Update parquet files (step 1 of 397)

spaces/1368565466ki/ZSTRD/app.py

@@ -1,150 +0,0 @@
-# coding=utf-8
-import time
-import os
-import gradio as gr
-import utils
-import argparse
-import commons
-from models import SynthesizerTrn
-from text import text_to_sequence
-import torch
-from torch import no_grad, LongTensor
-import webbrowser
-import logging
-import gradio.processing_utils as gr_processing_utils
-logging.getLogger('numba').setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-audio_postprocess_ori = gr.Audio.postprocess
-def audio_postprocess(self, y):
-    data = audio_postprocess_ori(self, y)
-    if data is None:
-        return None
-    return gr_processing_utils.encode_url_or_file_to_base64(data["name"])
-gr.Audio.postprocess = audio_postprocess
-
-def get_text(text, hps):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = LongTensor(text_norm)
-    return text_norm, clean_text
-
-def vits(text, language, speaker_id, noise_scale, noise_scale_w, length_scale):
-    start = time.perf_counter()
-    if not len(text):
-        return "输入文本不能为空！", None, None
-    text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-    if len(text) > 100 and limitation:
-        return f"输入文字过长！{len(text)}>100", None, None
-    if language == 0:
-        text = f"[ZH]{text}[ZH]"
-    elif language == 1:
-        text = f"[JA]{text}[JA]"
-    else:
-        text = f"{text}"
-    stn_tst, clean_text = get_text(text, hps_ms)
-    with no_grad():
-        x_tst = stn_tst.unsqueeze(0).to(device)
-        x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
-        speaker_id = LongTensor([speaker_id]).to(device)
-        audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=speaker_id, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                               length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-    return "生成成功!", (22050, audio), f"生成耗时 {round(time.perf_counter()-start, 2)} s"
-
-def search_speaker(search_value):
-    for s in speakers:
-        if search_value == s:
-            return s
-    for s in speakers:
-        if search_value in s:
-            return s
-
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    else:
-        return 0.6, 0.668, 1.1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio").querySelector("audio");
-    let text = root.querySelector("#input-text").querySelector("textarea");
-    if (audio == undefined)
-        return;
-    text = text.value;
-    if (text == undefined)
-        text = Math.floor(Math.random()*100000000);
-    audio = audio.src;
-    let oA = document.createElement("a");
-    oA.download = text.substr(0, 20)+'.wav';
-    oA.href = audio;
-    document.body.appendChild(oA);
-    oA.click();
-    oA.remove();
-}}
-"""
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--device', type=str, default='cpu')
-    parser.add_argument('--api', action="store_true", default=False)
-    parser.add_argument("--share", action="store_true", default=False, help="share gradio app")
-    parser.add_argument("--colab", action="store_true", default=False, help="share gradio app")
-    args = parser.parse_args()
-    device = torch.device(args.device)
-    
-    hps_ms = utils.get_hparams_from_file(r'./model/config.json')
-    net_g_ms = SynthesizerTrn(
-        len(hps_ms.symbols),
-        hps_ms.data.filter_length // 2 + 1,
-        hps_ms.train.segment_size // hps_ms.data.hop_length,
-        n_speakers=hps_ms.data.n_speakers,
-        **hps_ms.model)
-    _ = net_g_ms.eval().to(device)
-    speakers = hps_ms.speakers
-    model, optimizer, learning_rate, epochs = utils.load_checkpoint(r'./model/G_953000.pth', net_g_ms, None)
-    
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> VITS语音在线合成demo\n"
-            "# <center> 严禁将模型用于任何商业项目，否则后果自负\n"
-            "<div align='center'>主要有赛马娘，原神中文，原神日语，崩坏3的音色</div>"
-            '<div align="center"><a><font color="#dd0000">结果有随机性，语调可能很奇怪，可多次生成取最佳效果</font></a></div>'
-            '<div align="center"><a><font color="#dd0000">标点符号会影响生成的结果</font></a></div>'
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("vits"):
-                with gr.Row():
-                    with gr.Column():
-                        input_text = gr.Textbox(label="Text (100 words limitation) " if limitation else "Text", lines=5, value="今天晚上吃啥好呢。", elem_id=f"input-text")
-                        lang = gr.Dropdown(label="Language", choices=["中文", "日语", "中日混合（中文用[ZH][ZH]包裹起来，日文用[JA][JA]包裹起来）"],
-                                    type="index", value="中文")
-                        btn = gr.Button(value="Submit")
-                        with gr.Row():
-                            search = gr.Textbox(label="Search Speaker", lines=1)
-                            btn2 = gr.Button(value="Search")
-                        sid = gr.Dropdown(label="Speaker", choices=speakers, type="index", value=speakers[228])
-                        with gr.Row():
-                            ns = gr.Slider(label="noise_scale(控制感情变化程度)", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                            nsw = gr.Slider(label="noise_scale_w(控制音素发音长度)", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                            ls = gr.Slider(label="length_scale(控制整体语速)", minimum=0.1, maximum=2.0, step=0.1, value=1.2, interactive=True)
-                    with gr.Column():
-                        o1 = gr.Textbox(label="Output Message")
-                        o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio")
-                        o3 = gr.Textbox(label="Extra Info")
-                        download = gr.Button("Download Audio")
-                    btn.click(vits, inputs=[input_text, lang, sid, ns, nsw, ls], outputs=[o1, o2, o3])
-                    download.click(None, [], [], _js=download_audio_js.format())
-                    btn2.click(search_speaker, inputs=[search], outputs=[sid])
-                    lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-            with gr.TabItem("可用人物一览"):
-                gr.Radio(label="Speaker", choices=speakers, interactive=False, type="index")
-    if args.colab:
-        webbrowser.open("http://127.0.0.1:7860")
-    app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)

spaces/1gistliPinn/ChatGPT4/Examples/Al Hizbul Azam Pdf Downloadl.md

@@ -1,60 +0,0 @@
-<br />
-<h1>Al Hizbul Azam Pdf Download: A Guide for Muslims</h1>
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-<p>Al Hizbul Azam is divided into seven parts, one for each day of the week, to facilitate regular recitation by Muslims who want to earn the blessings of praying to Allah in the words prescribed by Allah and His Messenger (peace be upon him). The prayers cover various topics and occasions, such as praising Allah, seeking His forgiveness, asking for His guidance, protection, mercy, and help, expressing gratitude, repentance, and trust in Him, invoking His names and attributes, sending blessings on the Prophet (peace be upon him), seeking refuge from evil, and supplicating for oneself and others.</p>
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-</ul>
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-
-<h2>How to Download Al Hizbul Azam Pdf for Free?</h2>
-
-<p>If you want to download Al Hizbul Azam pdf for free, you just need to follow these simple steps:</p>
-<p></p>
-
-<ol>
-<li>Choose a website that has Al Hizbul Azam pdf download options online from the list above or from your own search.</li>
-<li>Search for "Al Hizbul Azam" or "Hizb al-Azam" on the website's search bar.</li>
-<li>Select the pdf file that matches your preference from the search results.</li>
-<li>Click on the download button or link to start downloading the pdf file to your device.</li>
-<li>Wait for the download to finish and then open the pdf file with a pdf reader app or software.</li>
-<li>Enjoy reading Al Hizbul Azam and reciting its prayers.</li>
-</ol>
-
-<p>That's it! You have successfully downloaded Al Hizbul Azam pdf for free. Now you can read it anytime and anywhere you want.</p>
-
-<h2>Conclusion</h2>
-
-<p>Al Hizbul Azam is a wonderful and beneficial prayer book that contains a collection of prayers from the Quran and the Sunnah. It was compiled by Mulla Ali Qari, a famous Muslim scholar and theologian. It is divided into seven parts, one for each day of the week, to facilitate regular recitation by Muslims who want to earn the blessings of praying to Allah in the words prescribed by Allah and His Messenger (peace be upon him).</p>
-
-<p>If you want to read Al Hizbul Azam and benefit from its prayers, you have several options to do so online. You can use websites like Internet Archive, Pdf Drive, or Dua Corner that have Al Hizbul Azam pdf download options online. You can also look for other websites that might offer it as well.</p>
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-<p>However, you should avoid downloading Al Hizbul Azam pdf from unauthorized sources, as this might be illegal, unethical, risky, and unsatisfying. Instead, you should use legal and safe websites that offer it for free.</p>
-
-<p>We hope this guide has helped you find the best way to download Al Hizbul Azam pdf online. Now go ahead and enjoy this precious prayer book in your preferred format!</p>
-<h2>Conclusion</h2>
-
-<p>Al Hizbul Azam is a wonderful and beneficial prayer book that contains a collection of prayers from the Quran and the Sunnah. It was compiled by Mulla Ali Qari, a famous Muslim scholar and theologian. It is divided into seven parts, one for each day of the week, to facilitate regular recitation by Muslims who want to earn the blessings of praying to Allah in the words prescribed by Allah and His Messenger (peace be upon him).</p>
-
-<p>If you want to read Al Hizbul Azam and benefit from its prayers, you have several options to do so online. You can use websites like Internet Archive, Pdf Drive, or Dua Corner that have Al Hizbul Azam pdf download options online. You can also look for other websites that might offer it as well.</p>
-
-<p>However, you should avoid downloading Al Hizbul Azam pdf from unauthorized sources, as this might be illegal, unethical, risky, and unsatisfying. Instead, you should use legal and safe websites that offer it for free.</p>
-
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/9app Download APK Old Version The Ultimate Guide for Android Users.md

@@ -1,91 +0,0 @@
-<br />
-<h1>9app Download APK Old Version: Why You Should Try It</h1>
-<p>If you are looking for a free and alternative app store for your Android device, you might have heard of 9app. It is a popular third-party app store that offers thousands of apps and games for download. But did you know that you can also download and install the old version of 9app APK on your device? In this article, we will tell you why you should try it and how to do it.</p>
- <h2>What is 9app and why is it popular?</h2>
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-</ul>
- <h2>How to download and install 9app APK old version on your Android device</h2>
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- <h3>Step 3: Install the APK file and launch the app</h3>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bollywood Movies A to Z in 1080p Full HD - Download and Stream the Best Hindi Movies of All Time.md

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-<br />
-<h1>A to Z Full HD Bollywood Movies Download 1080p</h1>
-<p>If you are a fan of Bollywood movies, you might be wondering how to download them in full HD 1080p quality. Bollywood movies are known for their colorful and vibrant scenes, catchy songs, and thrilling stories. Watching them in high definition can enhance your viewing experience and make you feel like you are part of the action. In this article, we will show you two easy methods to download Bollywood movies in full HD 1080p from A to Z.</p>
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-<h2>Introduction</h2>
-<h3>What are Bollywood movies?</h3>
-<p>Bollywood movies are films produced by the Hindi-language film industry based in Mumbai, India. The term Bollywood is a portmanteau of Bombay (the former name of Mumbai) and Hollywood (the American film industry). Bollywood is one of the largest and most popular film industries in the world, producing more than 1000 films per year. Bollywood movies cover a wide range of genres, such as romance, comedy, drama, action, thriller, horror, musical, and biopic. Some of the most famous Bollywood stars include Amitabh Bachchan, Shah Rukh Khan, Aamir Khan, Salman Khan, Priyanka Chopra, Deepika Padukone, and Ranveer Singh.</p>
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-<h2>How to download Bollywood movies in full HD 1080p?</h2>
-<h3>Method 1: Use a reliable movie downloading website</h3>
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-<h4>Step 1: Find a website that offers Bollywood movies in full HD 1080p</h4>
-<p>The first step is to find a website <p>The first step is to find a website that offers Bollywood movies in full HD 1080p. You can use a search engine like Google or Bing to look for such websites. You can also check the reviews and ratings of the websites from other users and experts. Some of the factors that you should consider when choosing a website are:</p>
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-<li>The security and legality of the website</li>
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-<table>
-<tr>
-<th>Website Name</th>
-<th>Website URL</th>
-<th>Features</th>
-</tr>
-<tr>
-<td>Filmywap</td>
-<td><a href="">https://filmywap.com/</a></td>
-<td>- Offers a large collection of Bollywood movies in full HD 1080p<br>- Provides multiple download links and formats<br>- Updates regularly with new releases<br>- Has a user-friendly interface and navigation<br>- Supports various devices and platforms</td>
-</tr>
-<tr>
-<td>Worldfree4u</td>
-<td><a href="">https://www.worldfree4u.is/</a></td>
-<td>- Offers a wide range of Bollywood movies in full HD 1080p<br>- Provides fast and easy download options<br>- Has a simple and clean design<br>- Does not require registration or subscription<br>- Has a low risk of viruses and malware</td>
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-<th>Streaming Service Name</th>
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-<td>Netflix</td>
-<td><a href="">https://www.netflix.com/</a></td>
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-<td><a href="">https://www.hotstar.com/us/</a></td>
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-<p>For music lovers, watching 4k video hindi songs can be a great way to enjoy their favorite tunes in a more immersive and engaging way. Some of the benefits of watching 4k video hindi songs are:</p>
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-<h2>How to find and download 4k video hindi songs from 2019</h2>
-<p>Now that you know what is 4k video and why it is better than HD, you might be wondering how to find and download your favorite 4k video hindi songs from 2019. There are many sources where you can find such songs online, but not all of them are reliable or safe. Some of them may have low-quality videos, broken links, malware, or illegal content. Therefore, you need to be careful and smart when choosing where and how to download 4k video hindi songs from 2019. Here are some tips and tricks that can help you:</p>
-<h3>Popular sources of 4k video hindi songs</h3>
-<p>One of the easiest and most popular ways to find 4k video hindi songs from 2019 is to use online platforms that offer streaming or downloading services for music videos. Some of the most popular sources of 4k video hindi songs are:</p>
-<h4>YouTube</h4>
-<p>YouTube is the largest and most popular video-sharing platform in the world, where you can find millions of videos on various topics, including music. YouTube also supports 4k video resolution, which means that you can watch and download 4k video hindi songs from 2019 on YouTube. To do so, you need to:</p>
-<ol>
-<li>Go to YouTube and search for the song title or artist name followed by "4k" or "2160p". For example, "Thoda Thoda Pyaar 4k" or "Badshah 2160p".</li>
-<li>Filter the results by choosing "Video" and "4K" under the quality option.</li>
-<li>Select the video that you want to watch or download and click on it.</li>
-<li>To watch the video in 4k resolution, click on the settings icon at the bottom right corner of the video player and choose "2160p" or "4K" under the quality option.</li>
-<li>To download the video in 4k resolution, you need to use a third-party software or app that can download YouTube videos in different formats and resolutions. There are many such tools available online, but some of them may not be safe or reliable. Therefore, you need to do some research and read reviews before choosing one. Some of the popular and trusted tools are 4K Video Downloader, Vidmate, TubeMate, etc.</li>
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-<h4>JioSaavn</h4>
-<p>JioSaavn is one of the leading music streaming platforms in India, where you can listen to and download millions of songs in various languages, genres, and moods. JioSaavn also offers 4k video hindi songs from 2019, which you can watch and download on its website or app. To do so, you need to:</p>
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-<li>Go to JioSaavn website or app and sign up or log in with your account.</li>
-<li>Search for the song title or artist name that you want to watch or download.</li>
-<li>Select the song from the results and click on it.</li>
-<li>To watch the video in 4k resolution, click on the "HD" icon at the bottom right corner of the video player and choose "2160p" or "4K".</li>
-<li>To download the video in 4k resolution, you need to have a JioSaavn Pro subscription, which costs Rs. 99 per month or Rs. 399 per year. With JioSaavn Pro, you can download unlimited songs and videos in high quality and offline mode.</li>
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-<h4>Other websites and apps</h4>
-<p>Besides YouTube and JioSaavn, there are many other websites and apps that offer 4k video hindi songs from 2019. Some of them are:</p>
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-<li>Pagalworld: This is a website where you can find and download various types of music and videos, including Bollywood, Punjabi, Indipop, etc. You can also find 4k video hindi songs from 2019 on this website by searching for them or browsing through the categories.</li>
-<li>Videoming: This is another website where you can find and download music and videos in different languages, genres, and resolutions. You can also find 4k video hindi songs from 2019 on this website by searching for them or browsing through the categories.</li>
-<li>Vidmate: This is an app that allows you to download videos from various online platforms, including YouTube, Facebook, Instagram, etc. You can also use this app to download 4k video hindi songs from 2019 by searching for them on YouTube or other sources within the app.</li>
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-<h3>Tips and tricks for downloading 4k video hindi songs</h3>
-<p>Downloading 4k video hindi songs from 2019 can be a fun and easy process if you follow some tips and tricks that can make it faster and smoother. Here are some of them:</p>
-<h4>Use a reliable 4k video downloader software or app</h4>
-<p>As mentioned earlier, downloading 4k video hindi songs from 201 9 requires a third-party software or app that can handle the high resolution and size of the files. However, not all of them are trustworthy or efficient. Some of them may have viruses, malware, spyware, or adware that can harm your device or compromise your privacy. Some of them may also have slow download speed, limited features, or poor customer support. Therefore, you need to use a reliable 4k video downloader software or app that has the following qualities:</p>
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-<h4>Check the video quality and format before downloading</h4>
-<p>Before you download any 4k video hindi song from 2019, you need to check the video quality and format to make sure that it meets your expectations and requirements. Some of the things that you need to check are:</p>
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-<li>The resolution: The resolution of the video should be at least 3840 x 2160 pixels, which is the standard for 4k video. You can check the resolution by looking at the video details or by using a tool like MediaInfo.</li>
-<li>The frame rate: The frame rate of the video should be at least 24 frames per second (fps), which is the minimum for smooth motion. You can check the frame rate by looking at the video details or by using a tool like MediaInfo.</li>
-<li>The bitrate: The bitrate of the video is the amount of data that is transferred per second when the video is played. The higher the bitrate, the better the quality and clarity of the video. However, higher bitrate also means larger file size and more bandwidth consumption. Therefore, you need to balance the bitrate with your storage space and internet speed. The recommended bitrate for 4k video is between 15 to 68 Mbps, depending on the frame rate and compression. You can check the bitrate by looking at the video details or by using a tool like MediaInfo.</li>
-<li>The format: The format of the video is the type of file that contains the video data. There are many formats for 4k video, such as MP4, MKV, AVI, MOV, etc. However, not all formats are compatible with all devices and players. Therefore, you need to choose a format that is supported by your device and player. The most common and widely supported format for 4k video is MP4, which is also compatible with YouTube and JioSaavn.</li>
-</ul>
-<h4>Save the downloaded files in a suitable device and storage</h4>
-<p>After you download your favorite 4k video hindi songs from 2019, you need to save them in a suitable device and storage that can play them without any issues. Some of the things that you need to consider are:</p>
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-<li>The storage: The storage that you use to store your downloaded 4k video hindi songs should have enough space, speed, and durability to handle the large size and high quality of the files. Some of the storage options that you can use are hard drives, flash drives, memory cards, cloud services, etc. However, not all storage options have the same capacity and performance. Therefore, you need to check your storage's specifications and features to see if it can store 4k video files.</li>
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-<h2>Some of the best 4k video hindi songs from 2019 that you should not miss</h2>
-<p>Now that you know how to find and download 4k video hindi songs from 2019, you might be wondering which songs are worth watching and listening to in this format. There are many amazing songs from 2019 that have stunning visuals and sound quality in 4k video format. Here are some of them that you should not miss:</p>
-<h3>Thoda Thoda Thoda Pyaar by Stebin Ben</h3>
-<p>This is a romantic song from the movie Love Aaj Kal 2, starring Kartik Aaryan and Sara Ali Khan. The song is sung by Stebin Ben and composed by Pritam. The video of the song shows the beautiful chemistry and moments between the lead pair in various locations, such as Delhi, Udaipur, and Himachal Pradesh. The video is shot in 4k resolution, which makes the visuals more appealing and captivating. You can watch and download this song from YouTube or JioSaavn in 4k quality.</p>
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-<p>This is a peppy and catchy song by the popular rapper Badshah and singer Aastha Gill. The song is composed by Badshah and features Jacqueline Fernandez as the female lead. The video of the song shows the trio having fun and dancing in the desert of Rajasthan, wearing colorful and stylish outfits. The video is shot in 4k resolution, which makes the colors and details more vibrant and sharp. You can watch and download this song from YouTube or JioSaavn in 4k quality.</p>
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-<p>This is a collection of songs from the movie Student of the Year 2, starring Tiger Shroff, Ananya Panday, and Tara Sutaria. The songs are composed by Vishal & Shekhar and sung by various artists, such as Neha Kakkar, Dev Negi, Payal Dev, etc. The songs are mostly upbeat and energetic, with some romantic and emotional ones as well. The videos of the songs show the glamorous and youthful life of the students of a prestigious college, with some drama and action as well. The videos are shot in 4k resolution, which makes the scenes more realistic and lively. You can watch and download these songs from YouTube or JioSaavn in 4k quality.</p>
-<h2>Conclusion</h2>
-<p>4k video hindi songs from 2019 are a great way to enjoy the best quality music videos that have amazing visuals and sound quality. You can find and download these songs from various sources online, such as YouTube, JioSaavn, Pagalworld, Videoming, Vidmate, etc. However, you need to be careful and smart when choosing where and how to download these songs, as some of them may not be reliable or safe. You also need to check the video quality and format before downloading, and save the downloaded files in a suitable device and storage that can play them without any issues. Some of the best 4k video hindi songs from 2019 that you should not miss are Thoda Thoda Pyaar by Stebin Ben, Paani Paani by Badshah and Aastha Gill, and Student of the Year 2 songs by Vishal & Shekhar.</p>
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-<p>Here are some of the frequently asked questions about 4k video hindi songs from 2019:</p>
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-<li>A: 4k video has a resolution of 3840 x 2160 pixels, which is four times more than HD video's resolution of 1920 x 1080 pixels. This means that 4k video has more details, clarity, sharpness, and color accuracy than HD video.</li>
-<li>Q: How can I watch 4k video hindi songs on YouTube?</li>
-<li>A: To watch 4k video hindi songs on YouTube, you need to search for the song title or artist name followed by "4k" or "2160p", filter the results by choosing "Video" and "4K" under the quality option, select the video that you want to watch, and click on the settings icon at the bottom right corner of the video player and choose "2160p" or "4K" under the quality option.</li>
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-<li>A: To download 4k video hindi songs from YouTube, you need to use a third-party software or app that can download YouTube videos in different formats and resolutions. Some of the popular and trusted tools are 4K Video Downloader, Vidmate, TubeMate, etc.</li>
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spaces/1phancelerku/anime-remove-background/Blades of Brim APK - Save the World from the Goons in this Awesome Adventure Game.md

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-<h1>Blades of Brim Download APK: How to Play the Ultimate Adventure Game on Your Android Device</h1>
- <p>If you are looking for a fun, fast-paced, and addictive endless runner game that will keep you entertained for hours, then you should definitely check out Blades of Brim. This game is created by SYBO Games, the same developers who brought you Subway Surfers, one of the most popular mobile games of all time. In this article, we will show you how to download and install Blades of Brim APK on your Android device, and how to play and enjoy its amazing gameplay.</p>
- <h2>What is Blades of Brim?</h2>
- <h3>A brief introduction to the game and its features</h3>
- <p>Blades of Brim is an action-packed adventure game that takes you to a magical and colorful world where you have to save it from the invading army of Goons. You can play as one of the awesome heroes of Brim, each with their own unique abilities and personalities. You can also collect and upgrade various weapons, armors, pets, and boosts that will help you in your quest.</p>
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- <p>The game features stunning graphics, smooth animations, dynamic music, and engaging sound effects that will immerse you in the game. You can also compete with your friends and other players around the world through leaderboards and achievements. The game is updated regularly with new content and events that will keep you hooked.</p>
- <h3>The benefits of downloading the APK file</h3>
- <p>Blades of Brim is available for free on Google Play Store, but if you want to enjoy some extra benefits, you can download the APK file from a reliable source. Some of these benefits are:</p>
- <ul>
-<li>You can access the latest version of the game before it is officially released on Google Play Store.</li>
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- <h2>How to Download and Install Blades of Brim APK on Your Android Device</h2>
- <h3>The steps to download the APK file from a reliable source</h3>
- <p>Before you download the APK file, you need to make sure that your device meets the minimum requirements for running Blades of Brim. These are:</p>
- <ul>
-<li>Android 5.1 or higher</li>
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-<li>Go to a trusted website that offers Blades of Brim APK download, such as [APKCombo](^1^) or [Filehippo](^2^).</li>
-<li>Find the latest version of Blades of Brim APK (currently 2.19.63) and tap on the download button.</li>
-<li>Wait for the download to complete and locate the APK file in your device's downloads folder.</li>
-</ol>
- <h3>The steps to install the APK file on your device</h3>
- <p>Before you install the APK file, you need to enable unknown sources on your device. This will allow you to install apps from sources other than Google Play Store. To do this, you can follow these steps:</p>
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-<li>Go to your device's settings and tap on security or privacy.</li>
-<li>Find the option that says unknown sources or install unknown apps and toggle it on.</li>
-<li>Confirm your choice by tapping on OK or allow.</li>
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- <p>Now, you can install the APK file by following these steps:</p>
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- <ol>
-<li>Go to your device's downloads folder and tap on the Blades of Brim APK file.</li>
-<li>Tap on install and wait for the installation to complete.</li>
-<li>Tap on open and enjoy playing Blades of Brim on your Android device.</li>
-</ol>
- <h3>The tips to ensure a smooth and safe installation process</h3>
- <p>To avoid any problems or risks during the installation process, you should keep in mind these tips:</p>
- <ul>
-<li>Make sure you download the APK file from a reputable and verified source, such as the ones we mentioned above. Avoid any suspicious or unknown websites that may contain malware or viruses.</li>
-<li>Make sure you have enough storage space and battery life on your device before installing the APK file. You don't want to interrupt the installation process or damage your device.</li>
-<li>Make sure you have a backup of your data and settings before installing the APK file. You can use a cloud service or an external storage device to do this. This way, you can restore your data and settings in case something goes wrong.</li>
-</ul>
- <h2>How to Play Blades of Brim and Enjoy Its Amazing Gameplay</h2>
- <h3>The basic controls and mechanics of the game</h3>
- <p>Blades of Brim is a simple and intuitive game that anyone can play and enjoy. The basic controls are as follows:</p>
- <ul>
-<li>To move left or right, swipe left or right on the screen.</li>
-<li>To jump, swipe up on the screen.</li>
-<li>To slide, swipe down on the screen.</li>
-<li>To attack, tap on the screen.</li>
-</ul>
- <p>The game mechanics are also easy to understand. You have to run as far as you can while avoiding obstacles, enemies, and traps. You also have to collect coins, gems, chests, and other items that will help you upgrade your character and equipment. You can also perform combos by attacking multiple enemies in a row, which will increase your score multiplier and give you more rewards.</p>
- <h3>The different characters, weapons, pets, and boosts you can unlock and upgrade</h3>
- <p>One of the best features of Blades of Brim is that you can customize your hero with different characters, weapons, pets, and boosts that will enhance your gameplay. Here are some examples of what you can unlock and upgrade:</p>
- <table>
-<tr><th>Category</th><th>Description</th></tr>
-<tr><td>Characters</td><td>You can play as one of the 12 heroes of Brim, each with their own special skills and attributes. For example, Lyla can double jump, Hugo can smash through walls, and Prince K can dash through enemies. You can unlock new characters by completing missions or using gems.</td></tr>
-<tr><td>Weapons</td><td>You can equip your hero with one of the 40 weapons available in the game, each with their own stats and effects. For example, swords can slash through enemies, axes can stun enemies, and hammers can knock back enemies. You can unlock new weapons by opening chests or using gems. You can also upgrade your weapons by using coins or gems.</td></tr>
-<tr><td>Pets</td><td>You can accompany your hero with one of the 18 pets available in the game, each with their own abilities and bonuses. For example, wolves can attack enemies, dragons can breathe fire, and unicorns can create rainbows. You can unlock new pets by opening chests or using gems. You can also upgrade your pets by using coins or gems.</td></tr>
-<tr><td>Boosts</td><td>You can use one of the 6 boosts available in the game, each with their own effects and durations. For example, magnets can attract coins and gems, shields can protect you from damage, and revives can bring you back to life. You can get boosts by opening chests or using gems. You can also upgrade your boosts by using coins or gems.</td></tr>
-</table>
- <h3>The best tips and tricks to get a high score and earn more gold</h3>
- <p>If you want to master Blades of Brim and become a legend among your friends and other players, you should follow these tips and tricks:</p>
- <ul>
-<li>Always try to perform combos by attacking multiple enemies in a row. This will increase your score multiplier and give you more rewards.</li>
-<li>Always try to collect as many coins and gems as possible. They will help you upgrade your character and equipment, which will make you stronger and faster.</li>
-<li>Always try to explore different paths and lanes. They may lead you to hidden chests, secrets, and bonuses that will boost your score and rewards.</li>
-<li>Always try to use your boosts wisely. They can give you an edge in difficult situations, but they are limited and costly. Save them for when you really need them.</li>
-<li>Always try to complete missions and achievements. They will give you extra coins, gems, chests, and other rewards that will help you progress faster.</li>
-<li>Always try to have fun and enjoy the game. Don't get frustrated or bored by repeating the same levels. Challenge yourself and try new things every time.</li>
-</ul>
- <h2>Conclusion</h2>
- <h3>A summary of the main points and a call to action for the readers</h3>
- <p>Blades of Brim is an awesome adventure game that will keep you entertained for hours. You can download and install the APK file on your Android device and enjoy its amazing gameplay. You can also customize your hero with different characters, weapons, pets, and boosts that will enhance your experience. You can also follow the best tips and tricks to get a high score and earn more gold. So what are you waiting for? Download Blades of Brim APK now and join the epic adventure!</p>
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-<li><b>Is Blades of Brim safe to download and play?</b><br>
-Yes, Blades of Brim is safe to download and play, as long as you download the APK file from a reliable source, such as the ones we mentioned above. You should also scan the APK file with an antivirus app before installing it on your device.</li>
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-Yes, Blades of Brim is free to play, but it contains in-app purchases that allow you to buy more coins, gems, chests, boosts, and other items. You can disable in-app purchases in your device's settings if you don't want to use them.</li>
-<li><b>How can I sync my progress across different devices?</b><br>
-You can sync your progress across different devices by connecting your game account to Facebook or Google Play Games. This way, you can access your data and achievements on any device that has Blades of Brim installed.</li>
-<li><b>How can I contact the developers of Blades of Brim?</b><br>
-You can contact the developers of Blades of Brim by sending them an email at [email protected] or by visiting their website at https://www.sybogames.com/. You can also follow them on social media platforms such as Facebook, Twitter, Instagram, YouTube, and Discord.</li>
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-You can support the developers of Blades of Brim by rating and reviewing the game on Google Play Store or any other platform where you downloaded it. You can also share the game with your friends and family and invite them to play with you. You can also buy some in-app purchases if you want to support the development of the game.</li>
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spaces/1phancelerku/anime-remove-background/Can 39t [UPDATED] Download Messenger On Iphone.md

