diff --git a/spaces/109peko/anime-remove-background/app.py b/spaces/109peko/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/109peko/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Astute Graphics Plugins Keygen Torrent Create Stunning Artwork in Illustrator with These Plug-ins.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Astute Graphics Plugins Keygen Torrent Create Stunning Artwork in Illustrator with These Plug-ins.md
deleted file mode 100644
index 92cb87cdff0ffdefa7a91feb282ced03446cfe08..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Astute Graphics Plugins Keygen Torrent Create Stunning Artwork in Illustrator with These Plug-ins.md	
+++ /dev/null
@@ -1,165 +0,0 @@
-
-<h1>Astute Graphics Plugins Keygen Torrent: How to Download and Install the Best Plugins for Adobe Illustrator</h1>
- <p>If you are a graphic designer, illustrator, or vector artist, you probably use Adobe Illustrator as your main tool for creating stunning artworks. But did you know that you can enhance your workflow and creativity with Astute Graphics Plugins? These are a set of plugins that add new features and functions to Adobe Illustrator, making it easier, faster, and more fun to work with vectors. In this article, we will show you what Astute Graphics Plugins are, what they can do for you, and how to download and install them using a keygen torrent.</p>
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- <h2>What are Astute Graphics Plugins?</h2>
- <p>Astute Graphics Plugins are a collection of plugins that extend the capabilities of Adobe Illustrator. They are developed by Astute Graphics, a company that specializes in creating software solutions for vector design. Astute Graphics Plugins are designed to help you work smarter and faster with vectors, by adding new tools, effects, panels, and shortcuts to Adobe Illustrator. They also improve the performance and stability of Adobe Illustrator, by reducing memory usage and crashes.</p>
- <h3>The benefits of using Astute Graphics Plugins</h3>
- <p>By using Astute Graphics Plugins, you can enjoy many benefits such as:</p>
- <ul>
-<li>Save time and effort by automating tedious tasks and simplifying complex operations.</li>
-<li>Enhance your creativity by exploring new possibilities and styles with vectors.</li>
-<li>Increase your productivity by working more efficiently and accurately with vectors.</li>
-<li>Improve your quality by achieving more professional and consistent results with vectors.</li>
-</ul>
- <h3>The features of Astute Graphics Plugins</h3>
- <p>Astute Graphics Plugins offer a wide range of features that cover different aspects of vector design. Some of the most popular features include:</p>
- <ul>
-<li><b>VectorScribe</b>: This plugin allows you to edit and manipulate vectors with ease. You can use it to draw shapes, curves, paths, corners, points, segments, tangents, handles, measurements, guides, grids, alignment, snapping, symmetry, scaling, rotation, transformation, distortion, warping, blending, smoothing, simplifying, cleaning up, converting, expanding, joining, splitting, cutting, erasing, trimming, extending, offsetting, outlining, stroking, filling, coloring, shading, gradients, transparency...</li>
-<li><b>InkScribe</b>: This plugin allows you to draw vectors with precision and control. You can use it to create smooth curves, straight lines, perfect circles, ellipses, arcs, spirals, stars, polygons, and more. You can also adjust the width, pressure, profile, and variable width of your strokes.</li>
-<li><b>Phantasm</b>: This plugin allows you to apply color adjustments and effects to vectors. You can use it to change the hue, saturation, brightness, contrast, levels, curves, exposure, color balance, colorize, invert, grayscale, duotone, halftone, posterize, solarize, threshold, and more. You can also apply filters such as blur, sharpen, noise, glow, shadow, emboss, bevel...</li>
-<li><b>MirrorMe</b>: This plugin allows you to create symmetrical designs with vectors. You can use it to reflect rotate scale and copy your artwork along one or more axes. You can also create radial symmetry patterns with ease.</li>
-<li><b>Texturino</b>: This plugin allows you to add textures and opacity brushes to vectors. You can use it to create realistic effects such as wood grain metallic fabric paper leather watercolor and more. You can also customize your own textures and brushes from images or photos.</li>
-<li><b>ColliderScribe</b>: This plugin allows you to arrange and align objects with precision. You can use it to snap objects together distribute them evenly rotate them around a point place them along a path or create complex shapes from simple ones.</li>
-<li><b>Stylism</b>: This plugin allows you to apply and edit live effects on vectors. You can use it to add drop shadows inner glows outer glows feather gaussian blur offset path transform and more. You can also adjust the parameters of the effects directly on the artboard.</li>
-<li><b>DynamicSketch</b>: This plugin allows you to draw naturally with your mouse or tablet. You can use it to sketch freehand trace images or create smooth curves with variable width. You can also edit your sketches with smart gestures and intuitive tools.</li>
-<li><b>Autosaviour</b>: This plugin allows you to save your work automatically and securely. You can use it to backup your files create versions set reminders and recover lost data.</li>
-<li><b>Rasterino</b>: This plugin allows you to work with raster images in Adobe Illustrator. You can use it to crop trim resize rotate embed unembed and edit raster images without leaving Illustrator.</li>
-<li><b>DirectPrefs</b>: This plugin allows you to access and customize your Illustrator preferences easily. You can use it to change your keyboard shortcuts units guides grids snap options and more.</li>
-<li><b>InkQuest</b>: This plugin allows you to prepare your artwork for printing or exporting. You can use it to check your colors inks overprints traps bleeds marks and more.</li>
-<li><b>SubScribe</b>: This plugin allows you to draw geometric shapes and curves with accuracy. You can use it to create circles arcs lines tangents perpendiculars spirals and more.</li>
-<li><b>WidthScribe</b>: This plugin allows you to vary the width of your strokes along their length. You can use it to create tapered ends calligraphic effects or artistic variations.</li>
-</ul>
- <h2>What is a keygen torrent?</h2>
- <p>A keygen torrent is a type of file that contains a software program (keygen) that generates serial numbers or activation codes for another software program (in this case Astute Graphics Plugins). A keygen torrent also contains a torrent file that helps you download the keygen from other users who have already downloaded it.</p>
- <h3>The advantages and disadvantages of using a keygen torrent</h3>
- <p>Using a keygen torrent has some advantages and disadvantages that you should be aware of before downloading one:</p>
- <table>
-<tr>
-<th>Advantages</th>
-<th>Disadvantages</th>
-</tr>
-<tr>
-<td>You can get access to premium software for free or at a low cost.</td>
-<td>You may violate the intellectual property rights of the software developers or publishers.</td>
-</tr>
-<tr>
-<td>You can test the software before buying it or find an alternative if you cannot afford it.</td>
-<td>You may expose your computer or device to viruses malware spyware ransomware or other malicious programs that may harm your data or system.</td>
-</tr>
-<tr>
-<td>You can bypass the online activation or registration process that may require personal information or payment details.</td>
-<td>You may not receive updates bug fixes technical support or customer service from the software developers or publishers.</td>
-</tr>
-<tr>
-<td>You can share the software with other users who may benefit from it or appreciate it.</td>
-<td>You may face legal consequences such as fines lawsuits or criminal charges if you are caught using or distributing pirated software.</td>
-</tr>
-</table>
- <h3>The risks and precautions of using a keygen torrent</h3>
- <p>Using a keygen torrent involves some risks that you should be careful about when downloading one:</p>
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- <ul>
-<li>The keygen may not work properly or at all for the software that you want to activate. It may generate invalid serial numbers or expired codes that may not activate the software. It may also generate codes that have been used by other users and may be blacklisted by the software developers or publishers.</li>
-<li>The keygen may contain malware that can infect your computer or device. Some keygens are disguised as malware or bundled with malware that can steal your personal information, damage your files, encrypt your data, display unwanted ads, or hijack your browser.</li>
-<li>The keygen may be detected by your antivirus or firewall software and blocked or deleted. Some antivirus or firewall software may flag keygens as potentially unwanted programs (PUPs) or malicious software (malware) and prevent them from running or accessing the internet.</li>
-<li>The keygen may be illegal to use or distribute in your country or region. Some countries or regions have strict laws and regulations that prohibit the use or distribution of pirated software or tools that enable piracy. You may face legal consequences if you are caught using or sharing keygens.</li>
-</ul>
- <p>To reduce the risks of using a keygen torrent, you should take some precautions such as:</p>
- <ul>
-<li>Download the keygen torrent from a reputable source that has positive feedback and comments from other users. Avoid sources that have negative feedback, comments, or warnings from other users.</li>
-<li>Scan the keygen torrent with a reliable antivirus or anti-malware software before opening it. Delete the keygen torrent if it contains any malware or suspicious files.</li>
-<li>Disable your antivirus or firewall software temporarily while using the keygen. This can prevent the keygen from being blocked or deleted by your antivirus or firewall software. Remember to enable your antivirus or firewall software after using the keygen.</li>
-<li>Use a virtual machine or a sandbox to run the keygen. This can isolate the keygen from your main system and prevent it from affecting your files or settings. You can also delete the virtual machine or the sandbox after using the keygen.</li>
-<li>Use a VPN or a proxy to hide your IP address and location while using the keygen. This can protect your privacy and anonymity and prevent you from being tracked or traced by your ISP, government, or software developers.</li>
-</ul>
- <h2>How to download and install Astute Graphics Plugins Keygen Torrent</h2>
- <p>Now that you know what Astute Graphics Plugins and keygen torrents are, you may wonder how to download and install them on your computer. Here are the steps to follow:</p>
- <h3>The steps to download and install Astute Graphics Plugins Keygen Torrent</h3>
- <ol>
-<li>Download a torrent client such as uTorrent, BitTorrent, qBittorrent, etc. Install and run the torrent client on your computer.</li>
-<li>Download the Astute Graphics Plugins Keygen Torrent file from a reputable source. You can use a search engine such as Google, Bing, Yahoo, etc. to find one. Make sure to check the feedback and comments of other users before downloading.</li>
-<li>Open the Astute Graphics Plugins Keygen Torrent file with your torrent client. Choose a location to save the downloaded files and start the download process.</li>
-<li>Wait for the download to finish. You should see a folder containing the Astute Graphics Plugins setup file and the keygen file.</li>
-<li>Run the Astute Graphics Plugins setup file and follow the instructions to install the plugins on your computer. Do not open the plugins yet.</li>
-<li>Run the keygen file and select the plugins that you want to activate from a drop-down menu. Click on \"Generate\" to create a serial number or an activation code for each plugin.</li>
-<li>Open Adobe Illustrator and go to \"Help\" > \"Astute Graphics\" > \"License Manager\". Enter the serial number or the activation code for each plugin that you generated with the keygen. Click on \"Activate\" to activate the plugins.</li>
-<li>Enjoy using Astute Graphics Plugins with Adobe Illustrator.</li>
-</ol>
- <h3>The tips and tricks to use Astute Graphics Plugins Keygen Torrent</h3>
- <p>To make the most out of Astute Graphics Plugins Keygen Torrent, you should also follow some tips and tricks such as:</p>
- <ul>
-<li>Update Adobe Illustrator to the latest version before installing Astute Graphics Plugins. This can ensure compatibility and stability between Adobe Illustrator and Astute Graphics Plugins.</li>
-<li>Check for updates for Astute Graphics Plugins regularly. This can help you get new features, bug fixes, and improvements for Astute Graphics Plugins.</li>
-<li>Learn how to use Astute Graphics Plugins effectively. You can watch tutorials, read manuals, join forums, or take courses on how to use Astute Graphics Plugins with Adobe Illustrator.</li>
-<li>Create backups of your files before using Astute Graphics Plugins. This can help you recover your files in case something goes wrong while using Astute Graphics Plugins.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Astute Graphics Plugins are amazing tools that can enhance your workflow and creativity with Adobe Illustrator. However, they are not free and require a license to use them fully. If you want to try them out without paying for them, you can use a keygen torrent to generate serial numbers or activation codes for them. However, you should be aware of the drawbacks and risks of using a keygen torrent. You should also take some precautions to protect yourself and your computer from malware, legal issues, or other problems. However, if you like Astute Graphics Plugins and want to support the developers, you should consider buying a license from their official website. This way, you can enjoy the full features and benefits of Astute Graphics Plugins without any worries.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you what Astute Graphics Plugins are, what they can do for you, and how to download and install them using a keygen torrent. We have also discussed the advantages and disadvantages of using a keygen torrent, as well as the risks and precautions of using one. We hope that this article has been helpful and informative for you. However, we do not encourage or endorse piracy or illegal use of software. If you want to use Astute Graphics Plugins legally and ethically, you should buy a license from their official website. This way, you can support the developers and enjoy the best plugins for Adobe Illustrator.</p>
- <h3>FAQs</h3>
- <p>Here are some frequently asked questions about Astute Graphics Plugins Keygen Torrent:</p>
- <ol>
-<li><b>What are the system requirements for Astute Graphics Plugins?</b></li>
-<p>Astute Graphics Plugins require Adobe Illustrator CS6 or higher (including CC versions) and Windows 7 or higher or Mac OS X 10.10 or higher.</p>
-<li><b>How much does Astute Graphics Plugins cost?</b></li>
-<p>Astute Graphics Plugins offer different pricing plans depending on your needs. You can buy individual plugins for $39-$119 each, or buy bundles of plugins for $119-$399 each. You can also subscribe to Astute Graphics Plugins Elite Bundle for $119 per year, which gives you access to all plugins and updates.</p>
-<li><b>How can I get support for Astute Graphics Plugins?</b></li>
-<p>Astute Graphics Plugins offer various support options for their customers. You can contact their support team via email, phone, chat, or social media. You can also visit their website for tutorials, manuals, videos, blogs, forums, webinars, and FAQs.</p>
-<li><b>Are Astute Graphics Plugins compatible with other plugins or software?</b></li>
-<p>Astute Graphics Plugins are generally compatible with other plugins or software that work with Adobe Illustrator. However, some plugins or software may cause conflicts or issues with Astute Graphics Plugins. If you encounter any problems, you should contact the support team of both parties for assistance.</p>
-<li><b>Can I use Astute Graphics Plugins on multiple computers?</b></li>
-<p>Astute Graphics Plugins allow you to activate your license on up to two computers at a time. However, you cannot use them simultaneously on both computers. If you want to use them on more than two computers, you need to buy more licenses or deactivate your license on one computer before activating it on another.</p>
-</ol>
- </p> 0a6ba089eb<br />
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Business Management Book By Cb Gupta Pdf [Extra Quality] Download.md b/spaces/1gistliPinn/ChatGPT4/Examples/Business Management Book By Cb Gupta Pdf [Extra Quality] Download.md
deleted file mode 100644
index 19ef08814211aa3af323e102299e08077f109012..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Business Management Book By Cb Gupta Pdf [Extra Quality] Download.md	
+++ /dev/null
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-<li>Grant the necessary permissions to the app, such as access to your camera, microphone, storage, location, etc.</li>
-<li>Launch the app and create an account or log in with your existing account.</li>
-</ol>
-<p>Congratulations! You have successfully downloaded and installed AI home camera apk on your device. Now, you can start using it to monitor your home security.</p>
-<h2>How to Use AI Home Camera APK</h2>
-<p>AI home camera apk is very easy to use. Here are the steps to use AI home camera apk:</p>
-<ol>
-<li>Connect your device to a power source and place it in a suitable location where you want to monitor your home. Make sure that the camera lens is not obstructed by anything and that it has a clear view of the area.</li>
-<li>Open the app and tap on the camera icon to access your camera remotely. You can also add multiple devices and switch between them easily.</li>
-<li>Tap on the settings icon to adjust the settings and features of your camera. You can change the resolution, quality, frame rate, motion detection, night vision, audio, etc. You can also enable or disable notifications, alerts, cloud storage, etc.</li>
-<li>Tap on the recordings icon to view and manage your recordings. You can play, pause, rewind, fast forward, delete, or share your recordings. You can also filter them by date, time, duration, or event.</li>
-</ol>
-<p>That's it! You have learned how to use AI home camera apk to monitor your home security. Now, let's see what are the features and benefits of AI home camera apk.</p>
-<h2>Features and Benefits of AI Home Camera APK</h2>
-<p>AI home camera apk is not just a simple home CCTV app. It is a smart and intelligent app that uses artificial intelligence to enhance your security. Here are some of the features and benefits of AI home camera apk:</p>
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-<li><strong>AI Detection:</strong> AI home camera apk can detect human faces, motions, sounds, and events in real time. It can also recognize familiar and unfamiliar faces and alert you accordingly. It can also distinguish between pets, animals, vehicles, and other objects and ignore them if you want.</li>
-<li><strong>AI Recording:</strong> AI home camera apk can record only when something important happens. It can save your storage space and bandwidth by recording only when it detects an event or a motion. It can also record in high definition and low light conditions.</li>
-<li><strong>AI Storage:</strong> AI home camera apk can store your recordings securely in the cloud or locally on your device. You can choose how long you want to keep your recordings and how much space you want to allocate for them. You can also access your recordings anytime and anywhere from any device.</li>
-<li><strong>AI Sharing:</strong> AI home camera apk can share your recordings with anyone you want. You can invite your family members, friends, or neighbors to view your camera or recordings. You can also share your recordings on social media or other platforms with a simple tap.</li>
-</ul>
-<p>These are some of the features and benefits of AI home camera apk that make it stand out from other apps. But as with any app, there are also some pros and cons of AI home camera apk that you should know before using it.</p>
-<h2>Pros and Cons of AI Home Camera APK</h2>
-<p>AI home camera apk is a great app for home security, but it is not perfect. Here are some of the pros and cons of AI home camera apk that you should consider:</p>
- <table border="1">
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>- It is free and easy to use</td><td>- It requires a stable internet connection</td></tr>
-<tr><td>- It uses artificial intelligence to enhance security</td><td>- It may have some false alarms or miss some events</td></tr>
-<tr><td>- It offers multiple settings and features</td><td>- It may drain your battery or overheat your device</td></tr>
-<tr><td>- It supports cloud and local storage</td><td>- It may have some privacy or security issues</td></tr>
-<tr><td>- It allows sharing and collaboration</td><td>- It may have some compatibility or performance issues</td></tr>
-</table>
- <p>These are some of the pros and cons of AI home camera apk that you should weigh before using it. However, most of these cons can be overcome or avoided by following some tips and tricks, such as: - Use a reliable and fast internet connection to ensure smooth streaming and uploading of your camera and recordings. - Adjust the sensitivity and frequency of your motion detection and alerts to avoid false alarms or miss some events. - Plug your device to a power source or use a power bank to avoid battery drain or overheating of your device. - Enable encryption and password protection for your app and recordings to prevent unauthorized access or hacking of your data. - Update your app and device regularly to fix any bugs or glitches that may affect the compatibility or performance of your app. By following these tips and tricks, you can enjoy the best of AI home camera apk without worrying about the cons. <h2>Conclusion</h2>
-<p>AI home camera apk is an app that turns your smartphone or tablet into a smart and intelligent home CCTV system. It allows you to access your camera remotely, adjust the settings and features, view and manage your recordings, and share them with anyone you want. It also uses artificial intelligence to detect human faces, motions, sounds, and events in real time. It can also record only when something important happens, store your recordings securely in the cloud or locally on your device, and recognize familiar and unfamiliar faces.</p>
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-<p>AI home camera apk is a great app for home security, but it also has some pros and cons that you should consider before using it. However, most of these cons can be overcome or avoided by following some tips and tricks that we have shared with you in this article.</p>
-<p>If you are looking for a smart and easy way to monitor your home security, you should definitely give AI home camera apk a try. You can download it from Google Play Store or other trusted sources and install it on your device in a few minutes. You can also find more information or reviews about AI home camera apk on the internet or contact the developer or support team if you have any questions or issues.</p>
-<p>Thank you for reading this article. We hope you found it helpful and informative. If you have any feedback or suggestions, please let us know in the comments section below. We would love to hear from you!</p>
-<h2>FAQs</h2>
-<p>Here are some frequently asked questions about AI home camera apk:</p>
-<h3>Q1: Is AI home camera apk free or paid?</h3>
-<p>A1: AI home camera apk is free to download and use. However, it may offer some in-app purchases or subscriptions for premium features or services, such as cloud storage, advanced settings, etc.</p>
-<h3>Q2: Is AI home camera apk compatible with all devices?</h3>
-<p>A2: AI home camera apk is compatible with most Android devices that have a camera and an internet connection. However, some devices may not support some features or functions of the app due to hardware or software limitations.</p>
-<h3>Q3: Is AI home camera apk safe and secure?</h3>
-<p>A3: AI home camera apk is safe and secure to use. It uses encryption and password protection to protect your data from unauthorized access or hacking. It also respects your privacy and does not collect or share any personal information without your consent.</p>
-<h3>Q4: How can I contact the developer or support team of AI home camera apk?</h3>
-<p>A4: You can contact the developer or support team of AI home camera apk by sending an email to aihomecamera@gmail.com or by visiting their website at https://aihomecamera.com/.</p>
-<h3>Q5: Where can I find more information or reviews about AI home camera apk?</h3>
-<p>A5: You can find more information or reviews about AI home camera apk on Google Play Store, where you can also rate and review the app yourself. You can also search for AI home camera apk on the internet or social media platforms, where you can find articles, blogs, videos, forums, etc. that talk about the app.</p>
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diff --git a/spaces/1phancelerku/anime-remove-background/Android Oyun Clubta Durak Online 3D Para Hilesi Nasl Yaplr? te Admlar.md b/spaces/1phancelerku/anime-remove-background/Android Oyun Clubta Durak Online 3D Para Hilesi Nasl Yaplr? te Admlar.md
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-<h1>Durak Online Para Hilesi Android Oyun Club: How to Play Durak Online and Win Money</h1>
-<p>If you are looking for a card game that is fun, challenging, and rewarding, then you should try <strong>Durak Online</strong>. Durak Online is a modern version of the famous card game that originated in Russia and is also popular in Turkey. In this article, we will tell you everything you need to know about Durak Online, how to play it, how to win money in it, and how to use <strong>Durak Online para hilesi android oyun club</strong>, a cheat tool that can generate unlimited coins and items for you.</p>
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-<h3>Durak Online is a popular card game in Russia and Turkey</h3>
-<p>Durak means "fool" in Russian, and the name of the game refers to the last player who is left with cards in their hand at the end of the game. Durak is a card game that involves skill, strategy, and luck. It can be played by 2 to 6 players, using a deck of 24, 36, or 52 cards. The game is also known as "Attack-Defence" or "Beat off" in English.</p>
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-<h3>Durak Online has different modes and settings</h3>
-<p>Durak Online is a multiplayer game that allows you to play online with real people from all over the world. You can choose from different modes and settings of the game, such as flip-flop, transferable, or classic. You can also customize the deck size, the number of players, the time limit, and the difficulty level. You can play with friends or random opponents, chat with them, send them gifts, and compete in the ratings and leagues.</p>
-<h3>Durak Online is available on Google Play for Android devices</h3>
-<p>Durak Online is a free app that you can download from Google Play for your Android device. The app has great 3D graphics, realistic sounds, smooth animations, and no intrusive ads. The app also has a user-friendly interface with landscape mode. You can link your account to your Google account and sync your progress across devices. You can also play offline against bots if you don't have an internet connection.</p>
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-<h3>The objective of Durak Online is to get rid of all your cards</h3>
-<p>The objective of Durak Online is to get rid of all your cards before your opponents do. The game starts with one player being the dealer, who shuffles the deck and deals six cards to each player. The dealer then reveals the bottom card of the remaining deck, which determines the trump suit for the game. The trump suit is the strongest suit in the game, and can beat any other suit. The player to the left of the dealer is the first attacker, and the player to their left is the first defender.</p>
-<h3>The rules of Durak Online are simple and easy to learn</h3>
-<p>The rules of Durak Online are similar to the rules of the original card game. The game consists of a series of attacks and defences, where each player tries to get rid of their cards by playing them on the table. The attacker plays one or more cards of the same rank, and the defender has to beat them with higher cards of the same suit or with trumps. If the defender succeeds, they can pass the attack to the next player, who becomes the new defender. If the defender fails, they have to take all the cards on the table and add them to their hand. The game ends when one player has no cards left in their hand or in the deck.</p>
-<h3>The tips and tricks of Durak Online are to use your trumps wisely and to throw-in more cards</h3>