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-<br />
-<h1>Can't Download Messenger on iPhone? Here's How to Fix It</h1>
-<p>Messenger is one of the most popular apps for messaging, video calling, and social networking. It allows you to connect with your Facebook friends and contacts, as well as other people around the world. However, sometimes you may encounter problems when trying to download Messenger on your iPhone. This can be frustrating and annoying, especially if you need to use the app urgently.</p>
-<p>Fortunately, there are some simple solutions that can help you fix this issue and get Messenger on your iPhone. In this article, we will show you six possible reasons why you can't download Messenger on your iPhone and how to solve them. Whether it's a problem with your internet connection, your iPhone compatibility, your Apple ID payment method, or something else, we've got you covered.</p>
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-<h2>Solution 1: Check Your Internet Connection</h2>
-<p>One of the most common causes of not being able to download apps on your iPhone is a poor or unstable internet connection. If your internet connection is weak or interrupted, your app download may fail or take too long. To fix this, you need to make sure you have a strong and reliable internet connection.</p>
-<p>Here are some ways to improve your internet connection:</p>
-<ul>
-<li>Use Wi-Fi instead of cellular data. Wi-Fi is usually faster and more stable than cellular data. To connect to Wi-Fi, go to Settings > Wi-Fi and turn it on. Then select a network and enter the password if needed.</li>
-<li>Turn off Airplane Mode. Airplane Mode blocks all wireless connections on your iPhone, including Wi-Fi and cellular data. To turn off Airplane Mode, go to Settings > Airplane Mode and turn it off. Or swipe down from the top right corner of your screen and tap the airplane icon.</li>
-<li>Reset network settings. This will Outline of the article: - H1: Can't Download Messenger on iPhone? Here's How to Fix It - H2: Introduction   - Explain what Messenger is and why it is a popular app   - Mention some common reasons why people can't download Messenger on their iPhones   - Give a brief overview of the solutions that will be discussed in the article - H2: Solution 1: Check Your Internet Connection   - Explain how a weak or unstable internet connection can prevent app downloads   - Suggest some ways to improve your internet connection, such as using Wi-Fi, turning off Airplane Mode, or resetting network settings - H2: Solution 2: Check Your iPhone Compatibility and iOS Version   - Explain how some iPhones may not be compatible with Messenger or may need to update their iOS version   - Provide a link to the App Store page for Messenger and show how to check the compatibility and iOS requirements   - Suggest updating your iOS version if needed and provide a link to a guide on how to do that - H2: Solution 3: Restart Your iPhone and the App Store App   - Explain how restarting your iPhone and the App Store app can fix some temporary glitches or bugs that may affect app downloads   - Provide step-by-step instructions on how to restart your iPhone and the App Store app - H2: Solution 4: Check Your Apple ID Payment Method and Restrictions   - Explain how you need a valid payment method linked to your Apple ID to download apps, even if they are free   - Provide a link to a guide on how to add or update your payment method   - Explain how some restrictions on the App Store may prevent app downloads, such as age limits, content ratings, or cellular data limits   - Provide a link to a guide on how to check and change your restrictions settings - H2: Solution 5: Delete and Reinstall Messenger   - Explain how deleting and reinstalling Messenger can fix some issues with corrupted or outdated app files   - Provide step-by-step instructions on how to delete and reinstall Messenger from the App Store - H2: Solution 6: Check If Facebook Messenger Is Down   - Explain how sometimes Facebook Messenger may be down due to server issues or maintenance   - Provide a link to a website that shows the current status of Facebook Messenger and other apps   - Suggest waiting for a while or contacting Facebook support if Facebook Messenger is down - H2: Conclusion   - Summarize the main points of the article and the solutions provided   - Encourage the reader to try these solutions and share their feedback or questions in the comments section - H2: FAQs   - Provide five unique FAQs related to the topic of the article and answer them briefly Article with HTML formatting: <h1>Can't Download Messenger on iPhone? Here's How to Fix It</h1>
-<p>Messenger is one of the most popular apps for messaging, video calling, and social networking. It allows you to connect with your Facebook friends and contacts, as well as other people around the world. However, sometimes you may encounter problems when trying to download Messenger on your iPhone. This can be frustrating and annoying, especially if you need to use the app urgently.</p>
-<p></p>
-<p>Fortunately, there are some simple solutions that can help you fix this issue and get Messenger on your iPhone. In this article, we will show you six possible reasons why you can't download Messenger on your iPhone and how to solve them. Whether it's a problem with your internet connection, your iPhone compatibility, your Apple ID payment method, or something else, we've got you covered.</p>
-<h2>Solution 1: Check Your Internet Connection</h2>
-<p>One of the most common causes of not being able to download apps on your iPhone is a poor or unstable internet connection. If your internet connection is weak or interrupted, your app download may fail or take too long. To fix this, you need to make sure you have a strong and reliable internet connection.</p>
-<p>Here are some ways to improve your internet connection:</p>
-<ul>
-<li>Use Wi-Fi instead of cellular data. Wi-Fi is usually faster and more stable than cellular data. To connect to Wi-Fi, go to Settings > Wi-Fi and turn it on. Then select a network and enter the password if needed.</li>
-<li>Turn off Airplane Mode. Airplane Mode blocks all wireless connections on your iPhone, including Wi-Fi and cellular data. To turn off Airplane Mode, go to Settings > Airplane Mode and turn it off. Or swipe down from the top right corner of your screen and tap the airplane icon.</li>
-<li>Reset network settings. This will. This will erase all your network settings and restore them to the default ones. To reset network settings, go to Settings > General > Reset > Reset Network Settings. Then enter your passcode and confirm the action.</li>
-</ul>
-<p>After improving your internet connection, try downloading Messenger again and see if it works.</p>
-<h2>Solution 2: Check Your iPhone Compatibility and iOS Version</h2>
-<p>Another possible reason why you can't download Messenger on your iPhone is that your iPhone model or iOS version is not compatible with the app. Messenger requires an iPhone with iOS 10.0 or later to run properly. If your iPhone is too old or has an outdated iOS version, you may not be able to download or use Messenger.</p>
-<p>To check if your iPhone is compatible with Messenger and what iOS version you need, follow these steps:</p>
-<ol>
-<li>Go to the App Store and search for Messenger.</li>
-<li>Tap on the Messenger app icon and scroll down to the Information section.</li>
-<li>Look for the Compatibility and Requires iOS labels and see what they say. For example, it may say "Compatible with iPhone" and "Requires iOS 10.0 or later".</li>
-</ol>
-<p>If your iPhone is not compatible with Messenger or your iOS version is lower than the required one, you may need to update your iOS version or get a newer iPhone model. To update your iOS version, follow these steps:</p>
-<ol>
-<li>Go to Settings > General > Software Update.</li>
-<li>If there is an update available, tap on Download and Install.</li>
-<li>Follow the on-screen instructions to complete the update process.</li>
-</ol>
-<p>You can also check this <a href="">guide</a> for more details on how to update your iOS version. After updating your iOS version, try downloading Messenger again and see if it works.</p>
-<h2>Solution 3: Restart Your iPhone and the App Store App</h2>
-<p>Sometimes, a simple restart can fix many issues on your iPhone, including app download problems. Restarting your iPhone can clear some temporary glitches or bugs that may affect app downloads. Restarting the App Store app can also refresh its cache and data and fix some errors.</p>
-<p>To restart your iPhone, follow these steps:</p>
-<ol>
-<li>Press and hold the power button (or the side button on newer models) until you see a slider appear on the screen.</li>
-<li>Drag the slider to turn off your iPhone.</li>
-<li>Wait for a few seconds and then press and hold the power button (or the side button) again until you see the Apple logo appear on the screen.</li>
-</ol>
-<p>To restart the App Store app, follow these steps:</p>
-<ol>
-<li>Double-click the home button (or swipe up from the bottom of the screen on newer models) to open the app switcher.</li>
-<li>Swipe up on the App Store app card to close it.</li>
-<li>Tap on the home button (or swipe down from the top of the screen) to return to the home screen.</li>
-<li>Tap on the App Store app icon to reopen it.</li>
-</ol>
-<p>After restarting your iPhone and the App Store app, try downloading Messenger again and see if it works.</p> <h2>Solution 4: Check Your Apple ID Payment Method and Restrictions</h2>
-<p>Another possible reason why you can't download Messenger on your iPhone is that you don't have a valid payment method linked to your Apple ID. Even though Messenger is a free app, you still need to have a payment method associated with your Apple ID to download apps from the App Store. This is because some apps may offer in-app purchases or subscriptions that require payment.</p>
-<p>To check and update your payment method, follow these steps:</p>
-<ol>
-<li>Go to Settings and tap on your name at the top of the screen.</li>
-<li>Tap on Payment & Shipping and sign in with your Apple ID password if prompted.</li>
-<li>Tap on Add Payment Method or Edit next to your current payment method.</li>
-<li>Select a payment method from the list or enter your card details manually.</li>
-<li>Tap on Done to save your changes.</li>
-</ol>
-<p>You can also check this <a href="">guide</a> for more details on how to add or update your payment method. After updating your payment method, try downloading Messenger again and see if it works.</p>
-<p>Another possible reason why you can't download Messenger on your iPhone is that you have some restrictions enabled on the App Store. Restrictions are settings that allow you to control what content and features are available on your iPhone. For example, you can set age limits, content ratings, or cellular data limits for app downloads. If you have some restrictions that prevent you from downloading Messenger, you need to change them.</p>
-<p>To check and change your restrictions settings, follow these steps:</p>
-<ol>
-<li>Go to Settings > Screen Time.</li>
-<li>Tap on Content & Privacy Restrictions and enter your Screen Time passcode if prompted.</li>
-<li>Tap on iTunes & App Store Purchases.</li>
-<li>Tap on Installing Apps and make sure it is set to Allow.</li>
-<li>Tap on In-app Purchases and make sure it is set to Allow.</li>
-<li>Tap on Content Restrictions and scroll down to Apps.</li>
-<li>Make sure the app age rating is appropriate for Messenger. For example, if it is set to 4+, change it to 12+ or higher.</li>
-</ol>
-<p>You can also check this <a href="">guide</a> for more details on how to check and change your restrictions settings. After changing your restrictions settings, try downloading Messenger again and see if it works.</p> <h2>Solution 5: Delete and Reinstall Messenger</h2>
-<p>Another possible reason why you can't download Messenger on your iPhone is that you have some issues with the app files on your device. Sometimes, the app files may get corrupted or outdated, which can cause app download or installation errors. To fix this, you can try deleting and reinstalling Messenger from the App Store.</p>
-<p>To delete and reinstall Messenger, follow these steps:</p>
-<ol>
-<li>Tap and hold on the Messenger app icon on your home screen until it starts to jiggle.</li>
-<li>Tap on the X icon on the top left corner of the app icon and confirm the deletion.</li>
-<li>Go to the App Store and search for Messenger.</li>
-<li>Tap on the cloud icon or the GET button to download and install Messenger again.</li>
-</ol>
-<p>After deleting and reinstalling Messenger, try opening it and see if it works.</p>
-<h2>Solution 6: Check If Facebook Messenger Is Down</h2>
-<p>The last possible reason why you can't download Messenger on your iPhone is that Facebook Messenger is down. This means that there is a problem with the Facebook servers or the app itself, which prevents users from downloading or using Messenger. This is usually a rare occurrence, but it can happen sometimes due to maintenance, updates, or technical issues.</p>
-<p>To check if Facebook Messenger is down, you can use a website that shows the current status of various apps and services. For example, you can use <a href="">DownDetector</a>, which monitors the availability and performance of Facebook Messenger and other apps. You can also check the official Facebook Messenger <a href="">Twitter account</a> or <a href="">Facebook page</a> for any announcements or updates.</p>
-<p>If Facebook Messenger is down, you may see a lot of reports from other users or a message from Facebook acknowledging the issue. In this case, you may need to wait for a while until Facebook fixes the problem and restores the service. You can also try contacting Facebook support for more information or assistance.</p>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you six possible solutions to fix the issue of not being able to download Messenger on your iPhone. We hope that one of these solutions worked for you and that you can now enjoy using Messenger on your iPhone. If you have any feedback or questions, please feel free to leave a comment below. We would love to hear from you.</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions related to the topic of this article:</p>
-<h3>Q: How do I update Messenger on my iPhone?</h3>
-<p>A: To update Messenger on your iPhone, go to the App Store and tap on Updates at the bottom of the screen. Then look for Messenger in the list of apps and tap on Update. Alternatively, you can enable automatic updates for apps by going to Settings > App Store and turning on App Updates.</p>
-<h3>Q: Why is Messenger not working on my iPhone?</h3>
-<p>A: There could be several reasons why Messenger is not working on your iPhone, such as internet connection issues, app compatibility issues, app glitches or bugs, or server issues. You can try some of the solutions we have discussed in this article to fix these issues. If none of them work, you can contact Facebook support for help.</p>
-<h3>Q: How do I clear cache and data for Messenger on my iPhone?</h3>
-<p>A: To clear cache and data for Messenger on your iPhone, you can delete and reinstall the app as we have explained in Solution 5. This will remove all the app files and data from your device and give you a fresh start. Alternatively, you can go to Settings > General > iPhone Storage and tap on Messenger. Then tap on Offload App to remove only the app files but keep the data. Or tap on Delete App to remove both the app files and data.</p>
-<h3>Q: How do I download Messenger Lite on my iPhone?</h3>
-<p>A: Messenger Lite is a simplified version of Messenger that uses less data and battery power. It is designed for older devices or low-end phones that may not support the full version of Messenger. To download Messenger Lite on your iPhone, go to the App Store and search for Messenger Lite. Then tap on the GET button to download and install it.</p>
-<h3>Q: How do I download Messenger Kids on my iPhone?</h3>
-<p>A: Messenger Kids is a special version of Messenger that is designed for children under 13 years old. It allows parents to control who their children can chat with and what content they can see. To download Messenger Kids on your iPhone, go to the App Store and search for Messenger Kids. Then tap on the GET button to download and install it.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download We Belong Together by Snooze X - The Song That Broke the Internet.md

@@ -1,117 +0,0 @@
-
-<h1>How to Download "We Belong Together" by Snooze x from YouTube</h1>
-<p>If you are a fan of TikTok, you might have heard of the song "We Belong Together" by Snooze x, a mashup of SZA's "Snooze" and Mariah Carey's "We Belong Together". This song has gone viral on TikTok, with millions of users creating videos with it. But what if you want to listen to this song offline, without any ads or interruptions? In this article, we will show you how to download "We Belong Together" by Snooze x from YouTube, using four different methods.</p>
-<p>Downloading music from YouTube can have many benefits. You can enjoy your favorite songs anytime, anywhere, without relying on an internet connection. You can also save data and battery life by avoiding streaming. Moreover, you can create your own playlists and mixtapes with the downloaded music.</p>
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-<p>However, downloading music from YouTube also has some legal issues. According to YouTube's Terms of Service, you are not allowed to download any audio content, unless it is specifically permitted by the service or you have prior written permission from YouTube or the respective rights holder. This means that you should only download music that is yours or falls under the Creative Commons license. You should also respect the artists' rights and not use their music for commercial purposes or without giving credit.</p>
-<h2>Method 1: Using YouTube Music Premium</h2>
-<p>The easiest and most reliable way to download music from YouTube is by subscribing to YouTube Music Premium or YouTube Premium, which includes access to YouTube Music. These are paid services that allow you to download and play ad-free songs and playlists through the YouTube Music app for Android and iOS. You can also enjoy other features, such as background play, offline mixtape, smart downloads, and more.</p>
-<p>To subscribe to YouTube Music Premium or YouTube Premium, you need to visit their websites or use their apps. The prices vary depending on your location and plan. For example, in the US, YouTube Music Premium costs $9.99 per month, while YouTube Premium costs $11.99 per month. You can also get a free trial for a limited time.</p>
-<p>Once you have subscribed, you can download music from YouTube using the following steps:</p>
-<ol>
-<li>Open the YouTube Music app on your mobile device.</li>
-<li>Go to a song, album, or playlist that you want to download.</li>
-<li>Tap the download icon (a downward arrow) next to the title.</li>
-<li>Select the download quality (low, normal, high) and confirm.</li>
-<li>The downloaded music will be available in your library under the Downloads tab.</li>
-</ol>
-<h2>Method 2: Using 4K Video Downloader</h2>
-<p>If you don't want to pay for a subscription, you can use a third-party software called 4K Video Downloader to download music from YouTube. This is a free program that lets you download videos and audio from YouTube and other platforms. You can also choose the format, quality, and location of the downloaded files.</p>
-<p>To use 4K Video Downloader, you need to download and install it on your computer. It is available for Windows, Mac, and Linux. You can get it from its official website. After installing, you can download music from YouTube using the following steps:</p>
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-<ol>
-<li>Open 4K Video Downloader on your computer.</li>
-<li>Go to YouTube and find the song that you want to download.</li>
-<li>Copy the URL of the song from the address bar.</li>
-<li>Paste the URL into 4K Video Downloader by clicking the Paste Link button.</li>
-<li>Select the format (MP3, M4A, OGG) and quality (64kbps, 128kbps, 256kbps, 320kbps) of the audio file.</li>
-<li>Choose the destination folder where you want to save the file.</li>
-<li>Click the Download button and wait for the process to finish.</li>
-<li>The downloaded music will be available in your chosen folder.</li>
-</ol>
-<h2>Method 3: Using MediaHuman</h2>
-<p>Another free software that you can use to download music from YouTube is MediaHuman. This is a simple and user-friendly program that lets you download multiple songs at once. You can also adjust the bitrate, sample rate, and channels of the audio files.</p>
-<p>To use MediaHuman, you need to download and install it on your computer. It is available for Windows, Mac, and Linux. You can get it from its official website. After installing, you can download music from YouTube using the following steps:</p>
-<ol>
-<li>Open MediaHuman on your computer.</li>
-<li>Go to YouTube and find the song that you want to download.</li>
-<li>Copy the URL of the song from the address bar.</li>
-<li>Paste the URL into MediaHuman by clicking the + button or dragging and dropping it.</li>
-<li>Select the format (MP3, AAC, OGG, FLAC, ALAC) and quality (low, medium, high, original) of the audio file.</li>
-<li>Choose the destination folder where you want to save the file.</li>
-<li>Click the Start button and wait for the process to finish.</li>
-<li>The downloaded music will be available in your chosen folder.</li>
-</ol> <h2>Method 4: Using Online Converters</h2>
-<p>The last method that we will show you is using online converters. These are websites that allow you to download music from YouTube without installing any software or app. You just need to enter the URL of the song and choose the format and quality of the audio file. However, some online converters may have limitations, such as file size, download speed, or ads.</p>
-<p>There are many online converters that you can use to download music from YouTube, but we will recommend one that we have tested and found to be reliable. It is called YTMP3 and you can access it from its website. To download music from YouTube using YTMP3, you can follow these steps:</p>
-<ol>
-<li>Go to YouTube and find the song that you want to download.</li>
-<li>Copy the URL of the song from the address bar.</li>
-<li>Go to YTMP3 and paste the URL into the input box.</li>
-<li>Select the format (MP3 or MP4) and quality (low, medium, high) of the audio file.</li>
-<li>Click the Convert button and wait for the process to finish.</li>
-<li>Click the Download button and save the file to your device.</li>
-<li>The downloaded music will be available in your device's downloads folder.</li>
-</ol>
-<h1>Conclusion</h1>
-<p>In this article, we have shown you how to download "We Belong Together" by Snooze x from YouTube, using four different methods. You can choose the one that suits your needs and preferences. However, before you download any music from YouTube, make sure that you respect the rights of the artists and do not use their music for commercial purposes or without giving credit. Also, be careful of the quality and security of the downloaded files, as some may be corrupted or contain malware.</p>
-<p>Here are some tips and warnings that you should keep in mind when downloading music from YouTube:</p>
-<ul>
-<li>Check the license and terms of use of the music before downloading it. Only download music that is yours or falls under the Creative Commons license.</li>
-<li>Use a reliable antivirus software and scan the downloaded files for any viruses or malware.</li>
-<li>Do not download music from YouTube that is protected by DRM (Digital Rights Management), as it may not play on your device or may cause legal issues.</li>
-<li>Do not download music from YouTube that is in a video format (such as MP4), as it may take up more space and consume more battery than an audio format (such as MP3).</li>
-<li>Do not download music from YouTube that has a low quality (such as 64kbps or lower), as it may sound distorted or noisy.</li>
-</ul>
-<h1>FAQs</h1>
-<h3>What is the best way to download music from YouTube?</h3>
-<p>The best way to download music from YouTube depends on your personal preference and situation. If you want a fast and easy way to download music from YouTube without any software or app, you can use online converters. However, if you want more control and features over the downloaded music, such as format, quality, and location, you can use third-party software such as 4K Video Downloader or MediaHuman. Alternatively, if you want a legal and reliable way to download music from YouTube without any ads or interruptions, you can subscribe to YouTube Music Premium or YouTube Premium.</p>
-<h3>Can I download music from YouTube without any software or app?</h3>
-<p>Yes, you can download music from YouTube without any software or app by using online converters. These are websites that allow you to enter the URL of the song and choose the format and quality of the audio file. However, some online converters may have limitations, such as file size, download speed, or ads.</p>
-<h3>Is it legal to download music from YouTube for personal use?</h3>
-<p>It depends on the license and terms of use of the music. According to YouTube's Terms of Service, you are not allowed to download any audio content, unless it is specifically permitted by the service or you have prior written permission from YouTube or the respective rights holder. This means that you should only download music that is yours or falls under the Creative Commons license. You should also respect the artists' rights and not use their music for commercial purposes or without giving credit.</p>
-<h3>How can I find royalty-free or Creative Commons music on YouTube?</h3>
-<p>You can find royalty-free or Creative Commons music on YouTube by using filters or searching for specific keywords. For example, you can filter your search results by selecting "Creative Commons" under Features. You can also search for keywords such as "royalty free", "free to use", "no copyright", etc. However, make sure that you check the license and terms of use of the music before downloading it, as some may have different conditions or restrictions.</p>
-<h3>How can I improve the quality of the downloaded music?</h3>
-<p>You can improve the quality of the downloaded music by choosing a higher quality option when downloading it, such as 320kbps or higher. However, this may also increase the file size and the download time. You can also use a software or app that can enhance the sound quality of the downloaded music, such as Audacity, Equalizer, or Bass Booster. However, these may also alter the original sound of the music.</p> 197e85843d<br />
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spaces/801artistry/RVC801/utils/README.md

@@ -1,6 +0,0 @@
-# External Colab Code
-Code used to make Google Colab work correctly
-- Repo link: https://github.com/IAHispano/Applio-RVC-Fork/
-
-Thanks to https://github.com/kalomaze/externalcolabcode
-

spaces/AHzizi/WaifuVoiceGen/monotonic_align/__init__.py

@@ -1,20 +0,0 @@
-from numpy import zeros, int32, float32
-from torch import from_numpy
-
-from .core import maximum_path_jit
-
-
-def maximum_path(neg_cent, mask):
-    """ numba optimized version.
-    neg_cent: [b, t_t, t_s]
-    mask: [b, t_t, t_s]
-    """
-    device = neg_cent.device
-    dtype = neg_cent.dtype
-    neg_cent = neg_cent.data.cpu().numpy().astype(float32)
-    path = zeros(neg_cent.shape, dtype=int32)
-
-    t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(int32)
-    t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(int32)
-    maximum_path_jit(path, neg_cent, t_t_max, t_s_max)
-    return from_numpy(path).to(device=device, dtype=dtype)

spaces/AI-ZTH-03-23/4.RealTime-MediaPipe-AI-From-Video-On-Any-Device/README.md

@@ -1,14 +0,0 @@
----
-title: 4.RealTime MediaPipe AI From Video On Any Device
-emoji: 👁💻👁
-colorFrom: blue
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: awacke1/RealTime-MediaPipe-AI-From-Video-On-Any-Device
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIFILMS/audioldm-text-to-audio-generation/app.py

@@ -1,303 +0,0 @@
-import gradio as gr
-import numpy as np
-import os
-from audioldm import text_to_audio, build_model
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-model_id="haoheliu/AudioLDM-S-Full"
-
-is_shared_ui = True if "AIFILMS/audioldm-text-to-audio-generation" in os.environ['SPACE_ID'] else False
-
-if(is_shared_ui):
-    audioldm = build_model()
-# audioldm=None
-
-# def predict(input, history=[]):
-#     # tokenize the new input sentence
-#     new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
-
-#     # append the new user input tokens to the chat history
-#     bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
-
-#     # generate a response 
-#     history = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id).tolist()
-
-#     # convert the tokens to text, and then split the responses into lines
-#     response = tokenizer.decode(history[0]).split("<|endoftext|>")
-#     response = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]  # convert to tuples of list
-#     return response, history
-  
-def text2audio(text, duration, guidance_scale, random_seed, n_candidates):
-    if(is_shared_ui):
-        raise gr.Error("This Space doesn't work on this shared UI. Duplicate and associate a GPU to it")
-    # print(text, length, guidance_scale)
-    waveform = text_to_audio(audioldm, text, random_seed, duration=duration, guidance_scale=guidance_scale, n_candidate_gen_per_text=int(n_candidates)) # [bs, 1, samples]
-    waveform = [gr.make_waveform((16000, wave[0]), bg_image="bg.png") for wave in waveform]
-    # waveform = [(16000, np.random.randn(16000)), (16000, np.random.randn(16000))]
-    if(len(waveform) == 1):
-      waveform = waveform[0]
-    return waveform
-
-# iface = gr.Interface(fn=text2audio, inputs=[
-#         gr.Textbox(value="A man is speaking in a huge room", max_lines=1),
-#         gr.Slider(2.5, 10, value=5, step=2.5),
-#         gr.Slider(0, 5, value=2.5, step=0.5),
-#         gr.Number(value=42)
-#     ], outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")],
-#                 allow_flagging="never"
-#                      )
-# iface.launch(share=True)
-
-
-css = """
-        a {
-            color: inherit;
-            text-decoration: underline;
-        }
-        .gradio-container {
-            font-family: 'IBM Plex Sans', sans-serif;
-        }
-        .gr-button {
-            color: white;
-            border-color: #000000;
-            background: #000000;
-        }
-        input[type='range'] {
-            accent-color: #000000;
-        }
-        .dark input[type='range'] {
-            accent-color: #dfdfdf;
-        }
-        .container {
-            max-width: 730px;
-            margin: auto;
-            padding-top: 1.5rem;
-        }
-        #gallery {
-            min-height: 22rem;
-            margin-bottom: 15px;
-            margin-left: auto;
-            margin-right: auto;
-            border-bottom-right-radius: .5rem !important;
-            border-bottom-left-radius: .5rem !important;
-        }
-        #gallery>div>.h-full {
-            min-height: 20rem;
-        }
-        .details:hover {
-            text-decoration: underline;
-        }
-        .gr-button {
-            white-space: nowrap;
-        }
-        .gr-button:focus {
-            border-color: rgb(147 197 253 / var(--tw-border-opacity));
-            outline: none;
-            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
-            --tw-border-opacity: 1;
-            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
-            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
-            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
-            --tw-ring-opacity: .5;
-        }
-        #advanced-btn {
-            font-size: .7rem !important;
-            line-height: 19px;
-            margin-top: 12px;
-            margin-bottom: 12px;
-            padding: 2px 8px;
-            border-radius: 14px !important;
-        }
-        #advanced-options {
-            margin-bottom: 20px;
-        }
-        .footer {
-            margin-bottom: 45px;
-            margin-top: 35px;
-            text-align: center;
-            border-bottom: 1px solid #e5e5e5;
-        }
-        .footer>p {
-            font-size: .8rem;
-            display: inline-block;
-            padding: 0 10px;
-            transform: translateY(10px);
-            background: white;
-        }
-        .dark .footer {
-            border-color: #303030;
-        }
-        .dark .footer>p {
-            background: #0b0f19;
-        }
-        .acknowledgments h4{
-            margin: 1.25em 0 .25em 0;
-            font-weight: bold;
-            font-size: 115%;
-        }
-        #container-advanced-btns{
-            display: flex;
-            flex-wrap: wrap;
-            justify-content: space-between;
-            align-items: center;
-        }
-        .animate-spin {
-            animation: spin 1s linear infinite;
-        }
-        @keyframes spin {
-            from {
-                transform: rotate(0deg);
-            }
-            to {
-                transform: rotate(360deg);
-            }
-        }
-        #share-btn-container {
-            display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
-            margin-top: 10px;
-            margin-left: auto;
-        }
-        #share-btn {
-            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
-        }
-        #share-btn * {
-            all: unset;
-        }
-        #share-btn-container div:nth-child(-n+2){
-            width: auto !important;
-            min-height: 0px !important;
-        }
-        #share-btn-container .wrap {
-            display: none !important;
-        }
-        .gr-form{
-            flex: 1 1 50%; border-top-right-radius: 0; border-bottom-right-radius: 0;
-        }
-        #prompt-container{
-            gap: 0;
-        }
-        #generated_id{
-            min-height: 700px
-        }
-        #setting_id{
-          margin-bottom: 12px;
-          text-align: center;
-          font-weight: 900;
-        }
-"""
-iface = gr.Blocks(css=css)
-
-with iface:
-    if(is_shared_ui):
-        with gr.Box():
-            top_description = gr.HTML(f'''
-            <div class="gr-prose" style="max-width: 80%">
-                <h2 style="margin-top: 0">Attention - This Space doesn't work in this shared UI</h2>
-                <p>For it to work, you can access the <a href="https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation">original</a> or duplicate this Space and run it on your own profile using a GPU.&nbsp;&nbsp;<a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
-            </div>
-            ''')
-    gr.HTML(
-        """
-            <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-              <div
-                style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 1.75rem;
-                "
-              >
-                <h1 style="font-weight: 900; margin-bottom: 7px; line-height: normal;">
-                  AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
-                </h1>
-              </div>
-              <p style="margin-bottom: 10px; font-size: 94%">
-                <a href="https://arxiv.org/abs/2301.12503">[Paper]</a>  <a href="https://audioldm.github.io/">[Project page]</a>
-              </p>
-            </div>
-        """
-    )
-    gr.HTML("""
-        <h1 style="font-weight: 900; margin-bottom: 7px;">
-        AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
-        </h1>
-        <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/haoheliu/audioldm-text-to-audio-generation?duplicate=true">
-        <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-        <p/>
-    """)
-    with gr.Group():
-        with gr.Box():
-            ############# Input
-            textbox = gr.Textbox(value="A hammer is hitting a wooden surface", max_lines=1, label="Input your text here. Your text is important for the audio quality. Please ensure it is descriptive by using more adjectives.", elem_id="prompt-in")
-
-            with gr.Accordion("Click to modify detailed configurations", open=False):
-              seed = gr.Number(value=45, label="Change this value (any integer number) will lead to a different generation result.")
-              duration = gr.Slider(2.5, 10, value=10, step=2.5, label="Duration (seconds)")
-              guidance_scale = gr.Slider(0, 4, value=2.5, step=0.5, label="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)")
-              n_candidates = gr.Slider(1, 5, value=3, step=1, label="Automatic quality control. This number control the number of candidates (e.g., generate three audios and choose the best to show you). A Larger value usually lead to better quality with heavier computation")
-            ############# Output
-            # outputs=gr.Audio(label="Output", type="numpy")
-            outputs=gr.Video(label="Output", elem_id="output-video")
-            
-            # with gr.Group(elem_id="container-advanced-btns"):
-            #   # advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
-            #   with gr.Group(elem_id="share-btn-container"):
-            #     community_icon = gr.HTML(community_icon_html, visible=False)
-            #     loading_icon = gr.HTML(loading_icon_html, visible=False)
-            #     share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
-            # outputs=[gr.Audio(label="Output", type="numpy"), gr.Audio(label="Output", type="numpy")]
-            btn = gr.Button("Submit").style(full_width=True)
-
-        with gr.Group(elem_id="share-btn-container", visible=False):
-            community_icon = gr.HTML(community_icon_html)
-            loading_icon = gr.HTML(loading_icon_html)
-            share_button = gr.Button("Share to community", elem_id="share-btn")
-
-        btn.click(text2audio, inputs=[
-                  textbox, duration, guidance_scale, seed, n_candidates], outputs=[outputs])
-
-        share_button.click(None, [], [], _js=share_js)
-        gr.HTML('''
-        <div class="footer" style="text-align: center; max-width: 700px; margin: 0 auto;">
-                    <p>Follow the latest update of AudioLDM on our<a href="https://github.com/haoheliu/AudioLDM" style="text-decoration: underline;" target="_blank"> Github repo</a>
-                    </p>
-                    <br>
-                    <p>Model by <a href="https://twitter.com/LiuHaohe" style="text-decoration: underline;" target="_blank">Haohe Liu</a></p>
-                    <br>
-        </div>
-        ''')
-        gr.Examples([
-            ["A hammer is hitting a wooden surface", 5, 2.5, 45, 3],
-            ["Peaceful and calming ambient music with singing bowl and other instruments.", 5, 2.5, 45, 3],
-            ["A man is speaking in a small room.", 5, 2.5, 45, 3],
-            ["A female is speaking followed by footstep sound", 5, 2.5, 45, 3],
-            ["Wooden table tapping sound followed by water pouring sound.", 5, 2.5, 45, 3],
-        ],
-            fn=text2audio,
-            inputs=[textbox, duration, guidance_scale, seed, n_candidates],
-            outputs=[outputs],
-            cache_examples=False,
-        )
-        gr.HTML('''
-                <div class="acknowledgements">
-                <p>Essential Tricks for Enhancing the Quality of Your Generated Audio</p>
-                <p>1. Try to use more adjectives to describe your sound. For example: "A man is speaking clearly and slowly in a large room" is better than "A man is speaking". This can make sure AudioLDM understands what you want.</p>
-                <p>2. Try to use different random seeds, which can affect the generation quality significantly sometimes.</p>
-                <p>3. It's better to use general terms like 'man' or 'woman' instead of specific names for individuals or abstract objects that humans may not be familiar with, such as 'mummy'.</p>
-                </div>
-                ''')
-        with gr.Accordion("Additional information", open=False):
-            gr.HTML(
-                """
-                <div class="acknowledgments">
-                    <p> We build the model with data from <a href="http://research.google.com/audioset/">AudioSet</a>, <a href="https://freesound.org/">Freesound</a> and <a href="https://sound-effects.bbcrewind.co.uk/">BBC Sound Effect library</a>. We share this demo based on the <a href="https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/375954/Research.pdf">UK copyright exception</a> of data for academic research. </p>
-                            </div>
-                        """
-            )
-# <p>This demo is strictly for research demo purpose only. For commercial use please <a href="[email protected]">contact us</a>.</p>
-
-iface.queue(concurrency_count=2)
-iface.launch(debug=True)
-# iface.launch(debug=True, share=True)