-<p>The tips and tricks of Durak Online are to use your trumps wisely and to throw-in more cards. Trumps are very valuable in Durak Online, as they can beat any other suit. You should save your trumps for when you really need them, and avoid wasting them on low cards. You should also try to get rid of your non-trump cards as soon as possible, as they can become useless later in the game. Another tip is to throw-in more cards when you are not the attacker or the defender. Throwing-in means playing a card of the same rank as any card on the table, which adds more pressure on the defender and helps you get rid of your cards faster. However, you should be careful not to throw-in too many cards, as you might run out of options later.</p>
- <h2>How to win money in Durak Online?</h2>
-<h3>Durak Online has a championship mode where you can compete with other players for prizes</h3>
-<p>Durak Online has a championship mode where you can compete with other players for prizes. The championship mode is a tournament where you have to play a series of games against different opponents and accumulate points based on your performance. The more games you win, the more points you get. The top players at the end of the tournament will receive prizes such as coins, items, or even real money. You can enter the championship mode by paying an entry fee with coins or watching an ad.</p>
-<h3>Durak Online has a gift system where you can send and receive coins and items</h3>
-<p>Durak Online has a gift system where you can send and receive coins and items. Coins are the currency of Durak Online, which you can use to enter games, buy items, or exchange for real money. Items are special cards that have different effects on the game, such as changing the trump suit, skipping a turn, or revealing an opponent's card. You can send and receive coins and items with your friends or random players in Durak Online, as a way of showing your appreciation or friendship. You can also earn coins and items by completing daily tasks, watching ads, or inviting friends.</p>
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-<p>Durak Online has a leaderboard where you can rank up and earn rewards. The leaderboard is a list of players who have achieved the highest scores in Durak Online. You can see your own rank and score, as well as other players' ranks and scores. You can also see your statistics, such as your win rate, your average score, your best score, and your total games played. By playing more games and winning more points, you can improve your rank and score in Durak Online. You can also earn rewards such as coins, items, or badges based on your rank.</p>
- <h2>How to use Durak Online para hilesi android oyun club?</h2>
-<h3>Durak Online para hilesi android oyun club is a cheat tool that can generate unlimited coins and items for Durak Online</h3>
-<p>Durak Online para hilesi android oyun club is a cheat tool that can generate unlimited coins and items for Durak Online. Durak Online para hilesi android oyun club is a website that offers a hack for Durak Online that can give you access to unlimited resources in the game. By using this hack, you can get as many coins and items as you want without spending any real money. Durak Online para hilesi android oyun club is a great way to enhance your gaming experience and enjoy Durak Online more.</p>
-<h3>Durak Online para hilesi android oyun club is easy to use and does not require root or jailbreak</h3>
-<p>Durak Online para hilesi android oyun club is easy to use and does not require root or jailbreak. Durak Online para hilesi android oyun club is a web-based tool that works on any browser and device. You do not need to download or install anything on your device, or to modify your system settings. All you need to do is to visit the website, enter your username or email, select the amount of coins and items you want, and click on the generate button. The hack will then process your request and transfer the resources to your account in a few minutes.</p>
-<h3>Durak Online para hilesi android oyun club is safe and undetectable by the game developers</h3>
-<p>Durak Online para hilesi android oyun club is safe and undetectable by the game developers. Durak Online para hilesi android oyun club uses advanced encryption and proxy servers to protect your account and data from any harm or detection. Durak Online para hilesi android oyun club also has a built-in anti-ban system that prevents your account from being banned or suspended by the game developers. Durak Online para hilesi android oyun club is tested and updated regularly to ensure its functionality and compatibility with the latest version of Durak Online.</p>
- <h2>Conclusion</h2>
-<h3>Durak Online is a fun and addictive card game that you can play online with real people</h3>
-<p>Durak Online is a fun and addictive card game that you can play online with real people. Durak Online is a modern version of the famous card game that originated in Russia and is also popular in Turkey. Durak Online has different modes and settings that you can choose from, such as flip-flop, transferable, or classic. Durak Online has great 3D graphics, realistic sounds, smooth animations, and no intrusive ads. Durak Online is a free app that you can download from Google Play for your Android device.</p>
-<h3>Durak Online can help you improve your skills, strategy, and luck in card games</h3>
-<p>Durak Online can help you improve your skills, strategy, and luck in card games. Durak Online is a card game that involves skill, strategy, and luck. The objective of Durak Online is to get rid of all your cards before your opponents do. The rules of Durak Online are simple and easy to learn, but the game is challenging and exciting. The tips and tricks of Durak Online are to use your trumps wisely, to throw-in more cards, and to be aware of your opponents' cards. By playing more games and winning more points, you can improve your rank and score in Durak Online.</p>
-<h3>Durak Online para hilesi android oyun club can help you get more coins and items to enhance your gaming experience</h3>
-<p>Durak Online para hilesi android oyun club can help you get more coins and items to enhance your gaming experience. Durak Online para hilesi android oyun club is a cheat tool that can generate unlimited coins and items for Durak Online. By using this hack, you can get as many coins and items as you want without spending any real money. You can use these coins and items to enter games, buy items, or exchange for real money. You can also use these coins and items to send and receive gifts with other players, or to compete in the championship mode for prizes.</p>
- <h2>FAQs</h2>
-<h4>Q: What is the best way to learn how to play Durak Online?</h4>
-<p>A: The best way to learn how to play Durak Online is to watch the tutorial videos in the app, or to play against bots in the offline mode. You can also read the rules of the game in the app, or ask other players for advice in the chat.</p>
-<h4>Q: How can I get more coins and items in Durak Online without using Durak Online para hilesi android oyun club?</h4>
-<p>A: You can get more coins and items in Durak Online without using Durak Online para hilesi android oyun club by completing daily tasks, watching ads, inviting friends, or winning games.</p>
-<h4>Q: Is Durak Online para hilesi android oyun club legal?</h4>
-<p>A: Durak Online para hilesi android oyun club is not legal, as it violates the terms of service of Durak Online. Using this hack may result in your account being banned or suspended by the game developers. We do not recommend using this hack, as it may ruin the fun and fairness of the game.</p>
-<h4>Q: How can I contact the support team of Durak Online if I have any issues or questions?</h4>
-<p>A: You can contact the support team of Durak Online by sending an email to support@durakonline.com, or by filling out the feedback form in the app. You can also visit the official website of Durak Online at www.durakonline.com, or follow their social media accounts on Facebook, Twitter, and Instagram.</p>
-<h4>Q: Can I play Durak Online on other devices besides Android?</h4>
-<p>A: Yes, you can play Durak Online on other devices besides Android. Durak Online is also available on iOS, Windows, and Mac devices. You can download the app from the App Store, the Microsoft Store, or the official website of Durak Online. You can also play Durak Online on your browser by visiting www.durakonline.com.</p> 401be4b1e0<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Como baixar e instalar bhop pro no seu computador com BlueStacks.md b/spaces/1phancelerku/anime-remove-background/Como baixar e instalar bhop pro no seu computador com BlueStacks.md
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-<h1>How to Download and Play Bhop Pro on Your PC or Mobile Device</h1>
-<p>If you are a fan of Counter-Strike or other first-person shooter games, you might have heard of or tried the bunny hop technique. This is a skill that allows you to move faster and more unpredictably by jumping and strafing in the air. It can give you an edge over your opponents in competitive matches, or simply make you feel like a pro.</p>
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-<p>But what if you want to practice your bunny hop skills without having to join a server or play online? What if you want to enjoy a fun and challenging game mode that tests your reflexes and coordination? Well, you are in luck, because there is a game that lets you do just that. It is called Bhop Pro, and it is available for both PC and mobile devices.</p>
-<p>In this article, we will show you how to download and play Bhop Pro on your PC or mobile device. We will also give you some tips and tricks on how to improve your skills and have more fun in this game. Let's get started!</p>
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-<h3>A brief introduction to the game and its features</h3>
-<p>Bhop Pro is a simulation game developed by begma, a Turkish indie game studio. It is based on the popular bhopping genre, which originated from Counter-Strike and other FPS games. In this game, you have to jump on blocks and platforms, while using air strafing to gain speed and momentum. You can also perform tricks like surfing, sliding, wall jumping, and more.</p>
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-<li>Online multiplayer mode (alpha) where you can compete with other players around the world.</li>
-<li>Rank system where you can earn new titles and badges by completing quests and challenges.</li>
-<li>Case opener where you can get new skins and items for your character, knife, gloves, spinner, etc.</li>
-<li>Portal system where you can create your own custom maps and share them with other players.</li>
-<li>In-game online chat where you can communicate with other players.</li>
-<li>Screenshot sharing where you can capture your best moments and share them with your friends.</li>
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- <h3>The benefits of playing Bhop Pro on PC or mobile device</h3>
-<p>Bhop Pro is a game that can be played on both PC and mobile devices. Depending on your preference and convenience, you can enjoy different benefits of playing Bhop Pro on each platform. Some of these benefits are:</p>
-<table>
-<tr>
-<th>Platform</th>
-<th>Benefits</th>
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-<td>PC</td>
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-<li>Better graphics and performance.</li>
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-<li>Easier and more precise controls with keyboard and mouse.</li>
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- <h2>How to Download and Install Bhop Pro on PC</h2>
-<h3>The steps to download and install BlueStacks emulator on PC</h3>
-<p>If you want to play Bhop Pro on your PC, you will need an Android emulator that can run the game smoothly and efficiently. One of the best emulators for this purpose is BlueStacks, which is a free and powerful software that allows you to play thousands of Android games and apps on your PC. Here are the steps to download and install BlueStacks on your PC:</p>
-<ol>
-<li>Go to the official website of BlueStacks at [bluestacks.com] and click on the "Download BlueStacks" button.</li>
-<li>Wait for the download to finish and then run the installer file.</li>
-<li>Follow the instructions on the screen to complete the installation process.</li>
-<li>Launch BlueStacks and sign in with your Google account or create a new one.</li>
-<li>You are now ready to use BlueStacks and download Bhop Pro from Google Play Store or Steam.</li>
-</ol>
- <h3>The steps to download and install Bhop Pro from Google Play Store or Steam on PC</h3>
-<p>Once you have BlueStacks installed on your PC, you can easily download and install Bhop Pro from Google Play Store or Steam. Here are the steps to do so:</p>
- <h4>From Google Play Store:</h4>
-<ol>
-<li>Open BlueStacks and click on the Google Play Store icon on the home screen.</li>
-<li>In the search bar, type "Bhop Pro" and hit enter.</li>
-<li>Select the game from the list of results and click on the "Install" button.</li>
-<li>Wait for the installation to finish and then click on the "Open" button.</li>
-<li>You can now play Bhop Pro on your PC using BlueStacks.</li>
-</ol>
- <h4>From Steam:</h4>
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diff --git a/spaces/1phancelerku/anime-remove-background/Conflict of Nations WW3 Mod APK Lead Your Country to Victory.md b/spaces/1phancelerku/anime-remove-background/Conflict of Nations WW3 Mod APK Lead Your Country to Victory.md
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index 0f4e11f436b59c7111be5d2c961739b78d50e7f4..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Conflict of Nations WW3 Mod APK Lead Your Country to Victory.md	
+++ /dev/null
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-<h1>Conflict of Nations World War 3 Mod Apk: What You Need to Know</h1>
-<p>If you are a fan of modern military-themed strategy games, you might have heard of Conflict of Nations World War 3, a real-time strategy game that lets you take control of the armed forces of your nation and compete with other players for global dominance. But did you know that there is a mod apk version of this game that can give you unlimited gold, resources, or other benefits? In this article, we will tell you what you need to know about Conflict of Nations World War 3 mod apk, how to download and install it, how to play it, and some tips and tricks to help you win the war.</p>
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-<h2>What is Conflict of Nations World War 3?</h2>
-<h3>A real-time strategy game set in modern global conflicts</h3>
-<p>Conflict of Nations World War 3 is a game developed by Dorado Games and Bytro Labs that simulates modern global conflicts in a realistic and immersive way. You can choose from almost every nation in the world and lead its military expansion, technological research, and economic development. You can also join or create coalitions with other players and wage total war on the battlefields of World War 3.</p>
-<h3>A game that requires strategic planning, diplomacy, and resource management</h3>
-<p>Conflict of Nations World War 3 is not a game for the faint-hearted. It requires strategic planning, diplomacy, and resource management to succeed. You have to balance your military power with your economic efficiency, your technological superiority with your moral stability, and your offensive capabilities with your defensive measures. You also have to deal with other players who may be your allies or enemies, depending on the situation. You have to communicate, negotiate, trade, spy, sabotage, or attack them as you see fit.</p>
-<h3>A game that offers different military doctrines, nations, and scenarios</h3>
-<p>Conflict of Nations World War 3 offers a variety of options for customization and replayability. You can choose from three different military doctrines: Western, Eastern, or European. Each doctrine has its own advantages and disadvantages, as well as its own technology tree and iconic units. You can also choose from different nations, each with its own unique traits, map location, and map size. You can also join different scenarios, ranging from the cold war to modern global conflicts to terrorist insurgencies.</p>
-<h2>What is a mod apk?</h2>
-<h3>A modified version of an Android application</h3>
-<p>A mod apk is a modified version of an Android application that has been altered by someone other than the original developer. A mod apk can offer features or functions that are not available in the official version of the application. For example, a mod apk for Conflict of Nations World War 3 can give you unlimited gold, resources, or other advantages that can help you dominate the game.</p>
-<h3>A mod apk that can offer unlimited gold, resources, or other advantages</h3>
-<p>One of the main reasons why some players look for a mod apk for Conflict of Nations World War 3 is to get unlimited gold, resources, or other advantages. Gold is the premium currency of the game that can be used to speed up research, construction, or recruitment. Resources are the basic materials that are needed to build and maintain your army and economy. Other advantages can include unlocking all units and technologies, increasing your morale and stability, or disabling ads and pop-ups.</p>
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-<h2>How to download and install Conflict of Nations World War 3 mod apk?</h2>
-<h3>Find a reliable source for the mod apk file</h3>
-<p>The first step to download and install Conflict of Nations World War 3 mod apk is to find a reliable source for the mod apk file. You can search online for websites or forums that offer mod apk files for various games. However, you have to be careful and cautious when choosing a source, as some of them may contain viruses, malware, or fake files. You can check the reviews, ratings, comments, or feedback from other users to verify the credibility and safety of the source.</p>
-<h3>Enable unknown sources on your device settings</h3>
-<p>The second step to download and install Conflict of Nations World War 3 mod apk is to enable unknown sources on your device settings. This is because most Android devices do not allow the installation of applications from sources other than the official Google Play Store. To enable unknown sources, you have to go to your device settings, then security, then unknown sources, and then toggle it on. You may also have to grant some permissions or accept some warnings before proceeding.</p>
-<h3>Download and install the mod apk file</h3>
-<p>The third step to download and install Conflict of Nations World War 3 mod apk is to download and install the mod apk file. You can do this by clicking on the download link or button from the source website or forum. You may have to wait for a few seconds or minutes for the download to complete. After that, you can open the downloaded file and follow the instructions to install it on your device. You may have to overwrite or uninstall the original version of the game before installing the mod apk version.</p>
-<h2>How to play Conflict of Nations World War 3 mod apk?</h2>
-<h3>Choose your nation and doctrine</h3>
-<p>The first step to play Conflict of Nations World War 3 mod apk is to choose your nation and doctrine. You can select from almost every nation in the world and lead its military expansion, technological research, and economic development. You can also choose from three different military doctrines: Western, Eastern, or European. Each doctrine has its own advantages and disadvantages, as well as its own technology tree and iconic units.</p>
-<h3>Build your army and economy</h3>
-<p>The second step to play Conflict of Nations World War 3 mod apk is to build your army and economy. You have to balance your military power with your economic efficiency, your technological superiority with your moral stability, and your offensive capabilities with your defensive measures. You have to produce and manage resources such as oil, food, metal, rare earths, components, money, manpower, and electricity. You also have to research and upgrade your units and technologies such as infantry, tanks, aircraft, ships, submarines, missiles, drones, satellites, nuclear weapons, and more.</p>
-<h3>Form alliances and wage war</h3>
-<p>The third step to play Conflict of Nations World War 3 mod apk is to form alliances and wage war. You can join or create coalitions with other players and cooperate or compete with them for global dominance. You can also communicate, negotiate, trade, spy, sabotage, or attack other players as you see fit. You have to use diplomacy and communication wisely, as your actions and words can have consequences. You also have to be prepared for the challenges and threats that may arise from the dynamic and unpredictable world of World War 3.</p>
-<h2>Tips and tricks for Conflict of Nations World War 3 mod apk</h2>
-<h3>Know your nation's strengths and weaknesses</h3>
-<p>One of the tips and tricks for Conflict of Nations World War 3 mod apk is to know your nation's strengths and weaknesses. Each nation has its own unique traits, map location, and map size that can affect its performance and strategy. For example, some nations have access to more resources, some have better geographical advantages, some have stronger military units, and some have faster research speed. You have to use your nation's strengths to your advantage and compensate for its weaknesses.</p>
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-<p>A final tip and trick for Conflict of Nations World War 3 mod apk is to use diplomacy and communication wisely. You have to interact with other players in a smart and respectful way. You have to communicate your intentions, needs, and expectations clearly and honestly. You have to negotiate fair and beneficial deals with your allies or enemies. You have to spy on your rivals or enemies to gather information or disrupt their plans. You also have to avoid unnecessary conflicts or provocations that can damage your reputation or relationships.</p>
-<h2>Conclusion</h2>
-<p>Conflict of Nations World War 3 mod apk is a modified version of an Android application that can offer unlimited gold, resources, or other advantages for the game Conflict of Nations World War 3, a real-time strategy game that simulates modern global conflicts in a realistic and immersive way. However, it can also pose risks such as malware, bans, or legal issues. To download and install it, you have to find a reliable source for the mod apk file, enable unknown sources on your device settings, and download and install the mod apk file. To play it, you have to choose your nation and doctrine, build your army and economy, and form alliances and wage war. To win the war, you have to know your nation's strengths and weaknesses, research and upgrade your units and technologies, and use diplomacy and communication wisely.</p>
-<h2>FAQs</h2>
-<ul>
-<li><b>What is the difference between Conflict of Nations World War 3 mod apk and the official version?</b></li>
-<p>The main difference between Conflict of Nations World War 3 mod apk and the official version is that the mod apk version can offer unlimited gold, resources, or other advantages that are not available in the official version.</p>
-<li><b>Is Conflict of Nations World War 3 mod apk safe to use?</b></li>
-<p>Conflict of Nations World War 3 mod apk is not completely safe to use. It can pose risks such as malware, bans, or legal issues. Therefore, you should use it at your own risk and discretion.</p>
-<li><b>How can I get more gold in Conflict of Nations World War 3 mod apk?</b></li>
-<p>You can get more gold in Conflict of Nations World War 3 mod apk by using the unlimited gold feature that the mod apk version offers. Alternatively, you can also get more gold by completing missions, achievements, or participating in events in the game.</p>
-<li><b>Can I play Conflict of Nations World War 3 mod apk offline?</b></li>
-<p>No, you cannot play Conflict of Nations World War 3 mod apk offline. The game requires an internet connection to run and sync with the servers and other players.</p>
-<li><b>Can I play Conflict of Nations World War 3 mod apk with my friends?</b></li>
-<p>Yes, you can play Conflict of Nations World War 3 mod apk with your friends. You can join or create coalitions with your friends and cooperate or compete with them for global dominance. You can also chat, trade, or fight with them in the game.</p>
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Lagu Give Me Your Forever by Zack Tabudlo - I want you to know I love you the most.md b/spaces/1phancelerku/anime-remove-background/Download Lagu Give Me Your Forever by Zack Tabudlo - I want you to know I love you the most.md
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--- a/spaces/1phancelerku/anime-remove-background/Download Lagu Give Me Your Forever by Zack Tabudlo - I want you to know I love you the most.md	
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-<p>You can choose any format and quality that suits your preferences and device capacity. However, you should always check the compatibility of the format and quality with your device before downloading it. Some devices may not support certain formats or qualities.</p>
- <h3>Step 3: Click on the download button and wait for the process to finish</h3>
-<p>The final step is to click on the download button and wait for the process to finish. The download button may vary depending on the platform that you are using. It may be labeled as "Download", "Save", "Add", or something else. It may also have an icon of a downward arrow or a cloud.</p>
-<p>Once you click on the download button, you will see a progress bar or a notification that shows how much time is left until the download is complete. You should not close or exit the platform while the download is in progress. You should also make sure that you have a stable internet connection and enough battery power on your device.</p>
-<p>After the download is finished, you will find the song in your device's music library or folder. You can then play it offline and on any device that supports it. You can also transfer it to other devices using a USB cable or a wireless connection.</p>
- <h2>Benefits of Downloading Lagu Zack Tabudlo I Want You to Know</h2>
-<p>By downloading lagu Zack Tabudlo I Want You to Know, you can enjoy several benefits:</p>
- <h3>Enjoy the song offline and on any device</h3>
-<p>One of the main benefits of downloading lagu Zack Tabudlo I Want You to Know is that you can enjoy the song offline and on any device. This means that you don't need an internet connection or a subscription fee to listen to the song whenever and wherever you want. You can also play it on a free trial period or a discount for certain users. You should check the pricing and payment options of the platform that you choose before downloading the song.</p>
- <h3>How long does it take to download lagu Zack Tabudlo I Want You to Know?</h3>
-<p>The time that it takes to download lagu Zack Tabudlo I Want You to Know depends on the format and quality of the song, the speed of your internet connection, and the capacity of your device. Generally, the higher the format and quality of the song, the longer it takes to download. The faster your internet connection, the shorter it takes to download. The more space you have on your device, the easier it is to download. You can estimate the time that it takes to download the song by looking at the file size and the download speed of the platform that you use.</p>
- <h3>What are some other songs by Zack Tabudlo that I can download?</h3>
-<p>Some other songs by Zack Tabudlo that you can download are:</p>
-<ul>
-<li><em>Binibini</em>: A catchy and upbeat song that praises the beauty and charm of a woman</li>
-<li><em>Hindi Ko Kaya</em>: A heartfelt and emotional song that expresses the pain and regret of losing a loved one</li>
-<li><em>Lost</em>: A mellow and soothing song that reflects on the feeling of being lost and lonely</li>
-<li><em>Heart Can't Lose</em>: A powerful and inspiring song that encourages overcoming challenges and following your dreams</li>
-</ul>
-<p>You can find these songs and more on the platforms that we mentioned earlier or on Zack Tabudlo's official website, YouTube channel, or social media accounts.</p>
- <h3>Where can I find more information about Zack Tabudlo and his music?</h3>
-<p>You can find more information about Zack Tabudlo and his music on his official website, YouTube channel, or social media accounts. Here are some links that you can visit:</p>
-<ul>
-<li>[Zack Tabudlo's official website]</li>
-<li>[Zack Tabudlo's YouTube channel]</li>
-<li>[Zack Tabudlo's Facebook page]</li>
-<li>[Zack Tabudlo's Instagram account]</li>
-<li>[Zack Tabudlo's Twitter account]</li>
-</ul>
-<p>You can also search for articles, interviews, reviews, or fan pages about Zack Tabudlo and his music on the web.</p> 197e85843d<br />
-<br />
-<br />