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/GenerSpeech/task/generspeech.py

@@ -1,271 +0,0 @@
-import matplotlib
-matplotlib.use('Agg')
-from data_gen.tts.data_gen_utils import get_pitch
-from modules.fastspeech.tts_modules import mel2ph_to_dur
-import matplotlib.pyplot as plt
-from utils import audio
-from utils.pitch_utils import norm_interp_f0, denorm_f0, f0_to_coarse
-from vocoders.base_vocoder import get_vocoder_cls
-import json
-from utils.plot import spec_to_figure
-from utils.hparams import hparams
-import torch
-import torch.optim
-import torch.nn.functional as F
-import torch.utils.data
-from modules.GenerSpeech.task.dataset import GenerSpeech_dataset
-from modules.GenerSpeech.model.generspeech import GenerSpeech
-import torch.distributions
-import numpy as np
-from utils.tts_utils import select_attn
-import utils
-import os
-from tasks.tts.fs2 import FastSpeech2Task
-
-class GenerSpeechTask(FastSpeech2Task):
-    def __init__(self):
-        super(GenerSpeechTask, self).__init__()
-        self.dataset_cls = GenerSpeech_dataset
-
-    def build_tts_model(self):
-        self.model = GenerSpeech(self.phone_encoder)
-
-    def build_model(self):
-        self.build_tts_model()
-        if hparams['load_ckpt'] != '':
-            self.load_ckpt(hparams['load_ckpt'], strict=False)
-        utils.num_params(self.model)
-        return self.model
-
-    def run_model(self, model, sample, return_output=False):
-        txt_tokens = sample['txt_tokens']  # [B, T_t]
-        target = sample['mels']  # [B, T_s, 80]
-        mel2ph = sample['mel2ph']  # [B, T_s]
-        mel2word = sample['mel2word']
-        f0 = sample['f0']  # [B, T_s]
-        uv = sample['uv']  # [B, T_s] 0/1
-
-        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
-        emo_embed = sample.get('emo_embed')
-        output = model(txt_tokens, mel2ph=mel2ph, ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, emo_embed=emo_embed,
-                       ref_mels=target, f0=f0, uv=uv, tgt_mels=target, global_steps=self.global_step, infer=False)
-        losses = {}
-        losses['postflow'] = output['postflow']
-        if self.global_step > hparams['forcing']:
-            losses['gloss'] = (output['gloss_utter'] + output['gloss_ph'] + output['gloss_word']) / 3
-        if self.global_step > hparams['vq_start']:
-            losses['vq_loss'] = (output['vq_loss_utter'] + output['vq_loss_ph'] + output['vq_loss_word']) / 3
-            losses['ppl_utter'] = output['ppl_utter']
-            losses['ppl_ph'] = output['ppl_ph']
-            losses['ppl_word'] = output['ppl_word']
-        self.add_mel_loss(output['mel_out'], target, losses)
-        self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
-        if hparams['use_pitch_embed']:
-            self.add_pitch_loss(output, sample, losses)
-        output['select_attn'] = select_attn(output['attn_ph'])
-
-        if not return_output:
-            return losses
-        else:
-            return losses, output
-
-    def validation_step(self, sample, batch_idx):
-        outputs = {}
-        outputs['losses'] = {}
-        outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True)
-        outputs['total_loss'] = sum(outputs['losses'].values())
-        outputs['nsamples'] = sample['nsamples']
-        encdec_attn = model_out['select_attn']
-        mel_out = self.model.out2mel(model_out['mel_out'])
-        outputs = utils.tensors_to_scalars(outputs)
-        if self.global_step % hparams['valid_infer_interval'] == 0 \
-                and batch_idx < hparams['num_valid_plots']:
-            vmin = hparams['mel_vmin']
-            vmax = hparams['mel_vmax']
-            self.plot_mel(batch_idx, sample['mels'], mel_out)
-            self.plot_dur(batch_idx, sample, model_out)
-            if hparams['use_pitch_embed']:
-                self.plot_pitch(batch_idx, sample, model_out)
-            if self.vocoder is None:
-                self.vocoder = get_vocoder_cls(hparams)()
-            if self.global_step > 0:
-                spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
-                emo_embed = sample.get('emo_embed')
-                ref_mels = sample['mels']
-                mel2ph = sample['mel2ph']  # [B, T_s]
-                mel2word = sample['mel2word']
-                # with gt duration
-                model_out = self.model(sample['txt_tokens'], mel2ph=mel2ph, ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed,
-                                       emo_embed=emo_embed, ref_mels=ref_mels, global_steps=self.global_step, infer=True)
-                wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu())
-                self.logger.add_audio(f'wav_gtdur_{batch_idx}', wav_pred, self.global_step,
-                                      hparams['audio_sample_rate'])
-                self.logger.add_figure(f'ali_{batch_idx}', spec_to_figure(encdec_attn[0]), self.global_step)
-                self.logger.add_figure(
-                    f'mel_gtdur_{batch_idx}',
-                    spec_to_figure(model_out['mel_out'][0], vmin, vmax), self.global_step)
-                # with pred duration
-                model_out = self.model(sample['txt_tokens'], ref_mel2ph=mel2ph, ref_mel2word=mel2word, spk_embed=spk_embed, emo_embed=emo_embed, ref_mels=ref_mels,
-                                       global_steps=self.global_step, infer=True)
-                self.logger.add_figure(
-                    f'mel_{batch_idx}',
-                    spec_to_figure(model_out['mel_out'][0], vmin, vmax), self.global_step)
-                wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu())
-                self.logger.add_audio(f'wav_{batch_idx}', wav_pred, self.global_step, hparams['audio_sample_rate'])
-            # gt wav
-            if self.global_step <= hparams['valid_infer_interval']:
-                mel_gt = sample['mels'][0].cpu()
-                wav_gt = self.vocoder.spec2wav(mel_gt)
-                self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, 22050)
-        return outputs
-
-    ############
-    # infer
-    ############
-    def test_step(self, sample, batch_idx):
-        spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
-        emo_embed = sample.get('emo_embed')
-        txt_tokens = sample['txt_tokens']
-        mel2ph, uv, f0 = None, None, None
-        ref_mel2word = sample['mel2word']
-        ref_mel2ph = sample['mel2ph']
-        ref_mels = sample['mels']
-        if hparams['use_gt_dur']:
-            mel2ph = sample['mel2ph']
-        if hparams['use_gt_f0']:
-            f0 = sample['f0']
-            uv = sample['uv']
-        global_steps = 200000
-        run_model = lambda: self.model(
-            txt_tokens, spk_embed=spk_embed, emo_embed=emo_embed, mel2ph=mel2ph, ref_mel2ph=ref_mel2ph, ref_mel2word=ref_mel2word,
-            f0=f0, uv=uv, ref_mels=ref_mels, global_steps=global_steps, infer=True)
-        outputs = run_model()
-        sample['outputs'] = self.model.out2mel(outputs['mel_out'])
-        sample['mel2ph_pred'] = outputs['mel2ph']
-        if hparams['use_pitch_embed']:
-            sample['f0'] = denorm_f0(sample['f0'], sample['uv'], hparams)
-            if hparams['pitch_type'] == 'ph':
-                sample['f0'] = torch.gather(F.pad(sample['f0'], [1, 0]), 1, sample['mel2ph'])
-            sample['f0_pred'] = outputs.get('f0_denorm')
-
-        return self.after_infer(sample)
-
-
-
-    def after_infer(self, predictions, sil_start_frame=0):
-        predictions = utils.unpack_dict_to_list(predictions)
-        assert len(predictions) == 1, 'Only support batch_size=1 in inference.'
-        prediction = predictions[0]
-        prediction = utils.tensors_to_np(prediction)
-        item_name = prediction.get('item_name')
-        text = prediction.get('text')
-        ph_tokens = prediction.get('txt_tokens')
-        mel_gt = prediction["mels"]
-        mel2ph_gt = prediction.get("mel2ph")
-        mel2ph_gt = mel2ph_gt if mel2ph_gt is not None else None
-        mel_pred = prediction["outputs"]
-        mel2ph_pred = prediction.get("mel2ph_pred")
-        f0_gt = prediction.get("f0")
-        f0_pred = prediction.get("f0_pred")
-
-        str_phs = None
-        if self.phone_encoder is not None and 'txt_tokens' in prediction:
-            str_phs = self.phone_encoder.decode(prediction['txt_tokens'], strip_padding=True)
-
-        if 'encdec_attn' in prediction:
-            encdec_attn = prediction['encdec_attn']  # (1, Tph, Tmel)
-            encdec_attn = encdec_attn[encdec_attn.max(-1).sum(-1).argmax(-1)]
-            txt_lengths = prediction.get('txt_lengths')
-            encdec_attn = encdec_attn.T[:, :txt_lengths]
-        else:
-            encdec_attn = None
-
-        wav_pred = self.vocoder.spec2wav(mel_pred, f0=f0_pred)
-        wav_pred[:sil_start_frame * hparams['hop_size']] = 0
-        gen_dir = self.gen_dir
-        base_fn = f'[{self.results_id:06d}][{item_name}][%s]'
-        # if text is not None:
-        #     base_fn += text.replace(":", "%3A")[:80]
-        base_fn = base_fn.replace(' ', '_')
-        if not hparams['profile_infer']:
-            os.makedirs(gen_dir, exist_ok=True)
-            os.makedirs(f'{gen_dir}/wavs', exist_ok=True)
-            os.makedirs(f'{gen_dir}/plot', exist_ok=True)
-            if hparams.get('save_mel_npy', False):
-                os.makedirs(f'{gen_dir}/npy', exist_ok=True)
-            if 'encdec_attn' in prediction:
-                os.makedirs(f'{gen_dir}/attn_plot', exist_ok=True)
-            self.saving_results_futures.append(
-                self.saving_result_pool.apply_async(self.save_result, args=[
-                    wav_pred, mel_pred, base_fn % 'TTS', gen_dir, str_phs, mel2ph_pred, encdec_attn]))
-
-            if mel_gt is not None and hparams['save_gt']:
-                wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt)
-                self.saving_results_futures.append(
-                    self.saving_result_pool.apply_async(self.save_result, args=[
-                        wav_gt, mel_gt, base_fn % 'Ref', gen_dir, str_phs, mel2ph_gt]))
-                if hparams['save_f0']:
-                    import matplotlib.pyplot as plt
-                    f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
-                    f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams)
-                    fig = plt.figure()
-                    plt.plot(f0_pred_, label=r'$\hat{f_0}$')
-                    plt.plot(f0_gt_, label=r'$f_0$')
-                    plt.legend()
-                    plt.tight_layout()
-                    plt.savefig(f'{gen_dir}/plot/[F0][{item_name}]{text}.png', format='png')
-                    plt.close(fig)
-
-            print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
-        self.results_id += 1
-        return {
-            'item_name': item_name,
-            'text': text,
-            'ph_tokens': self.phone_encoder.decode(ph_tokens.tolist()),
-            'wav_fn_pred': base_fn % 'TTS',
-            'wav_fn_gt': base_fn % 'Ref',
-        }
-
-
-
-    @staticmethod
-    def save_result(wav_out, mel, base_fn, gen_dir, str_phs=None, mel2ph=None, alignment=None):
-        audio.save_wav(wav_out, f'{gen_dir}/wavs/{base_fn}.wav', hparams['audio_sample_rate'],
-                       norm=hparams['out_wav_norm'])
-        fig = plt.figure(figsize=(14, 10))
-        spec_vmin = hparams['mel_vmin']
-        spec_vmax = hparams['mel_vmax']
-        heatmap = plt.pcolor(mel.T, vmin=spec_vmin, vmax=spec_vmax)
-        fig.colorbar(heatmap)
-        f0, _ = get_pitch(wav_out, mel, hparams)
-        f0 = f0 / 10 * (f0 > 0)
-        plt.plot(f0, c='white', linewidth=1, alpha=0.6)
-        if mel2ph is not None and str_phs is not None:
-            decoded_txt = str_phs.split(" ")
-            dur = mel2ph_to_dur(torch.LongTensor(mel2ph)[None, :], len(decoded_txt))[0].numpy()
-            dur = [0] + list(np.cumsum(dur))
-            for i in range(len(dur) - 1):
-                shift = (i % 20) + 1
-                plt.text(dur[i], shift, decoded_txt[i])
-                plt.hlines(shift, dur[i], dur[i + 1], colors='b' if decoded_txt[i] != '|' else 'black')
-                plt.vlines(dur[i], 0, 5, colors='b' if decoded_txt[i] != '|' else 'black',
-                           alpha=1, linewidth=1)
-        plt.tight_layout()
-        plt.savefig(f'{gen_dir}/plot/{base_fn}.png', format='png')
-        plt.close(fig)
-        if hparams.get('save_mel_npy', False):
-            np.save(f'{gen_dir}/npy/{base_fn}', mel)
-        if alignment is not None:
-            fig, ax = plt.subplots(figsize=(12, 16))
-            im = ax.imshow(alignment, aspect='auto', origin='lower',
-                           interpolation='none')
-            ax.set_xticks(np.arange(0, alignment.shape[1], 5))
-            ax.set_yticks(np.arange(0, alignment.shape[0], 10))
-            ax.set_ylabel("$S_p$ index")
-            ax.set_xlabel("$H_c$ index")
-            fig.colorbar(im, ax=ax)
-            fig.savefig(f'{gen_dir}/attn_plot/{base_fn}_attn.png', format='png')
-            plt.close(fig)
-
-
-

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

@@ -1,76 +0,0 @@
-import glob
-import json
-import os
-import re
-
-import librosa
-import torch
-
-import utils
-from modules.hifigan.hifigan import HifiGanGenerator
-from utils.hparams import hparams, set_hparams
-from vocoders.base_vocoder import register_vocoder
-from vocoders.pwg import PWG
-from vocoders.vocoder_utils import denoise
-
-
-def load_model(config_path, checkpoint_path):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    ckpt_dict = torch.load(checkpoint_path, map_location="cpu")
-    if '.yaml' in config_path:
-        config = set_hparams(config_path, global_hparams=False)
-        state = ckpt_dict["state_dict"]["model_gen"]
-    elif '.json' in config_path:
-        config = json.load(open(config_path, 'r'))
-        state = ckpt_dict["generator"]
-
-    model = HifiGanGenerator(config)
-    model.load_state_dict(state, strict=True)
-    model.remove_weight_norm()
-    model = model.eval().to(device)
-    print(f"| Loaded model parameters from {checkpoint_path}.")
-    print(f"| HifiGAN device: {device}.")
-    return model, config, device
-
-
-total_time = 0
-
-
-@register_vocoder
-class HifiGAN(PWG):
-    def __init__(self):
-        base_dir = hparams['vocoder_ckpt']
-        config_path = f'{base_dir}/config.yaml'
-        if os.path.exists(config_path):
-            ckpt = sorted(glob.glob(f'{base_dir}/model_ckpt_steps_*.ckpt'), key=
-            lambda x: int(re.findall(f'{base_dir}/model_ckpt_steps_(\d+).ckpt', x)[0]))[-1]
-            print('| load HifiGAN: ', ckpt)
-            self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt)
-        else:
-            config_path = f'{base_dir}/config.json'
-            ckpt = f'{base_dir}/generator_v1'
-            if os.path.exists(config_path):
-                self.model, self.config, self.device = load_model(config_path=config_path, checkpoint_path=ckpt)
-
-    def spec2wav(self, mel, **kwargs):
-        device = self.device
-        with torch.no_grad():
-            c = torch.FloatTensor(mel).unsqueeze(0).transpose(2, 1).to(device)
-            with utils.Timer('hifigan', print_time=hparams['profile_infer']):
-                f0 = kwargs.get('f0')
-                if f0 is not None and hparams.get('use_nsf'):
-                    f0 = torch.FloatTensor(f0[None, :]).to(device)
-                    y = self.model(c, f0).view(-1)
-                else:
-                    y = self.model(c).view(-1)
-        wav_out = y.cpu().numpy()
-        if hparams.get('vocoder_denoise_c', 0.0) > 0:
-            wav_out = denoise(wav_out, v=hparams['vocoder_denoise_c'])
-        return wav_out
-
-    # @staticmethod
-    # def wav2spec(wav_fn, **kwargs):
-    #     wav, _ = librosa.core.load(wav_fn, sr=hparams['audio_sample_rate'])
-    #     wav_torch = torch.FloatTensor(wav)[None, :]
-    #     mel = mel_spectrogram(wav_torch, hparams).numpy()[0]
-    #     return wav, mel.T

spaces/AIGC-Audio/AudioGPT/audio_to_text/captioning/utils/predict_nn.py

@@ -1,49 +0,0 @@
-import json
-import random
-import argparse
-import numpy as np
-from tqdm import tqdm
-from h5py import File
-import sklearn.metrics
-
-random.seed(1)
-
-parser = argparse.ArgumentParser()
-parser.add_argument("train_feature", type=str)
-parser.add_argument("train_corpus", type=str)
-parser.add_argument("pred_feature", type=str)
-parser.add_argument("output_json", type=str)
-
-args = parser.parse_args()
-train_embs = []
-train_idx_to_audioid = []
-with File(args.train_feature, "r") as store:
-    for audio_id, embedding in tqdm(store.items(), ascii=True):
-        train_embs.append(embedding[()])
-        train_idx_to_audioid.append(audio_id)
-
-train_annotation = json.load(open(args.train_corpus, "r"))["audios"]
-train_audioid_to_tokens = {}
-for item in train_annotation:
-    audio_id = item["audio_id"]
-    train_audioid_to_tokens[audio_id] = [cap_item["tokens"] for cap_item in item["captions"]]
-train_embs = np.stack(train_embs)
-
-
-pred_data = []
-pred_embs = []
-pred_idx_to_audioids = []
-with File(args.pred_feature, "r") as store:
-    for audio_id, embedding in tqdm(store.items(), ascii=True):
-        pred_embs.append(embedding[()])
-        pred_idx_to_audioids.append(audio_id)
-pred_embs = np.stack(pred_embs)
-
-similarity = sklearn.metrics.pairwise.cosine_similarity(pred_embs, train_embs)
-for idx, audio_id in enumerate(pred_idx_to_audioids):
-    train_idx = similarity[idx].argmax()
-    pred_data.append({
-        "filename": audio_id,
-        "tokens": random.choice(train_audioid_to_tokens[train_idx_to_audioid[train_idx]])
-    })
-json.dump({"predictions": pred_data}, open(args.output_json, "w"), ensure_ascii=False, indent=4)

spaces/AIGC-Audio/Make_An_Audio/ldm/modules/diffusionmodules/util.py

@@ -1,267 +0,0 @@
-# adopted from
-# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
-# and
-# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
-# and
-# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
-#
-# thanks!
-
-
-import os
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import repeat
-
-from ldm.util import instantiate_from_config
-
-
-def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
-    if schedule == "linear":
-        betas = (
-                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
-        )
-
-    elif schedule == "cosine":
-        timesteps = (
-                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
-        )
-        alphas = timesteps / (1 + cosine_s) * np.pi / 2
-        alphas = torch.cos(alphas).pow(2)
-        alphas = alphas / alphas[0]
-        betas = 1 - alphas[1:] / alphas[:-1]
-        betas = np.clip(betas, a_min=0, a_max=0.999)
-
-    elif schedule == "sqrt_linear":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
-    elif schedule == "sqrt":
-        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
-    else:
-        raise ValueError(f"schedule '{schedule}' unknown.")
-    return betas.numpy()
-
-
-def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
-    if ddim_discr_method == 'uniform':
-        c = num_ddpm_timesteps // num_ddim_timesteps
-        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
-    elif ddim_discr_method == 'quad':
-        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
-    else:
-        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
-
-    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
-    # add one to get the final alpha values right (the ones from first scale to data during sampling)
-    steps_out = ddim_timesteps + 1
-    if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
-    return steps_out
-
-
-def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
-    # select alphas for computing the variance schedule
-    alphas = alphacums[ddim_timesteps]
-    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
-
-    # according the the formula provided in https://arxiv.org/abs/2010.02502
-    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
-    if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
-              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
-    return sigmas, alphas, alphas_prev
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas)
-
-
-def extract_into_tensor(a, t, x_shape):
-    b, *_ = t.shape
-    out = a.gather(-1, t)
-    return out.reshape(b, *((1,) * (len(x_shape) - 1)))
-
-
-def checkpoint(func, inputs, params, flag):
-    """
-    Evaluate a function without caching intermediate activations, allowing for
-    reduced memory at the expense of extra compute in the backward pass.
-    :param func: the function to evaluate.
-    :param inputs: the argument sequence to pass to `func`.
-    :param params: a sequence of parameters `func` depends on but does not
-                   explicitly take as arguments.
-    :param flag: if False, disable gradient checkpointing.
-    """
-    if flag:
-        args = tuple(inputs) + tuple(params)
-        return CheckpointFunction.apply(func, len(inputs), *args)
-    else:
-        return func(*inputs)
-
-
-class CheckpointFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_tensors = list(args[:length])
-        ctx.input_params = list(args[length:])
-
-        with torch.no_grad():
-            output_tensors = ctx.run_function(*ctx.input_tensors)
-        return output_tensors
-
-    @staticmethod
-    def backward(ctx, *output_grads):
-        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
-        with torch.enable_grad():
-            # Fixes a bug where the first op in run_function modifies the
-            # Tensor storage in place, which is not allowed for detach()'d
-            # Tensors.
-            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
-            output_tensors = ctx.run_function(*shallow_copies)
-        input_grads = torch.autograd.grad(
-            output_tensors,
-            ctx.input_tensors + ctx.input_params,
-            output_grads,
-            allow_unused=True,
-        )
-        del ctx.input_tensors
-        del ctx.input_params
-        del output_tensors
-        return (None, None) + input_grads
-
-
-def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
-    """
-    Create sinusoidal timestep embeddings.
-    :param timesteps: a 1-D Tensor of N indices, one per batch element.
-                      These may be fractional.
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an [N x dim] Tensor of positional embeddings.
-    """
-    if not repeat_only:
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
-        ).to(device=timesteps.device)
-        args = timesteps[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-    else:
-        embedding = repeat(timesteps, 'b -> b d', d=dim)
-    return embedding
-
-
-def zero_module(module):
-    """
-    Zero out the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().zero_()
-    return module
-
-
-def scale_module(module, scale):
-    """
-    Scale the parameters of a module and return it.
-    """
-    for p in module.parameters():
-        p.detach().mul_(scale)
-    return module
-
-
-def mean_flat(tensor):
-    """
-    Take the mean over all non-batch dimensions.
-    """
-    return tensor.mean(dim=list(range(1, len(tensor.shape))))
-
-
-def normalization(channels):
-    """
-    Make a standard normalization layer.
-    :param channels: number of input channels.
-    :return: an nn.Module for normalization.
-    """
-    return GroupNorm32(32, channels)
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * torch.sigmoid(x)
-
-
-class GroupNorm32(nn.GroupNorm):
-    def forward(self, x):
-        return super().forward(x.float()).type(x.dtype)
-
-def conv_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D convolution module.
-    """
-    if dims == 1:
-        return nn.Conv1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.Conv2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.Conv3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-def linear(*args, **kwargs):
-    """
-    Create a linear module.
-    """
-    return nn.Linear(*args, **kwargs)
-
-
-def avg_pool_nd(dims, *args, **kwargs):
-    """
-    Create a 1D, 2D, or 3D average pooling module.
-    """
-    if dims == 1:
-        return nn.AvgPool1d(*args, **kwargs)
-    elif dims == 2:
-        return nn.AvgPool2d(*args, **kwargs)
-    elif dims == 3:
-        return nn.AvgPool3d(*args, **kwargs)
-    raise ValueError(f"unsupported dimensions: {dims}")
-
-
-class HybridConditioner(nn.Module):
-
-    def __init__(self, c_concat_config, c_crossattn_config):
-        super().__init__()
-        self.concat_conditioner = instantiate_from_config(c_concat_config)
-        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
-
-    def forward(self, c_concat, c_crossattn):
-        c_concat = self.concat_conditioner(c_concat)
-        c_crossattn = self.crossattn_conditioner(c_crossattn)
-        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
-
-
-def noise_like(shape, device, repeat=False):
-    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
-    noise = lambda: torch.randn(shape, device=device)
-    return repeat_noise() if repeat else noise()

spaces/AIGC-Audio/Make_An_Audio/vocoder/hifigan/modules.py

@@ -1,332 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from pathlib import Path
-import yaml
-import numpy as np
-from argparse import Namespace
-LRELU_SLOPE = 0.1
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size*dilation - dilation)/2)
-
-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)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.h = h
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            xt = c2(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.h = h
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            xt = c(xt)
-            x = xt + x
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, h):
-        super(Generator, self).__init__()
-        self.h = h
-        self.num_kernels = len(h.resblock_kernel_sizes)
-        self.num_upsamples = len(h.upsample_rates)
-        self.conv_pre = weight_norm(Conv1d(80, h.upsample_initial_channel, 7, 1, padding=3))
-        resblock = ResBlock1 if h.resblock == '1' else ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = h.upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
-                self.resblocks.append(resblock(h, ch, k, d))
-
-        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
-        self.ups.apply(init_weights)
-        self.conv_post.apply(init_weights)
-
-    def forward(self, x):
-        x = self.conv_pre(x)
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, 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()
-        remove_weight_norm(self.conv_pre)
-        remove_weight_norm(self.conv_post)
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        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(5, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        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, 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):
-        super(MultiPeriodDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorP(2),
-            DiscriminatorP(3),
-            DiscriminatorP(5),
-            DiscriminatorP(7),
-            DiscriminatorP(11),
-        ])
-
-    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)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
-            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
-            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
-            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiScaleDiscriminator(torch.nn.Module):
-    def __init__(self):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.discriminators = nn.ModuleList([
-            DiscriminatorS(use_spectral_norm=True),
-            DiscriminatorS(),
-            DiscriminatorS(),
-        ])
-        self.meanpools = nn.ModuleList([
-            AvgPool1d(4, 2, padding=2),
-            AvgPool1d(4, 2, padding=2)
-        ])
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            if i != 0:
-                y = self.meanpools[i-1](y)
-                y_hat = self.meanpools[i-1](y_hat)
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            fmap_rs.append(fmap_r)
-            y_d_gs.append(y_d_g)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-def feature_loss(fmap_r, fmap_g):
-    loss = 0
-    for dr, dg in zip(fmap_r, fmap_g):
-        for rl, gl in zip(dr, dg):
-            loss += torch.mean(torch.abs(rl - gl))
-
-    return loss*2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
-    loss = 0
-    r_losses = []
-    g_losses = []
-    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
-        r_loss = torch.mean((1-dr)**2)
-        g_loss = torch.mean(dg**2)
-        loss += (r_loss + g_loss)
-        r_losses.append(r_loss.item())
-        g_losses.append(g_loss.item())
-
-    return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
-    loss = 0
-    gen_losses = []
-    for dg in disc_outputs:
-        l = torch.mean((1-dg)**2)
-        gen_losses.append(l)
-        loss += l
-
-    return loss, gen_losses
-
-
-class VocoderHifigan(object):
-    def __init__(self, ckpt_vocoder,device='cuda'):
-        
-        with open(os.path.join(ckpt_vocoder,'args.yml'), 'r') as f:
-            vocoder_args = Namespace(**yaml.load(f, Loader=yaml.UnsafeLoader))
-
-        self.generator = Generator(vocoder_args)
-        netG_path = os.path.join(ckpt_vocoder,'best_netG.pt')
-        if os.path.exists(netG_path):
-            vocoder_sd = torch.load(netG_path, map_location='cpu')
-            self.generator.load_state_dict(vocoder_sd['generator'])
-        self.generator.eval()
-
-        self.device = device
-        self.generator.to(self.device)
-
-    def vocode(self, spec, global_step=None):
-        with torch.no_grad():
-            if isinstance(spec,np.ndarray):
-                spec = torch.from_numpy(spec).unsqueeze(0)
-            spec = spec.to(dtype=torch.float32,device=self.device)
-            return self.generator(spec).squeeze().cpu().numpy()
-
-class VocoderHifigan_noload(object):
-    def __init__(self, vocoder_args,device='cuda'):
-        self.generator = Generator(vocoder_args)
-        self.generator.eval()
-
-        self.device = device
-        self.generator.to(self.device)
-
-    def vocode(self, spec, global_step=None):
-        with torch.no_grad():
-            if isinstance(spec,np.ndarray):
-                spec = torch.from_numpy(spec).unsqueeze(0)
-            spec = spec.to(dtype=torch.float32,device=self.device)
-            return self.generator(spec).squeeze().cpu().numpy()

spaces/Aaaaaaaabdualh/poetry/app.py

@@ -1,53 +0,0 @@
-import gc 
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
-           ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
-            ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
-              ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
-                 ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
-                   ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
-                    ,1.0, 50, 1.0, 1.0, 114 ],['.'
-                       ,1.0, 50, 1.0, 1.0, 114]]
-                     
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND 
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed. Seed should be 0 or more (not empty) 
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long) 
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) 
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
-    if not int(seed) >= 0: seed=114
-    set_seed(seed)
-    gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
-              min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
-               num_beams=5, num_return_sequences=1)[0]["generated_text"]
-    poetry =""
-    for line in gen.split('.')[:-1]:
-        poetry += line #+ "\n"
-    return poetry
-poetry = gr.Interface(fn=sayPoetry, 
-                    inputs=[
-                        gr.Textbox(label="Enter short prompt or select from examples:"), 
-                        gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control  temperature'),
-                        gr.Slider(25, 100, step=1,value=50, label='control  top k'),
-                        gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control  top p'),
-                        gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control  penalty'),
-                        gr.Number(value=139750, precision=0, label='Seed'),
-                    ], 
-                outputs=[gr.Textbox(label="Generated Poetry:")], 
-                    
-                allow_flagging='never',
-                title='Arabic Poetry Generation Demo (updated Jan. 2023)',
-                description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
-                examples=samples,
-                cache_examples=False,
-                article = notes)
-poetry.launch()         

spaces/Abhilashvj/planogram-compliance/utils/loggers/clearml/clearml_utils.py

@@ -1,206 +0,0 @@
-"""Main Logger class for ClearML experiment tracking."""
-import glob
-import re
-from pathlib import Path
-
-import numpy as np
-import yaml
-
-from utils.plots import Annotator, colors
-
-try:
-    import clearml
-    from clearml import Dataset, Task
-
-    assert hasattr(
-        clearml, "__version__"
-    )  # verify package import not local dir
-except (ImportError, AssertionError):
-    clearml = None
-
-
-def construct_dataset(clearml_info_string):
-    """Load in a clearml dataset and fill the internal data_dict with its contents."""
-    dataset_id = clearml_info_string.replace("clearml://", "")
-    dataset = Dataset.get(dataset_id=dataset_id)
-    dataset_root_path = Path(dataset.get_local_copy())
-
-    # We'll search for the yaml file definition in the dataset
-    yaml_filenames = list(
-        glob.glob(str(dataset_root_path / "*.yaml"))
-        + glob.glob(str(dataset_root_path / "*.yml"))
-    )
-    if len(yaml_filenames) > 1:
-        raise ValueError(
-            "More than one yaml file was found in the dataset root, cannot determine which one contains "
-            "the dataset definition this way."
-        )
-    elif len(yaml_filenames) == 0:
-        raise ValueError(
-            "No yaml definition found in dataset root path, check that there is a correct yaml file "
-            "inside the dataset root path."
-        )
-    with open(yaml_filenames[0]) as f:
-        dataset_definition = yaml.safe_load(f)
-
-    assert set(dataset_definition.keys()).issuperset(
-        {"train", "test", "val", "nc", "names"}
-    ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
-
-    data_dict = dict()
-    data_dict["train"] = (
-        str((dataset_root_path / dataset_definition["train"]).resolve())
-        if dataset_definition["train"]
-        else None
-    )
-    data_dict["test"] = (
-        str((dataset_root_path / dataset_definition["test"]).resolve())
-        if dataset_definition["test"]
-        else None
-    )
-    data_dict["val"] = (
-        str((dataset_root_path / dataset_definition["val"]).resolve())
-        if dataset_definition["val"]
-        else None
-    )
-    data_dict["nc"] = dataset_definition["nc"]
-    data_dict["names"] = dataset_definition["names"]
-
-    return data_dict
-
-
-class ClearmlLogger:
-    """Log training runs, datasets, models, and predictions to ClearML.
-
-    This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default,
-    this information includes hyperparameters, system configuration and metrics, model metrics, code information and
-    basic data metrics and analyses.
-
-    By providing additional command line arguments to train.py, datasets,
-    models and predictions can also be logged.
-    """
-
-    def __init__(self, opt, hyp):
-        """
-        - Initialize ClearML Task, this object will capture the experiment
-        - Upload dataset version to ClearML Data if opt.upload_dataset is True
-
-        arguments:
-        opt (namespace) -- Commandline arguments for this run
-        hyp (dict) -- Hyperparameters for this run
-
-        """
-        self.current_epoch = 0
-        # Keep tracked of amount of logged images to enforce a limit
-        self.current_epoch_logged_images = set()
-        # Maximum number of images to log to clearML per epoch
-        self.max_imgs_to_log_per_epoch = 16
-        # Get the interval of epochs when bounding box images should be logged
-        self.bbox_interval = opt.bbox_interval
-        self.clearml = clearml
-        self.task = None
-        self.data_dict = None
-        if self.clearml:
-            self.task = Task.init(
-                project_name=opt.project
-                if opt.project != "runs/train"
-                else "YOLOv5",
-                task_name=opt.name if opt.name != "exp" else "Training",
-                tags=["YOLOv5"],
-                output_uri=True,
-                reuse_last_task_id=opt.exist_ok,
-                auto_connect_frameworks={"pytorch": False}
-                # We disconnect pytorch auto-detection, because we added manual model save points in the code
-            )
-            # ClearML's hooks will already grab all general parameters
-            # Only the hyperparameters coming from the yaml config file
-            # will have to be added manually!
-            self.task.connect(hyp, name="Hyperparameters")
-            self.task.connect(opt, name="Args")
-
-            # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent
-            self.task.set_base_docker(
-                "ultralytics/yolov5:latest",
-                docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"',
-                docker_setup_bash_script="pip install clearml",
-            )
-
-            # Get ClearML Dataset Version if requested
-            if opt.data.startswith("clearml://"):
-                # data_dict should have the following keys:
-                # names, nc (number of classes), test, train, val (all three relative paths to ../datasets)
-                self.data_dict = construct_dataset(opt.data)
-                # Set data to data_dict because wandb will crash without this information and opt is the best way
-                # to give it to them
-                opt.data = self.data_dict
-
-    def log_debug_samples(self, files, title="Debug Samples"):
-        """
-        Log files (images) as debug samples in the ClearML task.
-
-        arguments:
-        files (List(PosixPath)) a list of file paths in PosixPath format
-        title (str) A title that groups together images with the same values
-        """
-        for f in files:
-            if f.exists():
-                it = re.search(r"_batch(\d+)", f.name)
-                iteration = int(it.groups()[0]) if it else 0
-                self.task.get_logger().report_image(
-                    title=title,
-                    series=f.name.replace(it.group(), ""),
-                    local_path=str(f),
-                    iteration=iteration,
-                )
-
-    def log_image_with_boxes(
-        self, image_path, boxes, class_names, image, conf_threshold=0.25
-    ):
-        """
-        Draw the bounding boxes on a single image and report the result as a ClearML debug sample.
-
-        arguments:
-        image_path (PosixPath) the path the original image file
-        boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
-        class_names (dict): dict containing mapping of class int to class name
-        image (Tensor): A torch tensor containing the actual image data
-        """
-        if (
-            len(self.current_epoch_logged_images)
-            < self.max_imgs_to_log_per_epoch
-            and self.current_epoch >= 0
-        ):
-            # Log every bbox_interval times and deduplicate for any intermittend extra eval runs
-            if (
-                self.current_epoch % self.bbox_interval == 0
-                and image_path not in self.current_epoch_logged_images
-            ):
-                im = np.ascontiguousarray(
-                    np.moveaxis(
-                        image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2
-                    )
-                )
-                annotator = Annotator(im=im, pil=True)
-                for i, (conf, class_nr, box) in enumerate(
-                    zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])
-                ):
-                    color = colors(i)
-
-                    class_name = class_names[int(class_nr)]
-                    confidence_percentage = round(float(conf) * 100, 2)
-                    label = f"{class_name}: {confidence_percentage}%"
-
-                    if conf > conf_threshold:
-                        annotator.rectangle(box.cpu().numpy(), outline=color)
-                        annotator.box_label(
-                            box.cpu().numpy(), label=label, color=color
-                        )
-
-                annotated_image = annotator.result()
-                self.task.get_logger().report_image(
-                    title="Bounding Boxes",
-                    series=image_path.name,
-                    iteration=self.current_epoch,
-                    image=annotated_image,
-                )
-                self.current_epoch_logged_images.add(image_path)

spaces/AchyuthGamer/OpenGPT/client/css/style.css

@@ -1,18 +0,0 @@
-@import "./global.css";
-@import "./hljs.css";
-@import "./main.css";
-@import "./sidebar.css";
-@import "./conversation.css";
-@import "./message.css";
-@import "./stop-generating.css";
-@import "./typing.css";
-@import "./checkbox.css";
-@import "./label.css";
-@import "./button.css";
-@import "./buttons.css";
-@import "./dropdown.css";
-@import "./field.css";
-@import "./select.css";
-@import "./options.css";
-@import "./settings.css";
-@import "./message-input.css";

spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/deprecated/Forefront.py

@@ -1,40 +0,0 @@
-from __future__ import annotations
-
-import json
-
-import requests
-
-from ...typing import Any, CreateResult
-from ..base_provider import BaseProvider
-
-
-class Forefront(BaseProvider):
-    url                   = "https://forefront.com"
-    supports_stream       = True
-    supports_gpt_35_turbo = True
-
-    @staticmethod
-    def create_completion(
-        model: str,
-        messages: list[dict[str, str]],
-        stream: bool, **kwargs: Any) -> CreateResult:
-        
-        json_data = {
-            "text"          : messages[-1]["content"],
-            "action"        : "noauth",
-            "id"            : "",
-            "parentId"      : "",
-            "workspaceId"   : "",
-            "messagePersona": "607e41fe-95be-497e-8e97-010a59b2e2c0",
-            "model"         : "gpt-4",
-            "messages"      : messages[:-1] if len(messages) > 1 else [],
-            "internetMode"  : "auto",
-        }
-
-        response = requests.post("https://streaming.tenant-forefront-default.knative.chi.coreweave.com/free-chat",
-            json=json_data, stream=True)
-        
-        response.raise_for_status()
-        for token in response.iter_lines():
-            if b"delta" in token:
-                yield json.loads(token.decode().split("data: ")[1])["delta"]

spaces/AdVisual/MaskCut/Dockerfile

@@ -1,28 +0,0 @@
-FROM python:3.9
-
-WORKDIR /code
-
-# Install git
-RUN apt-get update && apt-get install -y git
-
-RUN useradd -m -u 1000 user
-
-USER user
-
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-WORKDIR $HOME/app
-
-# Install CutLER
-RUN git clone --recursive https://github.com/Ad-Visual/CutLER $HOME/app/CutLER
-
-RUN pip install --no-cache-dir --upgrade -r $HOME/app/CutLER/requirements.txt
-
-COPY ./requirements.txt $HOME/app/requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r $HOME/app/requirements.txt
-
-COPY --chown=user . $HOME/app
-
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

spaces/AgentVerse/agentVerse/agentverse/memory/sde_team.py

@@ -1,38 +0,0 @@
-import re
-from string import Template
-from typing import List
-
-from pydantic import Field, validator
-
-from agentverse.initialization import load_llm
-from agentverse.llms.base import BaseLLM
-from agentverse.message import Message
-
-from . import memory_registry
-from .base import BaseMemory
-
-
-@memory_registry.register("sde_team")
-class SdeTeamMemory(BaseMemory):
-    """SdeTeamMemory is a memory for SdeTeamEnvironment.
-    It is a simple memory that only stores the most recent info in the buffer.
-    TODO: add summarized history
-    """
-    buffer: str = Field(default="")
-
-    def add_message(self, messages: List[Message]) -> None:
-        new_lines = "\n".join([message.content for message in messages])
-        if messages[0].sender == "code_writer":
-            self.buffer = new_lines
-        elif messages[0].sender == "code_tester":
-            self.buffer += "\n\n"
-            self.buffer += new_lines
-        elif messages[0].sender == "code_reviewer":
-            self.buffer += "\n\n"
-            self.buffer += new_lines
-
-    def to_string(self, *args, **kwargs) -> str:
-        return self.buffer
-
-    def reset(self) -> None:
-        self.buffer = ""

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/bejeweled/board/chess/CreateChess.js

@@ -1,30 +0,0 @@
-import RandomSymbol from './RandomSymobl.js';
-
-var CreateChess = function (tileX, tileY, symbols) {
-    var scene = this.scene,
-        board = this.board,
-        scope = this.chessCallbackScope;
-
-    // Get symbol
-    var symbol = RandomSymbol(board, tileX, tileY, symbols, scope);
-    // Create game object
-    var gameObject;
-    if (scope) {
-        gameObject = this.chessCreateCallback.call(scope, board);
-    } else {
-        gameObject = this.chessCreateCallback(board);
-    }
-    // Set symbol, it also fires 'changedata_symbol' event
-    gameObject.setData('symbol', symbol);
-    // Add to board
-    board.addChess(gameObject, tileX, tileY, this.chessTileZ, true);
-    // behaviors
-    gameObject.rexMoveTo = this.rexBoard.add.moveTo(gameObject, this.chessMoveTo);
-
-    if (this.layer) {
-        // Move chess gameObject from scene to layer
-        this.layer.add(gameObject);
-    }
-}
-
-export default CreateChess;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/gridbuttons/Factory.d.ts

@@ -1,5 +0,0 @@
-import GridButtons from './GridButtons';
-
-export default function (
-    config?: GridButtons.IConfig
-): GridButtons;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateCircleMaskImage.js

@@ -1,22 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import CircleMaskImage from '../../circlemaskimage/CircleMaskImage.js';
-import SetTextureProperties from './utils/SetTextureProperties.js';
-
-var CreateCircleMaskImage = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-    var gameObject = new CircleMaskImage(scene, 0, 0, data.key, data.frame, data);
-
-    if (data.width !== undefined) {
-        gameObject.setDisplayWidth(data.width);
-    }
-    if (data.height !== undefined) {
-        gameObject.setDisplayHeight(data.height);
-    }
-
-    SetTextureProperties(gameObject, data);
-
-    scene.add.existing(gameObject);
-    return gameObject;
-}
-
-export default CreateCircleMaskImage;

spaces/Altinas/vits-uma-genshin-honkais/transforms.py

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

spaces/Amon1/ChatGPTForAcadamic/crazy_functions/test_project/latex/attention/background.tex

@@ -1,58 +0,0 @@
-The goal of reducing sequential computation also forms the foundation of the Extended Neural GPU \citep{extendedngpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as basic building block, computing hidden representations in parallel for all input and output positions. In these models, the number of operations required to relate signals from two arbitrary input or output positions grows in the distance between positions, linearly for ConvS2S and logarithmically for ByteNet. This makes it more difficult to learn dependencies between distant positions \citep{hochreiter2001gradient}. In the Transformer this is reduced to a constant number of operations, albeit at the cost of reduced effective resolution due to averaging attention-weighted positions, an effect we counteract with Multi-Head Attention as described in section~\ref{sec:attention}. 
-
-Self-attention, sometimes called intra-attention is an attention mechanism relating different positions of a single sequence in order to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, textual entailment and learning task-independent sentence representations \citep{cheng2016long, decomposableAttnModel, paulus2017deep, lin2017structured}.
-
-End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks \citep{sukhbaatar2015}.
-
-To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.
-In the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as \citep{neural_gpu, NalBytenet2017} and \citep{JonasFaceNet2017}.
-
-
-%\citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%For example,! in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at low computation cost, making it an essential ingredient in competitive recurrent models for machine translation.
-
-%A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-%After the seminal models introduced in \citep{sutskever14, bahdanau2014neural, cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation (MT) and language modeling with recurrent endoder-decoder and recurrent language models. Recent effort \citep{shazeer2017outrageously} has successfully combined the power of conditional computation with sequence models to train very large models for MT, pushing SOTA at lower computational cost.
-
-%Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state precludes processing all timesteps at once, instead requiring long sequences of sequential operations.  In practice, this results in greatly reduced computational efficiency, as on modern computing hardware, a single operation on a large batch is much faster than a large number of operations on small batches.  The problem gets worse at longer sequence lengths. Although sequential computation is not a severe bottleneck at inference time, as autoregressively generating each output requires all previous outputs, the inability to compute scores at all output positions at once hinders us from rapidly training our models over large datasets. Although impressive work such as \citep{Kuchaiev2017Factorization} is able to significantly accelerate the training of LSTMs with factorization tricks, we are still bound by the linear dependence on sequence length.
-
-%If the model could compute hidden states at each time step using only the inputs and outputs,  it would be liberated from the dependence on results from previous time steps during training. This line of thought is the foundation of recent efforts such as the Markovian neural GPU \citep{neural_gpu}, ByteNet \citep{NalBytenet2017} and ConvS2S \citep{JonasFaceNet2017}, all of which use convolutional neural networks as a building block to compute hidden representations simultaneously for all timesteps, resulting in $O(1)$ sequential time complexity. \citep{JonasFaceNet2017} report new SOTA on machine translation for English-to-German (EnDe), Enlish-to-French (EnFr) and English-to-Romanian language pairs. 
-
-%A crucial component for accurate sequence prediction is modeling cross-positional communication. For example, in MT, we must draw information from both input and previous output words to translate an output word accurately. An attention layer \citep{bahdanau2014neural} can connect a very large number of positions at a low computation cost, also $O(1)$ sequential time complexity, making it an essential ingredient in recurrent encoder-decoder architectures for MT. A natural question to ask then is, "Could we replace recurrence with attention?". \marginpar{Don't know if it's the most natural question to ask given the previous statements. Also, need to say that the complexity table summarizes these statements} Such a model would be blessed with the computational efficiency of attention and the power of cross-positional communication. In this work, show that pure attention models work remarkably well for MT, achieving new SOTA results on EnDe and EnFr, and can be trained in under $2$ days on xyz architecture. 
-
-
-
-%Note: Facebook model is no better than RNNs in this regard, since it requires a number of layers proportional to the distance you want to communicate.  Bytenet is more promising, since it requires a logarithmnic number of layers (does bytenet have SOTA results)?   
-
-%Note: An attention  layer can connect a very large number of positions at a low computation cost in O(1) sequential operations.  This is why encoder-decoder attention has been so successful in seq-to-seq models so far.  It is only natural, then, to also use attention to connect the timesteps of the same sequence.
-
-%Note: I wouldn't say that long sequences are not a problem during inference.  It would be great if we could infer with no long sequences.  We could just say later on that, while our training graph is constant-depth, our model still requires sequential operations in the decoder part during inference due to the autoregressive nature of the model.   
-
-%\begin{table}[h!]
-%\caption{Attention models are quite efficient for cross-positional communications when sequence length is smaller than channel depth. $n$ represents the sequence length and $d$ represents the channel depth.}
-%\label{tab:op_complexities}
-%\begin{center}
-%\vspace{-5pt}
-%\scalebox{0.75}{
-
-%\begin{tabular}{l|c|c|c}
-%\hline \hline
-%Layer Type & Receptive & Complexity & Sequential  \\
-%           & Field     &            & Operations  \\
-%\hline
-%Pointwise Feed-Forward & $1$ & $O(n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Recurrent & $n$ & $O(n \cdot d^2)$ & $O(n)$ \\
-%\hline
-%Convolutional & $r$ & $O(r \cdot n \cdot d^2)$ & $O(1)$ \\
-%\hline
-%Convolutional (separable) & $r$ & $O(r \cdot n \cdot d + n %\cdot d^2)$ & $O(1)$ \\
-%\hline
-%Attention & $r$ & $O(r \cdot n \cdot d)$ & $O(1)$ \\
-%\hline \hline
-%\end{tabular}
-%}
-%\end{center}
-%\end{table}

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/schedulers/heun.md

@@ -1,23 +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.
--->
-
-# Heun scheduler inspired by Karras et. al paper
-
-## Overview
-
-Algorithm 1 of [Karras et. al](https://arxiv.org/abs/2206.00364).
-Scheduler ported from @crowsonkb's https://github.com/crowsonkb/k-diffusion library:
-
-All credit for making this scheduler work goes to [Katherine Crowson](https://github.com/crowsonkb/)
-
-## HeunDiscreteScheduler
-[[autodoc]] HeunDiscreteScheduler

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/training/controlnet.md

@@ -1,329 +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.
--->
-
-# ControlNet
-
-[Adding Conditional Control to Text-to-Image Diffusion Models](https://arxiv.org/abs/2302.05543) (ControlNet) by Lvmin Zhang and Maneesh Agrawala.
-
-This example is based on the [training example in the original ControlNet repository](https://github.com/lllyasviel/ControlNet/blob/main/docs/train.md). It trains a ControlNet to fill circles using a [small synthetic dataset](https://huggingface.co/datasets/fusing/fill50k).
-
-## Installing the dependencies
-
-Before running the scripts, make sure to install the library's training dependencies.
-
-<Tip warning={true}>
-
-To successfully run the latest versions of the example scripts, we highly recommend **installing from source** and keeping the installation up to date. We update the example scripts frequently and install example-specific requirements.
-
-</Tip>
-
-To do this, execute the following steps in a new virtual environment:
-```bash
-git clone https://github.com/huggingface/diffusers
-cd diffusers
-pip install -e .
-```
-
-Then navigate into the [example folder](https://github.com/huggingface/diffusers/tree/main/examples/controlnet)
-```bash
-cd examples/controlnet
-```
-
-Now run:
-```bash
-pip install -r requirements.txt
-```
-
-And initialize an [🤗Accelerate](https://github.com/huggingface/accelerate/) environment with:
-
-```bash
-accelerate config
-```
-
-Or for a default 🤗Accelerate configuration without answering questions about your environment:
-
-```bash
-accelerate config default
-```
-
-Or if your environment doesn't support an interactive shell like a notebook:
-
-```python
-from accelerate.utils import write_basic_config
-
-write_basic_config()
-```
-
-## Circle filling dataset
-
-The original dataset is hosted in the ControlNet [repo](https://huggingface.co/lllyasviel/ControlNet/blob/main/training/fill50k.zip), but we re-uploaded it [here](https://huggingface.co/datasets/fusing/fill50k) to be compatible with 🤗 Datasets so that it can handle the data loading within the training script.
-
-Our training examples use [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) because that is what the original set of ControlNet models was trained on. However, ControlNet can be trained to augment any compatible Stable Diffusion model (such as [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4)) or [`stabilityai/stable-diffusion-2-1`](https://huggingface.co/stabilityai/stable-diffusion-2-1).
-
-To use your own dataset, take a look at the [Create a dataset for training](create_dataset) guide.
-
-## Training
-
-Download the following images to condition our training with:
-
-```sh
-wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png
-
-wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png
-```
-
-Specify the `MODEL_NAME` environment variable (either a Hub model repository id or a path to the directory containing the model weights) and pass it to the [`pretrained_model_name_or_path`](https://huggingface.co/docs/diffusers/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained.pretrained_model_name_or_path) argument.
-
-The training script creates and saves a `diffusion_pytorch_model.bin` file in your repository.
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=4 \
- --push_to_hub
-```
-
-This default configuration requires ~38GB VRAM.
-
-By default, the training script logs outputs to tensorboard. Pass `--report_to wandb` to use Weights &
-Biases.
-
-Gradient accumulation with a smaller batch size can be used to reduce training requirements to ~20 GB VRAM.
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4 \
-  --push_to_hub
-```
-
-## Training with multiple GPUs
-
-`accelerate` allows for seamless multi-GPU training. Follow the instructions [here](https://huggingface.co/docs/accelerate/basic_tutorials/launch)
-for running distributed training with `accelerate`. Here is an example command:
-
-```bash 
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch --mixed_precision="fp16" --multi_gpu train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=4 \
- --mixed_precision="fp16" \
- --tracker_project_name="controlnet-demo" \
- --report_to=wandb \
-  --push_to_hub
-```
-
-## Example results
-
-#### After 300 steps with batch size 8
-
-| |  | 
-|-------------------|:-------------------------:|
-| | red circle with blue background  | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_300_steps.png) |
-| | cyan circle with brown floral background | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_300_steps.png) |
-
-
-#### After 6000 steps with batch size 8:
-
-| |  | 
-|-------------------|:-------------------------:|
-| | red circle with blue background  | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![red circle with blue background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/red_circle_with_blue_background_6000_steps.png) |
-| | cyan circle with brown floral background | 
-![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png) | ![cyan circle with brown floral background](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/cyan_circle_with_brown_floral_background_6000_steps.png) |
-
-## Training on a 16 GB GPU
-
-Enable the following optimizations to train on a 16GB GPU:
-
-- Gradient checkpointing
-- bitsandbyte's 8-bit optimizer (take a look at the [installation]((https://github.com/TimDettmers/bitsandbytes#requirements--installation) instructions if you don't already have it installed)
-
-Now you can launch the training script:
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4 \
- --gradient_checkpointing \
- --use_8bit_adam \
-  --push_to_hub
-```
-
-## Training on a 12 GB GPU
-
-Enable the following optimizations to train on a 12GB GPU:
-- Gradient checkpointing
-- bitsandbyte's 8-bit optimizer (take a look at the [installation]((https://github.com/TimDettmers/bitsandbytes#requirements--installation) instructions if you don't already have it installed)
-- xFormers (take a look at the [installation](https://huggingface.co/docs/diffusers/training/optimization/xformers) instructions if you don't already have it installed)
-- set gradients to `None`
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --learning_rate=1e-5 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4 \
- --gradient_checkpointing \
- --use_8bit_adam \
- --enable_xformers_memory_efficient_attention \
- --set_grads_to_none \
-  --push_to_hub
-```
-
-When using `enable_xformers_memory_efficient_attention`, please make sure to install `xformers` by `pip install xformers`. 
-
-## Training on an 8 GB GPU
-
-We have not exhaustively tested DeepSpeed support for ControlNet. While the configuration does
-save memory, we have not confirmed whether the configuration trains successfully. You will very likely
-have to make changes to the config to have a successful training run.
-
-Enable the following optimizations to train on a 8GB GPU:
-- Gradient checkpointing
-- bitsandbyte's 8-bit optimizer (take a look at the [installation]((https://github.com/TimDettmers/bitsandbytes#requirements--installation) instructions if you don't already have it installed)
-- xFormers (take a look at the [installation](https://huggingface.co/docs/diffusers/training/optimization/xformers) instructions if you don't already have it installed)
-- set gradients to `None`
-- DeepSpeed stage 2 with parameter and optimizer offloading
-- fp16 mixed precision
-
-[DeepSpeed](https://www.deepspeed.ai/) can offload tensors from VRAM to either 
-CPU or NVME. This requires significantly more RAM (about 25 GB).
-
-You'll have to configure your environment with `accelerate config` to enable DeepSpeed stage 2.
-
-The configuration file should look like this:
-
-```yaml
-compute_environment: LOCAL_MACHINE
-deepspeed_config:
-  gradient_accumulation_steps: 4
-  offload_optimizer_device: cpu
-  offload_param_device: cpu
-  zero3_init_flag: false
-  zero_stage: 2
-distributed_type: DEEPSPEED
-```
-
-<Tip>
-
-See [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more DeepSpeed configuration options.
-
-<Tip>
-
-Changing the default Adam optimizer to DeepSpeed's Adam
-`deepspeed.ops.adam.DeepSpeedCPUAdam` gives a substantial speedup but
-it requires a CUDA toolchain with the same version as PyTorch. 8-bit optimizer
-does not seem to be compatible with DeepSpeed at the moment.
-
-```bash
-export MODEL_DIR="runwayml/stable-diffusion-v1-5"
-export OUTPUT_DIR="path to save model"
-
-accelerate launch train_controlnet.py \
- --pretrained_model_name_or_path=$MODEL_DIR \
- --output_dir=$OUTPUT_DIR \
- --dataset_name=fusing/fill50k \
- --resolution=512 \
- --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \
- --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \
- --train_batch_size=1 \
- --gradient_accumulation_steps=4 \
- --gradient_checkpointing \
- --enable_xformers_memory_efficient_attention \
- --set_grads_to_none \
- --mixed_precision fp16 \
- --push_to_hub
-```
-
-## Inference
-
-The trained model can be run with the [`StableDiffusionControlNetPipeline`].
-Set `base_model_path` and `controlnet_path` to the values `--pretrained_model_name_or_path` and 
-`--output_dir` were respectively set to in the training script.
-
-```py
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
-from diffusers.utils import load_image
-import torch
-
-base_model_path = "path to model"
-controlnet_path = "path to controlnet"
-
-controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
-pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    base_model_path, controlnet=controlnet, torch_dtype=torch.float16
-)
-
-# speed up diffusion process with faster scheduler and memory optimization
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-# remove following line if xformers is not installed
-pipe.enable_xformers_memory_efficient_attention()
-
-pipe.enable_model_cpu_offload()
-
-control_image = load_image("./conditioning_image_1.png")
-prompt = "pale golden rod circle with old lace background"
-
-# generate image
-generator = torch.manual_seed(0)
-image = pipe(prompt, num_inference_steps=20, generator=generator, image=control_image).images[0]
-
-image.save("./output.png")
-```