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Tank Hero The Ultimate Arcade Shooter Game MOD APK.md b/spaces/1phancelerku/anime-remove-background/Download Tank Hero The Ultimate Arcade Shooter Game MOD APK.md
deleted file mode 100644
index ea86480a5c611caad2fb8cc5a281e9bba6d7f99a..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Tank Hero The Ultimate Arcade Shooter Game MOD APK.md	
+++ /dev/null
@@ -1,91 +0,0 @@
-<br />
-<h1>Download Game Tank Hero Mod Apk: A Guide for Tank Lovers</h1>
-<p>If you are a fan of tank games, you might have heard of Tank Hero, a popular arcade shooter game that lets you control a powerful tank and blast your enemies in various levels. But did you know that you can download the mod apk version of Tank Hero and enjoy unlimited money, god mode, one hit kill, and other features? In this article, we will show you how to download and install Tank Hero mod apk, as well as some tips and tricks to master the game.</p>
- <h2>What is Tank Hero and why you should play it</h2>
-<p>Tank Hero is a 2D side-scrolling tank game developed by UP STUDIO. The game has over 120 levels, 3 unique environments, 5 weapons, and 5 types of AI tanks to fight against. You can also play online with other players in multiplayer mode. The game is simple to control: just drag to move and release to fire. You can also choose from different firing options, such as single shot, double shot, triple shot, etc. The game is fun and addictive, as you have to use your strategy and skills to defeat the enemies and clear the levels.</p>
-<h2>download game tank hero mod apk</h2><br /><p><b><b>Download</b> &mdash;&mdash;&mdash; <a href="https://jinyurl.com/2uNSeV">https://jinyurl.com/2uNSeV</a></b></p><br /><br />
- <h2>Features of Tank Hero</h2>
-<p>Some of the features that make Tank Hero stand out are:</p>
-<ul>
-<li><b>Awesome graphics and sound effects:</b> The game has colorful and cartoonish graphics that suit the arcade style. The sound effects are also realistic and immersive, making you feel like you are in a real tank battle.</li>
-<li><b>Various tanks and weapons:</b> You can collect and upgrade different tanks and weapons in the game. Each tank has its own characteristics, such as speed, armor, fire rate, etc. Each weapon also has its own effects, such as fireball, laser, missile, etc. You can mix and match different tanks and weapons to suit your play style.</li>
-<li><b>Unique talents:</b> You can also unlock and use over 100 talents in the game. Talents are special abilities that can boost your firepower, defense, speed, or other aspects. You can choose from random talents or customize your own talent combination.</li>
-</ul>
- <h2>How to download and install Tank Hero mod apk</h2>
-<p>If you want to enjoy more features and benefits in Tank Hero, you can download the mod apk version of the game. The mod apk version will give you unlimited money, god mode, one hit kill, and other features that will make the game easier and more fun. Here are the steps to download and install Tank Hero mod apk:</p>
-<ol>
-<li>Go to [this link](^1^) or [this link](^2^) or [this link](^3^) to download the Tank Hero mod apk file.</li>
-<li>After downloading the file, go to your device settings and enable the installation of unknown sources.</li>
-<li>Locate the downloaded file in your file manager and tap on it to install it.</li>
-<li>Wait for the installation to finish and then launch the game.</li>
-<li>Enjoy playing Tank Hero with unlimited money, god mode, one hit kill, and other features.</li>
-</ol>
- <h2>Tips and tricks to master Tank Hero</h2>
-<p>If you want to become a tank hero yourself, here are some tips and tricks that will help you improve your skills and performance in the game:</p>
- <h3>Choose the right tank and weapon for each level</h3>
-<p>As mentioned earlier, each tank and weapon has its own advantages and disadvantages. You should choose the ones that suit the level's difficulty, terrain, enemies, and objectives. For example, if the level has many obstacles or narrow spaces, you might want to use a fast tank with a laser weapon that can penetrate through walls. If the level has many enemies or bosses, you might want to use a tank with a tank with high armor and a fireball weapon that can deal splash damage. You can also switch tanks and weapons during the level if you find a better option.</p>
- <h3>Upgrade your tank and skills regularly</h3>
-<p>As you progress in the game, you will earn money and experience that you can use to upgrade your tank and skills. Upgrading your tank will increase its stats, such as speed, armor, fire rate, etc. Upgrading your skills will unlock new talents or improve the existing ones. You should always upgrade your tank and skills whenever you can, as they will make a big difference in your performance and survival.</p>
- <h3>Use the terrain and obstacles to your advantage</h3>
-<p>The game has various terrains and obstacles that can affect the gameplay. For example, some terrains can slow you down or speed you up, some obstacles can block your shots or reflect them, some items can heal you or give you extra ammo, etc. You should always be aware of your surroundings and use them to your advantage. For example, you can hide behind walls or rocks to avoid enemy fire, you can bounce your shots off walls or mirrors to hit enemies from different angles, you can use speed boosts or ramps to escape or chase enemies, etc.</p>
- <h2>Conclusion</h2>
-<p>Tank Hero is a fun and addictive tank game that will keep you entertained for hours. You can download the mod apk version of Tank Hero and enjoy unlimited money, god mode, one hit kill, and other features that will make the game easier and more fun. You can also follow our tips and tricks to master the game and become a tank hero yourself. So what are you waiting for? Download Tank Hero mod apk now and start blasting your enemies!</p>
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- <h3>FAQs</h3>
-<p>Here are some frequently asked questions about Tank Hero mod apk:</p>
-<ul>
-<li><b>Q: Is Tank Hero mod apk safe to download and install?</b></li>
-<li>A: Yes, Tank Hero mod apk is safe to download and install. It does not contain any viruses or malware that can harm your device. However, you should always download it from a trusted source and enable the installation of unknown sources in your device settings.</li>
-<li><b>Q: Do I need to root my device to use Tank Hero mod apk?</b></li>
-<li>A: No, you do not need to root your device to use Tank Hero mod apk. It works on both rooted and non-rooted devices.</li>
-<li><b>Q: Will Tank Hero mod apk affect my progress in the original game?</b></li>
-<li>A: No, Tank Hero mod apk will not affect your progress in the original game. It will create a separate game data that is independent from the original game. You can play both versions of the game without any interference.</li>
-<li><b>Q: Can I play online with other players in Tank Hero mod apk?</b></li>
-<li>A: Yes, you can play online with other players in Tank Hero mod apk. However, you might encounter some issues or errors when playing online, as the mod apk version might not be compatible with the latest version of the original game. You can try to update the mod apk version or switch to the original game if you want to play online smoothly.</li>
-<li><b>Q: How can I contact the developer of Tank Hero mod apk?</b></li>
-<li>A: You can contact the developer of Tank Hero mod apk by visiting their website [here] or by sending them an email at [this address].</li>
-</ul></p> 401be4b1e0<br />
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\ No newline at end of file
diff --git a/spaces/AHzizi/WaifuVoiceGen/app.py b/spaces/AHzizi/WaifuVoiceGen/app.py
deleted file mode 100644
index e41932ae3e0a20837c5740859b4be34253c59b82..0000000000000000000000000000000000000000
--- a/spaces/AHzizi/WaifuVoiceGen/app.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# coding=utf-8
-import os
-import re
-import argparse
-import utils
-import commons
-import json
-import torch
-import gradio as gr
-from models import SynthesizerTrn
-from text import text_to_sequence, _clean_text
-from torch import no_grad, LongTensor
-import gradio.processing_utils as gr_processing_utils
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit text and audio length in huggingface spaces
-
-hps_ms = utils.get_hparams_from_file(r'config/config.json')
-
-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, is_symbol):
-    text_norm, clean_text = text_to_sequence(text, hps.symbols, [] if is_symbol else 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 create_tts_fn(net_g_ms, speaker_id):
-    def tts_fn(text, language, noise_scale, noise_scale_w, length_scale, is_symbol):
-        text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
-        if limitation:
-            text_len = len(re.sub("\[([A-Z]{2})\]", "", text))
-            max_len = 100
-            if is_symbol:
-                max_len *= 3
-            if text_len > max_len:
-                return "Error: Text is too long", None
-        if not is_symbol:
-            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, is_symbol)
-        with no_grad():
-            x_tst = stn_tst.unsqueeze(0).to(device)
-            x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device)
-            sid = LongTensor([speaker_id]).to(device)
-            audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=noise_scale, noise_scale_w=noise_scale_w,
-                                   length_scale=length_scale)[0][0, 0].data.cpu().float().numpy()
-
-        return "Success", (22050, audio)
-    return tts_fn
-
-def create_to_symbol_fn(hps):
-    def to_symbol_fn(is_symbol_input, input_text, temp_lang):
-        if temp_lang == 0:
-            clean_text = f'[ZH]{input_text}[ZH]'
-        elif temp_lang == 1:
-            clean_text = f'[JA]{input_text}[JA]'
-        else:
-            clean_text = input_text
-        return _clean_text(clean_text, hps.data.text_cleaners) if is_symbol_input else ''
-
-    return to_symbol_fn
-def change_lang(language):
-    if language == 0:
-        return 0.6, 0.668, 1.2
-    elif language == 1:
-        return 0.6, 0.668, 1
-    else:
-        return 0.6, 0.668, 1
-
-download_audio_js = """
-() =>{{
-    let root = document.querySelector("body > gradio-app");
-    if (root.shadowRoot != null)
-        root = root.shadowRoot;
-    let audio = root.querySelector("#tts-audio-{audio_id}").querySelector("audio");
-    let text = root.querySelector("#input-text-{audio_id}").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")
-    args = parser.parse_args()
-    device = torch.device(args.device)
-    
-    models = []
-    with open("pretrained_models/info.json", "r", encoding="utf-8") as f:
-        models_info = json.load(f)
-    for i, info in models_info.items():
-        if not info['enable']:
-            continue
-        sid = info['sid']
-        name_en = info['name_en']
-        name_zh = info['name_zh']
-        title = info['title']
-        cover = f"pretrained_models/{i}/{info['cover']}"
-        example = info['example']
-        language = info['language']
-        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 if info['type'] == "multi" else 0,
-            **hps_ms.model)
-        utils.load_checkpoint(f'pretrained_models/{i}/{i}.pth', net_g_ms, None)
-        _ = net_g_ms.eval().to(device)
-        models.append((sid, name_en, name_zh, title, cover, example, language, net_g_ms, create_tts_fn(net_g_ms, sid), create_to_symbol_fn(hps_ms)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> vits-models\n"
-            "## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n"
-            "## <center> ·请不要生成会对个人以及组织造成侵害的内容\n"
-            "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=sayashi.vits-models)\n\n"
-            "[Open In Colab]"
-            "(https://colab.research.google.com/drive/10QOk9NPgoKZUXkIhhuVaZ7SYra1MPMKH?usp=share_link)"
-            " without queue and length limitation.(无需等待队列，并且没有长度限制)\n\n"
-            "[Finetune your own model](https://github.com/SayaSS/vits-finetuning)"
-        )
-
-        with gr.Tabs():
-            with gr.TabItem("EN"):
-                for (sid, name_en, name_zh, title, cover, example, language, net_g_ms, tts_fn, to_symbol_fn) in models:
-                    with gr.TabItem(name_en):
-                        with gr.Row():
-                            gr.Markdown(
-                                '<div align="center">'
-                                f'<a><strong>{title}</strong></a>'
-                                f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                                '</div>'
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                input_text = gr.Textbox(label="Text (100 words limitation)" if limitation else "Text", lines=5, value=example, elem_id=f"input-text-en-{name_en.replace(' ','')}")
-                                lang = gr.Dropdown(label="Language", choices=["Chinese", "Japanese", "Mix（wrap the Chinese text with [ZH][ZH], wrap the Japanese text with [JA][JA]）"],
-                                            type="index", value=language)
-                                with gr.Accordion(label="Advanced Options", open=False):
-                                    symbol_input = gr.Checkbox(value=False, label="Symbol input")
-                                    symbol_list = gr.Dataset(label="Symbol list", components=[input_text],
-                                                             samples=[[x] for x in hps_ms.symbols])
-                                    symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
-                                btn = gr.Button(value="Generate", variant="primary")
-                                with gr.Row():
-                                    ns = gr.Slider(label="noise_scale", minimum=0.1, maximum=1.0, step=0.1, value=0.6, interactive=True)
-                                    nsw = gr.Slider(label="noise_scale_w", minimum=0.1, maximum=1.0, step=0.1, value=0.668, interactive=True)
-                                    ls = gr.Slider(label="length_scale", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
-                            with gr.Column():
-                                o1 = gr.Textbox(label="Output Message")
-                                o2 = gr.Audio(label="Output Audio", elem_id=f"tts-audio-en-{name_en.replace(' ','')}")
-                                download = gr.Button("Download Audio")
-                            btn.click(tts_fn, inputs=[input_text, lang,  ns, nsw, ls, symbol_input], outputs=[o1, o2], api_name=f"tts-{name_en}")
-                            download.click(None, [], [], _js=download_audio_js.format(audio_id=f"en-{name_en.replace(' ', '')}"))
-                            lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-                            symbol_input.change(
-                                to_symbol_fn,
-                                [symbol_input, input_text, lang],
-                                [input_text]
-                            )
-                            symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
-                                              _js=f"""
-                            (i,symbols) => {{
-                                let root = document.querySelector("body > gradio-app");
-                                if (root.shadowRoot != null)
-                                    root = root.shadowRoot;
-                                let text_input = root.querySelector("#input-text-en-{name_en.replace(' ', '')}").querySelector("textarea");
-                                let startPos = text_input.selectionStart;
-                                let endPos = text_input.selectionEnd;
-                                let oldTxt = text_input.value;
-                                let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                text_input.value = result;
-                                let x = window.scrollX, y = window.scrollY;
-                                text_input.focus();
-                                text_input.selectionStart = startPos + symbols[i].length;
-                                text_input.selectionEnd = startPos + symbols[i].length;
-                                text_input.blur();
-                                window.scrollTo(x, y);
-                                return text_input.value;
-                            }}""")
-            with gr.TabItem("中文"):
-                for (sid, name_en, name_zh, title, cover, example, language,  net_g_ms, tts_fn, to_symbol_fn) in models:
-                    with gr.TabItem(name_zh):
-                        with gr.Row():
-                            gr.Markdown(
-                                '<div align="center">'
-                                f'<a><strong>{title}</strong></a>'
-                                f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else ""
-                                '</div>'
-                            )
-                        with gr.Row():
-                            with gr.Column():
-                                input_text = gr.Textbox(label="文本 (100字上限)" if limitation else "文本", lines=5, value=example, elem_id=f"input-text-zh-{name_zh}")
-                                lang = gr.Dropdown(label="语言", 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"if language == "Chinese" else "日语")
-                                with gr.Accordion(label="高级选项", open=False):
-                                    symbol_input = gr.Checkbox(value=False, label="符号输入")
-                                    symbol_list = gr.Dataset(label="符号列表", components=[input_text],
-                                                             samples=[[x] for x in hps_ms.symbols])
-                                    symbol_list_json = gr.Json(value=hps_ms.symbols, visible=False)
-                                btn = gr.Button(value="生成", variant="primary")
-                                with gr.Row():
-                                    ns = gr.Slider(label="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="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="控制整体语速", minimum=0.1, maximum=2.0, step=0.1, value=1.2 if language=="Chinese" else 1, interactive=True)
-                            with gr.Column():
-                                o1 = gr.Textbox(label="输出信息")
-                                o2 = gr.Audio(label="输出音频", elem_id=f"tts-audio-zh-{name_zh}")
-                                download = gr.Button("下载音频")
-                            btn.click(tts_fn, inputs=[input_text, lang,  ns, nsw, ls, symbol_input], outputs=[o1, o2])
-                            download.click(None, [], [], _js=download_audio_js.format(audio_id=f"zh-{name_zh}"))
-                            lang.change(change_lang, inputs=[lang], outputs=[ns, nsw, ls])
-                            symbol_input.change(
-                                to_symbol_fn,
-                                [symbol_input, input_text, lang],
-                                [input_text]
-                            )
-                            symbol_list.click(None, [symbol_list, symbol_list_json], [input_text],
-                                              _js=f"""
-                            (i,symbols) => {{
-                                let root = document.querySelector("body > gradio-app");
-                                if (root.shadowRoot != null)
-                                    root = root.shadowRoot;
-                                let text_input = root.querySelector("#input-text-zh-{name_zh}").querySelector("textarea");
-                                let startPos = text_input.selectionStart;
-                                let endPos = text_input.selectionEnd;
-                                let oldTxt = text_input.value;
-                                let result = oldTxt.substring(0, startPos) + symbols[i] + oldTxt.substring(endPos);
-                                text_input.value = result;
-                                let x = window.scrollX, y = window.scrollY;
-                                text_input.focus();
-                                text_input.selectionStart = startPos + symbols[i].length;
-                                text_input.selectionEnd = startPos + symbols[i].length;
-                                text_input.blur();
-                                window.scrollTo(x, y);
-                                return text_input.value;
-                            }}""")
-    app.queue(concurrency_count=1, api_open=args.api).launch(share=args.share)
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/modules/conv.py b/spaces/AIConsultant/MusicGen/audiocraft/modules/conv.py
deleted file mode 100644
index d115cbf8729b642ed78608bd00a4d0fd5afae6fd..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/modules/conv.py
+++ /dev/null
@@ -1,243 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
-                                 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'weight_norm':
-        return weight_norm(module)
-    elif norm == 'spectral_norm':
-        return spectral_norm(module)
-    else:
-        # We already check was in CONV_NORMALIZATION, so any other choice
-        # doesn't need reparametrization.
-        return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
-    """Return the proper normalization module. If causal is True, this will ensure the returned
-    module is causal, or return an error if the normalization doesn't support causal evaluation.
-    """
-    assert norm in CONV_NORMALIZATIONS
-    if norm == 'time_group_norm':
-        if causal:
-            raise ValueError("GroupNorm doesn't support causal evaluation.")
-        assert isinstance(module, nn.modules.conv._ConvNd)
-        return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
-    else:
-        return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
-                                 padding_total: int = 0) -> int:
-    """See `pad_for_conv1d`."""
-    length = x.shape[-1]
-    n_frames = (length - kernel_size + padding_total) / stride + 1
-    ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
-    return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
-    """Pad for a convolution to make sure that the last window is full.
-    Extra padding is added at the end. This is required to ensure that we can rebuild
-    an output of the same length, as otherwise, even with padding, some time steps
-    might get removed.
-    For instance, with total padding = 4, kernel size = 4, stride = 2:
-        0 0 1 2 3 4 5 0 0   # (0s are padding)
-        1   2   3           # (output frames of a convolution, last 0 is never used)
-        0 0 1 2 3 4 5 0     # (output of tr. conv., but pos. 5 is going to get removed as padding)
-            1 2 3 4         # once you removed padding, we are missing one time step !
-    """
-    extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-    return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
-    """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
-    If this is the case, we insert extra 0 padding to the right before the reflection happen.
-    """
-    length = x.shape[-1]
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    if mode == 'reflect':
-        max_pad = max(padding_left, padding_right)
-        extra_pad = 0
-        if length <= max_pad:
-            extra_pad = max_pad - length + 1
-            x = F.pad(x, (0, extra_pad))
-        padded = F.pad(x, paddings, mode, value)
-        end = padded.shape[-1] - extra_pad
-        return padded[..., :end]
-    else:
-        return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
-    """Remove padding from x, handling properly zero padding. Only for 1d!"""
-    padding_left, padding_right = paddings
-    assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    assert (padding_left + padding_right) <= x.shape[-1]
-    end = x.shape[-1] - padding_right
-    return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
-    """Wrapper around Conv1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConv2d(nn.Module):
-    """Wrapper around Conv2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose1d(nn.Module):
-    """Wrapper around ConvTranspose1d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, causal: bool = False, norm: str = 'none',
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
-        self.norm_type = norm
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class NormConvTranspose2d(nn.Module):
-    """Wrapper around ConvTranspose2d and normalization applied to this conv
-    to provide a uniform interface across normalization approaches.
-    """
-    def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
-        super().__init__()
-        self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
-        self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
-    def forward(self, x):
-        x = self.convtr(x)
-        x = self.norm(x)
-        return x
-
-
-class StreamableConv1d(nn.Module):
-    """Conv1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, dilation: int = 1,
-                 groups: int = 1, bias: bool = True, causal: bool = False,
-                 norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
-                 pad_mode: str = 'reflect'):
-        super().__init__()
-        # warn user on unusual setup between dilation and stride
-        if stride > 1 and dilation > 1:
-            warnings.warn("StreamableConv1d has been initialized with stride > 1 and dilation > 1"
-                          f" (kernel_size={kernel_size} stride={stride}, dilation={dilation}).")
-        self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
-                               dilation=dilation, groups=groups, bias=bias, causal=causal,
-                               norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.pad_mode = pad_mode
-
-    def forward(self, x):
-        B, C, T = x.shape
-        kernel_size = self.conv.conv.kernel_size[0]
-        stride = self.conv.conv.stride[0]
-        dilation = self.conv.conv.dilation[0]
-        kernel_size = (kernel_size - 1) * dilation + 1  # effective kernel size with dilations
-        padding_total = kernel_size - stride
-        extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
-        if self.causal:
-            # Left padding for causal
-            x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
-        return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
-    """ConvTranspose1d with some builtin handling of asymmetric or causal padding
-    and normalization.
-    """
-    def __init__(self, in_channels: int, out_channels: int,
-                 kernel_size: int, stride: int = 1, causal: bool = False,
-                 norm: str = 'none', trim_right_ratio: float = 1.,
-                 norm_kwargs: tp.Dict[str, tp.Any] = {}):
-        super().__init__()
-        self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
-                                          causal=causal, norm=norm, norm_kwargs=norm_kwargs)
-        self.causal = causal
-        self.trim_right_ratio = trim_right_ratio
-        assert self.causal or self.trim_right_ratio == 1., \
-            "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
-        assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
-    def forward(self, x):
-        kernel_size = self.convtr.convtr.kernel_size[0]
-        stride = self.convtr.convtr.stride[0]
-        padding_total = kernel_size - stride
-
-        y = self.convtr(x)
-
-        # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
-        # removed at the very end, when keeping only the right length for the output,
-        # as removing it here would require also passing the length at the matching layer
-        # in the encoder.
-        if self.causal:
-            # Trim the padding on the right according to the specified ratio
-            # if trim_right_ratio = 1.0, trim everything from right
-            padding_right = math.ceil(padding_total * self.trim_right_ratio)
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        else:
-            # Asymmetric padding required for odd strides
-            padding_right = padding_total // 2
-            padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
-        return y
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/utils/best_state.py b/spaces/AIConsultant/MusicGen/audiocraft/utils/best_state.py
deleted file mode 100644
index f5ad551432ad5cb0f83278b5d2100f9aa287958b..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/utils/best_state.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import defaultdict
-import logging
-import typing as tp
-
-import flashy
-import torch
-
-from ..optim import ModuleDictEMA
-from .utils import copy_state
-
-
-logger = logging.getLogger(__name__)
-
-
-class BestStateDictManager(flashy.state.StateDictSource):
-    """BestStateDictManager maintains a copy of best state_dict() for registered sources.