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/loading.md

@@ -1,442 +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.
--->
-
-
-
-# 파이프라인, 모델, 스케줄러 불러오기
-
-기본적으로 diffusion 모델은 다양한 컴포넌트들(모델, 토크나이저, 스케줄러) 간의 복잡한 상호작용을 기반으로 동작합니다. 디퓨저스(Diffusers)는 이러한 diffusion 모델을 보다 쉽고 간편한 API로 제공하는 것을 목표로 설계되었습니다. [`DiffusionPipeline`]은 diffusion 모델이 갖는 복잡성을 하나의 파이프라인 API로 통합하고, 동시에 이를 구성하는 각각의 컴포넌트들을 태스크에 맞춰 유연하게 커스터마이징할 수 있도록 지원하고 있습니다.
-
-diffusion 모델의 훈련과 추론에 필요한 모든 것은 [`DiffusionPipeline.from_pretrained`] 메서드를 통해 접근할 수 있습니다. (이 말의 의미는 다음 단락에서 보다 자세하게 다뤄보도록 하겠습니다.)
-
-이 문서에서는 설명할 내용은 다음과 같습니다.
-
-* 허브를 통해 혹은 로컬로 파이프라인을 불러오는 법
-
-* 파이프라인에 다른 컴포넌트들을 적용하는 법
-* 오리지널 체크포인트가 아닌 variant를 불러오는 법  (variant란 기본으로 설정된 `fp32`가 아닌 다른  부동 소수점 타입(예: `fp16`)을 사용하거나 Non-EMA 가중치를 사용하는 체크포인트들을 의미합니다.)
-* 모델과 스케줄러를 불러오는 법
-
-
-
-## Diffusion 파이프라인
-
-<Tip>
-
-💡 [`DiffusionPipeline`] 클래스가 동작하는 방식에 보다 자세한 내용이 궁금하다면,  [DiffusionPipeline explained](#diffusionpipeline에-대해-알아보기) 섹션을 확인해보세요.
-
-</Tip>
-
-[`DiffusionPipeline`] 클래스는 diffusion 모델을 [허브](https://huggingface.co/models?library=diffusers)로부터 불러오는 가장 심플하면서 보편적인 방식입니다. [`DiffusionPipeline.from_pretrained`] 메서드는 적합한 파이프라인 클래스를 자동으로 탐지하고, 필요한 구성요소(configuration)와 가중치(weight) 파일들을 다운로드하고 캐싱한 다음, 해당 파이프라인 인스턴스를 반환합니다.
-
-```python
-from diffusers import DiffusionPipeline
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-pipe = DiffusionPipeline.from_pretrained(repo_id)
-```
-
-물론 [`DiffusionPipeline`] 클래스를 사용하지 않고, 명시적으로 직접 해당 파이프라인 클래스를 불러오는 것도 가능합니다. 아래 예시 코드는 위 예시와 동일한 인스턴스를 반환합니다. 
-
-```python
-from diffusers import StableDiffusionPipeline
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionPipeline.from_pretrained(repo_id)
-```
-
-[CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)이나 [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) 같은 체크포인트들의 경우, 하나 이상의 다양한 태스크에 활용될 수 있습니다. (예를 들어 위의 두 체크포인트의 경우, text-to-image와 image-to-image에 모두 활용될 수 있습니다.)  만약 이러한 체크포인트들을 기본 설정 태스크가 아닌 다른 태스크에 활용하고자 한다면, 해당 태스크에 대응되는 파이프라인(task-specific pipeline)을 사용해야 합니다.
-
-```python
-from diffusers import StableDiffusionImg2ImgPipeline
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id)
-```
-
-
-
-### 로컬 파이프라인
-
-파이프라인을 로컬로 불러오고자 한다면, `git-lfs`를 사용하여 직접 체크포인트를 로컬 디스크에 다운로드 받아야 합니다. 아래의 명령어를 실행하면 `./stable-diffusion-v1-5`란 이름으로 폴더가 로컬디스크에 생성됩니다.
-
-```bash
-git lfs install
-git clone https://huggingface.co/runwayml/stable-diffusion-v1-5
-```
-
-그런 다음 해당 로컬 경로를 [`~DiffusionPipeline.from_pretrained`] 메서드에 전달합니다.
-
-```python
-from diffusers import DiffusionPipeline
-
-repo_id = "./stable-diffusion-v1-5"
-stable_diffusion = DiffusionPipeline.from_pretrained(repo_id)
-```
-
-위의 예시코드처럼 만약 `repo_id`가 로컬 패스(local path)라면, [`~DiffusionPipeline.from_pretrained`] 메서드는 이를 자동으로 감지하여 허브에서 파일을 다운로드하지 않습니다. 만약 로컬 디스크에 저장된 파이프라인 체크포��트가 최신 버전이 아닐 경우에도, 최신 버전을 다운로드하지 않고 기존 로컬 디스크에 저장된 체크포인트를 사용한다는 것을 의미합니다.
-
-
-
-### 파이프라인 내부의 컴포넌트 교체하기
-
-파이프라인 내부의 컴포넌트들은 호환 가능한 다른 컴포넌트로 교체될 수 있습니다. 이와 같은 컴포넌트 교체가 중요한 이유는 다음과 같습니다. 
-
-- 어떤 스케줄러를 사용할 것인가는 생성속도와 생성품질 간의 트레이드오프를 정의하는 중요한 요소입니다.
-- diffusion 모델 내부의 컴포넌트들은 일반적으로 각각 독립적으로 훈련되기 때문에, 더 좋은 성능을 보여주는 컴포넌트가 있다면 그걸로 교체하는 식으로 성능을 향상시킬 수 있습니다.
-- 파인 튜닝 단계에서는 일반적으로 UNet 혹은 텍스트 인코더와 같은 일부 컴포넌트들만 훈련하게 됩니다.
-
-어떤 스케줄러들이 호환가능한지는 `compatibles` 속성을 통해 확인할 수 있습니다.
-
-```python
-from diffusers import DiffusionPipeline
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-stable_diffusion = DiffusionPipeline.from_pretrained(repo_id)
-stable_diffusion.scheduler.compatibles
-```
-
-이번에는 [`SchedulerMixin.from_pretrained`] 메서드를 사용해서, 기존 기본 스케줄러였던 [`PNDMScheduler`]를 보다 우수한 성능의 [`EulerDiscreteScheduler`]로 바꿔봅시다. 스케줄러를 로드할 때는 `subfolder` 인자를 통해, 해당 파이프라인의 레포지토리에서 [스케줄러에 관한 하위폴더](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/scheduler)를  명시해주어야 합니다. 
-
-그 다음 새롭게 생성한 [`EulerDiscreteScheduler`] 인스턴스를 [`DiffusionPipeline`]의 `scheduler` 인자에 전달합니다.
-
-```python
-from diffusers import DiffusionPipeline, EulerDiscreteScheduler, DPMSolverMultistepScheduler
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-
-scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
-
-stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler)
-```
-
-### 세이프티 체커
-
-스테이블 diffusion과 같은 diffusion 모델들은 유해한 이미지를 생성할 수도 있습니다. 이를 예방하기 위해 디퓨저스는 생성된 이미지의 유해성을 판단하는 [세이프티 체커(safety checker)](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) 기능을 지원하고 있습니다. 만약 세이프티 체커의 사용을 원하지 않는다면, `safety_checker` 인자에 `None`을 전달해주시면 됩니다.
-
-```python
-from diffusers import DiffusionPipeline
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=None)
-```
-
-### 컴포넌트 재사용
-
-복수의 파이프라인에 동일한 모델이 반복적으로 사용한다면, 굳이 해당 모델의 동일한 가중치를 중복으로 RAM에 불러올 필요는 없을 것입니다.  [`~DiffusionPipeline.components`] 속성을 통해 파이프라인 내부의 컴포넌트들을 참조할 수 있는데, 이번 단락에서는 이를 통해 동일한 모델 가중치를 RAM에 중복으로 불러오는 것을 방지하는 법에 대해 알아보겠습니다.
-
-```python
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
-
-model_id = "runwayml/stable-diffusion-v1-5"
-stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id)
-
-components = stable_diffusion_txt2img.components
-```
-
-그 다음 위 예시 코드에서 선언한 `components` 변수를 다른 파이프라인에 전달함으로써, 모델의 가중치를 중복으로 RAM에 로딩하지 않고, 동일한 컴포넌트를 재사용할 수 있습니다.
-
-```python
-stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components)
-```
-
-물론 각각의 컴포넌트들을 따로 따로 파이프라인에 전달할 수도 있습니다.  예를 들어 `stable_diffusion_txt2img` 파이프라인 안의 컴포넌트들 가운데서 세이프티 체커(`safety_checker`)와 피쳐 익스트랙터(`feature_extractor`)를 제외한 컴포넌트들만 `stable_diffusion_img2img` 파이프라인에서 재사용하는 방식 역시 가능합니다. 
-
-```python
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
-
-model_id = "runwayml/stable-diffusion-v1-5"
-stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id)
-stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(
-    vae=stable_diffusion_txt2img.vae,
-    text_encoder=stable_diffusion_txt2img.text_encoder,
-    tokenizer=stable_diffusion_txt2img.tokenizer,
-    unet=stable_diffusion_txt2img.unet,
-    scheduler=stable_diffusion_txt2img.scheduler,
-    safety_checker=None,
-    feature_extractor=None,
-    requires_safety_checker=False,
-)
-```
-
-## Checkpoint variants
-
-Variant란 일반적으로 다음과 같은 체크포인트들을 의미합니다.
-
--  `torch.float16`과 같이 정밀도는 더 낮지만, 용량 역시 더 작은 부동소수점 타입의 가중치를 사용하는 체크포인트. *(다만 이와 같은 variant의 경우, 추가적인 훈련과 CPU환경에서의 구동이 불가능합니다.)* 
-- Non-EMA 가중치를 사용하는 체크포인트. *(Non-EMA 가중치의 경우, 파인 튜닝 단계에서 사용하는 것이 권장되는데, 추론 단계에선 사용하지 않는 것이 권장됩니다.)*
-
-<Tip>
-
-💡 모델 구조는 동일하지만 서로 다른 학습 환경에서 서로 다른 데이터셋으로 학습된 체크포인트들이 있을 경우, 해당 체크포인트들은 variant 단계가 아닌 레포지토리 단계에서 분리되어 관리되어야 합니다. (즉, 해당 체크포인트들은 서로 다른 레포지토리에서 따로 관리되어야 합니다. 예시: [`stable-diffusion-v1-4`], [`stable-diffusion-v1-5`]). 
-
-</Tip>
-
-| **checkpoint type** | **weight name**                     | **argument for loading weights** |
-| ------------------- | ----------------------------------- | -------------------------------- |
-| original            | diffusion_pytorch_model.bin         |                                  |
-| floating point      | diffusion_pytorch_model.fp16.bin    | `variant`, `torch_dtype`         |
-| non-EMA             | diffusion_pytorch_model.non_ema.bin | `variant`                        |
-
-variant를 로드할 때 2개의 중요한 argument가 있습니다.
-
-* `torch_dtype`은 불러올 체크포인트의 부동소수점을 정의합니다. 예를 들어 `torch_dtype=torch.float16`을 명시함으로써 가중치의 부동소수점 타입을 `fl16`으로 변환할 수 있습니다. (만약 따로 설정하지 않을 경우, 기본값으로 `fp32` 타입의 가중치가 로딩됩니다.) 또한 `variant` 인자를 명시하지 않은 채로 체크포인트를 불러온 다음, 해당 체크포인트를 `torch_dtype=torch.float16` 인자를 통해 `fp16` 타입으로 변환하는 것 역시 가능합니다. 이 경우 기본으로 설정된 `fp32` 가중치가 먼저 다운로드되고, 해당 가중치들을 불러온 다음 `fp16` 타입으로 변환하게 됩니다.
-* `variant` 인자는 레포지토리에서 어떤 variant를 불러올 것인가를 정의합니다. 가령  [`diffusers/stable-diffusion-variants`](https://huggingface.co/diffusers/stable-diffusion-variants/tree/main/unet) 레포지토리로부터 `non_ema` 체크포인트를 불러오고자 한다면, `variant="non_ema"` 인자를 전달해야 합니다.
-
-```python
-from diffusers import DiffusionPipeline
-
-# load fp16 variant
-stable_diffusion = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16
-)
-# load non_ema variant
-stable_diffusion = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema")
-```
-
-다른 부동소수점 타입의 가중치 혹은 non-EMA 가중치를 사용하는 체크포인트를 저장하기 위해서는, [`DiffusionPipeline.save_pretrained`] 메서드를 사용해야 하며, 이 때 `variant` 인자를 명시해줘야 합니다. 원래의 체크포인트와 동일한 폴더에 variant를 저장해야 하며, 이렇게 하면 동일한 폴더에서 오리지널 체크포인트과 variant를 모두 불러올 수 있습니다.
-
-```python
-from diffusers import DiffusionPipeline
-
-# save as fp16 variant
-stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="fp16")
-# save as non-ema variant
-stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema")
-```
-
-만약 variant를 기존 폴더에 저장하지 않을 경우, `variant` 인자를 반드시 명시해야 합니다. 그렇게 하지 않을 경우 원래의 오리지널 체크포인트를 찾을 수 없게 되기 때문에 에러가 발생합니다.
-
-```python
-# 👎 this won't work
-stable_diffusion = DiffusionPipeline.from_pretrained("./stable-diffusion-v1-5", torch_dtype=torch.float16)
-# 👍 this works
-stable_diffusion = DiffusionPipeline.from_pretrained(
-    "./stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16
-)
-```
-
-### 모델 불러오기
-
-모델들은 [`ModelMixin.from_pretrained`] 메서드를 통해 불러올 수 있습니다. 해당 메서드는 최신 버전의 모델 가중치 파일과 설정 파일(configurations)을 다운로드하고 캐싱합니다. 만약 이러한 파일들이 최신 버전으로 로컬 캐시에 저장되어 있다면, [`ModelMixin.from_pretrained`]는 굳이 해당 파일들을 다시 다운로드하지 않으며, 그저 캐시에 있는 최신 파일들을 재사용합니다.
-
-모델은 `subfolder` 인자에 명시된 하위 폴더로부터 로드됩니다. 예를 들어 `runwayml/stable-diffusion-v1-5`의 UNet 모델의 가중치는 [`unet`](https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/unet) 폴더에 저장되��� 있습니다.
-
-```python
-from diffusers import UNet2DConditionModel
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-model = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet")
-```
-
-혹은 [해당 모델의 레포지토리](https://huggingface.co/google/ddpm-cifar10-32/tree/main)로부터 다이렉트로 가져오는 것 역시 가능합니다. 
-
-```python
-from diffusers import UNet2DModel
-
-repo_id = "google/ddpm-cifar10-32"
-model = UNet2DModel.from_pretrained(repo_id)
-```
-
-또한 앞서 봤던 `variant` 인자를 명시함으로써, Non-EMA나 `fp16`의 가중치를 가져오는 것 역시 가능합니다. 
-
-```python
-from diffusers import UNet2DConditionModel
-
-model = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet", variant="non-ema")
-model.save_pretrained("./local-unet", variant="non-ema")
-```
-
-### 스케줄러
-
-스케줄러들은 [`SchedulerMixin.from_pretrained`] 메서드를 통해 불러올 수 있습니다. 모델과 달리 스케줄러는 별도의 가중치를 갖지 않으며, 따라서 당연히 별도의 학습과정을 요구하지 않습니다. 이러한 스케줄러들은 (해당 스케줄러 하위폴더의) configration 파일을 통해 정의됩니다. 
-
-여러개의 스케줄러를 불러온다고 해서 많은 메모리를 소모하는 것은 아니며, 다양한 스케줄러들에 동일한 스케줄러 configration을  적용하는 것 역시 가능합니다. 다음 예시 코드에서 불러오는 스케줄러들은 모두 [`StableDiffusionPipeline`]과 호환되는데, 이는 곧 해당 스케줄러들에 동일한 스케줄러 configration 파일을 적용할 수 있음을 의미합니다.
-
-```python
-from diffusers import StableDiffusionPipeline
-from diffusers import (
-    DDPMScheduler,
-    DDIMScheduler,
-    PNDMScheduler,
-    LMSDiscreteScheduler,
-    EulerDiscreteScheduler,
-    EulerAncestralDiscreteScheduler,
-    DPMSolverMultistepScheduler,
-)
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-
-ddpm = DDPMScheduler.from_pretrained(repo_id, subfolder="scheduler")
-ddim = DDIMScheduler.from_pretrained(repo_id, subfolder="scheduler")
-pndm = PNDMScheduler.from_pretrained(repo_id, subfolder="scheduler")
-lms = LMSDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
-euler_anc = EulerAncestralDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
-euler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler")
-dpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler")
-
-# replace `dpm` with any of `ddpm`, `ddim`, `pndm`, `lms`, `euler_anc`, `euler`
-pipeline = StableDiffusionPipeline.from_pretrained(repo_id, scheduler=dpm)
-```
-
-### DiffusionPipeline에 대해 알아보기
-
-클래스 메서드로서  [`DiffusionPipeline.from_pretrained`]은 2가지를 담당합니다.
-
-- 첫째로, `from_pretrained` 메서드는 최신 버전의 파이프라인을 다운로드하고, 캐시에 저장합니다. 이미 로컬 캐시에 최신 버전의 파이프라인이 저장되어 있다면, [`DiffusionPipeline.from_pretrained`]은 해당 파일들을 다시 다운로드하지 않고, 로컬 캐시에 저장되어 있는 파이프라인을 불러옵니다.
--  `model_index.json` 파일을 통해 체크포인트에 대응되는 적합한 파이프라인 클래스로 불러옵니다.
-
-파이프라인의 폴더 구조는 해당 파이프라인 클래스의 구조와 직접적으로 일치합니다. 예를 들어 [`StableDiffusionPipeline`] 클래스는 [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) 레포지토리와 대응되는 구조를 갖습니다.
-
-```python
-from diffusers import DiffusionPipeline
-
-repo_id = "runwayml/stable-diffusion-v1-5"
-pipeline = DiffusionPipeline.from_pretrained(repo_id)
-print(pipeline)
-```
-
-위의 코드 출력 결과를 확인해보면, `pipeline`은 [`StableDiffusionPipeline`]의 인스턴스이며, 다음과 같이 총 7개의 컴포넌트로 구성된다는 것을 알 수 있습니다.
-
-- `"feature_extractor"`: [`~transformers.CLIPFeatureExtractor`]의 인스턴스
-- `"safety_checker"`: 유해한 컨텐츠를 스크리닝하기 위한 [컴포넌트](https://github.com/huggingface/diffusers/blob/e55687e1e15407f60f32242027b7bb8170e58266/src/diffusers/pipelines/stable_diffusion/safety_checker.py#L32)
-- `"scheduler"`: [`PNDMScheduler`]의 인스턴스
-- `"text_encoder"`: [`~transformers.CLIPTextModel`]의 인스턴스
-- `"tokenizer"`: a [`~transformers.CLIPTokenizer`]의 인스턴스
-- `"unet"`: [`UNet2DConditionModel`]의 인스턴스
-- `"vae"` [`AutoencoderKL`]의 인스턴스
-
-```json
-StableDiffusionPipeline {
-  "feature_extractor": [
-    "transformers",
-    "CLIPImageProcessor"
-  ],
-  "safety_checker": [
-    "stable_diffusion",
-    "StableDiffusionSafetyChecker"
-  ],
-  "scheduler": [
-    "diffusers",
-    "PNDMScheduler"
-  ],
-  "text_encoder": [
-    "transformers",
-    "CLIPTextModel"
-  ],
-  "tokenizer": [
-    "transformers",
-    "CLIPTokenizer"
-  ],
-  "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}
-```
-
-파이프라인 인스턴스의 컴포넌트들을  [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5)의 폴더 구조와 비교해볼 경우, 각각의 컴포넌트마다 별도의 폴더가 있음을 확인할 수 있습니다.
-
-```
-.
-├── feature_extractor
-│   └── preprocessor_config.json
-├── model_index.json
-├── safety_checker
-│   ├── config.json
-│   └── pytorch_model.bin
-├── scheduler
-│   └── scheduler_config.json
-├── text_encoder
-│   ├── config.json
-│   └── pytorch_model.bin
-├── tokenizer
-│   ├── merges.txt
-│   ├── special_tokens_map.json
-│   ├── tokenizer_config.json
-│   └── vocab.json
-├── unet
-│   ├── config.json
-│   ├── diffusion_pytorch_model.bin
-└── vae
-    ├── config.json
-    ├── diffusion_pytorch_model.bin
-```
-
-또한 각각의 컴포넌트들을 파이프라인 인스턴스의 속성으로써 참조할 수 있습니다. 
-
-```py
-pipeline.tokenizer
-```
-
-```python
-CLIPTokenizer(
-    name_or_path="/root/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/39593d5650112b4cc580433f6b0435385882d819/tokenizer",
-    vocab_size=49408,
-    model_max_length=77,
-    is_fast=False,
-    padding_side="right",
-    truncation_side="right",
-    special_tokens={
-        "bos_token": AddedToken("<|startoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
-        "eos_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
-        "unk_token": AddedToken("<|endoftext|>", rstrip=False, lstrip=False, single_word=False, normalized=True),
-        "pad_token": "<|endoftext|>",
-    },
-)
-```
-
-모든 파이프라인은 `model_index.json` 파일을 통해 [`DiffusionPipeline`]에 다음과 같은 정보를 전달합니다.
-
-- `_class_name` 는 어떤 파이프라인 클래스를 사용해야 하는지에 대해 알려줍니다.
-- `_diffusers_version`는 어떤 버전의 디퓨저스로 파이프라인 안의 모델들이 만들어졌는지를 알려줍니다.
-- 그 다음은 각각의 컴포넌트들이 어떤 라이브러리의 어떤 클래스로 만들어졌는지에 대해 알려줍니다. (아래 예시에서 `"feature_extractor" : ["transformers", "CLIPImageProcessor"]`의 경우, `feature_extractor` 컴포넌트는 `transformers` 라이브러리의 `CLIPImageProcessor` 클래스를 통해 만들어졌다는 것을 의미합니다.)
-
-```json
-{
-  "_class_name": "StableDiffusionPipeline",
-  "_diffusers_version": "0.6.0",
-  "feature_extractor": [
-    "transformers",
-    "CLIPImageProcessor"
-  ],
-  "safety_checker": [
-    "stable_diffusion",
-    "StableDiffusionSafetyChecker"
-  ],
-  "scheduler": [
-    "diffusers",
-    "PNDMScheduler"
-  ],
-  "text_encoder": [
-    "transformers",
-    "CLIPTextModel"
-  ],
-  "tokenizer": [
-    "transformers",
-    "CLIPTokenizer"
-  ],
-  "unet": [
-    "diffusers",
-    "UNet2DConditionModel"
-  ],
-  "vae": [
-    "diffusers",
-    "AutoencoderKL"
-  ]
-}
-```
-

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_prior.py

@@ -1,538 +0,0 @@
-from typing import List, Optional, Union
-
-import PIL
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
-
-from ...models import PriorTransformer
-from ...schedulers import UnCLIPScheduler
-from ...utils import (
-    is_accelerate_available,
-    is_accelerate_version,
-    logging,
-    randn_tensor,
-    replace_example_docstring,
-)
-from ..kandinsky import KandinskyPriorPipelineOutput
-from ..pipeline_utils import DiffusionPipeline
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
-        >>> import torch
-
-        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
-        >>> pipe_prior.to("cuda")
-        >>> prompt = "red cat, 4k photo"
-        >>> image_emb, negative_image_emb = pipe_prior(prompt).to_tuple()
-
-        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
-        >>> pipe.to("cuda")
-        >>> image = pipe(
-        ...     image_embeds=image_emb,
-        ...     negative_image_embeds=negative_image_emb,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=50,
-        ... ).images
-        >>> image[0].save("cat.png")
-        ```
-"""
-
-EXAMPLE_INTERPOLATE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline
-        >>> from diffusers.utils import load_image
-        >>> import PIL
-        >>> import torch
-        >>> from torchvision import transforms
-
-        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
-        ... )
-        >>> pipe_prior.to("cuda")
-        >>> img1 = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/cat.png"
-        ... )
-        >>> img2 = load_image(
-        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
-        ...     "/kandinsky/starry_night.jpeg"
-        ... )
-        >>> images_texts = ["a cat", img1, img2]
-        >>> weights = [0.3, 0.3, 0.4]
-        >>> out = pipe_prior.interpolate(images_texts, weights)
-        >>> pipe = KandinskyV22Pipeline.from_pretrained(
-        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
-        ... )
-        >>> pipe.to("cuda")
-        >>> image = pipe(
-        ...     image_embeds=out.image_embeds,
-        ...     negative_image_embeds=out.negative_image_embeds,
-        ...     height=768,
-        ...     width=768,
-        ...     num_inference_steps=50,
-        ... ).images[0]
-        >>> image.save("starry_cat.png")
-        ```
-"""
-
-
-class KandinskyV22PriorPipeline(DiffusionPipeline):
-    """
-    Pipeline for generating image prior for Kandinsky
-
-    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:
-        prior ([`PriorTransformer`]):
-            The canonincal unCLIP prior to approximate the image embedding from the text embedding.
-        image_encoder ([`CLIPVisionModelWithProjection`]):
-            Frozen image-encoder.
-        text_encoder ([`CLIPTextModelWithProjection`]):
-            Frozen text-encoder.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        scheduler ([`UnCLIPScheduler`]):
-            A scheduler to be used in combination with `prior` to generate image embedding.
-        image_processor ([`CLIPImageProcessor`]):
-            A image_processor to be used to preprocess image from clip.
-    """
-
-    _exclude_from_cpu_offload = ["prior"]
-
-    def __init__(
-        self,
-        prior: PriorTransformer,
-        image_encoder: CLIPVisionModelWithProjection,
-        text_encoder: CLIPTextModelWithProjection,
-        tokenizer: CLIPTokenizer,
-        scheduler: UnCLIPScheduler,
-        image_processor: CLIPImageProcessor,
-    ):
-        super().__init__()
-
-        self.register_modules(
-            prior=prior,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            scheduler=scheduler,
-            image_encoder=image_encoder,
-            image_processor=image_processor,
-        )
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_INTERPOLATE_DOC_STRING)
-    def interpolate(
-        self,
-        images_and_prompts: List[Union[str, PIL.Image.Image, torch.FloatTensor]],
-        weights: List[float],
-        num_images_per_prompt: int = 1,
-        num_inference_steps: int = 25,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        negative_prior_prompt: Optional[str] = None,
-        negative_prompt: str = "",
-        guidance_scale: float = 4.0,
-        device=None,
-    ):
-        """
-        Function invoked when using the prior pipeline for interpolation.
-
-        Args:
-            images_and_prompts (`List[Union[str, PIL.Image.Image, torch.FloatTensor]]`):
-                list of prompts and images to guide the image generation.
-            weights: (`List[float]`):
-                list of weights for each condition in `images_and_prompts`
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            generator (`torch.Generator` or `List[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`.
-            negative_prior_prompt (`str`, *optional*):
-                The prompt not to guide the prior diffusion process. Ignored when not using guidance (i.e., ignored if
-                `guidance_scale` is less than `1`).
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt not to guide the image generation. Ignored when not using guidance (i.e., ignored if
-                `guidance_scale` is less than `1`).
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                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.
-
-        Examples:
-
-        Returns:
-            [`KandinskyPriorPipelineOutput`] or `tuple`
-        """
-
-        device = device or self.device
-
-        if len(images_and_prompts) != len(weights):
-            raise ValueError(
-                f"`images_and_prompts` contains {len(images_and_prompts)} items and `weights` contains {len(weights)} items - they should be lists of same length"
-            )
-
-        image_embeddings = []
-        for cond, weight in zip(images_and_prompts, weights):
-            if isinstance(cond, str):
-                image_emb = self(
-                    cond,
-                    num_inference_steps=num_inference_steps,
-                    num_images_per_prompt=num_images_per_prompt,
-                    generator=generator,
-                    latents=latents,
-                    negative_prompt=negative_prior_prompt,
-                    guidance_scale=guidance_scale,
-                ).image_embeds.unsqueeze(0)
-
-            elif isinstance(cond, (PIL.Image.Image, torch.Tensor)):
-                if isinstance(cond, PIL.Image.Image):
-                    cond = (
-                        self.image_processor(cond, return_tensors="pt")
-                        .pixel_values[0]
-                        .unsqueeze(0)
-                        .to(dtype=self.image_encoder.dtype, device=device)
-                    )
-
-                image_emb = self.image_encoder(cond)["image_embeds"].repeat(num_images_per_prompt, 1).unsqueeze(0)
-
-            else:
-                raise ValueError(
-                    f"`images_and_prompts` can only contains elements to be of type `str`, `PIL.Image.Image` or `torch.Tensor`  but is {type(cond)}"
-                )
-
-            image_embeddings.append(image_emb * weight)
-
-        image_emb = torch.cat(image_embeddings).sum(dim=0)
-
-        out_zero = self(
-            negative_prompt,
-            num_inference_steps=num_inference_steps,
-            num_images_per_prompt=num_images_per_prompt,
-            generator=generator,
-            latents=latents,
-            negative_prompt=negative_prior_prompt,
-            guidance_scale=guidance_scale,
-        )
-        zero_image_emb = out_zero.negative_image_embeds if negative_prompt == "" else out_zero.image_embeds
-
-        return KandinskyPriorPipelineOutput(image_embeds=image_emb, negative_image_embeds=zero_image_emb)
-
-    # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
-    def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            if latents.shape != shape:
-                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
-            latents = latents.to(device)
-
-        latents = latents * scheduler.init_noise_sigma
-        return latents
-
-    # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline.get_zero_embed
-    def get_zero_embed(self, batch_size=1, device=None):
-        device = device or self.device
-        zero_img = torch.zeros(1, 3, self.image_encoder.config.image_size, self.image_encoder.config.image_size).to(
-            device=device, dtype=self.image_encoder.dtype
-        )
-        zero_image_emb = self.image_encoder(zero_img)["image_embeds"]
-        zero_image_emb = zero_image_emb.repeat(batch_size, 1)
-        return zero_image_emb
-
-    # Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_prior.KandinskyPriorPipeline._encode_prompt
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-    ):
-        batch_size = len(prompt) if isinstance(prompt, list) else 1
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-        text_mask = text_inputs.attention_mask.bool().to(device)
-
-        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
-            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-
-        text_encoder_output = self.text_encoder(text_input_ids.to(device))
-
-        prompt_embeds = text_encoder_output.text_embeds
-        text_encoder_hidden_states = text_encoder_output.last_hidden_state
-
-        prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-        text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-        text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_text_mask = uncond_input.attention_mask.bool().to(device)
-            negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device))
-
-            negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds
-            uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-
-            seq_len = negative_prompt_embeds.shape[1]
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len)
-
-            seq_len = uncond_text_encoder_hidden_states.shape[1]
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
-            uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view(
-                batch_size * num_images_per_prompt, seq_len, -1
-            )
-            uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0)
-
-            # done duplicates
-
-            # 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
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
-
-            text_mask = torch.cat([uncond_text_mask, text_mask])
-
-        return prompt_embeds, text_encoder_hidden_states, text_mask
-
-    def enable_model_cpu_offload(self, gpu_id=0):
-        r"""
-        Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
-        to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
-        method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
-        `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
-        """
-        if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
-            from accelerate import cpu_offload_with_hook
-        else:
-            raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
-
-        device = torch.device(f"cuda:{gpu_id}")
-
-        if self.device.type != "cpu":
-            self.to("cpu", silence_dtype_warnings=True)
-            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
-
-        hook = None
-        for cpu_offloaded_model in [self.text_encoder, self.prior]:
-            _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
-
-        # We'll offload the last model manually.
-        self.prior_hook = hook
-
-        _, hook = cpu_offload_with_hook(self.image_encoder, device, prev_module_hook=self.prior_hook)
-
-        self.final_offload_hook = hook
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: int = 1,
-        num_inference_steps: int = 25,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        guidance_scale: float = 4.0,
-        output_type: Optional[str] = "pt",  # pt only
-        return_dict: bool = True,
-    ):
-        """
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            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.
-            num_inference_steps (`int`, *optional*, defaults to 100):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            generator (`torch.Generator` or `List[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`.
-            guidance_scale (`float`, *optional*, defaults to 4.0):
-                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.
-            output_type (`str`, *optional*, defaults to `"pt"`):
-                The output format of the generate image. Choose between: `"np"` (`np.array`) or `"pt"`
-                (`torch.Tensor`).
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
-
-        Examples:
-
-        Returns:
-            [`KandinskyPriorPipelineOutput`] or `tuple`
-        """
-
-        if isinstance(prompt, str):
-            prompt = [prompt]
-        elif not isinstance(prompt, list):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if isinstance(negative_prompt, str):
-            negative_prompt = [negative_prompt]
-        elif not isinstance(negative_prompt, list) and negative_prompt is not None:
-            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
-
-        # if the negative prompt is defined we double the batch size to
-        # directly retrieve the negative prompt embedding
-        if negative_prompt is not None:
-            prompt = prompt + negative_prompt
-            negative_prompt = 2 * negative_prompt
-
-        device = self._execution_device
-
-        batch_size = len(prompt)
-        batch_size = batch_size * num_images_per_prompt
-
-        do_classifier_free_guidance = guidance_scale > 1.0
-        prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt(
-            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
-        )
-
-        # prior
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        prior_timesteps_tensor = self.scheduler.timesteps
-
-        embedding_dim = self.prior.config.embedding_dim
-
-        latents = self.prepare_latents(
-            (batch_size, embedding_dim),
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-            self.scheduler,
-        )
-
-        for i, t in enumerate(self.progress_bar(prior_timesteps_tensor)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-            predicted_image_embedding = self.prior(
-                latent_model_input,
-                timestep=t,
-                proj_embedding=prompt_embeds,
-                encoder_hidden_states=text_encoder_hidden_states,
-                attention_mask=text_mask,
-            ).predicted_image_embedding
-
-            if do_classifier_free_guidance:
-                predicted_image_embedding_uncond, predicted_image_embedding_text = predicted_image_embedding.chunk(2)
-                predicted_image_embedding = predicted_image_embedding_uncond + guidance_scale * (
-                    predicted_image_embedding_text - predicted_image_embedding_uncond
-                )
-
-            if i + 1 == prior_timesteps_tensor.shape[0]:
-                prev_timestep = None
-            else:
-                prev_timestep = prior_timesteps_tensor[i + 1]
-
-            latents = self.scheduler.step(
-                predicted_image_embedding,
-                timestep=t,
-                sample=latents,
-                generator=generator,
-                prev_timestep=prev_timestep,
-            ).prev_sample
-
-        latents = self.prior.post_process_latents(latents)
-
-        image_embeddings = latents
-
-        # if negative prompt has been defined, we retrieve split the image embedding into two
-        if negative_prompt is None:
-            zero_embeds = self.get_zero_embed(latents.shape[0], device=latents.device)
-
-            if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-                self.final_offload_hook.offload()
-        else:
-            image_embeddings, zero_embeds = image_embeddings.chunk(2)
-
-            if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
-                self.prior_hook.offload()
-
-        if output_type not in ["pt", "np"]:
-            raise ValueError(f"Only the output types `pt` and `np` are supported not output_type={output_type}")
-
-        if output_type == "np":
-            image_embeddings = image_embeddings.cpu().numpy()
-            zero_embeds = zero_embeds.cpu().numpy()
-
-        if not return_dict:
-            return (image_embeddings, zero_embeds)
-
-        return KandinskyPriorPipelineOutput(image_embeds=image_embeddings, negative_image_embeds=zero_embeds)

spaces/Andy1621/uniformer_image_detection/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py

@@ -1,11 +0,0 @@
-_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
-# model settings
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w40',
-    backbone=dict(
-        type='HRNet',
-        extra=dict(
-            stage2=dict(num_channels=(40, 80)),
-            stage3=dict(num_channels=(40, 80, 160)),
-            stage4=dict(num_channels=(40, 80, 160, 320)))),
-    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))

spaces/Andy1621/uniformer_image_detection/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py