-
-    BestStateDictManager has two main attributes:
-        states (dict): State dict of the registered StateDictSource.
-        param_ids (dict): Dict of parameter ids for registered states from ModuleDictEMA and other sources.
-
-    When registering new sources, the BestStateDictManager will ensure two conflicting sources between
-    ModuleDictEMA and original modules are not both registered as it would otherwise create ambiguity about
-    what to consider for best state.
-
-    Args:
-        device (torch.device or str): Device on which we keep the copy.
-        dtype (torch.dtype): Data type for the state parameters.
-    """
-    def __init__(self, device: tp.Union[torch.device, str] = 'cpu',
-                 dtype: tp.Optional[torch.dtype] = None):
-        self.device = device
-        self.states: dict = {}
-        self.param_ids: dict = defaultdict(dict)
-        self.dtype = dtype
-
-    def _get_parameter_ids(self, state_dict):
-        return {id(p): name for name, p in state_dict.items() if isinstance(p, torch.Tensor)}
-
-    def _validate_no_parameter_ids_overlap(self, name: str, param_ids: dict):
-        for registered_name, registered_param_ids in self.param_ids.items():
-            if registered_name != name:
-                overlap = set.intersection(registered_param_ids.keys(), param_ids.keys())
-                assert len(overlap) == 0, f"Found {len(overlap)} / {len(param_ids.keys())} overlapping parameters"
-                f" in {name} and already registered {registered_name}: {' '.join(overlap)}"
-
-    def update(self, name: str, source: flashy.state.StateDictSource):
-        if name not in self.states:
-            raise ValueError(f"{name} missing from registered states.")
-        self.states[name] = copy_state(source.state_dict(), device=self.device, dtype=self.dtype)
-
-    def register(self, name: str, source: flashy.state.StateDictSource):
-        if name in self.states:
-            raise ValueError(f"{name} already present in states.")
-        # Registering parameter ids for EMA and non-EMA states allows us to check that
-        # there is no overlap that would create ambiguity about how to handle the best state
-        param_ids = self._get_parameter_ids(source.state_dict())
-        if isinstance(source, ModuleDictEMA):
-            logger.debug(f"Registering to best state: ModuleDictEMA '{name}' with {len(param_ids)} params")
-            self._validate_no_parameter_ids_overlap(name, param_ids)
-            self.param_ids[name] = param_ids
-        else:
-            logger.debug(f"Registering to best state: StateDictSource '{name}' with {len(param_ids)} params")
-            self._validate_no_parameter_ids_overlap('base', param_ids)
-            self.param_ids['base'].update(param_ids)
-        # Register state
-        self.states[name] = copy_state(source.state_dict(), device=self.device, dtype=self.dtype)
-
-    def state_dict(self) -> flashy.state.StateDict:
-        return self.states
-
-    def load_state_dict(self, state: flashy.state.StateDict):
-        for name, sub_state in state.items():
-            for k, v in sub_state.items():
-                self.states[name][k].copy_(v)
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/nn/schedulers.py b/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/nn/schedulers.py
deleted file mode 100644
index ce7494b136a77d7724ab97ae2cdc56e6c9694214..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_speech/utils/nn/schedulers.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import numpy as np
-
-
-class NoneSchedule(object):
-    def __init__(self, optimizer, lr):
-        self.optimizer = optimizer
-        self.constant_lr = lr
-        self.step(0)
-
-    def step(self, num_updates):
-        self.lr = self.constant_lr
-        for param_group in self.optimizer.param_groups:
-            param_group['lr'] = self.lr
-        return self.lr
-
-    def get_lr(self):
-        return self.optimizer.param_groups[0]['lr']
-
-    def get_last_lr(self):
-        return self.get_lr()
-
-
-class RSQRTSchedule(NoneSchedule):
-    def __init__(self, optimizer, lr, warmup_updates, hidden_size):
-        self.optimizer = optimizer
-        self.constant_lr = lr
-        self.warmup_updates = warmup_updates
-        self.hidden_size = hidden_size
-        self.lr = lr
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = self.lr
-        self.step(0)
-
-    def step(self, num_updates):
-        constant_lr = self.constant_lr
-        warmup = min(num_updates / self.warmup_updates, 1.0)
-        rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5
-        rsqrt_hidden = self.hidden_size ** -0.5
-        self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-6)
-        for param_group in self.optimizer.param_groups:
-            param_group['lr'] = self.lr
-        return self.lr
-
-
-class WarmupSchedule(NoneSchedule):
-    def __init__(self, optimizer, lr, warmup_updates):
-        self.optimizer = optimizer
-        self.constant_lr = self.lr = lr
-        self.warmup_updates = warmup_updates
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = self.lr
-        self.step(0)
-
-    def step(self, num_updates):
-        constant_lr = self.constant_lr
-        warmup = min(num_updates / self.warmup_updates, 1.0)
-        self.lr = max(constant_lr * warmup, 1e-7)
-        for param_group in self.optimizer.param_groups:
-            param_group['lr'] = self.lr
-        return self.lr
-
-
-class CosineSchedule(NoneSchedule):
-    def __init__(self, optimizer, lr, warmup_updates, total_updates):
-        self.optimizer = optimizer
-        self.constant_lr = lr
-        self.warmup_updates = warmup_updates
-        self.total_updates = total_updates
-        self.lr = lr
-        self.assign_learning_rate(self.optimizer, self.lr)
-        self.step(0)
-
-    def assign_learning_rate(self, optimizer, new_lr):
-        for param_group in optimizer.param_groups:
-            param_group["lr"] = new_lr
-
-    def _warmup_lr(self, base_lr, warmup_length, step):
-        return base_lr * (step + 1) / warmup_length
-
-    def step(self, num_updates):
-        if num_updates < self.warmup_updates:
-            lr = self._warmup_lr(self.lr, self.warmup_updates, num_updates)
-        else:
-            e = num_updates - self.warmup_updates
-            es = self.total_updates - self.warmup_updates
-            lr = 0.5 * (1 + np.cos(np.pi * e / es)) * self.lr
-        self.assign_learning_rate(self.optimizer, lr)
-        return lr
-
-
-if __name__ == '__main__':
-    import numpy as np
-    import matplotlib.pyplot as plt
-    import torch
-
-    def plot_scheduler(scheduler, label=None):
-        y = np.array([scheduler.step(x) for x in range(0,160000, 10)])
-        x = np.arange(0,160000, 10)
-        plt.plot(x, y, label=label)
-
-    dummy_model = torch.nn.Linear(10,10)
-    dummy_optimizer = torch.optim.Adam(dummy_model.parameters())
-    rsqrt = CosineSchedule(dummy_optimizer, lr=0.0005, warmup_updates=10000, total_updates=160000)
-    plot_scheduler(rsqrt, "8000")
-    plt.savefig("0.png")
diff --git a/spaces/AbelKidane/headdetector/README.md b/spaces/AbelKidane/headdetector/README.md
deleted file mode 100644
index 0c870719982a1ba94bc28c964b1750e4f19304ef..0000000000000000000000000000000000000000
--- a/spaces/AbelKidane/headdetector/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Headdetector
-emoji: 🏆
-colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/api.py b/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/api.py
deleted file mode 100644
index 9a6e194545c40ec263e65a140678b53a5a2abd54..0000000000000000000000000000000000000000
--- a/spaces/Adapter/T2I-Adapter/ldm/modules/extra_condition/midas/api.py
+++ /dev/null
@@ -1,175 +0,0 @@
-# based on https://github.com/isl-org/MiDaS
-import os
-
-import cv2
-import torch
-import torch.nn as nn
-from torchvision.transforms import Compose
-
-from ldm.modules.extra_condition.midas.midas.dpt_depth import DPTDepthModel
-from ldm.modules.extra_condition.midas.midas.midas_net import MidasNet
-from ldm.modules.extra_condition.midas.midas.midas_net_custom import MidasNet_small
-from ldm.modules.extra_condition.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
-
-
-ISL_PATHS = {
-    "dpt_large": "models/dpt_large-midas-2f21e586.pt",
-    "dpt_hybrid": "models/dpt_hybrid-midas-501f0c75.pt",
-    "midas_v21": "",
-    "midas_v21_small": "",
-}
-
-remote_model_path = "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt"
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def load_midas_transform(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load transform only
-    if model_type == "dpt_large":  # DPT-Large
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    elif model_type == "midas_v21_small":
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    else:
-        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return transform
-
-
-def load_model(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load network
-    model_path = ISL_PATHS[model_type]
-    if model_type == "dpt_large":  # DPT-Large
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        if not os.path.exists(model_path):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(remote_model_path, model_dir='models')
-
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitb_rn50_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        model = MidasNet(model_path, non_negative=True)
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    elif model_type == "midas_v21_small":
-        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
-                               non_negative=True, blocks={'expand': True})
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    else:
-        print(f"model_type '{model_type}' not implemented, use: --model_type large")
-        assert False
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return model.eval(), transform
-
-
-class MiDaSInference(nn.Module):
-    MODEL_TYPES_TORCH_HUB = [
-        "DPT_Large",
-        "DPT_Hybrid",
-        "MiDaS_small"
-    ]
-    MODEL_TYPES_ISL = [
-        "dpt_large",
-        "dpt_hybrid",
-        "midas_v21",
-        "midas_v21_small",
-    ]
-
-    def __init__(self, model_type):
-        super().__init__()
-        assert (model_type in self.MODEL_TYPES_ISL)
-        model, _ = load_model(model_type)
-        self.model = model
-        self.model.train = disabled_train
-
-    def forward(self, x):
-        # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
-        # NOTE: we expect that the correct transform has been called during dataloading.
-        with torch.no_grad():
-            prediction = self.model(x)
-            prediction = torch.nn.functional.interpolate(
-                prediction.unsqueeze(1),
-                size=x.shape[2:],
-                mode="bicubic",
-                align_corners=False,
-            )
-        assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
-        return prediction
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/PushIntoBounds.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/PushIntoBounds.js
deleted file mode 100644
index e9b7fdf52e0b21a75161db9648a91da96d459e90..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/PushIntoBounds.js
+++ /dev/null
@@ -1,15 +0,0 @@
-import GetViewport from '../../../plugins/utils/system/GetViewport.js';
-
-var PushIntoBounds = function (bounds) {
-    if (bounds === undefined) {
-        bounds = GetViewport(this.scene);
-    }
-
-    this.left = Math.max(this.left, bounds.left);
-    this.right = Math.min(this.right, bounds.right);
-    this.top = Math.max(this.top, bounds.top);
-    this.bottom = Math.min(this.bottom, bounds.bottom);
-    return this;
-}
-
-export default PushIntoBounds;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetChildrenWidth.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetChildrenWidth.js
deleted file mode 100644
index 06ea5bba98170a646a5bc3d6fc585d967a585771..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/GetChildrenWidth.js
+++ /dev/null
@@ -1,19 +0,0 @@
-var GetChildrenWidth = function () {
-    if (this.rexSizer.hidden) {
-        return 0;
-    }
-
-    var result = 0;
-    var children = this.sizerChildren;
-    var child, padding, childWidth;
-    for (var key in children) {
-        child = children[key];
-
-        padding = child.rexSizer.padding;
-        childWidth = this.getChildWidth(child) + padding.left + padding.right;
-        result = Math.max(childWidth, result);
-    }
-    return result + this.space.left + this.space.right;
-}
-
-export default GetChildrenWidth;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ExpandMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ExpandMethods.js
deleted file mode 100644
index e2e08c956a51cedfb6aa1f4e64bfdc4cf23c0765..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/sizer/ExpandMethods.js
+++ /dev/null
@@ -1,11 +0,0 @@
-export default {
-    getChildExpand(gameObject) {
-        return this.getSizerConfig(gameObject).expand;
-    },
-
-    setChildExpand(gameObject, expand) {
-        this.getSizerConfig(gameObject).expand = expand;
-        return this;
-    },
-
-}
\ No newline at end of file
diff --git a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/symbols.py b/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/symbols.py
deleted file mode 100644
index 789e9df25d3d93d1976ef22d15d77f51d170ed00..0000000000000000000000000000000000000000
--- a/spaces/Al-Chan/Vits_League_of_Legends_Yuumi_TTS/text/symbols.py
+++ /dev/null
@@ -1,76 +0,0 @@
-'''
-Defines the set of symbols used in text input to the model.
-'''
-
-# japanese_cleaners
-# _pad        = '_'
-# _punctuation = ',.!?-'
-# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
-
-
-'''# japanese_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
-'''
-
-
-'''# korean_cleaners
-_pad        = '_'
-_punctuation = ',.!?…~'
-_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
-'''
-
-'''# chinese_cleaners
-_pad        = '_'
-_punctuation = '，。！？—…'
-_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
-'''
-
-# # zh_ja_mixture_cleaners
-# _pad        = '_'
-# _punctuation = ',.!?-~…'
-# _letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
-
-
-'''# sanskrit_cleaners
-_pad        = '_'
-_punctuation = '।'
-_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
-'''
-
-'''# cjks_cleaners
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
-'''
-
-'''# thai_cleaners
-_pad        = '_'
-_punctuation = '.!? '
-_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
-'''
-
-# # cjke_cleaners2
-_pad        = '_'
-_punctuation = ',.!?-~…'
-_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
-
-
-'''# shanghainese_cleaners
-_pad        = '_'
-_punctuation = ',.!?…'
-_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
-'''
-
-'''# chinese_dialect_cleaners
-_pad        = '_'
-_punctuation = ',.!?~…─'
-_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
-'''
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")
diff --git a/spaces/AlbertoFH98/CastenaApp/utils.py b/spaces/AlbertoFH98/CastenaApp/utils.py
deleted file mode 100644
index 2016c51fc064eb4ae5002c848665660368844a63..0000000000000000000000000000000000000000
--- a/spaces/AlbertoFH98/CastenaApp/utils.py
+++ /dev/null
@@ -1,291 +0,0 @@
-# -- Utils .py file
-# -- Libraries
-from   typing                      import Any, Dict, List, Mapping, Optional
-from   pydantic                    import Extra, Field, root_validator
-from   langchain.llms.base         import LLM
-from   langchain.utils             import get_from_dict_or_env
-from   langchain.vectorstores      import Chroma
-from   langchain.text_splitter     import RecursiveCharacterTextSplitter
-from   langchain.chains            import RetrievalQA
-from   langchain.document_loaders  import TextLoader
-from   langchain.embeddings        import HuggingFaceEmbeddings
-from   googletrans                 import Translator
-import streamlit as st
-import together
-import textwrap
-import spacy
-import os
-import re
-
-os.environ["TOGETHER_API_KEY"] = "6101599d6e33e3bda336b8d007ca22e35a64c72cfd52c2d8197f663389fc50c5"
-
-# -- LLM class
-class TogetherLLM(LLM):
-    """Together large language models."""
-
-    model: str = "togethercomputer/llama-2-70b-chat"
-    """model endpoint to use"""
-
-    together_api_key: str = os.environ["TOGETHER_API_KEY"]
-    """Together API key"""
-
-    temperature: float = 0.7
-    """What sampling temperature to use."""
-
-    max_tokens: int = 512
-    """The maximum number of tokens to generate in the completion."""
-
-    class Config:
-        extra = Extra.forbid
-
-    @root_validator()
-    def validate_environment(cls, values: Dict) -> Dict:
-        """Validate that the API key is set."""
-        api_key = get_from_dict_or_env(
-            values, "together_api_key", "TOGETHER_API_KEY"
-        )
-        values["together_api_key"] = api_key
-        return values
-
-    @property
-    def _llm_type(self) -> str:
-        """Return type of LLM."""
-        return "together"
-
-    def clean_duplicates(self, transcription: str) -> str:
-      lines = transcription.strip().split('\n')
-
-      new_transcription = []
-
-      for linea in lines:
-          if linea.replace('CONTEXT:/n/n ', '').replace('/n', '') not in new_transcription and linea != '':
-              new_transcription.append(linea.replace('CONTEXT:/n/n ', '').replace('/n', ''))
-      # Create new transcription without duplicates
-      new_transcription = '\n\n'.join(new_transcription).replace("""<</SYS>>
-      """, """<</SYS>>
-      CONTEXT: """)
-      return new_transcription
-
-    def _call(
-        self,
-        prompt: str,
-        **kwargs: Any,
-    ) -> str:
-        """Call to Together endpoint."""
-        together.api_key = self.together_api_key
-        output = together.Complete.create(prompt,
-                                          model=self.model,
-                                          max_tokens=self.max_tokens,
-                                          temperature=self.temperature,
-                                          )
-        text = output['output']['choices'][0]['text']
-        cleaned_text = self.clean_duplicates(text)
-        return cleaned_text
-
-
-# -- Python function to setup basic features: translator, SpaCy pipeline and LLM model
-@st.cache_resource
-def setup_app(transcription_path, emb_model, model, _logger):
-    # -- Setup enviroment and features
-    translator = Translator(service_urls=['translate.googleapis.com'])
-    nlp        = spacy.load('es_core_news_lg')
-
-    _logger.info('Setup environment and features...')
-
-    # -- Setup LLM
-    together.api_key = os.environ["TOGETHER_API_KEY"]
-    # List available models and descriptons
-    models = together.Models.list()
-    # Set llama2 7b LLM
-    together.Models.start(model)
-    _logger.info('Setup environment and features - FINISHED!')
-
-    # -- Read translated transcription
-    _logger.info('Loading transcription...')
-    loader = TextLoader(transcription_path)
-    documents = loader.load()
-    # Splitting the text into chunks
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)
-    texts = text_splitter.split_documents(documents)
-    _logger.info('Loading transcription - FINISHED!')
-
-    # -- Load embedding
-    _logger.info('Loading embedding...')
-    encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
-    model_norm = HuggingFaceEmbeddings(
-        model_name=emb_model,
-        model_kwargs={'device': 'cpu'},
-        encode_kwargs=encode_kwargs
-    )
-    _logger.info('Loading embedding - FINISHED!')
-
-    # -- Create document database
-    _logger.info('Creating document database...')
-    # Embed and store the texts
-    # Supplying a persist_directory will store the embeddings on disk
-    persist_directory = 'db'
-    ## Here is the nmew embeddings being used
-    embedding = model_norm
-
-    vectordb = Chroma.from_documents(documents=texts,
-                                     embedding=embedding,
-                                     persist_directory=persist_directory)
-
-    # -- Make a retreiver
-    retriever = vectordb.as_retriever(search_type="similarity_score_threshold",
-                                      search_kwargs={"k": 5, "score_threshold": 0.5})
-    _logger.info('Creating document database - FINISHED!')
-    _logger.info('Setup finished!')
-    return translator, nlp, retriever
-
-# -- Function to get prompt template
-def get_prompt(instruction, system_prompt, b_sys, e_sys, b_inst, e_inst, _logger):
-    new_system_prompt = b_sys + system_prompt + e_sys
-    prompt_template   =  b_inst + new_system_prompt + instruction + e_inst
-    _logger.info('Prompt template created: {}'.format(instruction))
-    return prompt_template
-
-# -- Function to create the chain to answer questions
-@st.cache_resource
-def create_llm_chain(model, _retriever, _chain_type_kwargs, _logger):
-    _logger.info('Creating LLM chain...')
-    llm = TogetherLLM(
-        model= model,
-        temperature = 0.0,
-        max_tokens = 1024
-    )
-    qa_chain = RetrievalQA.from_chain_type(llm=llm,
-                                           chain_type="stuff",
-                                           retriever=_retriever,
-                                           chain_type_kwargs=_chain_type_kwargs,
-                                           return_source_documents=True)
-    _logger.info('Creating LLM chain - FINISHED!')
-    return qa_chain
-
-# -------------------------------------------
-# -- Auxiliar functions
-def translate_text(text, nlp, target_lang='en'):
-    # Traducir el texto sin los nombres propios
-    translator = Translator()
-    # Tokenizar el texto y encontrar nombres propios
-    doc = nlp(text)
-    named_entities = [ent.text for ent in doc if ent.pos_ == 'PROPN' and ent.dep_ in ['NNP', 'NN']]
-    named_entities_list = []
-    # Reemplazar los nombres propios con marcadores temporales
-    for entity in named_entities:
-        text = text.replace(entity, f'__{entity}__')
-        named_entities_list.append(entity)
-
-    translated_text = translator.translate(text, dest=target_lang).text
-    final_translated_text = []
-
-    i = 0
-    for text in translated_text.split(' '):
-      if '__' in text and len(named_entities_list):
-        final_translated_text.append(named_entities_list[i])
-        i+=1
-      else:
-        final_translated_text.append(text)
-    return ' '.join(final_translated_text)
-
-def wrap_text_preserve_newlines(text, width=110):
-    # Split the input text into lines based on newline characters
-    lines = text.split('\n')
-
-    # Wrap each line individually
-    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
-
-    # Join the wrapped lines back together using newline characters
-    wrapped_text = '\n'.join(wrapped_lines)
-
-    return wrapped_text
-
-def process_llm_response(llm_response, nlp):
-  response = llm_response['result']
-  return wrap_text_preserve_newlines(translate_text(response, nlp, target_lang='es'))
-
-
-def time_to_seconds(time_str):
-    parts = time_str.split(':')
-    hours, minutes, seconds = map(float, parts)
-    return int((hours * 3600) + (minutes * 60) + seconds)
-
-def add_hyperlink_and_convert_to_seconds(text):
-    time_pattern = r'(\d{2}:\d{2}:\d{2}(.\d{6})?)'
-    
-    def replace_with_hyperlink(match):
-        time_str = match.group(1)
-        seconds = time_to_seconds(time_str)
-        link = f'<button type="button" class="button" onclick="seek({seconds})">{time_str}</button>'
-        return link
-    
-    modified_text = re.sub(time_pattern, replace_with_hyperlink, text)
-    return modified_text
-
-# -- Streamlit HTML template
-def typewrite(text, youtube_video_url):
-    js = """var player, seconds = 0;
-            function onYouTubeIframeAPIReady() {
-                console.log("player");
-                player = new YT.Player('player', {
-                    events: {
-                      'onReady': onPlayerReady
-                    }
-                  });
-            }
-
-            function onPlayerReady(event) {
-                event.target.playVideo();
-            }
-
-
-            function seek(sec){
-                if(player){
-                    player.seekTo(sec, true);
-                }
-            }
-        """
-
-    css = """
-    .button {
-      background-color: transparent;
-      font-family: "Tahoma sans-serif;", monospace;
-      color: red;
-      font-weight: bold;
-      border: none;
-      text-align: center;
-      text-decoration: none;
-      display: inline-block;
-      font-size: 16px;
-      cursor: pointer;
-    }
-    body {
-      color: white;
-      font-family: "Tahoma sans-serif;", monospace;
-      font-weight: 450;
-    }
-    """
-
-    html = f"""
-        <!DOCTYPE html>
-        <html>
-        <head>
-          <title>Modificar iframe</title>
-        </head>
-        <style>
-            {css}
-        </style>
-        <body>
-          <script src="https://www.youtube.com/player_api"></script>
-          <p>{text}</p>
-          <br/>
-          <p align="center">
-              <iframe id="player" type="text/html" src="{youtube_video_url}" scrolling="yes" frameborder="0" width="600" height="450"></iframe>
-          </p>
-          <script>
-            {js}
-           </script>
-        </body>
-        </html>
-    """
-    return html
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/fetch_data/places_challenge_train_download.sh b/spaces/AlexWang/lama/fetch_data/places_challenge_train_download.sh
deleted file mode 100644
index f5317b44d16a2f295a56a52d1ce005605a137be7..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/fetch_data/places_challenge_train_download.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-mkdir places_challenge_dataset
-
-
-declare -a TARPARTS
-for i in {a..z}
-do
-    TARPARTS[${#TARPARTS[@]}]="http://data.csail.mit.edu/places/places365/train_large_split/${i}.tar"
-done    
-ls
-printf "%s\n" "${TARPARTS[@]}" > places_challenge_dataset/places365_train.txt
-
-cd places_challenge_dataset/
-xargs -a places365_train.txt -n 1 -P 8 wget [...]