@@ -1,14 +0,0 @@
-_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
-model = dict(
-    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'))

spaces/Andy1621/uniformer_image_detection/mmdet/core/mask/structures.py

@@ -1,1024 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import cv2
-import mmcv
-import numpy as np
-import pycocotools.mask as maskUtils
-import torch
-from mmcv.ops.roi_align import roi_align
-
-
-class BaseInstanceMasks(metaclass=ABCMeta):
-    """Base class for instance masks."""
-
-    @abstractmethod
-    def rescale(self, scale, interpolation='nearest'):
-        """Rescale masks as large as possible while keeping the aspect ratio.
-        For details can refer to `mmcv.imrescale`.
-
-        Args:
-            scale (tuple[int]): The maximum size (h, w) of rescaled mask.
-            interpolation (str): Same as :func:`mmcv.imrescale`.
-
-        Returns:
-            BaseInstanceMasks: The rescaled masks.
-        """
-
-    @abstractmethod
-    def resize(self, out_shape, interpolation='nearest'):
-        """Resize masks to the given out_shape.
-
-        Args:
-            out_shape: Target (h, w) of resized mask.
-            interpolation (str): See :func:`mmcv.imresize`.
-
-        Returns:
-            BaseInstanceMasks: The resized masks.
-        """
-
-    @abstractmethod
-    def flip(self, flip_direction='horizontal'):
-        """Flip masks alone the given direction.
-
-        Args:
-            flip_direction (str): Either 'horizontal' or 'vertical'.
-
-        Returns:
-            BaseInstanceMasks: The flipped masks.
-        """
-
-    @abstractmethod
-    def pad(self, out_shape, pad_val):
-        """Pad masks to the given size of (h, w).
-
-        Args:
-            out_shape (tuple[int]): Target (h, w) of padded mask.
-            pad_val (int): The padded value.
-
-        Returns:
-            BaseInstanceMasks: The padded masks.
-        """
-
-    @abstractmethod
-    def crop(self, bbox):
-        """Crop each mask by the given bbox.
-
-        Args:
-            bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ).
-
-        Return:
-            BaseInstanceMasks: The cropped masks.
-        """
-
-    @abstractmethod
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device,
-                        interpolation='bilinear'):
-        """Crop and resize masks by the given bboxes.
-
-        This function is mainly used in mask targets computation.
-        It firstly align mask to bboxes by assigned_inds, then crop mask by the
-        assigned bbox and resize to the size of (mask_h, mask_w)
-
-        Args:
-            bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4)
-            out_shape (tuple[int]): Target (h, w) of resized mask
-            inds (ndarray): Indexes to assign masks to each bbox,
-                shape (N,) and values should be between [0, num_masks - 1].
-            device (str): Device of bboxes
-            interpolation (str): See `mmcv.imresize`
-
-        Return:
-            BaseInstanceMasks: the cropped and resized masks.
-        """
-
-    @abstractmethod
-    def expand(self, expanded_h, expanded_w, top, left):
-        """see :class:`Expand`."""
-
-    @property
-    @abstractmethod
-    def areas(self):
-        """ndarray: areas of each instance."""
-
-    @abstractmethod
-    def to_ndarray(self):
-        """Convert masks to the format of ndarray.
-
-        Return:
-            ndarray: Converted masks in the format of ndarray.
-        """
-
-    @abstractmethod
-    def to_tensor(self, dtype, device):
-        """Convert masks to the format of Tensor.
-
-        Args:
-            dtype (str): Dtype of converted mask.
-            device (torch.device): Device of converted masks.
-
-        Returns:
-            Tensor: Converted masks in the format of Tensor.
-        """
-
-    @abstractmethod
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=0,
-                  interpolation='bilinear'):
-        """Translate the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            offset (int | float): The offset for translate.
-            direction (str): The translate direction, either "horizontal"
-                or "vertical".
-            fill_val (int | float): Border value. Default 0.
-            interpolation (str): Same as :func:`mmcv.imtranslate`.
-
-        Returns:
-            Translated masks.
-        """
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """Shear the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            magnitude (int | float): The magnitude used for shear.
-            direction (str): The shear direction, either "horizontal"
-                or "vertical".
-            border_value (int | tuple[int]): Value used in case of a
-                constant border. Default 0.
-            interpolation (str): Same as in :func:`mmcv.imshear`.
-
-        Returns:
-            ndarray: Sheared masks.
-        """
-
-    @abstractmethod
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """Rotate the masks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            angle (int | float): Rotation angle in degrees. Positive values
-                mean counter-clockwise rotation.
-            center (tuple[float], optional): Center point (w, h) of the
-                rotation in source image. If not specified, the center of
-                the image will be used.
-            scale (int | float): Isotropic scale factor.
-            fill_val (int | float): Border value. Default 0 for masks.
-
-        Returns:
-            Rotated masks.
-        """
-
-
-class BitmapMasks(BaseInstanceMasks):
-    """This class represents masks in the form of bitmaps.
-
-    Args:
-        masks (ndarray): ndarray of masks in shape (N, H, W), where N is
-            the number of objects.
-        height (int): height of masks
-        width (int): width of masks
-
-    Example:
-        >>> from mmdet.core.mask.structures import *  # NOQA
-        >>> num_masks, H, W = 3, 32, 32
-        >>> rng = np.random.RandomState(0)
-        >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int)
-        >>> self = BitmapMasks(masks, height=H, width=W)
-
-        >>> # demo crop_and_resize
-        >>> num_boxes = 5
-        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
-        >>> out_shape = (14, 14)
-        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
-        >>> device = 'cpu'
-        >>> interpolation = 'bilinear'
-        >>> new = self.crop_and_resize(
-        ...     bboxes, out_shape, inds, device, interpolation)
-        >>> assert len(new) == num_boxes
-        >>> assert new.height, new.width == out_shape
-    """
-
-    def __init__(self, masks, height, width):
-        self.height = height
-        self.width = width
-        if len(masks) == 0:
-            self.masks = np.empty((0, self.height, self.width), dtype=np.uint8)
-        else:
-            assert isinstance(masks, (list, np.ndarray))
-            if isinstance(masks, list):
-                assert isinstance(masks[0], np.ndarray)
-                assert masks[0].ndim == 2  # (H, W)
-            else:
-                assert masks.ndim == 3  # (N, H, W)
-
-            self.masks = np.stack(masks).reshape(-1, height, width)
-            assert self.masks.shape[1] == self.height
-            assert self.masks.shape[2] == self.width
-
-    def __getitem__(self, index):
-        """Index the BitmapMask.
-
-        Args:
-            index (int | ndarray): Indices in the format of integer or ndarray.
-
-        Returns:
-            :obj:`BitmapMasks`: Indexed bitmap masks.
-        """
-        masks = self.masks[index].reshape(-1, self.height, self.width)
-        return BitmapMasks(masks, self.height, self.width)
-
-    def __iter__(self):
-        return iter(self.masks)
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += f'num_masks={len(self.masks)}, '
-        s += f'height={self.height}, '
-        s += f'width={self.width})'
-        return s
-
-    def __len__(self):
-        """Number of masks."""
-        return len(self.masks)
-
-    def rescale(self, scale, interpolation='nearest'):
-        """See :func:`BaseInstanceMasks.rescale`."""
-        if len(self.masks) == 0:
-            new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
-            rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8)
-        else:
-            rescaled_masks = np.stack([
-                mmcv.imrescale(mask, scale, interpolation=interpolation)
-                for mask in self.masks
-            ])
-        height, width = rescaled_masks.shape[1:]
-        return BitmapMasks(rescaled_masks, height, width)
-
-    def resize(self, out_shape, interpolation='nearest'):
-        """See :func:`BaseInstanceMasks.resize`."""
-        if len(self.masks) == 0:
-            resized_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            resized_masks = np.stack([
-                mmcv.imresize(
-                    mask, out_shape[::-1], interpolation=interpolation)
-                for mask in self.masks
-            ])
-        return BitmapMasks(resized_masks, *out_shape)
-
-    def flip(self, flip_direction='horizontal'):
-        """See :func:`BaseInstanceMasks.flip`."""
-        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
-
-        if len(self.masks) == 0:
-            flipped_masks = self.masks
-        else:
-            flipped_masks = np.stack([
-                mmcv.imflip(mask, direction=flip_direction)
-                for mask in self.masks
-            ])
-        return BitmapMasks(flipped_masks, self.height, self.width)
-
-    def pad(self, out_shape, pad_val=0):
-        """See :func:`BaseInstanceMasks.pad`."""
-        if len(self.masks) == 0:
-            padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            padded_masks = np.stack([
-                mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
-                for mask in self.masks
-            ])
-        return BitmapMasks(padded_masks, *out_shape)
-
-    def crop(self, bbox):
-        """See :func:`BaseInstanceMasks.crop`."""
-        assert isinstance(bbox, np.ndarray)
-        assert bbox.ndim == 1
-
-        # clip the boundary
-        bbox = bbox.copy()
-        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
-        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
-        x1, y1, x2, y2 = bbox
-        w = np.maximum(x2 - x1, 1)
-        h = np.maximum(y2 - y1, 1)
-
-        if len(self.masks) == 0:
-            cropped_masks = np.empty((0, h, w), dtype=np.uint8)
-        else:
-            cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w]
-        return BitmapMasks(cropped_masks, h, w)
-
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device='cpu',
-                        interpolation='bilinear'):
-        """See :func:`BaseInstanceMasks.crop_and_resize`."""
-        if len(self.masks) == 0:
-            empty_masks = np.empty((0, *out_shape), dtype=np.uint8)
-            return BitmapMasks(empty_masks, *out_shape)
-
-        # convert bboxes to tensor
-        if isinstance(bboxes, np.ndarray):
-            bboxes = torch.from_numpy(bboxes).to(device=device)
-        if isinstance(inds, np.ndarray):
-            inds = torch.from_numpy(inds).to(device=device)
-
-        num_bbox = bboxes.shape[0]
-        fake_inds = torch.arange(
-            num_bbox, device=device).to(dtype=bboxes.dtype)[:, None]
-        rois = torch.cat([fake_inds, bboxes], dim=1)  # Nx5
-        rois = rois.to(device=device)
-        if num_bbox > 0:
-            gt_masks_th = torch.from_numpy(self.masks).to(device).index_select(
-                0, inds).to(dtype=rois.dtype)
-            targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape,
-                                1.0, 0, 'avg', True).squeeze(1)
-            resized_masks = (targets >= 0.5).cpu().numpy()
-        else:
-            resized_masks = []
-        return BitmapMasks(resized_masks, *out_shape)
-
-    def expand(self, expanded_h, expanded_w, top, left):
-        """See :func:`BaseInstanceMasks.expand`."""
-        if len(self.masks) == 0:
-            expanded_mask = np.empty((0, expanded_h, expanded_w),
-                                     dtype=np.uint8)
-        else:
-            expanded_mask = np.zeros((len(self), expanded_h, expanded_w),
-                                     dtype=np.uint8)
-            expanded_mask[:, top:top + self.height,
-                          left:left + self.width] = self.masks
-        return BitmapMasks(expanded_mask, expanded_h, expanded_w)
-
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=0,
-                  interpolation='bilinear'):
-        """Translate the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            offset (int | float): The offset for translate.
-            direction (str): The translate direction, either "horizontal"
-                or "vertical".
-            fill_val (int | float): Border value. Default 0 for masks.
-            interpolation (str): Same as :func:`mmcv.imtranslate`.
-
-        Returns:
-            BitmapMasks: Translated BitmapMasks.
-
-        Example:
-            >>> from mmdet.core.mask.structures import BitmapMasks
-            >>> self = BitmapMasks.random(dtype=np.uint8)
-            >>> out_shape = (32, 32)
-            >>> offset = 4
-            >>> direction = 'horizontal'
-            >>> fill_val = 0
-            >>> interpolation = 'bilinear'
-            >>> # Note, There seem to be issues when:
-            >>> # * out_shape is different than self's shape
-            >>> # * the mask dtype is not supported by cv2.AffineWarp
-            >>> new = self.translate(out_shape, offset, direction, fill_val,
-            >>>                      interpolation)
-            >>> assert len(new) == len(self)
-            >>> assert new.height, new.width == out_shape
-        """
-        if len(self.masks) == 0:
-            translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            translated_masks = mmcv.imtranslate(
-                self.masks.transpose((1, 2, 0)),
-                offset,
-                direction,
-                border_value=fill_val,
-                interpolation=interpolation)
-            if translated_masks.ndim == 2:
-                translated_masks = translated_masks[:, :, None]
-            translated_masks = translated_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(translated_masks, *out_shape)
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """Shear the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            magnitude (int | float): The magnitude used for shear.
-            direction (str): The shear direction, either "horizontal"
-                or "vertical".
-            border_value (int | tuple[int]): Value used in case of a
-                constant border.
-            interpolation (str): Same as in :func:`mmcv.imshear`.
-
-        Returns:
-            BitmapMasks: The sheared masks.
-        """
-        if len(self.masks) == 0:
-            sheared_masks = np.empty((0, *out_shape), dtype=np.uint8)
-        else:
-            sheared_masks = mmcv.imshear(
-                self.masks.transpose((1, 2, 0)),
-                magnitude,
-                direction,
-                border_value=border_value,
-                interpolation=interpolation)
-            if sheared_masks.ndim == 2:
-                sheared_masks = sheared_masks[:, :, None]
-            sheared_masks = sheared_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(sheared_masks, *out_shape)
-
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """Rotate the BitmapMasks.
-
-        Args:
-            out_shape (tuple[int]): Shape for output mask, format (h, w).
-            angle (int | float): Rotation angle in degrees. Positive values
-                mean counter-clockwise rotation.
-            center (tuple[float], optional): Center point (w, h) of the
-                rotation in source image. If not specified, the center of
-                the image will be used.
-            scale (int | float): Isotropic scale factor.
-            fill_val (int | float): Border value. Default 0 for masks.
-
-        Returns:
-            BitmapMasks: Rotated BitmapMasks.
-        """
-        if len(self.masks) == 0:
-            rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype)
-        else:
-            rotated_masks = mmcv.imrotate(
-                self.masks.transpose((1, 2, 0)),
-                angle,
-                center=center,
-                scale=scale,
-                border_value=fill_val)
-            if rotated_masks.ndim == 2:
-                # case when only one mask, (h, w)
-                rotated_masks = rotated_masks[:, :, None]  # (h, w, 1)
-            rotated_masks = rotated_masks.transpose(
-                (2, 0, 1)).astype(self.masks.dtype)
-        return BitmapMasks(rotated_masks, *out_shape)
-
-    @property
-    def areas(self):
-        """See :py:attr:`BaseInstanceMasks.areas`."""
-        return self.masks.sum((1, 2))
-
-    def to_ndarray(self):
-        """See :func:`BaseInstanceMasks.to_ndarray`."""
-        return self.masks
-
-    def to_tensor(self, dtype, device):
-        """See :func:`BaseInstanceMasks.to_tensor`."""
-        return torch.tensor(self.masks, dtype=dtype, device=device)
-
-    @classmethod
-    def random(cls,
-               num_masks=3,
-               height=32,
-               width=32,
-               dtype=np.uint8,
-               rng=None):
-        """Generate random bitmap masks for demo / testing purposes.
-
-        Example:
-            >>> from mmdet.core.mask.structures import BitmapMasks
-            >>> self = BitmapMasks.random()
-            >>> print('self = {}'.format(self))
-            self = BitmapMasks(num_masks=3, height=32, width=32)
-        """
-        from mmdet.utils.util_random import ensure_rng
-        rng = ensure_rng(rng)
-        masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype)
-        self = cls(masks, height=height, width=width)
-        return self
-
-
-class PolygonMasks(BaseInstanceMasks):
-    """This class represents masks in the form of polygons.
-
-    Polygons is a list of three levels. The first level of the list
-    corresponds to objects, the second level to the polys that compose the
-    object, the third level to the poly coordinates
-
-    Args:
-        masks (list[list[ndarray]]): The first level of the list
-            corresponds to objects, the second level to the polys that
-            compose the object, the third level to the poly coordinates
-        height (int): height of masks
-        width (int): width of masks
-
-    Example:
-        >>> from mmdet.core.mask.structures import *  # NOQA
-        >>> masks = [
-        >>>     [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ]
-        >>> ]
-        >>> height, width = 16, 16
-        >>> self = PolygonMasks(masks, height, width)
-
-        >>> # demo translate
-        >>> new = self.translate((16, 16), 4., direction='horizontal')
-        >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2])
-        >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4)
-
-        >>> # demo crop_and_resize
-        >>> num_boxes = 3
-        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
-        >>> out_shape = (16, 16)
-        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
-        >>> device = 'cpu'
-        >>> interpolation = 'bilinear'
-        >>> new = self.crop_and_resize(
-        ...     bboxes, out_shape, inds, device, interpolation)
-        >>> assert len(new) == num_boxes
-        >>> assert new.height, new.width == out_shape
-    """
-
-    def __init__(self, masks, height, width):
-        assert isinstance(masks, list)
-        if len(masks) > 0:
-            assert isinstance(masks[0], list)
-            assert isinstance(masks[0][0], np.ndarray)
-
-        self.height = height
-        self.width = width
-        self.masks = masks
-
-    def __getitem__(self, index):
-        """Index the polygon masks.
-
-        Args:
-            index (ndarray | List): The indices.
-
-        Returns:
-            :obj:`PolygonMasks`: The indexed polygon masks.
-        """
-        if isinstance(index, np.ndarray):
-            index = index.tolist()
-        if isinstance(index, list):
-            masks = [self.masks[i] for i in index]
-        else:
-            try:
-                masks = self.masks[index]
-            except Exception:
-                raise ValueError(
-                    f'Unsupported input of type {type(index)} for indexing!')
-        if len(masks) and isinstance(masks[0], np.ndarray):
-            masks = [masks]  # ensure a list of three levels
-        return PolygonMasks(masks, self.height, self.width)
-
-    def __iter__(self):
-        return iter(self.masks)
-
-    def __repr__(self):
-        s = self.__class__.__name__ + '('
-        s += f'num_masks={len(self.masks)}, '
-        s += f'height={self.height}, '
-        s += f'width={self.width})'
-        return s
-
-    def __len__(self):
-        """Number of masks."""
-        return len(self.masks)
-
-    def rescale(self, scale, interpolation=None):
-        """see :func:`BaseInstanceMasks.rescale`"""
-        new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
-        if len(self.masks) == 0:
-            rescaled_masks = PolygonMasks([], new_h, new_w)
-        else:
-            rescaled_masks = self.resize((new_h, new_w))
-        return rescaled_masks
-
-    def resize(self, out_shape, interpolation=None):
-        """see :func:`BaseInstanceMasks.resize`"""
-        if len(self.masks) == 0:
-            resized_masks = PolygonMasks([], *out_shape)
-        else:
-            h_scale = out_shape[0] / self.height
-            w_scale = out_shape[1] / self.width
-            resized_masks = []
-            for poly_per_obj in self.masks:
-                resized_poly = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    p[0::2] *= w_scale
-                    p[1::2] *= h_scale
-                    resized_poly.append(p)
-                resized_masks.append(resized_poly)
-            resized_masks = PolygonMasks(resized_masks, *out_shape)
-        return resized_masks
-
-    def flip(self, flip_direction='horizontal'):
-        """see :func:`BaseInstanceMasks.flip`"""
-        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
-        if len(self.masks) == 0:
-            flipped_masks = PolygonMasks([], self.height, self.width)
-        else:
-            flipped_masks = []
-            for poly_per_obj in self.masks:
-                flipped_poly_per_obj = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    if flip_direction == 'horizontal':
-                        p[0::2] = self.width - p[0::2]
-                    elif flip_direction == 'vertical':
-                        p[1::2] = self.height - p[1::2]
-                    else:
-                        p[0::2] = self.width - p[0::2]
-                        p[1::2] = self.height - p[1::2]
-                    flipped_poly_per_obj.append(p)
-                flipped_masks.append(flipped_poly_per_obj)
-            flipped_masks = PolygonMasks(flipped_masks, self.height,
-                                         self.width)
-        return flipped_masks
-
-    def crop(self, bbox):
-        """see :func:`BaseInstanceMasks.crop`"""
-        assert isinstance(bbox, np.ndarray)
-        assert bbox.ndim == 1
-
-        # clip the boundary
-        bbox = bbox.copy()
-        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
-        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
-        x1, y1, x2, y2 = bbox
-        w = np.maximum(x2 - x1, 1)
-        h = np.maximum(y2 - y1, 1)
-
-        if len(self.masks) == 0:
-            cropped_masks = PolygonMasks([], h, w)
-        else:
-            cropped_masks = []
-            for poly_per_obj in self.masks:
-                cropped_poly_per_obj = []
-                for p in poly_per_obj:
-                    # pycocotools will clip the boundary
-                    p = p.copy()
-                    p[0::2] -= bbox[0]
-                    p[1::2] -= bbox[1]
-                    cropped_poly_per_obj.append(p)
-                cropped_masks.append(cropped_poly_per_obj)
-            cropped_masks = PolygonMasks(cropped_masks, h, w)
-        return cropped_masks
-
-    def pad(self, out_shape, pad_val=0):
-        """padding has no effect on polygons`"""
-        return PolygonMasks(self.masks, *out_shape)
-
-    def expand(self, *args, **kwargs):
-        """TODO: Add expand for polygon"""
-        raise NotImplementedError
-
-    def crop_and_resize(self,
-                        bboxes,
-                        out_shape,
-                        inds,
-                        device='cpu',
-                        interpolation='bilinear'):
-        """see :func:`BaseInstanceMasks.crop_and_resize`"""
-        out_h, out_w = out_shape
-        if len(self.masks) == 0:
-            return PolygonMasks([], out_h, out_w)
-
-        resized_masks = []
-        for i in range(len(bboxes)):
-            mask = self.masks[inds[i]]
-            bbox = bboxes[i, :]
-            x1, y1, x2, y2 = bbox
-            w = np.maximum(x2 - x1, 1)
-            h = np.maximum(y2 - y1, 1)
-            h_scale = out_h / max(h, 0.1)  # avoid too large scale
-            w_scale = out_w / max(w, 0.1)
-
-            resized_mask = []
-            for p in mask:
-                p = p.copy()
-                # crop
-                # pycocotools will clip the boundary
-                p[0::2] -= bbox[0]
-                p[1::2] -= bbox[1]
-
-                # resize
-                p[0::2] *= w_scale
-                p[1::2] *= h_scale
-                resized_mask.append(p)
-            resized_masks.append(resized_mask)
-        return PolygonMasks(resized_masks, *out_shape)
-
-    def translate(self,
-                  out_shape,
-                  offset,
-                  direction='horizontal',
-                  fill_val=None,
-                  interpolation=None):
-        """Translate the PolygonMasks.
-
-        Example:
-            >>> self = PolygonMasks.random(dtype=np.int)
-            >>> out_shape = (self.height, self.width)
-            >>> new = self.translate(out_shape, 4., direction='horizontal')
-            >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2])
-            >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4)  # noqa: E501
-        """
-        assert fill_val is None or fill_val == 0, 'Here fill_val is not '\
-            f'used, and defaultly should be None or 0. got {fill_val}.'
-        if len(self.masks) == 0:
-            translated_masks = PolygonMasks([], *out_shape)
-        else:
-            translated_masks = []
-            for poly_per_obj in self.masks:
-                translated_poly_per_obj = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    if direction == 'horizontal':
-                        p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1])
-                    elif direction == 'vertical':
-                        p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0])
-                    translated_poly_per_obj.append(p)
-                translated_masks.append(translated_poly_per_obj)
-            translated_masks = PolygonMasks(translated_masks, *out_shape)
-        return translated_masks
-
-    def shear(self,
-              out_shape,
-              magnitude,
-              direction='horizontal',
-              border_value=0,
-              interpolation='bilinear'):
-        """See :func:`BaseInstanceMasks.shear`."""
-        if len(self.masks) == 0:
-            sheared_masks = PolygonMasks([], *out_shape)
-        else:
-            sheared_masks = []
-            if direction == 'horizontal':
-                shear_matrix = np.stack([[1, magnitude],
-                                         [0, 1]]).astype(np.float32)
-            elif direction == 'vertical':
-                shear_matrix = np.stack([[1, 0], [magnitude,
-                                                  1]]).astype(np.float32)
-            for poly_per_obj in self.masks:
-                sheared_poly = []
-                for p in poly_per_obj:
-                    p = np.stack([p[0::2], p[1::2]], axis=0)  # [2, n]
-                    new_coords = np.matmul(shear_matrix, p)  # [2, n]
-                    new_coords[0, :] = np.clip(new_coords[0, :], 0,
-                                               out_shape[1])
-                    new_coords[1, :] = np.clip(new_coords[1, :], 0,
-                                               out_shape[0])
-                    sheared_poly.append(
-                        new_coords.transpose((1, 0)).reshape(-1))
-                sheared_masks.append(sheared_poly)
-            sheared_masks = PolygonMasks(sheared_masks, *out_shape)
-        return sheared_masks
-
-    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
-        """See :func:`BaseInstanceMasks.rotate`."""
-        if len(self.masks) == 0:
-            rotated_masks = PolygonMasks([], *out_shape)
-        else:
-            rotated_masks = []
-            rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale)
-            for poly_per_obj in self.masks:
-                rotated_poly = []
-                for p in poly_per_obj:
-                    p = p.copy()
-                    coords = np.stack([p[0::2], p[1::2]], axis=1)  # [n, 2]
-                    # pad 1 to convert from format [x, y] to homogeneous
-                    # coordinates format [x, y, 1]
-                    coords = np.concatenate(
-                        (coords, np.ones((coords.shape[0], 1), coords.dtype)),
-                        axis=1)  # [n, 3]
-                    rotated_coords = np.matmul(
-                        rotate_matrix[None, :, :],
-                        coords[:, :, None])[..., 0]  # [n, 2, 1] -> [n, 2]
-                    rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0,
-                                                   out_shape[1])
-                    rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0,
-                                                   out_shape[0])
-                    rotated_poly.append(rotated_coords.reshape(-1))
-                rotated_masks.append(rotated_poly)
-            rotated_masks = PolygonMasks(rotated_masks, *out_shape)
-        return rotated_masks
-
-    def to_bitmap(self):
-        """convert polygon masks to bitmap masks."""
-        bitmap_masks = self.to_ndarray()
-        return BitmapMasks(bitmap_masks, self.height, self.width)
-
-    @property
-    def areas(self):
-        """Compute areas of masks.
-
-        This func is modified from `detectron2
-        <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
-        The function only works with Polygons using the shoelace formula.
-
-        Return:
-            ndarray: areas of each instance
-        """  # noqa: W501
-        area = []
-        for polygons_per_obj in self.masks:
-            area_per_obj = 0
-            for p in polygons_per_obj:
-                area_per_obj += self._polygon_area(p[0::2], p[1::2])
-            area.append(area_per_obj)
-        return np.asarray(area)
-
-    def _polygon_area(self, x, y):
-        """Compute the area of a component of a polygon.
-
-        Using the shoelace formula:
-        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
-
-        Args:
-            x (ndarray): x coordinates of the component
-            y (ndarray): y coordinates of the component
-
-        Return:
-            float: the are of the component
-        """  # noqa: 501
-        return 0.5 * np.abs(
-            np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
-
-    def to_ndarray(self):
-        """Convert masks to the format of ndarray."""
-        if len(self.masks) == 0:
-            return np.empty((0, self.height, self.width), dtype=np.uint8)
-        bitmap_masks = []
-        for poly_per_obj in self.masks:
-            bitmap_masks.append(
-                polygon_to_bitmap(poly_per_obj, self.height, self.width))
-        return np.stack(bitmap_masks)
-
-    def to_tensor(self, dtype, device):
-        """See :func:`BaseInstanceMasks.to_tensor`."""
-        if len(self.masks) == 0:
-            return torch.empty((0, self.height, self.width),
-                               dtype=dtype,
-                               device=device)
-        ndarray_masks = self.to_ndarray()
-        return torch.tensor(ndarray_masks, dtype=dtype, device=device)
-
-    @classmethod
-    def random(cls,
-               num_masks=3,
-               height=32,
-               width=32,
-               n_verts=5,
-               dtype=np.float32,
-               rng=None):
-        """Generate random polygon masks for demo / testing purposes.
-
-        Adapted from [1]_
-
-        References:
-            .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379  # noqa: E501
-
-        Example:
-            >>> from mmdet.core.mask.structures import PolygonMasks
-            >>> self = PolygonMasks.random()
-            >>> print('self = {}'.format(self))
-        """
-        from mmdet.utils.util_random import ensure_rng
-        rng = ensure_rng(rng)
-
-        def _gen_polygon(n, irregularity, spikeyness):
-            """Creates the polygon by sampling points on a circle around the
-            centre.  Random noise is added by varying the angular spacing
-            between sequential points, and by varying the radial distance of
-            each point from the centre.
-
-            Based on original code by Mike Ounsworth
-
-            Args:
-                n (int): number of vertices
-                irregularity (float): [0,1] indicating how much variance there
-                    is in the angular spacing of vertices. [0,1] will map to
-                    [0, 2pi/numberOfVerts]
-                spikeyness (float): [0,1] indicating how much variance there is
-                    in each vertex from the circle of radius aveRadius. [0,1]
-                    will map to [0, aveRadius]
-
-            Returns:
-                a list of vertices, in CCW order.
-            """
-            from scipy.stats import truncnorm
-            # Generate around the unit circle
-            cx, cy = (0.0, 0.0)
-            radius = 1
-
-            tau = np.pi * 2
-
-            irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n
-            spikeyness = np.clip(spikeyness, 1e-9, 1)
-
-            # generate n angle steps
-            lower = (tau / n) - irregularity
-            upper = (tau / n) + irregularity
-            angle_steps = rng.uniform(lower, upper, n)
-
-            # normalize the steps so that point 0 and point n+1 are the same
-            k = angle_steps.sum() / (2 * np.pi)
-            angles = (angle_steps / k).cumsum() + rng.uniform(0, tau)
-
-            # Convert high and low values to be wrt the standard normal range
-            # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html
-            low = 0
-            high = 2 * radius
-            mean = radius
-            std = spikeyness
-            a = (low - mean) / std
-            b = (high - mean) / std
-            tnorm = truncnorm(a=a, b=b, loc=mean, scale=std)
-
-            # now generate the points
-            radii = tnorm.rvs(n, random_state=rng)
-            x_pts = cx + radii * np.cos(angles)
-            y_pts = cy + radii * np.sin(angles)
-
-            points = np.hstack([x_pts[:, None], y_pts[:, None]])
-
-            # Scale to 0-1 space
-            points = points - points.min(axis=0)
-            points = points / points.max(axis=0)
-
-            # Randomly place within 0-1 space
-            points = points * (rng.rand() * .8 + .2)
-            min_pt = points.min(axis=0)
-            max_pt = points.max(axis=0)
-
-            high = (1 - max_pt)
-            low = (0 - min_pt)
-            offset = (rng.rand(2) * (high - low)) + low
-            points = points + offset
-            return points
-
-        def _order_vertices(verts):
-            """
-            References:
-                https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise
-            """
-            mlat = verts.T[0].sum() / len(verts)
-            mlng = verts.T[1].sum() / len(verts)
-
-            tau = np.pi * 2
-            angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) +
-                     tau) % tau
-            sortx = angle.argsort()
-            verts = verts.take(sortx, axis=0)
-            return verts
-
-        # Generate a random exterior for each requested mask
-        masks = []
-        for _ in range(num_masks):
-            exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9))
-            exterior = (exterior * [(width, height)]).astype(dtype)
-            masks.append([exterior.ravel()])
-
-        self = cls(masks, height, width)
-        return self
-
-
-def polygon_to_bitmap(polygons, height, width):
-    """Convert masks from the form of polygons to bitmaps.
-
-    Args:
-        polygons (list[ndarray]): masks in polygon representation
-        height (int): mask height
-        width (int): mask width
-
-    Return:
-        ndarray: the converted masks in bitmap representation
-    """
-    rles = maskUtils.frPyObjects(polygons, height, width)
-    rle = maskUtils.merge(rles)
-    bitmap_mask = maskUtils.decode(rle).astype(np.bool)
-    return bitmap_mask

spaces/AnimalEquality/chatbot/lv_recipe_chatbot/_modidx.py

@@ -1,52 +0,0 @@
-# Autogenerated by nbdev
-
-d = { 'settings': { 'branch': 'main',
-                'doc_baseurl': '/lv-recipe-chatbot',
-                'doc_host': 'https://animalequality.github.io',
-                'git_url': 'https://gitlab.com/animalequality/lv-recipe-chatbot',
-                'lib_path': 'lv_recipe_chatbot'},
-  'syms': { 'lv_recipe_chatbot.app': { 'lv_recipe_chatbot.app.ConversationBot': ('app.html#conversationbot', 'lv_recipe_chatbot/app.py'),
-                                       'lv_recipe_chatbot.app.ConversationBot.__init__': ( 'app.html#conversationbot.__init__',
-                                                                                           'lv_recipe_chatbot/app.py'),
-                                       'lv_recipe_chatbot.app.ConversationBot._get_bot_response': ( 'app.html#conversationbot._get_bot_response',
-                                                                                                    'lv_recipe_chatbot/app.py'),
-                                       'lv_recipe_chatbot.app.ConversationBot.init_agent_executor': ( 'app.html#conversationbot.init_agent_executor',
-                                                                                                      'lv_recipe_chatbot/app.py'),
-                                       'lv_recipe_chatbot.app.ConversationBot.reset': ( 'app.html#conversationbot.reset',
-                                                                                        'lv_recipe_chatbot/app.py'),
-                                       'lv_recipe_chatbot.app.ConversationBot.respond': ( 'app.html#conversationbot.respond',
-                                                                                          'lv_recipe_chatbot/app.py'),
-                                       'lv_recipe_chatbot.app.ConversationBot.run_img': ( 'app.html#conversationbot.run_img',
-                                                                                          'lv_recipe_chatbot/app.py'),
-                                       'lv_recipe_chatbot.app.create_demo': ('app.html#create_demo', 'lv_recipe_chatbot/app.py')},
-            'lv_recipe_chatbot.edamam_api': { 'lv_recipe_chatbot.edamam_api.foo': ( 'edamam_api.html#foo',
-                                                                                    'lv_recipe_chatbot/edamam_api.py')},
-            'lv_recipe_chatbot.engineer_prompt': {},
-            'lv_recipe_chatbot.ingredient_vision': { 'lv_recipe_chatbot.ingredient_vision.BlipImageCaptioning': ( 'ingredient_vision.html#blipimagecaptioning',
-                                                                                                                  'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.BlipImageCaptioning.__init__': ( 'ingredient_vision.html#blipimagecaptioning.__init__',
-                                                                                                                           'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.BlipImageCaptioning.inference': ( 'ingredient_vision.html#blipimagecaptioning.inference',
-                                                                                                                            'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.BlipVQA': ( 'ingredient_vision.html#blipvqa',
-                                                                                                      'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.BlipVQA.__init__': ( 'ingredient_vision.html#blipvqa.__init__',
-                                                                                                               'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.BlipVQA.inference': ( 'ingredient_vision.html#blipvqa.inference',
-                                                                                                                'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.VeganIngredientFinder': ( 'ingredient_vision.html#veganingredientfinder',
-                                                                                                                    'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.VeganIngredientFinder.__init__': ( 'ingredient_vision.html#veganingredientfinder.__init__',
-                                                                                                                             'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.VeganIngredientFinder.list_ingredients': ( 'ingredient_vision.html#veganingredientfinder.list_ingredients',
-                                                                                                                                     'lv_recipe_chatbot/ingredient_vision.py'),
-                                                     'lv_recipe_chatbot.ingredient_vision.format_image': ( 'ingredient_vision.html#format_image',
-                                                                                                           'lv_recipe_chatbot/ingredient_vision.py')},
-            'lv_recipe_chatbot.vegan_recipe_tools': { 'lv_recipe_chatbot.vegan_recipe_tools.RecipeSerpAPIWrapper': ( 'vegan_recipe_tools.html#recipeserpapiwrapper',
-                                                                                                                     'lv_recipe_chatbot/vegan_recipe_tools.py'),
-                                                      'lv_recipe_chatbot.vegan_recipe_tools.RecipeSerpAPIWrapper._process_response': ( 'vegan_recipe_tools.html#recipeserpapiwrapper._process_response',
-                                                                                                                                       'lv_recipe_chatbot/vegan_recipe_tools.py'),
-                                                      'lv_recipe_chatbot.vegan_recipe_tools.get_vegan_recipes_edamam_api': ( 'vegan_recipe_tools.html#get_vegan_recipes_edamam_api',
-                                                                                                                             'lv_recipe_chatbot/vegan_recipe_tools.py'),
-                                                      'lv_recipe_chatbot.vegan_recipe_tools.vegan_recipe_edamam_search': ( 'vegan_recipe_tools.html#vegan_recipe_edamam_search',
-                                                                                                                           'lv_recipe_chatbot/vegan_recipe_tools.py')}}}

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/command/sdist.py

@@ -1,210 +0,0 @@
-from distutils import log
-import distutils.command.sdist as orig
-import os
-import sys
-import io
-import contextlib
-from itertools import chain
-
-from .py36compat import sdist_add_defaults
-
-from .._importlib import metadata
-from .build import _ORIGINAL_SUBCOMMANDS
-
-_default_revctrl = list
-
-
-def walk_revctrl(dirname=''):
-    """Find all files under revision control"""
-    for ep in metadata.entry_points(group='setuptools.file_finders'):
-        for item in ep.load()(dirname):
-            yield item
-
-
-class sdist(sdist_add_defaults, orig.sdist):
-    """Smart sdist that finds anything supported by revision control"""
-
-    user_options = [
-        ('formats=', None,
-         "formats for source distribution (comma-separated list)"),
-        ('keep-temp', 'k',
-         "keep the distribution tree around after creating " +
-         "archive file(s)"),
-        ('dist-dir=', 'd',
-         "directory to put the source distribution archive(s) in "
-         "[default: dist]"),
-        ('owner=', 'u',
-         "Owner name used when creating a tar file [default: current user]"),
-        ('group=', 'g',
-         "Group name used when creating a tar file [default: current group]"),
-    ]
-
-    negative_opt = {}
-
-    README_EXTENSIONS = ['', '.rst', '.txt', '.md']
-    READMES = tuple('README{0}'.format(ext) for ext in README_EXTENSIONS)
-
-    def run(self):
-        self.run_command('egg_info')
-        ei_cmd = self.get_finalized_command('egg_info')
-        self.filelist = ei_cmd.filelist
-        self.filelist.append(os.path.join(ei_cmd.egg_info, 'SOURCES.txt'))
-        self.check_readme()
-
-        # Run sub commands
-        for cmd_name in self.get_sub_commands():
-            self.run_command(cmd_name)
-
-        self.make_distribution()
-
-        dist_files = getattr(self.distribution, 'dist_files', [])
-        for file in self.archive_files:
-            data = ('sdist', '', file)
-            if data not in dist_files:
-                dist_files.append(data)
-
-    def initialize_options(self):
-        orig.sdist.initialize_options(self)
-
-        self._default_to_gztar()
-
-    def _default_to_gztar(self):
-        # only needed on Python prior to 3.6.
-        if sys.version_info >= (3, 6, 0, 'beta', 1):
-            return
-        self.formats = ['gztar']
-
-    def make_distribution(self):
-        """
-        Workaround for #516
-        """
-        with self._remove_os_link():
-            orig.sdist.make_distribution(self)
-
-    @staticmethod
-    @contextlib.contextmanager
-    def _remove_os_link():
-        """
-        In a context, remove and restore os.link if it exists
-        """
-
-        class NoValue:
-            pass
-
-        orig_val = getattr(os, 'link', NoValue)
-        try:
-            del os.link
-        except Exception:
-            pass
-        try:
-            yield
-        finally:
-            if orig_val is not NoValue:
-                setattr(os, 'link', orig_val)
-
-    def add_defaults(self):
-        super().add_defaults()
-        self._add_defaults_build_sub_commands()
-
-    def _add_defaults_optional(self):
-        super()._add_defaults_optional()
-        if os.path.isfile('pyproject.toml'):
-            self.filelist.append('pyproject.toml')
-
-    def _add_defaults_python(self):
-        """getting python files"""
-        if self.distribution.has_pure_modules():
-            build_py = self.get_finalized_command('build_py')
-            self.filelist.extend(build_py.get_source_files())
-            self._add_data_files(self._safe_data_files(build_py))
-
-    def _add_defaults_build_sub_commands(self):
-        build = self.get_finalized_command("build")
-        missing_cmds = set(build.get_sub_commands()) - _ORIGINAL_SUBCOMMANDS
-        # ^-- the original built-in sub-commands are already handled by default.
-        cmds = (self.get_finalized_command(c) for c in missing_cmds)
-        files = (c.get_source_files() for c in cmds if hasattr(c, "get_source_files"))
-        self.filelist.extend(chain.from_iterable(files))
-
-    def _safe_data_files(self, build_py):
-        """
-        Since the ``sdist`` class is also used to compute the MANIFEST
-        (via :obj:`setuptools.command.egg_info.manifest_maker`),
-        there might be recursion problems when trying to obtain the list of
-        data_files and ``include_package_data=True`` (which in turn depends on
-        the files included in the MANIFEST).
-
-        To avoid that, ``manifest_maker`` should be able to overwrite this
-        method and avoid recursive attempts to build/analyze the MANIFEST.
-        """
-        return build_py.data_files
-
-    def _add_data_files(self, data_files):
-        """
-        Add data files as found in build_py.data_files.
-        """
-        self.filelist.extend(
-            os.path.join(src_dir, name)
-            for _, src_dir, _, filenames in data_files
-            for name in filenames
-        )
-
-    def _add_defaults_data_files(self):
-        try:
-            super()._add_defaults_data_files()
-        except TypeError:
-            log.warn("data_files contains unexpected objects")
-
-    def check_readme(self):
-        for f in self.READMES:
-            if os.path.exists(f):
-                return
-        else:
-            self.warn(
-                "standard file not found: should have one of " +
-                ', '.join(self.READMES)
-            )
-
-    def make_release_tree(self, base_dir, files):
-        orig.sdist.make_release_tree(self, base_dir, files)
-
-        # Save any egg_info command line options used to create this sdist
-        dest = os.path.join(base_dir, 'setup.cfg')
-        if hasattr(os, 'link') and os.path.exists(dest):
-            # unlink and re-copy, since it might be hard-linked, and
-            # we don't want to change the source version
-            os.unlink(dest)
-            self.copy_file('setup.cfg', dest)
-
-        self.get_finalized_command('egg_info').save_version_info(dest)
-
-    def _manifest_is_not_generated(self):
-        # check for special comment used in 2.7.1 and higher
-        if not os.path.isfile(self.manifest):
-            return False
-
-        with io.open(self.manifest, 'rb') as fp:
-            first_line = fp.readline()
-        return (first_line !=
-                '# file GENERATED by distutils, do NOT edit\n'.encode())
-
-    def read_manifest(self):
-        """Read the manifest file (named by 'self.manifest') and use it to
-        fill in 'self.filelist', the list of files to include in the source
-        distribution.
-        """
-        log.info("reading manifest file '%s'", self.manifest)
-        manifest = open(self.manifest, 'rb')
-        for line in manifest:
-            # The manifest must contain UTF-8. See #303.
-            try:
-                line = line.decode('UTF-8')
-            except UnicodeDecodeError:
-                log.warn("%r not UTF-8 decodable -- skipping" % line)
-                continue
-            # ignore comments and blank lines
-            line = line.strip()
-            if line.startswith('#') or not line:
-                continue
-            self.filelist.append(line)
-        manifest.close()

spaces/Atualli/yoloxTeste/configs/yolox_x.py

@@ -1,15 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-# Copyright (c) Megvii, Inc. and its affiliates.
-
-import os
-
-from yolox.exp import Exp as MyExp
-
-
-class Exp(MyExp):
-    def __init__(self):
-        super(Exp, self).__init__()
-        self.depth = 1.33
-        self.width = 1.25
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]

spaces/Audio-AGI/WavJourney/parse_voice.py

@@ -1,31 +0,0 @@
-import os
-import argparse
-from VoiceParser.model import VoiceParser
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--wav-path', type=str, help="Path of a wav file")
-    parser.add_argument('--wav-dir', type=str, help="Directory of wav files")
-    parser.add_argument('--out-dir', type=str, help="Directory of output npz files")
-    args = parser.parse_args()
-
-    if (args.wav_path is None and args.wav_dir is None) or (args.wav_path is not None and args.wav_dir is not None):
-        parser.error("Please provide either '--wav-path' or '--wav-dir', but not both.")
-
-    out_dir = args.out_dir
-
-    model = VoiceParser(device='cpu')
-
-    if args.wav_path is not None:
-        model.extract_acoustic_embed(args.wav_path, out_dir)
-        print(f'Sucessfully parsed {args.wav_path}')
-    else:
-        wav_name_list = os.listdir(args.wav_dir)
-        for wav_name in wav_name_list:
-            wav_path = os.path.join(args.wav_dir, wav_name)
-            model.extract_acoustic_embed(wav_path, out_dir)
-            print(f'Sucessfully parsed {wav_path}')
-
-
-if __name__ == '__main__':
-    main()

spaces/Awesimo/jojogan/e4e/training/coach.py

@@ -1,437 +0,0 @@
-import os
-import random
-import matplotlib
-import matplotlib.pyplot as plt
-
-matplotlib.use('Agg')
-
-import torch
-from torch import nn, autograd
-from torch.utils.data import DataLoader
-from torch.utils.tensorboard import SummaryWriter
-import torch.nn.functional as F
-
-from utils import common, train_utils
-from criteria import id_loss, moco_loss
-from configs import data_configs
-from datasets.images_dataset import ImagesDataset
-from criteria.lpips.lpips import LPIPS
-from models.psp import pSp
-from models.latent_codes_pool import LatentCodesPool
-from models.discriminator import LatentCodesDiscriminator
-from models.encoders.psp_encoders import ProgressiveStage
-from training.ranger import Ranger
-
-random.seed(0)
-torch.manual_seed(0)
-
-
-class Coach:
-    def __init__(self, opts, prev_train_checkpoint=None):
-        self.opts = opts
-
-        self.global_step = 0
-
-        self.device = 'cuda:0'
-        self.opts.device = self.device
-        # Initialize network
-        self.net = pSp(self.opts).to(self.device)
-
-        # Initialize loss
-        if self.opts.lpips_lambda > 0:
-            self.lpips_loss = LPIPS(net_type=self.opts.lpips_type).to(self.device).eval()
-        if self.opts.id_lambda > 0:
-            if 'ffhq' in self.opts.dataset_type or 'celeb' in self.opts.dataset_type:
-                self.id_loss = id_loss.IDLoss().to(self.device).eval()
-            else:
-                self.id_loss = moco_loss.MocoLoss(opts).to(self.device).eval()
-        self.mse_loss = nn.MSELoss().to(self.device).eval()
-
-        # Initialize optimizer
-        self.optimizer = self.configure_optimizers()
-
-        # Initialize discriminator
-        if self.opts.w_discriminator_lambda > 0:
-            self.discriminator = LatentCodesDiscriminator(512, 4).to(self.device)
-            self.discriminator_optimizer = torch.optim.Adam(list(self.discriminator.parameters()),
-                                                            lr=opts.w_discriminator_lr)
-            self.real_w_pool = LatentCodesPool(self.opts.w_pool_size)
-            self.fake_w_pool = LatentCodesPool(self.opts.w_pool_size)
-
-        # Initialize dataset
-        self.train_dataset, self.test_dataset = self.configure_datasets()
-        self.train_dataloader = DataLoader(self.train_dataset,
-                                           batch_size=self.opts.batch_size,
-                                           shuffle=True,
-                                           num_workers=int(self.opts.workers),
-                                           drop_last=True)
-        self.test_dataloader = DataLoader(self.test_dataset,
-                                          batch_size=self.opts.test_batch_size,
-                                          shuffle=False,
-                                          num_workers=int(self.opts.test_workers),
-                                          drop_last=True)
-
-        # Initialize logger
-        log_dir = os.path.join(opts.exp_dir, 'logs')
-        os.makedirs(log_dir, exist_ok=True)
-        self.logger = SummaryWriter(log_dir=log_dir)
-
-        # Initialize checkpoint dir
-        self.checkpoint_dir = os.path.join(opts.exp_dir, 'checkpoints')
-        os.makedirs(self.checkpoint_dir, exist_ok=True)
-        self.best_val_loss = None
-        if self.opts.save_interval is None:
-            self.opts.save_interval = self.opts.max_steps
-
-        if prev_train_checkpoint is not None:
-            self.load_from_train_checkpoint(prev_train_checkpoint)
-            prev_train_checkpoint = None
-
-    def load_from_train_checkpoint(self, ckpt):
-        print('Loading previous training data...')
-        self.global_step = ckpt['global_step'] + 1
-        self.best_val_loss = ckpt['best_val_loss']
-        self.net.load_state_dict(ckpt['state_dict'])
-
-        if self.opts.keep_optimizer:
-            self.optimizer.load_state_dict(ckpt['optimizer'])
-        if self.opts.w_discriminator_lambda > 0:
-            self.discriminator.load_state_dict(ckpt['discriminator_state_dict'])
-            self.discriminator_optimizer.load_state_dict(ckpt['discriminator_optimizer_state_dict'])
-        if self.opts.progressive_steps:
-            self.check_for_progressive_training_update(is_resume_from_ckpt=True)
-        print(f'Resuming training from step {self.global_step}')
-
-    def train(self):
-        self.net.train()
-        if self.opts.progressive_steps:
-            self.check_for_progressive_training_update()
-        while self.global_step < self.opts.max_steps:
-            for batch_idx, batch in enumerate(self.train_dataloader):
-                loss_dict = {}
-                if self.is_training_discriminator():
-                    loss_dict = self.train_discriminator(batch)
-                x, y, y_hat, latent = self.forward(batch)
-                loss, encoder_loss_dict, id_logs = self.calc_loss(x, y, y_hat, latent)
-                loss_dict = {**loss_dict, **encoder_loss_dict}
-                self.optimizer.zero_grad()
-                loss.backward()
-                self.optimizer.step()
-
-                # Logging related
-                if self.global_step % self.opts.image_interval == 0 or (
-                        self.global_step < 1000 and self.global_step % 25 == 0):
-                    self.parse_and_log_images(id_logs, x, y, y_hat, title='images/train/faces')
-                if self.global_step % self.opts.board_interval == 0:
-                    self.print_metrics(loss_dict, prefix='train')
-                    self.log_metrics(loss_dict, prefix='train')
-
-                # Validation related
-                val_loss_dict = None
-                if self.global_step % self.opts.val_interval == 0 or self.global_step == self.opts.max_steps:
-                    val_loss_dict = self.validate()
-                    if val_loss_dict and (self.best_val_loss is None or val_loss_dict['loss'] < self.best_val_loss):
-                        self.best_val_loss = val_loss_dict['loss']
-                        self.checkpoint_me(val_loss_dict, is_best=True)
-
-                if self.global_step % self.opts.save_interval == 0 or self.global_step == self.opts.max_steps:
-                    if val_loss_dict is not None:
-                        self.checkpoint_me(val_loss_dict, is_best=False)
-                    else:
-                        self.checkpoint_me(loss_dict, is_best=False)
-
-                if self.global_step == self.opts.max_steps:
-                    print('OMG, finished training!')
-                    break
-
-                self.global_step += 1
-                if self.opts.progressive_steps:
-                    self.check_for_progressive_training_update()
-
-    def check_for_progressive_training_update(self, is_resume_from_ckpt=False):
-        for i in range(len(self.opts.progressive_steps)):
-            if is_resume_from_ckpt and self.global_step >= self.opts.progressive_steps[i]:  # Case checkpoint
-                self.net.encoder.set_progressive_stage(ProgressiveStage(i))
-            if self.global_step == self.opts.progressive_steps[i]:   # Case training reached progressive step
-                self.net.encoder.set_progressive_stage(ProgressiveStage(i))
-
-    def validate(self):
-        self.net.eval()
-        agg_loss_dict = []
-        for batch_idx, batch in enumerate(self.test_dataloader):
-            cur_loss_dict = {}
-            if self.is_training_discriminator():
-                cur_loss_dict = self.validate_discriminator(batch)
-            with torch.no_grad():
-                x, y, y_hat, latent = self.forward(batch)
-                loss, cur_encoder_loss_dict, id_logs = self.calc_loss(x, y, y_hat, latent)
-                cur_loss_dict = {**cur_loss_dict, **cur_encoder_loss_dict}
-            agg_loss_dict.append(cur_loss_dict)
-
-            # Logging related
-            self.parse_and_log_images(id_logs, x, y, y_hat,
-                                      title='images/test/faces',
-                                      subscript='{:04d}'.format(batch_idx))
-
-            # For first step just do sanity test on small amount of data
-            if self.global_step == 0 and batch_idx >= 4:
-                self.net.train()
-                return None  # Do not log, inaccurate in first batch
-
-        loss_dict = train_utils.aggregate_loss_dict(agg_loss_dict)
-        self.log_metrics(loss_dict, prefix='test')
-        self.print_metrics(loss_dict, prefix='test')
-
-        self.net.train()
-        return loss_dict
-
-    def checkpoint_me(self, loss_dict, is_best):
-        save_name = 'best_model.pt' if is_best else 'iteration_{}.pt'.format(self.global_step)
-        save_dict = self.__get_save_dict()
-        checkpoint_path = os.path.join(self.checkpoint_dir, save_name)
-        torch.save(save_dict, checkpoint_path)
-        with open(os.path.join(self.checkpoint_dir, 'timestamp.txt'), 'a') as f:
-            if is_best:
-                f.write(
-                    '**Best**: Step - {}, Loss - {:.3f} \n{}\n'.format(self.global_step, self.best_val_loss, loss_dict))
-            else:
-                f.write('Step - {}, \n{}\n'.format(self.global_step, loss_dict))
-
-    def configure_optimizers(self):
-        params = list(self.net.encoder.parameters())
-        if self.opts.train_decoder:
-            params += list(self.net.decoder.parameters())
-        else:
-            self.requires_grad(self.net.decoder, False)
-        if self.opts.optim_name == 'adam':
-            optimizer = torch.optim.Adam(params, lr=self.opts.learning_rate)
-        else:
-            optimizer = Ranger(params, lr=self.opts.learning_rate)
-        return optimizer
-
-    def configure_datasets(self):
-        if self.opts.dataset_type not in data_configs.DATASETS.keys():
-            Exception('{} is not a valid dataset_type'.format(self.opts.dataset_type))
-        print('Loading dataset for {}'.format(self.opts.dataset_type))
-        dataset_args = data_configs.DATASETS[self.opts.dataset_type]
-        transforms_dict = dataset_args['transforms'](self.opts).get_transforms()
-        train_dataset = ImagesDataset(source_root=dataset_args['train_source_root'],
-                                      target_root=dataset_args['train_target_root'],
-                                      source_transform=transforms_dict['transform_source'],
-                                      target_transform=transforms_dict['transform_gt_train'],
-                                      opts=self.opts)
-        test_dataset = ImagesDataset(source_root=dataset_args['test_source_root'],
-                                     target_root=dataset_args['test_target_root'],
-                                     source_transform=transforms_dict['transform_source'],
-                                     target_transform=transforms_dict['transform_test'],
-                                     opts=self.opts)
-        print("Number of training samples: {}".format(len(train_dataset)))
-        print("Number of test samples: {}".format(len(test_dataset)))
-        return train_dataset, test_dataset
-
-    def calc_loss(self, x, y, y_hat, latent):
-        loss_dict = {}
-        loss = 0.0
-        id_logs = None
-        if self.is_training_discriminator():  # Adversarial loss
-            loss_disc = 0.
-            dims_to_discriminate = self.get_dims_to_discriminate() if self.is_progressive_training() else \
-                list(range(self.net.decoder.n_latent))
-
-            for i in dims_to_discriminate:
-                w = latent[:, i, :]
-                fake_pred = self.discriminator(w)
-                loss_disc += F.softplus(-fake_pred).mean()
-            loss_disc /= len(dims_to_discriminate)
-            loss_dict['encoder_discriminator_loss'] = float(loss_disc)
-            loss += self.opts.w_discriminator_lambda * loss_disc
-
-        if self.opts.progressive_steps and self.net.encoder.progressive_stage.value != 18:  # delta regularization loss
-            total_delta_loss = 0
-            deltas_latent_dims = self.net.encoder.get_deltas_starting_dimensions()
-
-            first_w = latent[:, 0, :]
-            for i in range(1, self.net.encoder.progressive_stage.value + 1):
-                curr_dim = deltas_latent_dims[i]
-                delta = latent[:, curr_dim, :] - first_w
-                delta_loss = torch.norm(delta, self.opts.delta_norm, dim=1).mean()
-                loss_dict[f"delta{i}_loss"] = float(delta_loss)
-                total_delta_loss += delta_loss
-            loss_dict['total_delta_loss'] = float(total_delta_loss)
-            loss += self.opts.delta_norm_lambda * total_delta_loss
-
-        if self.opts.id_lambda > 0:  # Similarity loss
-            loss_id, sim_improvement, id_logs = self.id_loss(y_hat, y, x)
-            loss_dict['loss_id'] = float(loss_id)
-            loss_dict['id_improve'] = float(sim_improvement)
-            loss += loss_id * self.opts.id_lambda
-        if self.opts.l2_lambda > 0:
-            loss_l2 = F.mse_loss(y_hat, y)
-            loss_dict['loss_l2'] = float(loss_l2)
-            loss += loss_l2 * self.opts.l2_lambda
-        if self.opts.lpips_lambda > 0:
-            loss_lpips = self.lpips_loss(y_hat, y)
-            loss_dict['loss_lpips'] = float(loss_lpips)
-            loss += loss_lpips * self.opts.lpips_lambda
-        loss_dict['loss'] = float(loss)
-        return loss, loss_dict, id_logs
-
-    def forward(self, batch):
-        x, y = batch
-        x, y = x.to(self.device).float(), y.to(self.device).float()
-        y_hat, latent = self.net.forward(x, return_latents=True)
-        if self.opts.dataset_type == "cars_encode":
-            y_hat = y_hat[:, :, 32:224, :]
-        return x, y, y_hat, latent
-
-    def log_metrics(self, metrics_dict, prefix):
-        for key, value in metrics_dict.items():
-            self.logger.add_scalar('{}/{}'.format(prefix, key), value, self.global_step)
-
-    def print_metrics(self, metrics_dict, prefix):
-        print('Metrics for {}, step {}'.format(prefix, self.global_step))
-        for key, value in metrics_dict.items():
-            print('\t{} = '.format(key), value)
-
-    def parse_and_log_images(self, id_logs, x, y, y_hat, title, subscript=None, display_count=2):
-        im_data = []
-        for i in range(display_count):
-            cur_im_data = {
-                'input_face': common.log_input_image(x[i], self.opts),
-                'target_face': common.tensor2im(y[i]),
-                'output_face': common.tensor2im(y_hat[i]),
-            }
-            if id_logs is not None:
-                for key in id_logs[i]:
-                    cur_im_data[key] = id_logs[i][key]
-            im_data.append(cur_im_data)
-        self.log_images(title, im_data=im_data, subscript=subscript)
-
-    def log_images(self, name, im_data, subscript=None, log_latest=False):
-        fig = common.vis_faces(im_data)
-        step = self.global_step
-        if log_latest:
-            step = 0
-        if subscript:
-            path = os.path.join(self.logger.log_dir, name, '{}_{:04d}.jpg'.format(subscript, step))
-        else:
-            path = os.path.join(self.logger.log_dir, name, '{:04d}.jpg'.format(step))
-        os.makedirs(os.path.dirname(path), exist_ok=True)
-        fig.savefig(path)
-        plt.close(fig)
-
-    def __get_save_dict(self):
-        save_dict = {
-            'state_dict': self.net.state_dict(),
-            'opts': vars(self.opts)
-        }
-        # save the latent avg in state_dict for inference if truncation of w was used during training
-        if self.opts.start_from_latent_avg:
-            save_dict['latent_avg'] = self.net.latent_avg
-
-        if self.opts.save_training_data:  # Save necessary information to enable training continuation from checkpoint
-            save_dict['global_step'] = self.global_step
-            save_dict['optimizer'] = self.optimizer.state_dict()
-            save_dict['best_val_loss'] = self.best_val_loss
-            if self.opts.w_discriminator_lambda > 0:
-                save_dict['discriminator_state_dict'] = self.discriminator.state_dict()
-                save_dict['discriminator_optimizer_state_dict'] = self.discriminator_optimizer.state_dict()
-        return save_dict
-
-    def get_dims_to_discriminate(self):
-        deltas_starting_dimensions = self.net.encoder.get_deltas_starting_dimensions()
-        return deltas_starting_dimensions[:self.net.encoder.progressive_stage.value + 1]
-
-    def is_progressive_training(self):
-        return self.opts.progressive_steps is not None
-
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Discriminator ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
-
-    def is_training_discriminator(self):
-        return self.opts.w_discriminator_lambda > 0
-
-    @staticmethod
-    def discriminator_loss(real_pred, fake_pred, loss_dict):
-        real_loss = F.softplus(-real_pred).mean()
-        fake_loss = F.softplus(fake_pred).mean()
-
-        loss_dict['d_real_loss'] = float(real_loss)
-        loss_dict['d_fake_loss'] = float(fake_loss)
-
-        return real_loss + fake_loss
-
-    @staticmethod
-    def discriminator_r1_loss(real_pred, real_w):
-        grad_real, = autograd.grad(
-            outputs=real_pred.sum(), inputs=real_w, create_graph=True
-        )
-        grad_penalty = grad_real.pow(2).reshape(grad_real.shape[0], -1).sum(1).mean()
-
-        return grad_penalty
-
-    @staticmethod
-    def requires_grad(model, flag=True):
-        for p in model.parameters():
-            p.requires_grad = flag
-
-    def train_discriminator(self, batch):
-        loss_dict = {}
-        x, _ = batch
-        x = x.to(self.device).float()
-        self.requires_grad(self.discriminator, True)
-
-        with torch.no_grad():
-            real_w, fake_w = self.sample_real_and_fake_latents(x)
-        real_pred = self.discriminator(real_w)
-        fake_pred = self.discriminator(fake_w)
-        loss = self.discriminator_loss(real_pred, fake_pred, loss_dict)
-        loss_dict['discriminator_loss'] = float(loss)
-
-        self.discriminator_optimizer.zero_grad()
-        loss.backward()
-        self.discriminator_optimizer.step()
-
-        # r1 regularization
-        d_regularize = self.global_step % self.opts.d_reg_every == 0
-        if d_regularize:
-            real_w = real_w.detach()
-            real_w.requires_grad = True
-            real_pred = self.discriminator(real_w)
-            r1_loss = self.discriminator_r1_loss(real_pred, real_w)
-
-            self.discriminator.zero_grad()
-            r1_final_loss = self.opts.r1 / 2 * r1_loss * self.opts.d_reg_every + 0 * real_pred[0]
-            r1_final_loss.backward()
-            self.discriminator_optimizer.step()
-            loss_dict['discriminator_r1_loss'] = float(r1_final_loss)
-
-        # Reset to previous state
-        self.requires_grad(self.discriminator, False)
-
-        return loss_dict
-
-    def validate_discriminator(self, test_batch):
-        with torch.no_grad():
-            loss_dict = {}
-            x, _ = test_batch
-            x = x.to(self.device).float()
-            real_w, fake_w = self.sample_real_and_fake_latents(x)
-            real_pred = self.discriminator(real_w)
-            fake_pred = self.discriminator(fake_w)
-            loss = self.discriminator_loss(real_pred, fake_pred, loss_dict)
-            loss_dict['discriminator_loss'] = float(loss)
-            return loss_dict
-
-    def sample_real_and_fake_latents(self, x):
-        sample_z = torch.randn(self.opts.batch_size, 512, device=self.device)
-        real_w = self.net.decoder.get_latent(sample_z)
-        fake_w = self.net.encoder(x)
-        if self.is_progressive_training():  # When progressive training, feed only unique w's
-            dims_to_discriminate = self.get_dims_to_discriminate()
-            fake_w = fake_w[:, dims_to_discriminate, :]
-        if self.opts.use_w_pool:
-            real_w = self.real_w_pool.query(real_w)
-            fake_w = self.fake_w_pool.query(fake_w)
-        if fake_w.ndim == 3:
-            fake_w = fake_w[:, 0, :]
-        return real_w, fake_w

spaces/BeeMon/dreambooth-training/convertosd.py

@@ -1,302 +0,0 @@
-# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
-# *Only* converts the UNet, VAE, and Text Encoder.
-# Does not convert optimizer state or any other thing.
-
-import argparse
-import os.path as osp
-import re
-
-import torch
-import gc
-
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
-    # (stable-diffusion, HF Diffusers)
-    ("in_layers.0", "norm1"),
-    ("in_layers.2", "conv1"),
-    ("out_layers.0", "norm2"),
-    ("out_layers.3", "conv2"),
-    ("emb_layers.1", "time_emb_proj"),
-    ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
-    # loop over downblocks/upblocks
-
-    for j in range(2):
-        # loop over resnets/attentions for downblocks
-        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
-        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-        if i < 3:
-            # no attention layers in down_blocks.3
-            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
-            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-    for j in range(3):
-        # loop over resnets/attentions for upblocks
-        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
-        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-        if i > 0:
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3*i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-    if i < 3:
-        # no downsample in down_blocks.3
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
-        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        # no upsample in up_blocks.3
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
-        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{j}."
-    sd_mid_res_prefix = f"middle_block.{2*j}."
-    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
-    # buyer beware: this is a *brittle* function,
-    # and correct output requires that all of these pieces interact in
-    # the exact order in which I have arranged them.
-    mapping = {k: k for k in unet_state_dict.keys()}
-    for sd_name, hf_name in unet_conversion_map:
-        mapping[hf_name] = sd_name
-    for k, v in mapping.items():
-        if "resnets" in k:
-            for sd_part, hf_part in unet_conversion_map_resnet:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    for k, v in mapping.items():
-        for sd_part, hf_part in unet_conversion_map_layer:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
-    return new_state_dict
-
-
-# ================#
-# VAE Conversion #
-# ================#
-
-vae_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("nin_shortcut", "conv_shortcut"),
-    ("norm_out", "conv_norm_out"),
-    ("mid.attn_1.", "mid_block.attentions.0."),
-]
-
-for i in range(4):
-    # down_blocks have two resnets
-    for j in range(2):
-        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
-        sd_down_prefix = f"encoder.down.{i}.block.{j}."
-        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
-
-    if i < 3:
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
-        sd_downsample_prefix = f"down.{i}.downsample."
-        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"up.{3-i}.upsample."
-        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
-
-    # up_blocks have three resnets
-    # also, up blocks in hf are numbered in reverse from sd
-    for j in range(3):
-        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
-        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
-        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
-
-# this part accounts for mid blocks in both the encoder and the decoder
-for i in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{i}."
-    sd_mid_res_prefix = f"mid.block_{i+1}."
-    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-vae_conversion_map_attn = [
-    # (stable-diffusion, HF Diffusers)
-    ("norm.", "group_norm."),
-    ("q.", "query."),
-    ("k.", "key."),
-    ("v.", "value."),
-    ("proj_out.", "proj_attn."),
-]
-
-
-def reshape_weight_for_sd(w):
-    # convert HF linear weights to SD conv2d weights
-    return w.reshape(*w.shape, 1, 1)
-
-
-def convert_vae_state_dict(vae_state_dict):
-    mapping = {k: k for k in vae_state_dict.keys()}
-    for k, v in mapping.items():
-        for sd_part, hf_part in vae_conversion_map:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    for k, v in mapping.items():
-        if "attentions" in k:
-            for sd_part, hf_part in vae_conversion_map_attn:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
-    weights_to_convert = ["q", "k", "v", "proj_out"]
-    print("Converting to CKPT ...") 
-    for k, v in new_state_dict.items():
-        for weight_name in weights_to_convert:
-            if f"mid.attn_1.{weight_name}.weight" in k:
-                print(f"Reshaping {k} for SD format")
-                new_state_dict[k] = reshape_weight_for_sd(v)
-    return new_state_dict
-
-
-# =========================#
-# Text Encoder Conversion #
-# =========================#
-
-
-textenc_conversion_lst = [
-    # (stable-diffusion, HF Diffusers)
-    ("resblocks.", "text_model.encoder.layers."),
-    ("ln_1", "layer_norm1"),
-    ("ln_2", "layer_norm2"),
-    (".c_fc.", ".fc1."),
-    (".c_proj.", ".fc2."),
-    (".attn", ".self_attn"),
-    ("ln_final.", "transformer.text_model.final_layer_norm."),
-    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
-    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
-]
-protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
-textenc_pattern = re.compile("|".join(protected.keys()))
-
-# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
-code2idx = {"q": 0, "k": 1, "v": 2}
-
-
-def convert_text_enc_state_dict_v20(text_enc_dict):
-    new_state_dict = {}
-    capture_qkv_weight = {}
-    capture_qkv_bias = {}
-    for k, v in text_enc_dict.items():
-        if (
-            k.endswith(".self_attn.q_proj.weight")
-            or k.endswith(".self_attn.k_proj.weight")
-            or k.endswith(".self_attn.v_proj.weight")
-        ):
-            k_pre = k[: -len(".q_proj.weight")]
-            k_code = k[-len("q_proj.weight")]
-            if k_pre not in capture_qkv_weight:
-                capture_qkv_weight[k_pre] = [None, None, None]
-            capture_qkv_weight[k_pre][code2idx[k_code]] = v
-            continue
-
-        if (
-            k.endswith(".self_attn.q_proj.bias")
-            or k.endswith(".self_attn.k_proj.bias")
-            or k.endswith(".self_attn.v_proj.bias")
-        ):
-            k_pre = k[: -len(".q_proj.bias")]
-            k_code = k[-len("q_proj.bias")]
-            if k_pre not in capture_qkv_bias:
-                capture_qkv_bias[k_pre] = [None, None, None]
-            capture_qkv_bias[k_pre][code2idx[k_code]] = v
-            continue
-
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
-        new_state_dict[relabelled_key] = v
-
-    for k_pre, tensors in capture_qkv_weight.items():
-        if None in tensors:
-            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)
-
-    for k_pre, tensors in capture_qkv_bias.items():
-        if None in tensors:
-            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)
-
-    return new_state_dict
-
-
-def convert_text_enc_state_dict(text_enc_dict):
-    return text_enc_dict
-
-
-def convert(model_path, checkpoint_path):    
-    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
-    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
-    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
-
-    # Convert the UNet model
-    unet_state_dict = torch.load(unet_path, map_location="cpu")
-    unet_state_dict = convert_unet_state_dict(unet_state_dict)
-    unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
-
-    # Convert the VAE model
-    vae_state_dict = torch.load(vae_path, map_location="cpu")
-    vae_state_dict = convert_vae_state_dict(vae_state_dict)
-    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
-
-    # Convert the text encoder model
-    text_enc_dict = torch.load(text_enc_path, map_location="cpu")
-
-    # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
-    is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
-
-    if is_v20_model:
-        # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
-        text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()}
-        text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict)
-        text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
-    else:
-        text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
-        text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
-
-    # Put together new checkpoint
-    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
-    state_dict = {k: v.half() for k, v in state_dict.items()}
-    state_dict = {"state_dict": state_dict}
-    torch.save(state_dict, checkpoint_path)
-    del state_dict, text_enc_dict, vae_state_dict, unet_state_dict
-    torch.cuda.empty_cache()
-    gc.collect()
-    

spaces/Benson/text-generation/Examples/Descargar Abuela 5 Tiempo Para Despertar Mod Apk.md