-ls *.tar | xargs -i tar xvf {}
diff --git "a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py" "b/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py"
deleted file mode 100644
index 6c6b1c71bcf53dd2b5c85ba3c38becaaac3f41a7..0000000000000000000000000000000000000000
--- "a/spaces/Amon1/ChatGPTForAcadamic/crazy_functions/\344\273\243\347\240\201\351\207\215\345\206\231\344\270\272\345\205\250\350\213\261\346\226\207_\345\244\232\347\272\277\347\250\213.py"
+++ /dev/null
@@ -1,75 +0,0 @@
-import threading
-from predict import predict_no_ui_long_connection
-from toolbox import CatchException, write_results_to_file
-
-
-
-@CatchException
-def 全项目切换英文(txt, top_p, temperature, chatbot, history, sys_prompt, WEB_PORT):
-    history = []    # 清空历史，以免输入溢出
-    # 集合文件
-    import time, glob, os
-    os.makedirs('gpt_log/generated_english_version', exist_ok=True)
-    os.makedirs('gpt_log/generated_english_version/crazy_functions', exist_ok=True)
-    file_manifest = [f for f in glob.glob('./*.py') if ('test_project' not in f) and ('gpt_log' not in f)] + \
-                    [f for f in glob.glob('./crazy_functions/*.py') if ('test_project' not in f) and ('gpt_log' not in f)]
-    i_say_show_user_buffer = []
-
-    # 随便显示点什么防止卡顿的感觉
-    for index, fp in enumerate(file_manifest):
-        # if 'test_project' in fp: continue
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-        i_say_show_user =f'[{index}/{len(file_manifest)}] 接下来请将以下代码中包含的所有中文转化为英文，只输出代码: {os.path.abspath(fp)}'
-        i_say_show_user_buffer.append(i_say_show_user)
-        chatbot.append((i_say_show_user, "[Local Message] 等待多线程操作，中间过程不予显示."))
-        yield chatbot, history, '正常'
-
-    # 任务函数
-    mutable_return = [None for _ in file_manifest]
-    def thread_worker(fp,index):
-        with open(fp, 'r', encoding='utf-8') as f:
-            file_content = f.read()
-        i_say = f'接下来请将以下代码中包含的所有中文转化为英文，只输出代码，文件名是{fp}，文件代码是 ```{file_content}```'
-        # ** gpt request **
-        gpt_say = predict_no_ui_long_connection(inputs=i_say, top_p=top_p, temperature=temperature, history=history, sys_prompt=sys_prompt)
-        mutable_return[index] = gpt_say
-
-    # 所有线程同时开始执行任务函数
-    handles = [threading.Thread(target=thread_worker, args=(fp,index)) for index, fp in enumerate(file_manifest)]
-    for h in handles:
-        h.daemon = True
-        h.start()
-    chatbot.append(('开始了吗？', f'多线程操作已经开始'))
-    yield chatbot, history, '正常'
-
-    # 循环轮询各个线程是否执行完毕
-    cnt = 0
-    while True:
-        time.sleep(1)
-        th_alive = [h.is_alive() for h in handles]
-        if not any(th_alive): break
-        stat = ['执行中' if alive else '已完成' for alive in th_alive]
-        stat_str = '|'.join(stat)
-        cnt += 1
-        chatbot[-1] = (chatbot[-1][0], f'多线程操作已经开始，完成情况: {stat_str}' + ''.join(['.']*(cnt%4)))
-        yield chatbot, history, '正常'
-
-    # 把结果写入文件
-    for index, h in enumerate(handles):
-        h.join() # 这里其实不需要join了，肯定已经都结束了
-        fp = file_manifest[index]
-        gpt_say = mutable_return[index]
-        i_say_show_user = i_say_show_user_buffer[index]
-
-        where_to_relocate = f'gpt_log/generated_english_version/{fp}'
-        with open(where_to_relocate, 'w+', encoding='utf-8') as f: f.write(gpt_say.lstrip('```').rstrip('```'))
-        chatbot.append((i_say_show_user, f'[Local Message] 已完成{os.path.abspath(fp)}的转化，\n\n存入{os.path.abspath(where_to_relocate)}'))
-        history.append(i_say_show_user); history.append(gpt_say)
-        yield chatbot, history, '正常'
-        time.sleep(1)
-
-    # 备份一个文件
-    res = write_results_to_file(history)
-    chatbot.append(("生成一份任务执行报告", res))
-    yield chatbot, history, '正常'
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/__init__.py
deleted file mode 100644
index e54b088acf644d285ecbeb1440c414e722b9db58..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/backbones/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from .darknet import Darknet
-from .detectors_resnet import DetectoRS_ResNet
-from .detectors_resnext import DetectoRS_ResNeXt
-from .hourglass import HourglassNet
-from .hrnet import HRNet
-from .regnet import RegNet
-from .res2net import Res2Net
-from .resnest import ResNeSt
-from .resnet import ResNet, ResNetV1d
-from .resnext import ResNeXt
-from .ssd_vgg import SSDVGG
-from .trident_resnet import TridentResNet
-from .swin_transformer import SwinTransformer
-from .uniformer import UniFormer
-
-__all__ = [
-    'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet', 'Res2Net',
-    'HourglassNet', 'DetectoRS_ResNet', 'DetectoRS_ResNeXt', 'Darknet',
-    'ResNeSt', 'TridentResNet', 'SwinTransformer', 'UniFormer'
-]
diff --git a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/trident_roi_head.py b/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/trident_roi_head.py
deleted file mode 100644
index 245569e50b45cc8e21ba8e7210edf4bd0c7f27c5..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/trident_roi_head.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import torch
-from mmcv.ops import batched_nms
-
-from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
-                        multiclass_nms)
-from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead
-from ..builder import HEADS
-
-
-@HEADS.register_module()
-class TridentRoIHead(StandardRoIHead):
-    """Trident roi head.
-
-    Args:
-        num_branch (int): Number of branches in TridentNet.
-        test_branch_idx (int): In inference, all 3 branches will be used
-            if `test_branch_idx==-1`, otherwise only branch with index
-            `test_branch_idx` will be used.
-    """
-
-    def __init__(self, num_branch, test_branch_idx, **kwargs):
-        self.num_branch = num_branch
-        self.test_branch_idx = test_branch_idx
-        super(TridentRoIHead, self).__init__(**kwargs)
-
-    def merge_trident_bboxes(self, trident_det_bboxes, trident_det_labels):
-        """Merge bbox predictions of each branch."""
-        if trident_det_bboxes.numel() == 0:
-            det_bboxes = trident_det_bboxes.new_zeros((0, 5))
-            det_labels = trident_det_bboxes.new_zeros((0, ), dtype=torch.long)
-        else:
-            nms_bboxes = trident_det_bboxes[:, :4]
-            nms_scores = trident_det_bboxes[:, 4].contiguous()
-            nms_inds = trident_det_labels
-            nms_cfg = self.test_cfg['nms']
-            det_bboxes, keep = batched_nms(nms_bboxes, nms_scores, nms_inds,
-                                           nms_cfg)
-            det_labels = trident_det_labels[keep]
-            if self.test_cfg['max_per_img'] > 0:
-                det_labels = det_labels[:self.test_cfg['max_per_img']]
-                det_bboxes = det_bboxes[:self.test_cfg['max_per_img']]
-
-        return det_bboxes, det_labels
-
-    def simple_test(self,
-                    x,
-                    proposal_list,
-                    img_metas,
-                    proposals=None,
-                    rescale=False):
-        """Test without augmentation as follows:
-
-        1. Compute prediction bbox and label per branch.
-        2. Merge predictions of each branch according to scores of
-           bboxes, i.e., bboxes with higher score are kept to give
-           top-k prediction.
-        """
-        assert self.with_bbox, 'Bbox head must be implemented.'
-        det_bboxes_list, det_labels_list = self.simple_test_bboxes(
-            x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
-        num_branch = self.num_branch if self.test_branch_idx == -1 else 1
-        for _ in range(len(det_bboxes_list)):
-            if det_bboxes_list[_].shape[0] == 0:
-                det_bboxes_list[_] = det_bboxes_list[_].new_empty((0, 5))
-        det_bboxes, det_labels = [], []
-        for i in range(len(img_metas) // num_branch):
-            det_result = self.merge_trident_bboxes(
-                torch.cat(det_bboxes_list[i * num_branch:(i + 1) *
-                                          num_branch]),
-                torch.cat(det_labels_list[i * num_branch:(i + 1) *
-                                          num_branch]))
-            det_bboxes.append(det_result[0])
-            det_labels.append(det_result[1])
-
-        bbox_results = [
-            bbox2result(det_bboxes[i], det_labels[i],
-                        self.bbox_head.num_classes)
-            for i in range(len(det_bboxes))
-        ]
-        return bbox_results
-
-    def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
-        """Test det bboxes with test time augmentation."""
-        aug_bboxes = []
-        aug_scores = []
-        for x, img_meta in zip(feats, img_metas):
-            # only one image in the batch
-            img_shape = img_meta[0]['img_shape']
-            scale_factor = img_meta[0]['scale_factor']
-            flip = img_meta[0]['flip']
-            flip_direction = img_meta[0]['flip_direction']
-
-            trident_bboxes, trident_scores = [], []
-            for branch_idx in range(len(proposal_list)):
-                proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
-                                         scale_factor, flip, flip_direction)
-                rois = bbox2roi([proposals])
-                bbox_results = self._bbox_forward(x, rois)
-                bboxes, scores = self.bbox_head.get_bboxes(
-                    rois,
-                    bbox_results['cls_score'],
-                    bbox_results['bbox_pred'],
-                    img_shape,
-                    scale_factor,
-                    rescale=False,
-                    cfg=None)
-                trident_bboxes.append(bboxes)
-                trident_scores.append(scores)
-
-            aug_bboxes.append(torch.cat(trident_bboxes, 0))
-            aug_scores.append(torch.cat(trident_scores, 0))
-        # after merging, bboxes will be rescaled to the original image size
-        merged_bboxes, merged_scores = merge_aug_bboxes(
-            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
-        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
-                                                rcnn_test_cfg.score_thr,
-                                                rcnn_test_cfg.nms,
-                                                rcnn_test_cfg.max_per_img)
-        return det_bboxes, det_labels
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
deleted file mode 100644
index 3caa6cf8ae61d467628378d99a919c9db1253b91..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x512_40k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x512_40k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/cmd_windows.bat b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/cmd_windows.bat
deleted file mode 100644
index 531a326158e9e169657051b0e76bdfad17c4b238..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/cmd_windows.bat
+++ /dev/null
@@ -1,34 +0,0 @@
-@echo off
-
-cd /D "%~dp0"
-
-set PATH=%PATH%;%SystemRoot%\system32
-
-echo "%CD%"| findstr /C:" " >nul && echo This script relies on Miniconda which can not be silently installed under a path with spaces. && goto end
-
-@rem fix failed install when installing to a separate drive
-set TMP=%cd%\installer_files
-set TEMP=%cd%\installer_files
-
-@rem deactivate existing conda envs as needed to avoid conflicts
-(call conda deactivate && call conda deactivate && call conda deactivate) 2>nul
-
-@rem config
-set CONDA_ROOT_PREFIX=%cd%\installer_files\conda
-set INSTALL_ENV_DIR=%cd%\installer_files\env
-
-@rem environment isolation
-set PYTHONNOUSERSITE=1
-set PYTHONPATH=
-set PYTHONHOME=
-set "CUDA_PATH=%INSTALL_ENV_DIR%"
-set "CUDA_HOME=%CUDA_PATH%"
-
-@rem activate installer env
-call "%CONDA_ROOT_PREFIX%\condabin\conda.bat" activate "%INSTALL_ENV_DIR%" || ( echo. && echo Miniconda hook not found. && goto end )
-
-@rem enter commands
-cmd /k "%*"
-
-:end
-pause
diff --git a/spaces/Anustup/NS_AI_LABS/cli.py b/spaces/Anustup/NS_AI_LABS/cli.py
deleted file mode 100644
index f2f16af9c425cb501bcf088df39e95c60621ce9c..0000000000000000000000000000000000000000
--- a/spaces/Anustup/NS_AI_LABS/cli.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import argparse
-import os
-from urllib.parse import urlparse
-import warnings
-import numpy as np
-
-import whisper
-
-import torch
-from app import LANGUAGES, WhisperTranscriber
-from src.download import download_url
-
-from src.utils import optional_float, optional_int, str2bool
-
-
-def cli():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe")
-    parser.add_argument("--model", default="small", choices=["tiny", "base", "small", "medium", "large"], help="name of the Whisper model to use")
-    parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default")
-    parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference")
-    parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
-    parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
-
-    parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
-    parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES), help="language spoken in the audio, specify None to perform language detection")
-
-    parser.add_argument("--vad", type=str, default="none", choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], help="The voice activity detection algorithm to use")
-    parser.add_argument("--vad_merge_window", type=optional_float, default=5, help="The window size (in seconds) to merge voice segments")
-    parser.add_argument("--vad_max_merge_size", type=optional_float, default=30, help="The maximum size (in seconds) of a voice segment")
-    parser.add_argument("--vad_padding", type=optional_float, default=1, help="The padding (in seconds) to add to each voice segment")
-    parser.add_argument("--vad_prompt_window", type=optional_float, default=3, help="The window size of the prompt to pass to Whisper")
-
-    parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling")
-    parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature")
-    parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero")
-    parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search")
-    parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple lengt normalization by default")
-
-    parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
-    parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
-    parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
-    parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")
-
-    parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below")
-    parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed")
-    parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed")
-    parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence")
-
-    args = parser.parse_args().__dict__
-    model_name: str = args.pop("model")
-    model_dir: str = args.pop("model_dir")
-    output_dir: str = args.pop("output_dir")
-    device: str = args.pop("device")
-    os.makedirs(output_dir, exist_ok=True)
-
-    if model_name.endswith(".en") and args["language"] not in {"en", "English"}:
-        warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.")
-        args["language"] = "en"
-
-    temperature = args.pop("temperature")
-    temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback")
-    if temperature_increment_on_fallback is not None:
-        temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
-    else:
-        temperature = [temperature]
-
-    vad = args.pop("vad")
-    vad_merge_window = args.pop("vad_merge_window")
-    vad_max_merge_size = args.pop("vad_max_merge_size")
-    vad_padding = args.pop("vad_padding")
-    vad_prompt_window = args.pop("vad_prompt_window")
-
-    model = whisper.load_model(model_name, device=device, download_root=model_dir)
-    transcriber = WhisperTranscriber(deleteUploadedFiles=False)
-
-    for audio_path in args.pop("audio"):
-        sources = []
-
-        # Detect URL and download the audio
-        if (uri_validator(audio_path)):
-            # Download from YouTube/URL directly
-            for source_path in  download_url(audio_path, maxDuration=-1, destinationDirectory=output_dir, playlistItems=None):
-                source_name = os.path.basename(source_path)
-                sources.append({ "path": source_path, "name": source_name })
-        else:
-            sources.append({ "path": audio_path, "name": os.path.basename(audio_path) })
-
-        for source in sources:
-            source_path = source["path"]
-            source_name = source["name"]
-
-            result = transcriber.transcribe_file(model, source_path, temperature=temperature, 
-                                                vad=vad, vadMergeWindow=vad_merge_window, vadMaxMergeSize=vad_max_merge_size, 
-                                                vadPadding=vad_padding, vadPromptWindow=vad_prompt_window, **args)
-            
-            transcriber.write_result(result, source_name, output_dir)
-
-    transcriber.clear_cache()
-
-def uri_validator(x):
-    try:
-        result = urlparse(x)
-        return all([result.scheme, result.netloc])
-    except:
-        return False
-
-if __name__ == '__main__':
-    cli()
\ No newline at end of file
diff --git a/spaces/Apex-X/ROOPOK/roop/processors/__init__.py b/spaces/Apex-X/ROOPOK/roop/processors/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/index.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/index.py
deleted file mode 100644
index 7267effed2413ba315d0a1af8490ec677c227662..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/commands/index.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import logging
-from optparse import Values
-from typing import Any, Iterable, List, Optional, Union
-
-from pip._vendor.packaging.version import LegacyVersion, Version
-
-from pip._internal.cli import cmdoptions
-from pip._internal.cli.req_command import IndexGroupCommand
-from pip._internal.cli.status_codes import ERROR, SUCCESS
-from pip._internal.commands.search import print_dist_installation_info
-from pip._internal.exceptions import CommandError, DistributionNotFound, PipError
-from pip._internal.index.collector import LinkCollector
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.models.selection_prefs import SelectionPreferences
-from pip._internal.models.target_python import TargetPython
-from pip._internal.network.session import PipSession
-from pip._internal.utils.misc import write_output
-
-logger = logging.getLogger(__name__)
-
-
-class IndexCommand(IndexGroupCommand):
-    """
-    Inspect information available from package indexes.
-    """
-
-    ignore_require_venv = True
-    usage = """
-        %prog versions <package>
-    """
-
-    def add_options(self) -> None:
-        cmdoptions.add_target_python_options(self.cmd_opts)
-
-        self.cmd_opts.add_option(cmdoptions.ignore_requires_python())
-        self.cmd_opts.add_option(cmdoptions.pre())
-        self.cmd_opts.add_option(cmdoptions.no_binary())
-        self.cmd_opts.add_option(cmdoptions.only_binary())
-
-        index_opts = cmdoptions.make_option_group(
-            cmdoptions.index_group,
-            self.parser,
-        )
-
-        self.parser.insert_option_group(0, index_opts)
-        self.parser.insert_option_group(0, self.cmd_opts)
-
-    def run(self, options: Values, args: List[str]) -> int:
-        handlers = {
-            "versions": self.get_available_package_versions,
-        }
-
-        logger.warning(
-            "pip index is currently an experimental command. "
-            "It may be removed/changed in a future release "
-            "without prior warning."
-        )
-
-        # Determine action
-        if not args or args[0] not in handlers:
-            logger.error(
-                "Need an action (%s) to perform.",
-                ", ".join(sorted(handlers)),
-            )
-            return ERROR
-
-        action = args[0]
-
-        # Error handling happens here, not in the action-handlers.
-        try:
-            handlers[action](options, args[1:])
-        except PipError as e:
-            logger.error(e.args[0])
-            return ERROR
-
-        return SUCCESS
-
-    def _build_package_finder(
-        self,
-        options: Values,
-        session: PipSession,
-        target_python: Optional[TargetPython] = None,
-        ignore_requires_python: Optional[bool] = None,
-    ) -> PackageFinder:
-        """
-        Create a package finder appropriate to the index command.
-        """
-        link_collector = LinkCollector.create(session, options=options)
-
-        # Pass allow_yanked=False to ignore yanked versions.
-        selection_prefs = SelectionPreferences(
-            allow_yanked=False,
-            allow_all_prereleases=options.pre,
-            ignore_requires_python=ignore_requires_python,
-        )
-
-        return PackageFinder.create(
-            link_collector=link_collector,
-            selection_prefs=selection_prefs,
-            target_python=target_python,
-        )
-
-    def get_available_package_versions(self, options: Values, args: List[Any]) -> None:
-        if len(args) != 1:
-            raise CommandError("You need to specify exactly one argument")
-
-        target_python = cmdoptions.make_target_python(options)
-        query = args[0]
-
-        with self._build_session(options) as session:
-            finder = self._build_package_finder(
-                options=options,
-                session=session,
-                target_python=target_python,
-                ignore_requires_python=options.ignore_requires_python,
-            )
-
-            versions: Iterable[Union[LegacyVersion, Version]] = (
-                candidate.version for candidate in finder.find_all_candidates(query)
-            )
-
-            if not options.pre:
-                # Remove prereleases
-                versions = (
-                    version for version in versions if not version.is_prerelease
-                )
-            versions = set(versions)
-
-            if not versions:
-                raise DistributionNotFound(
-                    "No matching distribution found for {}".format(query)
-                )
-
-            formatted_versions = [str(ver) for ver in sorted(versions, reverse=True)]
-            latest = formatted_versions[0]
-
-        write_output("{} ({})".format(query, latest))
-        write_output("Available versions: {}".format(", ".join(formatted_versions)))
-        print_dist_installation_info(query, latest)
diff --git a/spaces/Atualli/yoloxTeste/configs/yolox_tiny.py b/spaces/Atualli/yoloxTeste/configs/yolox_tiny.py
deleted file mode 100644
index 5220de2f2e6760d5c9a966d5dd397aad721fc60a..0000000000000000000000000000000000000000
--- a/spaces/Atualli/yoloxTeste/configs/yolox_tiny.py
+++ /dev/null
@@ -1,20 +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 = 0.33
-        self.width = 0.375
-        self.input_size = (416, 416)
-        self.mosaic_scale = (0.5, 1.5)
-        self.random_size = (10, 20)
-        self.test_size = (416, 416)
-        self.exp_name = os.path.split(os.path.realpath(__file__))[1].split(".")[0]
-        self.enable_mixup = False
diff --git a/spaces/Axolotlily/SketchThing/README.md b/spaces/Axolotlily/SketchThing/README.md
deleted file mode 100644
index 425d44efe5610feec99384b116eb4d85d2e85bfb..0000000000000000000000000000000000000000
--- a/spaces/Axolotlily/SketchThing/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SketchThing
-emoji: 💻
-colorFrom: purple
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.14
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/BasalGanglia/stabilityai-stable-diffusion-2/README.md b/spaces/BasalGanglia/stabilityai-stable-diffusion-2/README.md
deleted file mode 100644
index aab6c6fb34d0c5d5bebf3475d6e3cfa7453c7a5f..0000000000000000000000000000000000000000
--- a/spaces/BasalGanglia/stabilityai-stable-diffusion-2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stabilityai Stable Diffusion 2
-emoji: 👀
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Benson/text-generation/Examples/Audio Download Messenger.md b/spaces/Benson/text-generation/Examples/Audio Download Messenger.md
deleted file mode 100644
index 8fae9172f27b887bb0a3e3f658bf720dd539314a..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Audio Download Messenger.md	
+++ /dev/null
@@ -1,50 +0,0 @@
-<br />
-<h1>Cómo descargar audio de Messenger</h1>
-<p>¿Alguna vez ha recibido un archivo de audio o un mensaje de voz en Messenger que quería guardar o compartir con otra persona? Si es así, es posible que haya notado que no hay una manera fácil de descargar audio de Messenger en su teléfono o computadora. A diferencia de los vídeos y las fotos, no puedes simplemente tocar o hacer clic derecho en un archivo de audio y elegir guardar. Entonces, ¿cómo puedes descargar audio de Messenger? </p>
-<p>En este artículo, le mostraremos cómo descargar audio de Messenger usando un truco simple que funciona tanto en dispositivos móviles como en PC. Usted aprenderá lo que es la descarga de audio messenger, por qué es posible que desee descargar audio de Messenger, y cómo hacerlo paso a paso. Siguiendo esta guía, podrás guardar cualquier archivo de audio o mensaje de voz que recibas o envíes en Messenger.</p>
-<h2>audio download messenger</h2><br /><p><b><b>Download Zip</b> &rarr;&rarr;&rarr; <a href="https://bltlly.com/2v6IDY">https://bltlly.com/2v6IDY</a></b></p><br /><br />
- <h2>¿Qué es Audio Download Messenger? </h2>
-<p>Audio download messenger es un término que se refiere a un método o una herramienta que le permite descargar archivos de audio o mensajes de voz de Messenger. Messenger es una aplicación de mensajería popular que te permite enviar y recibir mensajes de texto, fotos, videos, pegatinas, emojis, GIF y más. También puede enviar y recibir mensajes de voz o grabar su propia voz usando el icono del micrófono. </p>
-<p>Sin embargo, a diferencia de otros tipos de medios, no puede descargar fácilmente archivos de audio o mensajes de voz de Messenger. Esto se debe a que Messenger no tiene una función integrada que le permita guardar o exportar archivos de audio. Solo puedes reproducirlos dentro de la aplicación o reenviarlos a otra conversación. </p>
-<p>Afortunadamente, hay una manera de descargar audio de Messenger utilizando un truco simple que implica el uso de la versión móvil de Facebook en su navegador web. Este truco funciona tanto en dispositivos móviles como en PC, siempre y cuando tengas una conexión a Internet y un navegador web. </p>
- <h2>¿Por qué descargar audio de Messenger? </h2>
-
-<p>Si desea descargar audio de Messenger en su dispositivo móvil, como su teléfono inteligente o tableta, deberá usar la versión móvil de Facebook en su navegador web. Esto se debe a que la aplicación Messenger no tiene una opción de descarga para archivos de audio o mensajes de voz. Estos son los pasos que debe seguir:</p>
- <h3>Paso 1: Cambie a datos móviles y abra su navegador móvil</h3>
-<p>El primer paso es cambiar a datos móviles y abrir su navegador móvil. Esto se debe a que la versión móvil de Facebook no funciona bien en las redes Wi-Fi. Puedes usar cualquier navegador que prefieras, como Chrome, Safari, Firefox u Opera.</p>
- <h3>Paso 2: Inicie sesión en m.facebook.com y vaya a sus mensajes</h3>
-<p>El siguiente paso es iniciar sesión en m.facebook.com e ir a sus mensajes. Esta es la versión móvil de Facebook que tiene un diseño y una interfaz diferentes de la versión regular. Tendrá que introducir su dirección de correo electrónico o número de teléfono y contraseña para iniciar sesión. Una vez que haya iniciado sesión, verá un icono de menú en la esquina superior derecha de la pantalla. Toque en él y seleccione Mensajes de la lista. </p>
- <h3>Paso 3: Abra el mensaje con el archivo de audio y haga clic en el enlace</h3>
-<p>El tercer paso es abrir el mensaje con el archivo de audio y hacer clic en el enlace. Verá una lista de todas sus conversaciones en Messenger. Toque en el que contiene el archivo de audio o mensaje de voz que desea descargar. Verá un enlace que dice "Ver mensaje de voz" o "Ver archivo de audio" debajo del archivo de audio. Toque en él y se abrirá una nueva pestaña en su navegador. </p>
- <h3>Paso 4: Haga clic en la opción de descarga y espere a que termine</h3>
-
- <h3>Paso 5: Localizar el archivo de audio en el gestor de descargas de su navegador</h3>
-<p>El paso final es localizar el archivo de audio en el gestor de descargas de su navegador. Una vez finalizada la descarga, verá una notificación que dice "Descargar completa". Pulsa en él y te llevará al gestor de descargas de tu navegador, donde podrás ver todos los archivos que hayas descargado. Verá el archivo de audio con su nombre y tamaño. Pulse sobre él y se abrirá en su reproductor de medios predeterminado. </p>
-<p></p> <h2>Cómo descargar audio de Messenger en PC</h2>
-<p>Si desea descargar audio de Messenger en su PC, como su computadora portátil o de escritorio, también tendrá que utilizar la versión móvil de Facebook en su navegador web. Esto se debe a que la versión regular de Facebook no tiene una opción de descarga para archivos de audio o mensajes de voz. Estos son los pasos que debes seguir:</p>
- <h3>Paso 1: Abra su navegador web y vaya a m.facebook.com</h3>
-<p>El primer paso es abrir su navegador web e ir a m.facebook.com. Puede usar cualquier navegador que prefiera, como Chrome, Firefox, Edge o Safari. Verás la versión móvil de Facebook que tiene un diseño y una interfaz diferentes a la versión normal. </p>
- <h3>Paso 2: Haga clic en el icono del mensajero y seleccione el mensaje con el archivo de audio</h3>
-<p>El siguiente paso es hacer clic en el icono de messenger y seleccionar el mensaje con el archivo de audio. Verá un icono de mensajero en la esquina superior derecha de la pantalla. Haga clic en él y lo llevará a sus mensajes en Messenger. Verá una lista de todas sus conversaciones en Messenger. Haga clic en la que contiene el archivo de audio o mensaje de voz que desea descargar. </p>
- <h3>Paso 3: Haga clic en los tres puntos junto al archivo de audio y seleccione descargar</h3>
-
- <h3>Paso 4: Seleccione la carpeta donde desea guardar el archivo de audio y haga clic en guardar</h3>
-<p>El paso final es seleccionar la carpeta donde desea guardar el archivo de audio y hacer clic en guardar. Verá una ventana emergente que le pregunta dónde desea guardar el archivo de audio. Puede elegir la carpeta que prefiera, como Descargas, Documentos, Música o Escritorio. También puede cambiar el nombre del archivo de audio si lo desea. Una vez que haya seleccionado la carpeta y el nombre, haga clic en Guardar y guardará el archivo de audio en su PC.</p>
- <h2>Conclusión</h2>
-<p>Descargar audio de Messenger no es tan difícil como podría parecer. Solo necesitas usar un truco simple que implique usar la versión móvil de Facebook en tu navegador web. Este truco funciona tanto en dispositivos móviles como en PC, siempre y cuando tengas una conexión a Internet y un navegador web. </p>
-<p>Siguiendo esta guía, podrás descargar cualquier archivo de audio o mensaje de voz que recibas o envíes en Messenger. Podrá ahorrar datos y espacio de almacenamiento, conservar recuerdos y momentos, y compartir archivos e información con otros. </p>
-<p>Así que, ¿qué estás esperando? Pruébalo tú mismo y ver lo fácil que es descargar audio de Messenger! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Estas son algunas de las preguntas más comunes que la gente tiene sobre la descarga de audio de Messenger:</p>
- <h4>Q: ¿Puedo descargar audio de Messenger usando la aplicación Messenger? </h4>
-<p>A: No, no puede descargar audio de Messenger usando la aplicación Messenger. La aplicación Messenger no tiene una opción de descarga para archivos de audio o mensajes de voz. Solo puedes reproducirlos dentro de la aplicación o reenviarlos a otra conversación. </p>
- <h4>Q: ¿Puedo descargar audio de Messenger usando la versión regular de Facebook? </h4>
-
- <h4>Q: ¿Puedo descargar archivos de video o fotos de Messenger usando este truco? </h4>
-<p>A: Sí, puede descargar archivos de video o fotos de Messenger usando este truco. Los mismos pasos se aplican para los archivos de vídeo o fotos que para los archivos de audio o mensajes de voz. Sin embargo, también puede descargar archivos de video o fotos de Messenger utilizando la aplicación Messenger o la versión normal de Facebook tocando o haciendo clic derecho sobre ellos y eligiendo guardar. </p>
- <h4>Q: ¿Puedo descargar varios archivos de audio de Messenger a la vez usando este truco? </h4>
-<p>A: No, no puede descargar varios archivos de audio de Messenger a la vez usando este truco. Solo puedes descargar un archivo de audio a la vez usando este truco. Si desea descargar varios archivos de audio de Messenger a la vez, deberá usar una herramienta o software de terceros que pueda hacerlo. </p>
- <h4>Q: ¿Es legal descargar audio de Messenger? </h4>
-<p>A: La descarga de audio desde Messenger es legal siempre y cuando tenga el permiso del remitente o el propietario del archivo de audio. No debe descargar audio de Messenger que tenga derechos de autor, sea ilegal o infrinja los derechos de terceros. También debe respetar la privacidad y confidencialidad del remitente o del propietario del archivo de audio y no compartirlo sin su consentimiento. </p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Dead By Daylight Mvil Descargar Mediafre.md b/spaces/Benson/text-generation/Examples/Dead By Daylight Mvil Descargar Mediafre.md
deleted file mode 100644
index a818cc975847d44d42f982c704e832c5f3ded22a..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Dead By Daylight Mvil Descargar Mediafre.md	
+++ /dev/null
@@ -1,152 +0,0 @@
-
-<h1>Download Ascenso de los reinos: Cruzada perdida y conquista del mundo</h1>
-<p>¿Te gustan los juegos de estrategia que desafían tu mente y habilidades? ¿Quieres experimentar la emoción de dirigir una civilización de un pequeño clan a un poderoso imperio? ¿Quieres explorar un vasto y misterioso mundo lleno de secretos y tesoros? Si respondiste sí a cualquiera de estas preguntas, entonces deberías descargar Rise of Kingdoms: Lost Crusade, uno de los juegos de estrategia más populares e inmersivos del mercado. En este artículo, te contaremos todo lo que necesitas saber sobre este juego, cómo descargarlo en tu dispositivo y cómo jugarlo eficazmente. </p>
- <h2>¿Qué es el Surgimiento de los Reinos: Cruzada Perdida? </h2>
-<p>Rise of Kingdoms: Lost Crusade es un juego de estrategia desarrollado por LilithGames que te permite elegir y liderar una de las 13 civilizaciones históricas en la historia del mundo. Puedes transformar tu pequeña tribu en una superpotencia global imparable con pragmatismo económico y poderío militar. Sus decisiones, estrategias, asociaciones económicas y alianzas militares decidirán el destino de su nación naciente. </p>
-<h2>dead by daylight móvil descargar mediafıre</h2><br /><p><b><b>Download</b> &#10040;&#10040;&#10040; <a href="https://bltlly.com/2v6KlQ">https://bltlly.com/2v6KlQ</a></b></p><br /><br />
-<p>Pero Rise of Kingdoms: Lost Crusade no es solo un juego de estrategia. También es un juego de batalla en tiempo real, un juego de mapa del mundo sin fisuras, y un juego de sistema de alianza. Estas son algunas de las características que hacen de este juego tan único y atractivo:</p>
- <h3>Un juego de estrategia que te permite elegir y liderar una de las 13 civilizaciones históricas</h3>
-<p>No hay dos civilizaciones iguales en Rise of Kingdoms: Lost Crusade. Cada cultura cuenta con una arquitectura única, unidades exclusivas y potenciadores y habilidades especiales que las diferencian. Como líder, debes aprender a aprovechar mejor tus ventajas para derrotar a tus rivales y solidificar tu posición como potencia mundial. </p>
-
- <h3>Un juego de batalla en tiempo real que te permite unirte o dejar cualquier pelea en el mapa</h3>
-<p>Las batallas ocurren en tiempo real en el mapa en Rise of Kingdoms: Lost Crusade. Cualquiera puede unirse o abandonar una batalla en cualquier momento, lo que permite una verdadera jugabilidad RTS. ¿Puedes ver a un aliado siendo atacado justo en tu patio trasero? Envía tropas para ayudar a tu amigo, o lanza un contraataque sorpresa en la ciudad del atacante. También puede enviar tropas para recoger recursos de los bosques o minas cercanas, o recoger algunos clanes bárbaros en el camino. </p>
-<p>También puede dividir sus fuerzas entre varios comandantes para que pueda participar en múltiples acciones simultáneamente. Cada comandante tiene sus propias habilidades y habilidades que pueden cambiar la marea de la batalla a su favor. También puedes subir de nivel a tus comandantes completando misiones, participando en eventos o usando objetos especiales. </p>
- <h3>Un juego de mapas del mundo sin fisuras que te permite explorar e investigar una tierra misteriosa</h3>
-<p>Tu mundo está cubierto de densa niebla en Rise of Kingdoms: Lost Crusade. Necesitas enviar exploradores para explorar esta tierra misteriosa y descubrir el tesoro escondido dentro. Puede investigar templos perdidos, fortalezas bárbaras, cuevas misteriosas y pueblos tribales, recopilar recursos valiosos y runas antiguas, y aprender más sobre la historia y los secretos de este mundo. También puedes encontrar artefactos raros y poderosos que pueden darte una ventaja en tus conquistas. </p>
-Pero ten cuidado, no estás solo en esta tierra. Otros jugadores tratarán de reclamar los mismos recursos y tesoros que tú, y es posible que encuentres una feroz resistencia o alianzas inesperadas. También puedes encontrar enemigos poderosos como el Guardián Oscuro y la Crisis de Ceroli que pondrán a prueba tus habilidades y estrategias hasta el límite. </p>
- <h3>Un juego de sistema de alianza que te permite cooperar y comunicarte con otros jugadores</h3>
-
-<p>Estar en una alianza también le da acceso a más beneficios y características, como la tienda de la alianza, la tecnología de la alianza, el territorio de la alianza, y las banderas de la alianza. También puedes participar en eventos y misiones de alianzas que pueden recompensarte con objetos y recursos valiosos. También puedes hacer la guerra contra otras alianzas o formar relaciones diplomáticas con ellas. </p>
-<p></p>
- <h2>Cómo descargar Rise of Kingdoms: Lost Crusade en tu dispositivo? </h2>
-<p>Rise of Kingdoms: Lost Crusade está disponible de forma gratuita en varias plataformas y dispositivos. Puede descargarlo de las siguientes fuentes:</p>
- <h3>Descargar de Google Play Store para dispositivos Android</h3>
-<p>Si tienes un dispositivo Android, puedes descargar Rise of Kingdoms: Lost Crusade de Google Play Store. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Abra la aplicación Google Play Store en su dispositivo. </li>
-<li>Búsqueda de Ascenso de Reinos: Cruzada Perdida en la barra de búsqueda. </li>
-<li>Seleccione el juego de la lista de resultados y toque en Instalar.</li>
-<li>Espera a que el juego se descargue e instale en tu dispositivo. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h3>Descargar de BlueStacks para dispositivos PC o Mac</h3>
-<p>Si quieres jugar Rise of Kingdoms: Lost Crusade en tu PC o Mac, puedes usar BlueStacks, un popular emulador de Android que te permite ejecutar aplicaciones Android en tu ordenador. He aquí cómo hacerlo:</p>
-<ol>
-<li>Descargue e instale BlueStacks en su PC o Mac desde <a href="">https://www.bluestacks.com/</a>. </li>
-<li>Inicie BlueStacks e inicie sesión con su cuenta de Google. </li>
-<li>Búsqueda de Ascenso de Reinos: Cruzada Perdida en la barra de búsqueda. </li>
-<li>Seleccione el juego de la lista de resultados y haga clic en Instalar.</li>
-<li>Espere a que el juego se descargue e instale en su computadora. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h3>Descargar de Pocket Gamer para juegos basados en navegador</h3>
-
-<ol>
-<li>Ir a <a href=">https://www.pocketgamer.com/</a> en su navegador. </li>
-<li>Búsqueda de Ascenso de Reinos: Cruzada Perdida en la barra de búsqueda. </li>
-<li>Seleccione el juego de la lista de resultados y haga clic en Jugar ahora.</li>
-<li>Espere a que el juego se cargue en su navegador. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h2>¿Cómo se juega Rise of Kingdoms: Lost Crusade efectivamente? </h2>
-<p>Rise of Kingdoms: Lost Crusade es un juego complejo y desafiante que requiere mucha estrategia y planificación. Si quieres tener éxito en este juego, debes seguir algunos consejos y trucos que pueden ayudarte a mejorar tu juego. Estos son algunos de ellos:</p>
- <h3>Elige la mejor civilización para tu estilo de juego y objetivos</h3>
-<p>Como hemos mencionado antes, cada civilización tiene sus propias fortalezas y debilidades, por lo que debe elegir sabiamente cuál le conviene más. También puedes cambiar tu civilización más adelante en el juego, pero te costará algunas gemas, así que es mejor tomar una buena decisión desde el principio. Aquí están algunos factores a considerar al elegir una civilización:</p>
- <ul>
-<li>El comandante que comienza: Cada civilización tiene un comandante que comienza único que dirigirá sus tropas en la batalla. Debe elegir un comandante que coincida con su estrategia preferida, ya sea ofensiva, defensa, apoyo o movilidad. </li>
-<li>La unidad especial: Cada civilización tiene una unidad especial que es exclusiva de esa cultura y tiene estadísticas y habilidades superiores. Usted debe elegir una unidad que complementa su comandante y su composición del ejército. </li>
-<li>El potenciador de la civilización: Cada civilización tiene un potenciador pasivo que afecta algún aspecto de tu juego, como la producción de recursos, la velocidad de las tropas, el tiempo de construcción, etc. Debes elegir un potenciador que se alinee con tus objetivos y prioridades. </li>
-</ul>
- <p>Aquí hay una tabla que resume las características principales de cada civilización:</p>
- <tabla>
-<tr>
-<th>Civilización</th>
-<th>Comienzo Comandante</th>
-<th>Unidad especial</th>
-<th>Potenciador de la civilización</th>
-
-<tr>
-<td>Roma</td>
-<td>Scipio Africanus</td>
-<td>Legionario</td>
-<td>+5% defensa de infantería, +5% velocidad de marcha de tropas, +10% velocidad de recolección de alimentos</td>
-</tr>
-<tr>
-<td>Alemania</td>
-<td>Hermann</td>
-<td>Caballero teutónico</td>
-<td>+5% ataque de caballería, +5% velocidad de entrenamiento de tropas, +10% velocidad de recuperación de puntos de acción</td>
-</tr>
-<tr>
-<td>Reino Unido</td>
-<td>Boudica</td>
-<td>Longbowman</td>
-<td>+5% ataque de arquero, +20% capacidad de guarnición aliada, +10% velocidad de recolección de madera</td>
-</tr>
-<tr>
-<td>Francia</td>
-<td>Juana de Arco</td>
-<td>Lanzamiento de Axeman</td>
-<td>+3% de salud para todas las unidades, +10% de velocidad de curación hospitalaria, +10% de velocidad de recolección de piedra</td>
-</tr>
-<tr>
-<td>España</td>
-<td>Pelagius</td>
-<td>Conquistador</td>
-<td>+5% de defensa de caballería, +10% de experiencia de unidades neutras, +10% de velocidad de recolección de oro</td>
-</tr>
-<tr>
-<td>China</td>
-<td>Sun Tzu</td>
-<td>Chu-Ko-Nu Crossbowman</td>
-<td>+3% velocidad de construcción, +3% velocidad de investigación, +5% velocidad de recuperación del punto de acción</td>
-</tr>
-<tr>
-<td>Japón</td>
-<td>Kusunoki Masashige</td>
-<td>Samurai</td>
-<td>+3% ataque de tropas, +5% velocidad de marcha de exploración, +30% producción de recursos en el mapa</td>
-</tr>
-<tr>
-<td>Corea</td>
-<td>Eulji Mundeok</td>
-<td>Hwarang Archer</td>
-<td>+5% defensa arquera, +15% capacidad hospitalaria, +3% velocidad de investigación</td></tr <tr ><td>Arabia ></td><td>Baibars ></td><td>Mamluk ></td><td>+5% salud de caballería, +10% daño a bárbaros y otras unidades neutras, +5% de daño de ataque de rally ></td></tr <tr ><td>Ottoman ></td><td>Osyman I ></td><td>Janissary ></td><td>+5% de daño de habilidad activa, +5% de bonificación de velocidad de marcha cuando la capacidad del ejército es superior al 50% ></td></tr <tr ><td>Byzantium ></td><td>Belaarius ></td><td>Cataphract ></td><td>+5% de defensa de caballería, +15% de bonificación de salud al atacar otras ciudades de gobernadores, -15% de consumo de piedra ></td></tr <tr ><td>Rusia ></td><td>Minnamoto no Yoshitsune ></td><td><td>Cossack Cavalry ></td><td>+5% de salud de la infantería, -20% de costo de entrenamiento de tropas, -15% de costo de sanación ></tr>
- <tr>
-
-</tabla>
- <h3>Construye y mejora tu ciudad y tropas para ganar más poder y recursos</h3>
-<p>Tu ciudad es el corazón de tu civilización en Rise of Kingdoms: Lost Crusade. Necesitas construir y actualizar varias estructuras que te proporcionarán diferentes beneficios y funciones, como producir recursos, entrenar tropas, investigar tecnologías, defender tu ciudad y más. También puedes decorar tu ciudad con varios temas y adornos para que se vea más atractiva y única. </p>
-<p>Tus tropas son la columna vertebral de tu ejército en Rise of Kingdoms: Lost Crusade. Necesitas entrenar y mejorar diferentes tipos de unidades que se adapten a tu estrategia y objetivos, como infantería, caballería, arqueros y unidades de asedio. También puedes desbloquear unidades especiales que son exclusivas de tu civilización o comandantes. También puedes curar a tus tropas heridas en el hospital o revivirlas en el santuario. </p>
- <h3>Investiga y desbloquea nuevas tecnologías y habilidades para mejorar tu rendimiento</h3>
-<p>La investigación es esencial en Rise of Kingdoms: Lost Crusade. Necesitas invertir tus recursos y tiempo en desbloquear nuevas tecnologías y habilidades que mejoren tu jugabilidad y te den ventaja sobre tus enemigos. Puede investigar varios temas en cuatro categorías: militar, economía, defensa y matemáticas. Cada categoría tiene sus propios beneficios y requisitos, por lo que necesitas priorizar lo que más necesitas. </p>
-<p>También puede desbloquear nuevas habilidades para sus comandantes mediante el uso de puntos de talento. Cada comandante tiene tres árboles de talentos que se centran en diferentes aspectos de sus habilidades, como liderazgo, conquista o apoyo. Puedes personalizar las habilidades de tus comandantes según tus preferencias y necesidades. </p>
- <h3>Explora y ataca a tus enemigos y bárbaros para ganar más recompensas y experiencia</h3>
-
-<p>Atacar es emocionante en Rise of Kingdoms: Lost Crusade. Puedes lanzar ataques contra las ciudades o puntos de recursos de tus enemigos para saquear sus recursos, dañar sus edificios, capturar a sus comandantes o incluso quemar sus ciudades. También puedes atacar bárbaros y otras unidades neutrales en el mapa para ganar más recompensas y experiencia. También puedes participar en varios eventos y desafíos que pondrán a prueba tus habilidades y estrategias. </p>
- <h3>Únete o crea una alianza para obtener más soporte y beneficios</h3>
-<p>Unirse o crear una alianza es crucial en Rise of Kingdoms: Lost Crusade. Puedes unirte a una alianza existente que coincida con tus objetivos y valores, o crear tu propia alianza con tus amigos u otros jugadores. Estar en una alianza te dará más apoyo y beneficios, como:</p>
- <ul>
-<li>Ayudándose unos a otros con la construcción, la investigación, la curación, etc.</li>
-<li>Compartir consejos y estrategias entre sí. </li>
-<li>Chatear entre sí usando la función de chat en el juego o mensajes de voz. </li>
-<li>Acceder a más características y beneficios, como la tienda de la alianza, la tecnología de la alianza, el territorio de la alianza, y las banderas de la alianza.</li>
-<li>Participar en eventos y misiones de alianzas que pueden recompensarte con objetos y recursos valiosos. </li>
-<li>Hacer la guerra contra otras alianzas o establecer relaciones diplomáticas con ellas. </li>
-</ul>
- <h2>Conclusión</h2>
-
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre el Surgimiento de los Reinos: Cruzada Perdida:</p>
- <h4>Q: ¿Cómo puedo cambiar mi civilización? </h4>
-<p>A: Usted puede cambiar su civilización usando un artículo especial llamado cambio de la civilización. Puedes obtener este artículo completando ciertas misiones o eventos, o comprándolo en la tienda VIP o en la estación de mensajería. Solo puedes cambiar tu civilización una vez gratis; después de eso, te costará algunas gemas. </p>
- <h4>Q: ¿Cómo puedo obtener más gemas? </h4>
-<p>A: Puedes obtener más gemas completando varias tareas y actividades en el juego, como: - Completando misiones y eventos que te recompensan con gemas. - Participar en el evento Gem Mine que le permite extraer gemas desde un punto de recurso especial. - Ganar el evento Wheel of Fortune que te permite girar una rueda para tener la oportunidad de ganar gemas y otros premios. - Comprar gemas en la tienda VIP o en la estación de mensajería con dinero real u otros recursos. - Reclamar gemas de las Recompensas Diarias, las Recompensas VIP o los Regalos de la Alianza.</p>
- <h4>Q: ¿Cómo consigo más comandantes? </h4>
-<p>A: Puede obtener más comandantes mediante el uso de diferentes artículos y métodos, tales como: - Abrir cofres que contienen esculturas o estrellas comandante. Puede obtener cofres de varias fuentes, como misiones, eventos, tienda VIP, estación de mensajería, etc. - Usando esculturas de comandante para convocar o actualizar a los comandantes. Puedes conseguir esculturas de comandante de cofres, eventos, Alliance Shop, Expedition Shop, etc. - Usando estrellas para mejorar el nivel de estrellas de tu comandante. Puedes obtener estrellas de cofres, eventos, tienda VIP, estación de mensajería, etc. - Usando esculturas universales o estrellas para convocar o actualizar a cualquier comandante de tu elección. Puedes conseguir esculturas universales o estrellas de cofres, eventos, tienda VIP, Courier Station, etc.</p>
- <h4>Q: ¿Cómo me uno o creo una alianza? </h4>
-
- <h4>Q: ¿Cómo me pongo en contacto con el servicio de atención al cliente? </h4>
-<p>A: Puede ponerse en contacto con el servicio de atención al cliente siguiendo estos pasos: - Toque en el botón Configuración en la esquina superior derecha de la pantalla. - Toque en el botón de servicio al cliente en la esquina inferior izquierda de la pantalla. - Toque en el botón Contáctenos en la esquina superior derecha de la pantalla. - Complete su información y su problema y toque en Enviar.</p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/relativedelta.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/relativedelta.py
deleted file mode 100644
index a9e85f7e6cd7488e6b2f4b249d5cf6af314c3859..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/relativedelta.py
+++ /dev/null
@@ -1,599 +0,0 @@
-# -*- coding: utf-8 -*-
-import datetime
-import calendar
-
-import operator
-from math import copysign
-
-from six import integer_types
-from warnings import warn
-
-from ._common import weekday
-
-MO, TU, WE, TH, FR, SA, SU = weekdays = tuple(weekday(x) for x in range(7))
-
-__all__ = ["relativedelta", "MO", "TU", "WE", "TH", "FR", "SA", "SU"]
-
-
-class relativedelta(object):
-    """
-    The relativedelta type is designed to be applied to an existing datetime and
-    can replace specific components of that datetime, or represents an interval
-    of time.