@@ -1,76 +0,0 @@
-
-<h1>Descargar Granny 5: Tiempo para despertar Mod APK</h1>
-<p>Si eres un fan de los juegos de terror, es posible que hayas oído hablar de la serie Granny. Estos son juegos donde tienes que escapar de una anciana espeluznante que quiere matarte. Los juegos son conocidos por su atmósfera de miedo, sustos de salto y rompecabezas. ¿Pero sabías que hay un juego hecho por fans llamado Granny 5: Time to Wake Up? Este es un juego que lleva el concepto de la abuela a un nuevo nivel, con una nueva ubicación, nuevos enemigos, nuevas armas y nuevos desafíos. En este artículo, le diremos todo lo que necesita saber sobre este juego, y cómo se puede descargar Granny 5: Time to Wake Up Mod APK gratis. </p>
- <h2>¿Qué es la abuela 5: Hora de despertar? </h2>
-<h3>Un juego de terror hecho por fans basado en la popular serie Granny</h3>
-<p>Granny 5: Time to Wake Up es un juego creado y desarrollado por A Twelve Studio, un fan de los juegos originales de Granny. El juego no es una secuela oficial o una parte de la franquicia Granny, sino más bien un homenaje y un homenaje a los juegos. El juego está inspirado en la jugabilidad, la mecánica y los personajes de la serie Granny, pero también añade su propio toque y originalidad. </p>
-<h2>descargar abuela 5 tiempo para despertar mod apk</h2><br /><p><b><b>DOWNLOAD</b> &#8250;&#8250;&#8250;&#8250;&#8250; <a href="https://bltlly.com/2v6K2k">https://bltlly.com/2v6K2k</a></b></p><br /><br />
- <h3>Una pesadilla en un hotel abandonado con la abuela y su hija</h3>
-<p>La historia del juego es que eres víctima de una pesadilla en un hotel "abandonado". Tu tarea es sobrevivir y dejar la pesadilla. Sin embargo, no será fácil, ya que se enfrentará a muchas dificultades. Los principales enemigos son la abuela y su hija, que escuchan todo y te perseguirán si te ven. También encontrarás a Slendrina en el sótano, quien te maldecirá si la miras. Tendrás que evitar trampas, encontrar llaves, resolver puzzles y usar armas para escapar de este infierno. </p>
- <h3>Un juego desafiante y emocionante con trampas, enemigos y rompecabezas</h3>
-
- <h2>¿Por qué descargar Granny 5: Tiempo para despertar Mod APK? </h2>
-<h3>Disfruta de la última versión del juego con nuevas características y actualizaciones</h3>
-<p>Granny 5: Time to Wake Up es un juego que es constantemente actualizado y mejorado por el desarrollador. El juego ha recibido muchas actualizaciones desde su lanzamiento en 2020, añadiendo nuevas habitaciones, armas, objetos, modos, dificultades, gráficos, sonidos y más. La última versión del juego es 1.4.2, que fue lanzado el 18 de junio de 2021. Esta versión añadió una nueva sala llamada "sala secreta", donde se puede encontrar una gran cantidad de huevos de Pascua y referencias a los populares YouTubers y personajes de terror. La versión también corrigió algunos errores y fallos que afectaron el juego. </p>
- <h3>Desbloquear todas las armas, objetos y modos de forma gratuita</h3>
-<p>Granny 5: Time to Wake Up es un juego gratuito que puedes descargar y jugar en tu dispositivo Android. Sin embargo, algunas de las armas, artículos y modos están bloqueados y requieren que veas anuncios o pagues dinero real para desbloquearlos. Por ejemplo, si quieres usar la escopeta o la pistola eléctrica, tienes que ver un anuncio antes de cada uso. Si quieres jugar en modo extremo o en modo pesadilla, tienes que pagar $0.99 o $1.99 respectivamente. Esto puede ser molesto y frustrante para algunos jugadores que quieren disfrutar del juego sin interrupciones ni limitaciones. </p>
- <p>Es por eso que le recomendamos que descargue Granny 5: Tiempo para despertar Mod APK lugar. Esta es una versión modificada del juego que te da acceso a todas las armas, objetos y modos de forma gratuita. No tienes que ver anuncios ni pagar nada para usarlos. También puedes jugar sin conexión a Internet. De esta manera, puedes tener más diversión y libertad en el juego. </p>
- <h2>Cómo descargar e instalar Granny 5: Tiempo para despertar Mod APK? </h2>
-<h3>Siga estos sencillos pasos para obtener la versión modificada del juego</h3>
-<p>Si desea descargar Granny 5: Tiempo para despertar Mod APK, usted tiene que seguir estos sencillos pasos:</p>
-<h4>Paso 1: Descargar el archivo APK de una fuente de confianza</h4>
-
-<h4>Paso 2: Habilitar fuentes desconocidas en la configuración del dispositivo</h4>
-<p>Lo siguiente que debe hacer es habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones que no son de Google Play Store. Para hacer esto, vaya a la configuración del dispositivo > seguridad > fuentes desconocidas > habilitar. </p>
-<h4>Paso 3: Instalar el archivo APK tocando en él</h4>
-<p>La tercera cosa que necesita hacer es instalar el archivo APK tocando en él. Verá una ventana emergente pidiendo su permiso para instalar la aplicación. Toque en "instalar" y espere a que termine el proceso de instalación. </p>
-<h4>Paso 4: Iniciar el juego y disfrutar de</h4>
-<p>Lo último que tienes que hacer es lanzar el juego y disfrutarlo. Verá que todas las armas, artículos y modos están desbloqueados y disponibles de forma gratuita. También puede jugar sin conexión a Internet o anuncios. </p>
-<p></p>
- <h2>Consejos y trucos para jugar Granny 5: Hora de despertar</h2>
-<h3>Conozca el diseño del hotel y encuentre las llaves y puertas ocultas</h3>
-
- <h3>Evite hacer ruido y esconderse de la abuela y su hija</h3>
-<p>Otra cosa importante que necesitas hacer en el juego es evitar hacer ruido y esconderte de la abuela y su hija. Pueden oír todo y vendrán tras de ti si te oyen. Puedes hacer ruido caminando por pisos que crujen, rompiendo ventanas o jarrones, golpeando objetos o muebles, abriendo o cerrando puertas o cajones en voz alta, usando armas o artículos que hagan ruido, o disparando trampas o alarmas. También puede hacer ruido hablando o gritando si tiene un micrófono habilitado. Si haces ruido, verás un icono de ojo rojo en la pantalla que indica que la abuela y su hija están alertas. También escucharás sus voces diciendo cosas como "te oigo", "¿Dónde estás?", "¿Quieres jugar conmigo?" o "No puedes escapar de mí". Si te ven, te perseguirán e intentarán matarte con sus armas o manos. Puede evitarlos escondiéndose en armarios, armarios, rejillas de ventilación o debajo de camas o mesas. También puede huir de ellos utilizando las escaleras, ascensores o puertas. Sin embargo, tienes que ser rápido e inteligente, ya que también pueden usar estas formas para atraparte. También puedes distraerlos tirando objetos o objetos en diferentes direcciones, o usando el spray de pimienta para cegarlos temporalmente. Tienes que tener cuidado de no mirar a Slendrina en el sótano, ya que te maldecirá y te hará perder la salud. Puedes evitarla alejándote de ella o usando la ballesta para dispararle. </p>
- <h3>Usa las armas sabiamente y no desperdicies munición</h3>
-
- <h3>Aléjate de Slendrina en el sótano para evitar su maldición</h3>
-<p>La cuarta cosa importante que tienes que hacer en el juego es dar la espalda a Slendrina en el sótano para evitar su maldición. Slendrina es la nieta de la abuela y la hija de Slenderman. Es una chica fantasmal que frecuenta la bodega del hotel. Ella aparecerá al azar delante de ti e intentará maldecirte con su mirada. Si la miras demasiado tiempo, perderás salud y eventualmente morirás. Puedes evitar su maldición alejándote de ella o disparándole con la ballesta. Sin embargo, tienes que ser rápido y preciso, ya que puede moverse rápido y desaparecer rápidamente. También hay que tener cuidado de no hacer ruido o alertar a la abuela y su hija al disparar Slendrina, ya que pueden escuchar los disparos. </p>
- <h2>Reseña de la abuela 5: Hora de despertar</h2>
-<h3>Un divertido y aterrador juego hecho por fans que rinde homenaje a los juegos originales de Granny</h3>
-<p>Granny 5: Time to Wake Up es un divertido y aterrador juego hecho por fans que rinde homenaje a los juegos originales de Granny. El juego está bien hecho y desarrollado por un fan de la serie Granny. El juego está inspirado en la jugabilidad , la mecánica, y los personajes de la serie Granny, pero también añade su propio toque y originalidad. El juego tiene una nueva ubicación, una nueva historia, nuevos enemigos, nuevas armas, nuevos objetos, nuevos modos, nuevas dificultades, nuevos gráficos, nuevos sonidos y más. El juego está lleno de sorpresas, sustos y desafíos que te mantendrán enganchado y entretenido. El juego es una gran manera de mostrar aprecio y respeto a los juegos originales de la abuela y sus creadores. </p>
- <h3>Un entorno bien diseñado e inmersivo con sonidos y gráficos espeluznantes</h3>
-
- <h3>Un juego desafiante y gratificante con diferentes modos y dificultades</h3>
-<p>Granny 5: Time to Wake Up tiene un juego desafiante y gratificante con diferentes modos y dificultades. El juego tiene cinco días o cinco oportunidades para escapar del hotel. Puede elegir entre diferentes modos y dificultades, que van desde fáciles hasta extremas. También puede personalizar algunos ajustes, como apagar la sangre o la música. El juego tiene muchas habitaciones, pasillos, escaleras, ascensores, rejillas de ventilación, armarios, cajones, cajas fuertes, cajas fuertes, barriles, cajas, camas, mesas, sillas, sofás, lámparas, relojes, pinturas, espejos, ventanas, puertas, llaves, armas, artículos, herramientas, notas y más. Tendrás que explorar el hotel y encontrar pistas, pistas y secretos que te ayudarán a escapar. También tendrás que usar armas, como una ballesta, una escopeta, un martillo, una palanca, una pistola eléctrica y un spray de pimienta, para defenderte de la abuela y su hija. Sin embargo, tendrás munición y recursos limitados, así que tendrás que usarlos sabiamente. El juego está lleno de sorpresas, sustos y desafíos que te mantendrán al borde de tu asiento. El juego tiene un juego desafiante y gratificante con diferentes modos y dificultades que pondrán a prueba tus habilidades y coraje. </p>
- <h2>Conclusión</h2>
-<p>Granny 5: Time to Wake Up es un juego de terror hecho por fans basado en la popular serie Granny. El juego es un homenaje y un homenaje a los juegos originales de Granny, pero también tiene su propio toque y originalidad. El juego tiene una nueva ubicación, una nueva historia, nuevos enemigos, nuevas armas, nuevos objetos, nuevos modos, nuevas dificultades, nuevos gráficos, nuevos sonidos y más. El juego tiene un entorno bien diseñado e inmersivo con sonidos espeluznantes y gráficos. El juego tiene un juego desafiante y gratificante con diferentes modos y dificultades. El juego es un juego divertido y aterrador que te mantendrá enganchado y entretenido. </p>
-
- <h2>Preguntas frecuentes</h2>
-<h3>Q: ¿Es la abuela 5: Hora de despertar una secuela oficial o una parte de la franquicia de la abuela? </h3>
-<p>A: No, Granny 5: Time to Wake Up no es una secuela oficial o una parte de la franquicia Granny. Es un juego creado y desarrollado por A Twelve Studio, un fan de los juegos originales de Granny. El juego no está afiliado o respaldado por DVloper o cualquier otro creador oficial de la serie Granny. </p>
- <h3>Q: ¿Es la abuela 5: tiempo de despertar seguro para descargar y jugar? </h3>
-<p>A: Sí, Abuelita 5: Tiempo de despertar es seguro para descargar y jugar. Sin embargo, hay que tener cuidado de no descargar ningún malware o virus junto con el archivo APK de la versión modificada del juego. Le recomendamos que utilice este enlace ya que es seguro y confiable. </p>
- <h3>Q: ¿Cuáles son los requisitos mínimos para jugar Granny 5: Hora de despertar en los dispositivos Android? </h3>
-<p>A: Los requisitos mínimos para jugar Granny 5: Tiempo para despertar en dispositivos Android son los siguientes:</p>
-<ul>
-<li>Versión para Android: 4.4 o superior</li>
-<li>RAM: 1 GB o superior</li>
-<li>Espacio de almacenamiento: 100 MB o superior</li>
-</ul>
- <h3>Q: ¿Cómo puedo contactar al desarrollador de Granny 5: Time to Wake Up? </h3>
-<p>A: Puedes contactar al desarrollador de Granny 5: Time to Wake Up usando los siguientes métodos:</p>
-<ul>
-<li>Correo electrónico: [email protected]</li>
-<li>Facebook: https://www.facebook.com/ATwelveStudio</li>
-<li>YouTube: https://www.youtube.com/channel/UCx8Zf0l9cX6n1zqY7g9yZ0A</li>
-</ul>
- <h3>P: ¿Dónde puedo encontrar más información sobre Granny 5: Time to Wake Up? </h3>
-<p>A: Puedes encontrar más información sobre Granny 5: Time to Wake Up visitando las siguientes fuentes:</p>
-<ul>
-<li>Google Play Store: https://play.google.com/store/apps/details?id=com.atwelvestudio.grannyfive</li>
-<li>Sitio web oficial: https:/atwelvestudio.wixsite.com/grannyfive</li>
-Página wiki: https://granny-five.fandom.com/wiki/Granny_5:_Time_to_Wake_Up_Wiki</li>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/_distutils_hack/__init__.py

@@ -1,222 +0,0 @@
-# don't import any costly modules
-import sys
-import os
-
-
-is_pypy = '__pypy__' in sys.builtin_module_names
-
-
-def warn_distutils_present():
-    if 'distutils' not in sys.modules:
-        return
-    if is_pypy and sys.version_info < (3, 7):
-        # PyPy for 3.6 unconditionally imports distutils, so bypass the warning
-        # https://foss.heptapod.net/pypy/pypy/-/blob/be829135bc0d758997b3566062999ee8b23872b4/lib-python/3/site.py#L250
-        return
-    import warnings
-
-    warnings.warn(
-        "Distutils was imported before Setuptools, but importing Setuptools "
-        "also replaces the `distutils` module in `sys.modules`. This may lead "
-        "to undesirable behaviors or errors. To avoid these issues, avoid "
-        "using distutils directly, ensure that setuptools is installed in the "
-        "traditional way (e.g. not an editable install), and/or make sure "
-        "that setuptools is always imported before distutils."
-    )
-
-
-def clear_distutils():
-    if 'distutils' not in sys.modules:
-        return
-    import warnings
-
-    warnings.warn("Setuptools is replacing distutils.")
-    mods = [
-        name
-        for name in sys.modules
-        if name == "distutils" or name.startswith("distutils.")
-    ]
-    for name in mods:
-        del sys.modules[name]
-
-
-def enabled():
-    """
-    Allow selection of distutils by environment variable.
-    """
-    which = os.environ.get('SETUPTOOLS_USE_DISTUTILS', 'local')
-    return which == 'local'
-
-
-def ensure_local_distutils():
-    import importlib
-
-    clear_distutils()
-
-    # With the DistutilsMetaFinder in place,
-    # perform an import to cause distutils to be
-    # loaded from setuptools._distutils. Ref #2906.
-    with shim():
-        importlib.import_module('distutils')
-
-    # check that submodules load as expected
-    core = importlib.import_module('distutils.core')
-    assert '_distutils' in core.__file__, core.__file__
-    assert 'setuptools._distutils.log' not in sys.modules
-
-
-def do_override():
-    """
-    Ensure that the local copy of distutils is preferred over stdlib.
-
-    See https://github.com/pypa/setuptools/issues/417#issuecomment-392298401
-    for more motivation.
-    """
-    if enabled():
-        warn_distutils_present()
-        ensure_local_distutils()
-
-
-class _TrivialRe:
-    def __init__(self, *patterns):
-        self._patterns = patterns
-
-    def match(self, string):
-        return all(pat in string for pat in self._patterns)
-
-
-class DistutilsMetaFinder:
-    def find_spec(self, fullname, path, target=None):
-        # optimization: only consider top level modules and those
-        # found in the CPython test suite.
-        if path is not None and not fullname.startswith('test.'):
-            return
-
-        method_name = 'spec_for_{fullname}'.format(**locals())
-        method = getattr(self, method_name, lambda: None)
-        return method()
-
-    def spec_for_distutils(self):
-        if self.is_cpython():
-            return
-
-        import importlib
-        import importlib.abc
-        import importlib.util
-
-        try:
-            mod = importlib.import_module('setuptools._distutils')
-        except Exception:
-            # There are a couple of cases where setuptools._distutils
-            # may not be present:
-            # - An older Setuptools without a local distutils is
-            #   taking precedence. Ref #2957.
-            # - Path manipulation during sitecustomize removes
-            #   setuptools from the path but only after the hook
-            #   has been loaded. Ref #2980.
-            # In either case, fall back to stdlib behavior.
-            return
-
-        class DistutilsLoader(importlib.abc.Loader):
-            def create_module(self, spec):
-                mod.__name__ = 'distutils'
-                return mod
-
-            def exec_module(self, module):
-                pass
-
-        return importlib.util.spec_from_loader(
-            'distutils', DistutilsLoader(), origin=mod.__file__
-        )
-
-    @staticmethod
-    def is_cpython():
-        """
-        Suppress supplying distutils for CPython (build and tests).
-        Ref #2965 and #3007.
-        """
-        return os.path.isfile('pybuilddir.txt')
-
-    def spec_for_pip(self):
-        """
-        Ensure stdlib distutils when running under pip.
-        See pypa/pip#8761 for rationale.
-        """
-        if self.pip_imported_during_build():
-            return
-        clear_distutils()
-        self.spec_for_distutils = lambda: None
-
-    @classmethod
-    def pip_imported_during_build(cls):
-        """
-        Detect if pip is being imported in a build script. Ref #2355.
-        """
-        import traceback
-
-        return any(
-            cls.frame_file_is_setup(frame) for frame, line in traceback.walk_stack(None)
-        )
-
-    @staticmethod
-    def frame_file_is_setup(frame):
-        """
-        Return True if the indicated frame suggests a setup.py file.
-        """
-        # some frames may not have __file__ (#2940)
-        return frame.f_globals.get('__file__', '').endswith('setup.py')
-
-    def spec_for_sensitive_tests(self):
-        """
-        Ensure stdlib distutils when running select tests under CPython.
-
-        python/cpython#91169
-        """
-        clear_distutils()
-        self.spec_for_distutils = lambda: None
-
-    sensitive_tests = (
-        [
-            'test.test_distutils',
-            'test.test_peg_generator',
-            'test.test_importlib',
-        ]
-        if sys.version_info < (3, 10)
-        else [
-            'test.test_distutils',
-        ]
-    )
-
-
-for name in DistutilsMetaFinder.sensitive_tests:
-    setattr(
-        DistutilsMetaFinder,
-        f'spec_for_{name}',
-        DistutilsMetaFinder.spec_for_sensitive_tests,
-    )
-
-
-DISTUTILS_FINDER = DistutilsMetaFinder()
-
-
-def add_shim():
-    DISTUTILS_FINDER in sys.meta_path or insert_shim()
-
-
-class shim:
-    def __enter__(self):
-        insert_shim()
-
-    def __exit__(self, exc, value, tb):
-        remove_shim()
-
-
-def insert_shim():
-    sys.meta_path.insert(0, DISTUTILS_FINDER)
-
-
-def remove_shim():
-    try:
-        sys.meta_path.remove(DISTUTILS_FINDER)
-    except ValueError:
-        pass

spaces/Big-Web/MMSD/env/Lib/site-packages/boto3/docs/client.py

@@ -1,28 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# https://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-from botocore.docs.client import ClientDocumenter
-
-
-class Boto3ClientDocumenter(ClientDocumenter):
-    def _add_client_creation_example(self, section):
-        section.style.start_codeblock()
-        section.style.new_line()
-        section.write('import boto3')
-        section.style.new_line()
-        section.style.new_line()
-        section.write(
-            'client = boto3.client(\'{service}\')'.format(
-                service=self._service_name
-            )
-        )
-        section.style.end_codeblock()

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/shape.py

@@ -1,135 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-
-
-# NOTE: This class should not be instantiated and its
-# ``traverse_and_document_shape`` method called directly. It should be
-# inherited from a Documenter class with the appropriate methods
-# and attributes.
-from botocore.utils import is_json_value_header
-
-
-class ShapeDocumenter:
-    EVENT_NAME = ''
-
-    def __init__(
-        self, service_name, operation_name, event_emitter, context=None
-    ):
-        self._service_name = service_name
-        self._operation_name = operation_name
-        self._event_emitter = event_emitter
-        self._context = context
-        if context is None:
-            self._context = {'special_shape_types': {}}
-
-    def traverse_and_document_shape(
-        self,
-        section,
-        shape,
-        history,
-        include=None,
-        exclude=None,
-        name=None,
-        is_required=False,
-    ):
-        """Traverses and documents a shape
-
-        Will take a self class and call its appropriate methods as a shape
-        is traversed.
-
-        :param section: The section to document.
-
-        :param history: A list of the names of the shapes that have been
-            traversed.
-
-        :type include: Dictionary where keys are parameter names and
-            values are the shapes of the parameter names.
-        :param include: The parameter shapes to include in the documentation.
-
-        :type exclude: List of the names of the parameters to exclude.
-        :param exclude: The names of the parameters to exclude from
-            documentation.
-
-        :param name: The name of the shape.
-
-        :param is_required: If the shape is a required member.
-        """
-        param_type = shape.type_name
-        if getattr(shape, 'serialization', {}).get('eventstream'):
-            param_type = 'event_stream'
-        if shape.name in history:
-            self.document_recursive_shape(section, shape, name=name)
-        else:
-            history.append(shape.name)
-            is_top_level_param = len(history) == 2
-            if hasattr(shape, 'is_document_type') and shape.is_document_type:
-                param_type = 'document'
-            getattr(
-                self,
-                f"document_shape_type_{param_type}",
-                self.document_shape_default,
-            )(
-                section,
-                shape,
-                history=history,
-                name=name,
-                include=include,
-                exclude=exclude,
-                is_top_level_param=is_top_level_param,
-                is_required=is_required,
-            )
-            if is_top_level_param:
-                self._event_emitter.emit(
-                    f"docs.{self.EVENT_NAME}.{self._service_name}.{self._operation_name}.{name}",
-                    section=section,
-                )
-            at_overlying_method_section = len(history) == 1
-            if at_overlying_method_section:
-                self._event_emitter.emit(
-                    f"docs.{self.EVENT_NAME}.{self._service_name}.{self._operation_name}.complete-section",
-                    section=section,
-                )
-            history.pop()
-
-    def _get_special_py_default(self, shape):
-        special_defaults = {
-            'document_type': '{...}|[...]|123|123.4|\'string\'|True|None',
-            'jsonvalue_header': '{...}|[...]|123|123.4|\'string\'|True|None',
-            'streaming_input_shape': 'b\'bytes\'|file',
-            'streaming_output_shape': 'StreamingBody()',
-            'eventstream_output_shape': 'EventStream()',
-        }
-        return self._get_value_for_special_type(shape, special_defaults)
-
-    def _get_special_py_type_name(self, shape):
-        special_type_names = {
-            'document_type': ':ref:`document<document>`',
-            'jsonvalue_header': 'JSON serializable',
-            'streaming_input_shape': 'bytes or seekable file-like object',
-            'streaming_output_shape': ':class:`.StreamingBody`',
-            'eventstream_output_shape': ':class:`.EventStream`',
-        }
-        return self._get_value_for_special_type(shape, special_type_names)
-
-    def _get_value_for_special_type(self, shape, special_type_map):
-        if is_json_value_header(shape):
-            return special_type_map['jsonvalue_header']
-        if hasattr(shape, 'is_document_type') and shape.is_document_type:
-            return special_type_map['document_type']
-        for special_type, marked_shape in self._context[
-            'special_shape_types'
-        ].items():
-            if special_type in special_type_map:
-                if shape == marked_shape:
-                    return special_type_map[special_type]
-        return None

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

@@ -1,39 +0,0 @@
-import abc
-
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata.base import BaseDistribution
-from pip._internal.req import InstallRequirement
-
-
-class AbstractDistribution(metaclass=abc.ABCMeta):
-    """A base class for handling installable artifacts.
-
-    The requirements for anything installable are as follows:
-
-     - we must be able to determine the requirement name
-       (or we can't correctly handle the non-upgrade case).
-
-     - for packages with setup requirements, we must also be able
-       to determine their requirements without installing additional
-       packages (for the same reason as run-time dependencies)
-
-     - we must be able to create a Distribution object exposing the
-       above metadata.
-    """
-
-    def __init__(self, req: InstallRequirement) -> None:
-        super().__init__()
-        self.req = req
-
-    @abc.abstractmethod
-    def get_metadata_distribution(self) -> BaseDistribution:
-        raise NotImplementedError()
-
-    @abc.abstractmethod
-    def prepare_distribution_metadata(
-        self,
-        finder: PackageFinder,
-        build_isolation: bool,
-        check_build_deps: bool,
-    ) -> None:
-        raise NotImplementedError()

spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/importlib_resources/_adapters.py

@@ -1,170 +0,0 @@
-from contextlib import suppress
-from io import TextIOWrapper
-
-from . import abc
-
-
-class SpecLoaderAdapter:
-    """
-    Adapt a package spec to adapt the underlying loader.
-    """
-
-    def __init__(self, spec, adapter=lambda spec: spec.loader):
-        self.spec = spec
-        self.loader = adapter(spec)
-
-    def __getattr__(self, name):
-        return getattr(self.spec, name)
-
-
-class TraversableResourcesLoader:
-    """
-    Adapt a loader to provide TraversableResources.
-    """
-
-    def __init__(self, spec):
-        self.spec = spec
-
-    def get_resource_reader(self, name):
-        return CompatibilityFiles(self.spec)._native()
-
-
-def _io_wrapper(file, mode='r', *args, **kwargs):
-    if mode == 'r':
-        return TextIOWrapper(file, *args, **kwargs)
-    elif mode == 'rb':
-        return file
-    raise ValueError(
-        "Invalid mode value '{}', only 'r' and 'rb' are supported".format(mode)
-    )
-
-
-class CompatibilityFiles:
-    """
-    Adapter for an existing or non-existent resource reader
-    to provide a compatibility .files().
-    """
-
-    class SpecPath(abc.Traversable):
-        """
-        Path tied to a module spec.
-        Can be read and exposes the resource reader children.
-        """
-
-        def __init__(self, spec, reader):
-            self._spec = spec
-            self._reader = reader
-
-        def iterdir(self):
-            if not self._reader:
-                return iter(())
-            return iter(
-                CompatibilityFiles.ChildPath(self._reader, path)
-                for path in self._reader.contents()
-            )
-
-        def is_file(self):
-            return False
-
-        is_dir = is_file
-
-        def joinpath(self, other):
-            if not self._reader:
-                return CompatibilityFiles.OrphanPath(other)
-            return CompatibilityFiles.ChildPath(self._reader, other)
-
-        @property
-        def name(self):
-            return self._spec.name
-
-        def open(self, mode='r', *args, **kwargs):
-            return _io_wrapper(self._reader.open_resource(None), mode, *args, **kwargs)
-
-    class ChildPath(abc.Traversable):
-        """
-        Path tied to a resource reader child.
-        Can be read but doesn't expose any meaningful children.
-        """
-
-        def __init__(self, reader, name):
-            self._reader = reader
-            self._name = name
-
-        def iterdir(self):
-            return iter(())
-
-        def is_file(self):
-            return self._reader.is_resource(self.name)
-
-        def is_dir(self):
-            return not self.is_file()
-
-        def joinpath(self, other):
-            return CompatibilityFiles.OrphanPath(self.name, other)
-
-        @property
-        def name(self):
-            return self._name
-
-        def open(self, mode='r', *args, **kwargs):
-            return _io_wrapper(
-                self._reader.open_resource(self.name), mode, *args, **kwargs
-            )
-
-    class OrphanPath(abc.Traversable):
-        """
-        Orphan path, not tied to a module spec or resource reader.
-        Can't be read and doesn't expose any meaningful children.
-        """
-
-        def __init__(self, *path_parts):
-            if len(path_parts) < 1:
-                raise ValueError('Need at least one path part to construct a path')
-            self._path = path_parts
-
-        def iterdir(self):
-            return iter(())
-
-        def is_file(self):
-            return False
-
-        is_dir = is_file
-
-        def joinpath(self, other):
-            return CompatibilityFiles.OrphanPath(*self._path, other)
-
-        @property
-        def name(self):
-            return self._path[-1]
-
-        def open(self, mode='r', *args, **kwargs):
-            raise FileNotFoundError("Can't open orphan path")
-
-    def __init__(self, spec):
-        self.spec = spec
-
-    @property
-    def _reader(self):
-        with suppress(AttributeError):
-            return self.spec.loader.get_resource_reader(self.spec.name)
-
-    def _native(self):
-        """
-        Return the native reader if it supports files().
-        """
-        reader = self._reader
-        return reader if hasattr(reader, 'files') else self
-
-    def __getattr__(self, attr):
-        return getattr(self._reader, attr)
-
-    def files(self):
-        return CompatibilityFiles.SpecPath(self.spec, self._reader)
-
-
-def wrap_spec(package):
-    """
-    Construct a package spec with traversable compatibility
-    on the spec/loader/reader.
-    """
-    return SpecLoaderAdapter(package.__spec__, TraversableResourcesLoader)