-
-    It is based on the specification of the excellent work done by M.-A. Lemburg
-    in his
-    `mx.DateTime <https://www.egenix.com/products/python/mxBase/mxDateTime/>`_ extension.
-    However, notice that this type does *NOT* implement the same algorithm as
-    his work. Do *NOT* expect it to behave like mx.DateTime's counterpart.
-
-    There are two different ways to build a relativedelta instance. The
-    first one is passing it two date/datetime classes::
-
-        relativedelta(datetime1, datetime2)
-
-    The second one is passing it any number of the following keyword arguments::
-
-        relativedelta(arg1=x,arg2=y,arg3=z...)
-
-        year, month, day, hour, minute, second, microsecond:
-            Absolute information (argument is singular); adding or subtracting a
-            relativedelta with absolute information does not perform an arithmetic
-            operation, but rather REPLACES the corresponding value in the
-            original datetime with the value(s) in relativedelta.
-
-        years, months, weeks, days, hours, minutes, seconds, microseconds:
-            Relative information, may be negative (argument is plural); adding
-            or subtracting a relativedelta with relative information performs
-            the corresponding arithmetic operation on the original datetime value
-            with the information in the relativedelta.
-
-        weekday: 
-            One of the weekday instances (MO, TU, etc) available in the
-            relativedelta module. These instances may receive a parameter N,
-            specifying the Nth weekday, which could be positive or negative
-            (like MO(+1) or MO(-2)). Not specifying it is the same as specifying
-            +1. You can also use an integer, where 0=MO. This argument is always
-            relative e.g. if the calculated date is already Monday, using MO(1)
-            or MO(-1) won't change the day. To effectively make it absolute, use
-            it in combination with the day argument (e.g. day=1, MO(1) for first
-            Monday of the month).
-
-        leapdays:
-            Will add given days to the date found, if year is a leap
-            year, and the date found is post 28 of february.
-
-        yearday, nlyearday:
-            Set the yearday or the non-leap year day (jump leap days).
-            These are converted to day/month/leapdays information.
-
-    There are relative and absolute forms of the keyword
-    arguments. The plural is relative, and the singular is
-    absolute. For each argument in the order below, the absolute form
-    is applied first (by setting each attribute to that value) and
-    then the relative form (by adding the value to the attribute).
-
-    The order of attributes considered when this relativedelta is
-    added to a datetime is:
-
-    1. Year
-    2. Month
-    3. Day
-    4. Hours
-    5. Minutes
-    6. Seconds
-    7. Microseconds
-
-    Finally, weekday is applied, using the rule described above.
-
-    For example
-
-    >>> from datetime import datetime
-    >>> from dateutil.relativedelta import relativedelta, MO
-    >>> dt = datetime(2018, 4, 9, 13, 37, 0)
-    >>> delta = relativedelta(hours=25, day=1, weekday=MO(1))
-    >>> dt + delta
-    datetime.datetime(2018, 4, 2, 14, 37)
-
-    First, the day is set to 1 (the first of the month), then 25 hours
-    are added, to get to the 2nd day and 14th hour, finally the
-    weekday is applied, but since the 2nd is already a Monday there is
-    no effect.
-
-    """
-
-    def __init__(self, dt1=None, dt2=None,
-                 years=0, months=0, days=0, leapdays=0, weeks=0,
-                 hours=0, minutes=0, seconds=0, microseconds=0,
-                 year=None, month=None, day=None, weekday=None,
-                 yearday=None, nlyearday=None,
-                 hour=None, minute=None, second=None, microsecond=None):
-
-        if dt1 and dt2:
-            # datetime is a subclass of date. So both must be date
-            if not (isinstance(dt1, datetime.date) and
-                    isinstance(dt2, datetime.date)):
-                raise TypeError("relativedelta only diffs datetime/date")
-
-            # We allow two dates, or two datetimes, so we coerce them to be
-            # of the same type
-            if (isinstance(dt1, datetime.datetime) !=
-                    isinstance(dt2, datetime.datetime)):
-                if not isinstance(dt1, datetime.datetime):
-                    dt1 = datetime.datetime.fromordinal(dt1.toordinal())
-                elif not isinstance(dt2, datetime.datetime):
-                    dt2 = datetime.datetime.fromordinal(dt2.toordinal())
-
-            self.years = 0
-            self.months = 0
-            self.days = 0
-            self.leapdays = 0
-            self.hours = 0
-            self.minutes = 0
-            self.seconds = 0
-            self.microseconds = 0
-            self.year = None
-            self.month = None
-            self.day = None
-            self.weekday = None
-            self.hour = None
-            self.minute = None
-            self.second = None
-            self.microsecond = None
-            self._has_time = 0
-
-            # Get year / month delta between the two
-            months = (dt1.year - dt2.year) * 12 + (dt1.month - dt2.month)
-            self._set_months(months)
-
-            # Remove the year/month delta so the timedelta is just well-defined
-            # time units (seconds, days and microseconds)
-            dtm = self.__radd__(dt2)
-
-            # If we've overshot our target, make an adjustment
-            if dt1 < dt2:
-                compare = operator.gt
-                increment = 1
-            else:
-                compare = operator.lt
-                increment = -1
-
-            while compare(dt1, dtm):
-                months += increment
-                self._set_months(months)
-                dtm = self.__radd__(dt2)
-
-            # Get the timedelta between the "months-adjusted" date and dt1
-            delta = dt1 - dtm
-            self.seconds = delta.seconds + delta.days * 86400
-            self.microseconds = delta.microseconds
-        else:
-            # Check for non-integer values in integer-only quantities
-            if any(x is not None and x != int(x) for x in (years, months)):
-                raise ValueError("Non-integer years and months are "
-                                 "ambiguous and not currently supported.")
-
-            # Relative information
-            self.years = int(years)
-            self.months = int(months)
-            self.days = days + weeks * 7
-            self.leapdays = leapdays
-            self.hours = hours
-            self.minutes = minutes
-            self.seconds = seconds
-            self.microseconds = microseconds
-
-            # Absolute information
-            self.year = year
-            self.month = month
-            self.day = day
-            self.hour = hour
-            self.minute = minute
-            self.second = second
-            self.microsecond = microsecond
-
-            if any(x is not None and int(x) != x
-                   for x in (year, month, day, hour,
-                             minute, second, microsecond)):
-                # For now we'll deprecate floats - later it'll be an error.
-                warn("Non-integer value passed as absolute information. " +
-                     "This is not a well-defined condition and will raise " +
-                     "errors in future versions.", DeprecationWarning)
-
-            if isinstance(weekday, integer_types):
-                self.weekday = weekdays[weekday]
-            else:
-                self.weekday = weekday
-
-            yday = 0
-            if nlyearday:
-                yday = nlyearday
-            elif yearday:
-                yday = yearday
-                if yearday > 59:
-                    self.leapdays = -1
-            if yday:
-                ydayidx = [31, 59, 90, 120, 151, 181, 212,
-                           243, 273, 304, 334, 366]
-                for idx, ydays in enumerate(ydayidx):
-                    if yday <= ydays:
-                        self.month = idx+1
-                        if idx == 0:
-                            self.day = yday
-                        else:
-                            self.day = yday-ydayidx[idx-1]
-                        break
-                else:
-                    raise ValueError("invalid year day (%d)" % yday)
-
-        self._fix()
-
-    def _fix(self):
-        if abs(self.microseconds) > 999999:
-            s = _sign(self.microseconds)
-            div, mod = divmod(self.microseconds * s, 1000000)
-            self.microseconds = mod * s
-            self.seconds += div * s
-        if abs(self.seconds) > 59:
-            s = _sign(self.seconds)
-            div, mod = divmod(self.seconds * s, 60)
-            self.seconds = mod * s
-            self.minutes += div * s
-        if abs(self.minutes) > 59:
-            s = _sign(self.minutes)
-            div, mod = divmod(self.minutes * s, 60)
-            self.minutes = mod * s
-            self.hours += div * s
-        if abs(self.hours) > 23:
-            s = _sign(self.hours)
-            div, mod = divmod(self.hours * s, 24)
-            self.hours = mod * s
-            self.days += div * s
-        if abs(self.months) > 11:
-            s = _sign(self.months)
-            div, mod = divmod(self.months * s, 12)
-            self.months = mod * s
-            self.years += div * s
-        if (self.hours or self.minutes or self.seconds or self.microseconds
-                or self.hour is not None or self.minute is not None or
-                self.second is not None or self.microsecond is not None):
-            self._has_time = 1
-        else:
-            self._has_time = 0
-
-    @property
-    def weeks(self):
-        return int(self.days / 7.0)
-
-    @weeks.setter
-    def weeks(self, value):
-        self.days = self.days - (self.weeks * 7) + value * 7
-
-    def _set_months(self, months):
-        self.months = months
-        if abs(self.months) > 11:
-            s = _sign(self.months)
-            div, mod = divmod(self.months * s, 12)
-            self.months = mod * s
-            self.years = div * s
-        else:
-            self.years = 0
-
-    def normalized(self):
-        """
-        Return a version of this object represented entirely using integer
-        values for the relative attributes.
-
-        >>> relativedelta(days=1.5, hours=2).normalized()
-        relativedelta(days=+1, hours=+14)
-
-        :return:
-            Returns a :class:`dateutil.relativedelta.relativedelta` object.
-        """
-        # Cascade remainders down (rounding each to roughly nearest microsecond)
-        days = int(self.days)
-
-        hours_f = round(self.hours + 24 * (self.days - days), 11)
-        hours = int(hours_f)
-
-        minutes_f = round(self.minutes + 60 * (hours_f - hours), 10)
-        minutes = int(minutes_f)
-
-        seconds_f = round(self.seconds + 60 * (minutes_f - minutes), 8)
-        seconds = int(seconds_f)
-
-        microseconds = round(self.microseconds + 1e6 * (seconds_f - seconds))
-
-        # Constructor carries overflow back up with call to _fix()
-        return self.__class__(years=self.years, months=self.months,
-                              days=days, hours=hours, minutes=minutes,
-                              seconds=seconds, microseconds=microseconds,
-                              leapdays=self.leapdays, year=self.year,
-                              month=self.month, day=self.day,
-                              weekday=self.weekday, hour=self.hour,
-                              minute=self.minute, second=self.second,
-                              microsecond=self.microsecond)
-
-    def __add__(self, other):
-        if isinstance(other, relativedelta):
-            return self.__class__(years=other.years + self.years,
-                                 months=other.months + self.months,
-                                 days=other.days + self.days,
-                                 hours=other.hours + self.hours,
-                                 minutes=other.minutes + self.minutes,
-                                 seconds=other.seconds + self.seconds,
-                                 microseconds=(other.microseconds +
-                                               self.microseconds),
-                                 leapdays=other.leapdays or self.leapdays,
-                                 year=(other.year if other.year is not None
-                                       else self.year),
-                                 month=(other.month if other.month is not None
-                                        else self.month),
-                                 day=(other.day if other.day is not None
-                                      else self.day),
-                                 weekday=(other.weekday if other.weekday is not None
-                                          else self.weekday),
-                                 hour=(other.hour if other.hour is not None
-                                       else self.hour),
-                                 minute=(other.minute if other.minute is not None
-                                         else self.minute),
-                                 second=(other.second if other.second is not None
-                                         else self.second),
-                                 microsecond=(other.microsecond if other.microsecond
-                                              is not None else
-                                              self.microsecond))
-        if isinstance(other, datetime.timedelta):
-            return self.__class__(years=self.years,
-                                  months=self.months,
-                                  days=self.days + other.days,
-                                  hours=self.hours,
-                                  minutes=self.minutes,
-                                  seconds=self.seconds + other.seconds,
-                                  microseconds=self.microseconds + other.microseconds,
-                                  leapdays=self.leapdays,
-                                  year=self.year,
-                                  month=self.month,
-                                  day=self.day,
-                                  weekday=self.weekday,
-                                  hour=self.hour,
-                                  minute=self.minute,
-                                  second=self.second,
-                                  microsecond=self.microsecond)
-        if not isinstance(other, datetime.date):
-            return NotImplemented
-        elif self._has_time and not isinstance(other, datetime.datetime):
-            other = datetime.datetime.fromordinal(other.toordinal())
-        year = (self.year or other.year)+self.years
-        month = self.month or other.month
-        if self.months:
-            assert 1 <= abs(self.months) <= 12
-            month += self.months
-            if month > 12:
-                year += 1
-                month -= 12
-            elif month < 1:
-                year -= 1
-                month += 12
-        day = min(calendar.monthrange(year, month)[1],
-                  self.day or other.day)
-        repl = {"year": year, "month": month, "day": day}
-        for attr in ["hour", "minute", "second", "microsecond"]:
-            value = getattr(self, attr)
-            if value is not None:
-                repl[attr] = value
-        days = self.days
-        if self.leapdays and month > 2 and calendar.isleap(year):
-            days += self.leapdays
-        ret = (other.replace(**repl)
-               + datetime.timedelta(days=days,
-                                    hours=self.hours,
-                                    minutes=self.minutes,
-                                    seconds=self.seconds,
-                                    microseconds=self.microseconds))
-        if self.weekday:
-            weekday, nth = self.weekday.weekday, self.weekday.n or 1
-            jumpdays = (abs(nth) - 1) * 7
-            if nth > 0:
-                jumpdays += (7 - ret.weekday() + weekday) % 7
-            else:
-                jumpdays += (ret.weekday() - weekday) % 7
-                jumpdays *= -1
-            ret += datetime.timedelta(days=jumpdays)
-        return ret
-
-    def __radd__(self, other):
-        return self.__add__(other)
-
-    def __rsub__(self, other):
-        return self.__neg__().__radd__(other)
-
-    def __sub__(self, other):
-        if not isinstance(other, relativedelta):
-            return NotImplemented   # In case the other object defines __rsub__
-        return self.__class__(years=self.years - other.years,
-                             months=self.months - other.months,
-                             days=self.days - other.days,
-                             hours=self.hours - other.hours,
-                             minutes=self.minutes - other.minutes,
-                             seconds=self.seconds - other.seconds,
-                             microseconds=self.microseconds - other.microseconds,
-                             leapdays=self.leapdays or other.leapdays,
-                             year=(self.year if self.year is not None
-                                   else other.year),
-                             month=(self.month if self.month is not None else
-                                    other.month),
-                             day=(self.day if self.day is not None else
-                                  other.day),
-                             weekday=(self.weekday if self.weekday is not None else
-                                      other.weekday),
-                             hour=(self.hour if self.hour is not None else
-                                   other.hour),
-                             minute=(self.minute if self.minute is not None else
-                                     other.minute),
-                             second=(self.second if self.second is not None else
-                                     other.second),
-                             microsecond=(self.microsecond if self.microsecond
-                                          is not None else
-                                          other.microsecond))
-
-    def __abs__(self):
-        return self.__class__(years=abs(self.years),
-                              months=abs(self.months),
-                              days=abs(self.days),
-                              hours=abs(self.hours),
-                              minutes=abs(self.minutes),
-                              seconds=abs(self.seconds),
-                              microseconds=abs(self.microseconds),
-                              leapdays=self.leapdays,
-                              year=self.year,
-                              month=self.month,
-                              day=self.day,
-                              weekday=self.weekday,
-                              hour=self.hour,
-                              minute=self.minute,
-                              second=self.second,
-                              microsecond=self.microsecond)
-
-    def __neg__(self):
-        return self.__class__(years=-self.years,
-                             months=-self.months,
-                             days=-self.days,
-                             hours=-self.hours,
-                             minutes=-self.minutes,
-                             seconds=-self.seconds,
-                             microseconds=-self.microseconds,
-                             leapdays=self.leapdays,
-                             year=self.year,
-                             month=self.month,
-                             day=self.day,
-                             weekday=self.weekday,
-                             hour=self.hour,
-                             minute=self.minute,
-                             second=self.second,
-                             microsecond=self.microsecond)
-
-    def __bool__(self):
-        return not (not self.years and
-                    not self.months and
-                    not self.days and
-                    not self.hours and
-                    not self.minutes and
-                    not self.seconds and
-                    not self.microseconds and
-                    not self.leapdays and
-                    self.year is None and
-                    self.month is None and
-                    self.day is None and
-                    self.weekday is None and
-                    self.hour is None and
-                    self.minute is None and
-                    self.second is None and
-                    self.microsecond is None)
-    # Compatibility with Python 2.x
-    __nonzero__ = __bool__
-
-    def __mul__(self, other):
-        try:
-            f = float(other)
-        except TypeError:
-            return NotImplemented
-
-        return self.__class__(years=int(self.years * f),
-                             months=int(self.months * f),
-                             days=int(self.days * f),
-                             hours=int(self.hours * f),
-                             minutes=int(self.minutes * f),
-                             seconds=int(self.seconds * f),
-                             microseconds=int(self.microseconds * f),
-                             leapdays=self.leapdays,
-                             year=self.year,
-                             month=self.month,
-                             day=self.day,
-                             weekday=self.weekday,
-                             hour=self.hour,
-                             minute=self.minute,
-                             second=self.second,
-                             microsecond=self.microsecond)
-
-    __rmul__ = __mul__
-
-    def __eq__(self, other):
-        if not isinstance(other, relativedelta):
-            return NotImplemented
-        if self.weekday or other.weekday:
-            if not self.weekday or not other.weekday:
-                return False
-            if self.weekday.weekday != other.weekday.weekday:
-                return False
-            n1, n2 = self.weekday.n, other.weekday.n
-            if n1 != n2 and not ((not n1 or n1 == 1) and (not n2 or n2 == 1)):
-                return False
-        return (self.years == other.years and
-                self.months == other.months and
-                self.days == other.days and
-                self.hours == other.hours and
-                self.minutes == other.minutes and
-                self.seconds == other.seconds and
-                self.microseconds == other.microseconds and
-                self.leapdays == other.leapdays and
-                self.year == other.year and
-                self.month == other.month and
-                self.day == other.day and
-                self.hour == other.hour and
-                self.minute == other.minute and
-                self.second == other.second and
-                self.microsecond == other.microsecond)
-
-    def __hash__(self):
-        return hash((
-            self.weekday,
-            self.years,
-            self.months,
-            self.days,
-            self.hours,
-            self.minutes,
-            self.seconds,
-            self.microseconds,
-            self.leapdays,
-            self.year,
-            self.month,
-            self.day,
-            self.hour,
-            self.minute,
-            self.second,
-            self.microsecond,
-        ))
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __div__(self, other):
-        try:
-            reciprocal = 1 / float(other)
-        except TypeError:
-            return NotImplemented
-
-        return self.__mul__(reciprocal)
-
-    __truediv__ = __div__
-
-    def __repr__(self):
-        l = []
-        for attr in ["years", "months", "days", "leapdays",
-                     "hours", "minutes", "seconds", "microseconds"]:
-            value = getattr(self, attr)
-            if value:
-                l.append("{attr}={value:+g}".format(attr=attr, value=value))
-        for attr in ["year", "month", "day", "weekday",
-                     "hour", "minute", "second", "microsecond"]:
-            value = getattr(self, attr)
-            if value is not None:
-                l.append("{attr}={value}".format(attr=attr, value=repr(value)))
-        return "{classname}({attrs})".format(classname=self.__class__.__name__,
-                                             attrs=", ".join(l))
-
-
-def _sign(x):
-    return int(copysign(1, x))
-
-# vim:ts=4:sw=4:et
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/__init__.py
deleted file mode 100644
index b6beddbe6d24d2949dc89ed07abfebd59d8b63b9..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/vcs/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Expose a limited set of classes and functions so callers outside of
-# the vcs package don't need to import deeper than `pip._internal.vcs`.
-# (The test directory may still need to import from a vcs sub-package.)
-# Import all vcs modules to register each VCS in the VcsSupport object.
-import pip._internal.vcs.bazaar
-import pip._internal.vcs.git
-import pip._internal.vcs.mercurial
-import pip._internal.vcs.subversion  # noqa: F401
-from pip._internal.vcs.versioncontrol import (  # noqa: F401
-    RemoteNotFoundError,
-    RemoteNotValidError,
-    is_url,
-    make_vcs_requirement_url,
-    vcs,
-)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/platformdirs/__main__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/platformdirs/__main__.py
deleted file mode 100644
index 7171f13114e09f47eb8981d39b7a838e8ab88acb..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/platformdirs/__main__.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from __future__ import annotations
-
-from pip._vendor.platformdirs import PlatformDirs, __version__
-
-PROPS = (
-    "user_data_dir",
-    "user_config_dir",
-    "user_cache_dir",
-    "user_state_dir",
-    "user_log_dir",
-    "user_documents_dir",
-    "user_runtime_dir",
-    "site_data_dir",
-    "site_config_dir",
-    "site_cache_dir",
-)
-
-
-def main() -> None:
-    app_name = "MyApp"
-    app_author = "MyCompany"
-
-    print(f"-- platformdirs {__version__} --")
-
-    print("-- app dirs (with optional 'version')")
-    dirs = PlatformDirs(app_name, app_author, version="1.0")
-    for prop in PROPS:
-        print(f"{prop}: {getattr(dirs, prop)}")
-
-    print("\n-- app dirs (without optional 'version')")
-    dirs = PlatformDirs(app_name, app_author)
-    for prop in PROPS:
-        print(f"{prop}: {getattr(dirs, prop)}")
-
-    print("\n-- app dirs (without optional 'appauthor')")
-    dirs = PlatformDirs(app_name)
-    for prop in PROPS:
-        print(f"{prop}: {getattr(dirs, prop)}")
-
-    print("\n-- app dirs (with disabled 'appauthor')")
-    dirs = PlatformDirs(app_name, appauthor=False)
-    for prop in PROPS:
-        print(f"{prop}: {getattr(dirs, prop)}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/BilalSardar/Text-To-image-AllModels/README.md b/spaces/BilalSardar/Text-To-image-AllModels/README.md
deleted file mode 100644
index 6232986d4f1345a7ab0763a1db0bac0a57c15d87..0000000000000000000000000000000000000000
--- a/spaces/BilalSardar/Text-To-image-AllModels/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Text To Image AllModels
-emoji: 🐠
-colorFrom: blue
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.15.0
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/grid_feats/dataset_mapper.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/grid_feats/dataset_mapper.py
deleted file mode 100644
index 8685993a029c6da87f22ed16c3dadc6bfd770232..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/grid_feats/dataset_mapper.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import logging
-import numpy as np
-import torch
-from fvcore.common.file_io import PathManager
-from PIL import Image
-
-from detectron2.data import detection_utils as utils
-from detectron2.data import transforms as T
-from detectron2.data import DatasetMapper
-from detectron2.structures import (
-    BitMasks,
-    Boxes,
-    BoxMode,
-    Instances,
-    Keypoints,
-    PolygonMasks,
-    polygons_to_bitmask,
-)
-
-
-def annotations_to_instances_with_attributes(annos, 
-                                             image_size, 
-                                             mask_format="polygon", 
-                                             load_attributes=False, 
-                                             max_attr_per_ins=16):
-    """
-    Extend the function annotations_to_instances() to support attributes
-    """
-    boxes = [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos]
-    target = Instances(image_size)
-    boxes = target.gt_boxes = Boxes(boxes)
-    boxes.clip(image_size)
-
-    classes = [obj["category_id"] for obj in annos]
-    classes = torch.tensor(classes, dtype=torch.int64)
-    target.gt_classes = classes
-
-    if len(annos) and "segmentation" in annos[0]:
-        segms = [obj["segmentation"] for obj in annos]
-        if mask_format == "polygon":
-            masks = PolygonMasks(segms)
-        else:
-            assert mask_format == "bitmask", mask_format
-            masks = []
-            for segm in segms:
-                if isinstance(segm, list):
-                    # polygon
-                    masks.append(polygons_to_bitmask(segm, *image_size))
-                elif isinstance(segm, dict):
-                    # COCO RLE
-                    masks.append(mask_util.decode(segm))
-                elif isinstance(segm, np.ndarray):
-                    assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format(
-                        segm.ndim
-                    )
-                    # mask array
-                    masks.append(segm)
-                else:
-                    raise ValueError(
-                        "Cannot convert segmentation of type '{}' to BitMasks!"
-                        "Supported types are: polygons as list[list[float] or ndarray],"
-                        " COCO-style RLE as a dict, or a full-image segmentation mask "
-                        "as a 2D ndarray.".format(type(segm))
-                    )
-            masks = BitMasks(
-                torch.stack([torch.from_numpy(np.ascontiguousarray(x)) for x in masks])
-            )
-        target.gt_masks = masks
-
-    if len(annos) and "keypoints" in annos[0]:
-        kpts = [obj.get("keypoints", []) for obj in annos]
-        target.gt_keypoints = Keypoints(kpts)
-
-    if len(annos) and load_attributes:
-        attributes = -torch.ones((len(annos), max_attr_per_ins), dtype=torch.int64)
-        for idx, anno in enumerate(annos):
-            if "attribute_ids" in anno:
-                for jdx, attr_id in enumerate(anno["attribute_ids"]):
-                    attributes[idx, jdx] = attr_id
-        target.gt_attributes = attributes
-
-    return target
-
-
-class AttributeDatasetMapper(DatasetMapper):
-    """
-    Extend DatasetMapper to support attributes.
-    """
-    def __init__(self, cfg, is_train=True):
-        super().__init__(cfg, is_train)
-
-        # fmt: off
-        self.attribute_on      = cfg.MODEL.ATTRIBUTE_ON
-        self.max_attr_per_ins  = cfg.INPUT.MAX_ATTR_PER_INS
-        # fmt: on
-
-    def __call__(self, dataset_dict):
-        dataset_dict = copy.deepcopy(dataset_dict)
-        image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
-        utils.check_image_size(dataset_dict, image)
-
-        if "annotations" not in dataset_dict:
-            image, transforms = T.apply_transform_gens(
-                ([self.crop_gen] if self.crop_gen else []) + self.tfm_gens, image
-            )
-        else:
-            if self.crop_gen:
-                crop_tfm = utils.gen_crop_transform_with_instance(
-                    self.crop_gen.get_crop_size(image.shape[:2]),
-                    image.shape[:2],
-                    np.random.choice(dataset_dict["annotations"]),
-                )
-                image = crop_tfm.apply_image(image)
-            image, transforms = T.apply_transform_gens(self.tfm_gens, image)
-            if self.crop_gen:
-                transforms = crop_tfm + transforms
-
-        image_shape = image.shape[:2]
-        dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
-
-        if self.load_proposals:
-            utils.transform_proposals(
-                dataset_dict, image_shape, transforms, self.min_box_side_len, self.proposal_topk
-            )
-
-        if not self.is_train:
-            dataset_dict.pop("annotations", None)
-            dataset_dict.pop("sem_seg_file_name", None)
-            return dataset_dict
-
-        if "annotations" in dataset_dict:
-            for anno in dataset_dict["annotations"]:
-                if not self.mask_on:
-                    anno.pop("segmentation", None)
-                if not self.keypoint_on:
-                    anno.pop("keypoints", None)
-                if not self.attribute_on:
-                    anno.pop("attribute_ids")
-
-            annos = [
-                utils.transform_instance_annotations(
-                    obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
-                )
-                for obj in dataset_dict.pop("annotations")
-                if obj.get("iscrowd", 0) == 0
-            ]
-            instances = annotations_to_instances_with_attributes(
-                annos, image_shape, mask_format=self.mask_format,
-                load_attributes=self.attribute_on, max_attr_per_ins=self.max_attr_per_ins
-            )
-            if self.crop_gen and instances.has("gt_masks"):
-                instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
-            dataset_dict["instances"] = utils.filter_empty_instances(instances)
-
-        if "sem_seg_file_name" in dataset_dict:
-            with PathManager.open(dataset_dict.pop("sem_seg_file_name"), "rb") as f:
-                sem_seg_gt = Image.open(f)
-                sem_seg_gt = np.asarray(sem_seg_gt, dtype="uint8")
-            sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
-            sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
-            dataset_dict["sem_seg"] = sem_seg_gt
-        return dataset_dict
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/dataset_loader.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/dataset_loader.py
deleted file mode 100644
index e46c7d8c2e982d9611bc500c12f29bd1bfe31e50..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/datasets/dataset_loader.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# --------------------------------------------------------
-
-from importlib import import_module
-
-class DatasetLoader:
-    def __init__(self, __C):
-        self.__C = __C
-
-        self.dataset = __C.DATASET
-        dataset_moudle_path = 'openvqa.datasets.' + self.dataset +'.' + self.dataset + '_loader'
-        self.dataset_moudle = import_module(dataset_moudle_path)
-
-    def DataSet(self):
-        return self.dataset_moudle.DataSet(self.__C)
-
-
-class EvalLoader:
-    def __init__(self, __C):
-        self.__C = __C
-
-        self.dataset = __C.DATASET
-        eval_moudle_path = 'openvqa.datasets.' + self.dataset + '.' + 'eval' + '.' + 'result_eval'
-        self.eval_moudle = import_module(eval_moudle_path)
-
-    def eval(self, __arg1, __arg2, __arg3, __arg4, __arg5, __arg6, __arg7):
-        return self.eval_moudle.eval(self.__C, __arg1, __arg2, __arg3, __arg4, __arg5, __arg6, __arg7)
diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_stl.cpp b/spaces/CVPR/LIVE/pybind11/tests/test_stl.cpp
deleted file mode 100644
index 928635788e484d98f3cc8cf701d9221bef0a8bac..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/test_stl.cpp
+++ /dev/null
@@ -1,324 +0,0 @@
-/*
-    tests/test_stl.cpp -- STL type casters
-
-    Copyright (c) 2017 Wenzel Jakob <wenzel.jakob@epfl.ch>
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include "constructor_stats.h"
-#include <pybind11/stl.h>
-
-#include <vector>
-#include <string>
-
-// Test with `std::variant` in C++17 mode, or with `boost::variant` in C++11/14
-#if PYBIND11_HAS_VARIANT
-using std::variant;
-#elif defined(PYBIND11_TEST_BOOST) && (!defined(_MSC_VER) || _MSC_VER >= 1910)
-#  include <boost/variant.hpp>
-#  define PYBIND11_HAS_VARIANT 1
-using boost::variant;
-
-namespace pybind11 { namespace detail {
-template <typename... Ts>
-struct type_caster<boost::variant<Ts...>> : variant_caster<boost::variant<Ts...>> {};
-
-template <>
-struct visit_helper<boost::variant> {
-    template <typename... Args>
-    static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) {
-        return boost::apply_visitor(args...);
-    }
-};
-}} // namespace pybind11::detail
-#endif
-
-PYBIND11_MAKE_OPAQUE(std::vector<std::string, std::allocator<std::string>>);
-
-/// Issue #528: templated constructor
-struct TplCtorClass {
-    template <typename T> TplCtorClass(const T &) { }
-    bool operator==(const TplCtorClass &) const { return true; }
-};
-
-namespace std {
-    template <>
-    struct hash<TplCtorClass> { size_t operator()(const TplCtorClass &) const { return 0; } };
-}
-
-
-template <template <typename> class OptionalImpl, typename T>
-struct OptionalHolder
-{
-    OptionalHolder() = default;
-    bool member_initialized() const {
-        return member && member->initialized;
-    }
-    OptionalImpl<T> member = T{};
-};
-
-
-TEST_SUBMODULE(stl, m) {
-    // test_vector
-    m.def("cast_vector", []() { return std::vector<int>{1}; });
-    m.def("load_vector", [](const std::vector<int> &v) { return v.at(0) == 1 && v.at(1) == 2; });
-    // `std::vector<bool>` is special because it returns proxy objects instead of references
-    m.def("cast_bool_vector", []() { return std::vector<bool>{true, false}; });
-    m.def("load_bool_vector", [](const std::vector<bool> &v) {
-        return v.at(0) == true && v.at(1) == false;
-    });
-    // Unnumbered regression (caused by #936): pointers to stl containers aren't castable
-    static std::vector<RValueCaster> lvv{2};
-    m.def("cast_ptr_vector", []() { return &lvv; });
-
-    // test_deque
-    m.def("cast_deque", []() { return std::deque<int>{1}; });
-    m.def("load_deque", [](const std::deque<int> &v) { return v.at(0) == 1 && v.at(1) == 2; });
-
-    // test_array
-    m.def("cast_array", []() { return std::array<int, 2> {{1 , 2}}; });
-    m.def("load_array", [](const std::array<int, 2> &a) { return a[0] == 1 && a[1] == 2; });
-
-    // test_valarray
-    m.def("cast_valarray", []() { return std::valarray<int>{1, 4, 9}; });
-    m.def("load_valarray", [](const std::valarray<int>& v) {
-        return v.size() == 3 && v[0] == 1 && v[1] == 4 && v[2] == 9;
-    });
-
-    // test_map
-    m.def("cast_map", []() { return std::map<std::string, std::string>{{"key", "value"}}; });
-    m.def("load_map", [](const std::map<std::string, std::string> &map) {
-        return map.at("key") == "value" && map.at("key2") == "value2";
-    });
-
-    // test_set
-    m.def("cast_set", []() { return std::set<std::string>{"key1", "key2"}; });
-    m.def("load_set", [](const std::set<std::string> &set) {
-        return set.count("key1") && set.count("key2") && set.count("key3");
-    });
-
-    // test_recursive_casting
-    m.def("cast_rv_vector", []() { return std::vector<RValueCaster>{2}; });
-    m.def("cast_rv_array", []() { return std::array<RValueCaster, 3>(); });
-    // NB: map and set keys are `const`, so while we technically do move them (as `const Type &&`),
-    // casters don't typically do anything with that, which means they fall to the `const Type &`
-    // caster.
-    m.def("cast_rv_map", []() { return std::unordered_map<std::string, RValueCaster>{{"a", RValueCaster{}}}; });
-    m.def("cast_rv_nested", []() {
-        std::vector<std::array<std::list<std::unordered_map<std::string, RValueCaster>>, 2>> v;
-        v.emplace_back(); // add an array
-        v.back()[0].emplace_back(); // add a map to the array
-        v.back()[0].back().emplace("b", RValueCaster{});
-        v.back()[0].back().emplace("c", RValueCaster{});
-        v.back()[1].emplace_back(); // add a map to the array
-        v.back()[1].back().emplace("a", RValueCaster{});
-        return v;
-    });
-    static std::array<RValueCaster, 2> lva;
-    static std::unordered_map<std::string, RValueCaster> lvm{{"a", RValueCaster{}}, {"b", RValueCaster{}}};
-    static std::unordered_map<std::string, std::vector<std::list<std::array<RValueCaster, 2>>>> lvn;
-    lvn["a"].emplace_back(); // add a list
-    lvn["a"].back().emplace_back(); // add an array
-    lvn["a"].emplace_back(); // another list
-    lvn["a"].back().emplace_back(); // add an array
-    lvn["b"].emplace_back(); // add a list
-    lvn["b"].back().emplace_back(); // add an array
-    lvn["b"].back().emplace_back(); // add another array
-    m.def("cast_lv_vector", []() -> const decltype(lvv) & { return lvv; });
-    m.def("cast_lv_array", []() -> const decltype(lva) & { return lva; });
-    m.def("cast_lv_map", []() -> const decltype(lvm) & { return lvm; });
-    m.def("cast_lv_nested", []() -> const decltype(lvn) & { return lvn; });
-    // #853:
-    m.def("cast_unique_ptr_vector", []() {
-        std::vector<std::unique_ptr<UserType>> v;
-        v.emplace_back(new UserType{7});
-        v.emplace_back(new UserType{42});
-        return v;
-    });
-
-    // test_move_out_container
-    struct MoveOutContainer {
-        struct Value { int value; };
-        std::list<Value> move_list() const { return {{0}, {1}, {2}}; }
-    };
-    py::class_<MoveOutContainer::Value>(m, "MoveOutContainerValue")
-        .def_readonly("value", &MoveOutContainer::Value::value);
-    py::class_<MoveOutContainer>(m, "MoveOutContainer")
-        .def(py::init<>())
-        .def_property_readonly("move_list", &MoveOutContainer::move_list);
-
-    // Class that can be move- and copy-constructed, but not assigned
-    struct NoAssign {
-        int value;
-
-        explicit NoAssign(int value = 0) : value(value) { }
-        NoAssign(const NoAssign &) = default;
-        NoAssign(NoAssign &&) = default;
-
-        NoAssign &operator=(const NoAssign &) = delete;
-        NoAssign &operator=(NoAssign &&) = delete;
-    };
-    py::class_<NoAssign>(m, "NoAssign", "Class with no C++ assignment operators")
-        .def(py::init<>())
-        .def(py::init<int>());
-
-
-    struct MoveOutDetector
-    {
-        MoveOutDetector() = default;
-        MoveOutDetector(const MoveOutDetector&) = default;
-        MoveOutDetector(MoveOutDetector&& other) noexcept
-         : initialized(other.initialized) {
-            // steal underlying resource
-            other.initialized = false;
-        }
-        bool initialized = true;
-    };
-    py::class_<MoveOutDetector>(m, "MoveOutDetector", "Class with move tracking")
-        .def(py::init<>())
-        .def_readonly("initialized", &MoveOutDetector::initialized);
-
-
-#ifdef PYBIND11_HAS_OPTIONAL
-    // test_optional
-    m.attr("has_optional") = true;
-
-    using opt_int = std::optional<int>;
-    using opt_no_assign = std::optional<NoAssign>;
-    m.def("double_or_zero", [](const opt_int& x) -> int {
-        return x.value_or(0) * 2;
-    });
-    m.def("half_or_none", [](int x) -> opt_int {
-        return x ? opt_int(x / 2) : opt_int();
-    });
-    m.def("test_nullopt", [](opt_int x) {
-        return x.value_or(42);
-    }, py::arg_v("x", std::nullopt, "None"));
-    m.def("test_no_assign", [](const opt_no_assign &x) {
-        return x ? x->value : 42;
-    }, py::arg_v("x", std::nullopt, "None"));
-
-    m.def("nodefer_none_optional", [](std::optional<int>) { return true; });
-    m.def("nodefer_none_optional", [](py::none) { return false; });
-
-    using opt_holder = OptionalHolder<std::optional, MoveOutDetector>;
-    py::class_<opt_holder>(m, "OptionalHolder", "Class with optional member")
-        .def(py::init<>())
-        .def_readonly("member", &opt_holder::member)
-        .def("member_initialized", &opt_holder::member_initialized);
-#endif
-
-#ifdef PYBIND11_HAS_EXP_OPTIONAL
-    // test_exp_optional
-    m.attr("has_exp_optional") = true;
-
-    using exp_opt_int = std::experimental::optional<int>;
-    using exp_opt_no_assign = std::experimental::optional<NoAssign>;
-    m.def("double_or_zero_exp", [](const exp_opt_int& x) -> int {
-        return x.value_or(0) * 2;
-    });
-    m.def("half_or_none_exp", [](int x) -> exp_opt_int {
-        return x ? exp_opt_int(x / 2) : exp_opt_int();
-    });
-    m.def("test_nullopt_exp", [](exp_opt_int x) {
-        return x.value_or(42);
-    }, py::arg_v("x", std::experimental::nullopt, "None"));
-    m.def("test_no_assign_exp", [](const exp_opt_no_assign &x) {
-        return x ? x->value : 42;
-    }, py::arg_v("x", std::experimental::nullopt, "None"));
-
-    using opt_exp_holder = OptionalHolder<std::experimental::optional, MoveOutDetector>;
-    py::class_<opt_exp_holder>(m, "OptionalExpHolder", "Class with optional member")
-        .def(py::init<>())
-        .def_readonly("member", &opt_exp_holder::member)
-        .def("member_initialized", &opt_exp_holder::member_initialized);
-#endif
-
-#ifdef PYBIND11_HAS_VARIANT
-    static_assert(std::is_same<py::detail::variant_caster_visitor::result_type, py::handle>::value,
-                  "visitor::result_type is required by boost::variant in C++11 mode");
-
-    struct visitor {
-        using result_type = const char *;
-
-        result_type operator()(int) { return "int"; }
-        result_type operator()(std::string) { return "std::string"; }
-        result_type operator()(double) { return "double"; }
-        result_type operator()(std::nullptr_t) { return "std::nullptr_t"; }
-    };
-
-    // test_variant
-    m.def("load_variant", [](variant<int, std::string, double, std::nullptr_t> v) {
-        return py::detail::visit_helper<variant>::call(visitor(), v);
-    });
-    m.def("load_variant_2pass", [](variant<double, int> v) {
-        return py::detail::visit_helper<variant>::call(visitor(), v);
-    });
-    m.def("cast_variant", []() {
-        using V = variant<int, std::string>;
-        return py::make_tuple(V(5), V("Hello"));
-    });
-#endif
-
-    // #528: templated constructor
-    // (no python tests: the test here is that this compiles)
-    m.def("tpl_ctor_vector", [](std::vector<TplCtorClass> &) {});
-    m.def("tpl_ctor_map", [](std::unordered_map<TplCtorClass, TplCtorClass> &) {});
-    m.def("tpl_ctor_set", [](std::unordered_set<TplCtorClass> &) {});
-#if defined(PYBIND11_HAS_OPTIONAL)
-    m.def("tpl_constr_optional", [](std::optional<TplCtorClass> &) {});
-#elif defined(PYBIND11_HAS_EXP_OPTIONAL)
-    m.def("tpl_constr_optional", [](std::experimental::optional<TplCtorClass> &) {});
-#endif
-
-    // test_vec_of_reference_wrapper
-    // #171: Can't return STL structures containing reference wrapper
-    m.def("return_vec_of_reference_wrapper", [](std::reference_wrapper<UserType> p4) {
-        static UserType p1{1}, p2{2}, p3{3};
-        return std::vector<std::reference_wrapper<UserType>> {
-            std::ref(p1), std::ref(p2), std::ref(p3), p4
-        };
-    });
-
-    // test_stl_pass_by_pointer
-    m.def("stl_pass_by_pointer", [](std::vector<int>* v) { return *v; }, "v"_a=nullptr);
-
-    // #1258: pybind11/stl.h converts string to vector<string>
-    m.def("func_with_string_or_vector_string_arg_overload", [](std::vector<std::string>) { return 1; });
-    m.def("func_with_string_or_vector_string_arg_overload", [](std::list<std::string>) { return 2; });
-    m.def("func_with_string_or_vector_string_arg_overload", [](std::string) { return 3; });
-
-    class Placeholder {
-    public:
-        Placeholder() { print_created(this); }
-        Placeholder(const Placeholder &) = delete;
-        ~Placeholder() { print_destroyed(this); }
-    };
-    py::class_<Placeholder>(m, "Placeholder");
-
-    /// test_stl_vector_ownership
-    m.def("test_stl_ownership",
-          []() {
-              std::vector<Placeholder *> result;
-              result.push_back(new Placeholder());
-              return result;
-          },
-          py::return_value_policy::take_ownership);
-
-    m.def("array_cast_sequence", [](std::array<int, 3> x) { return x; });
-
-    /// test_issue_1561
-    struct Issue1561Inner { std::string data; };
-    struct Issue1561Outer { std::vector<Issue1561Inner> list; };
-
-    py::class_<Issue1561Inner>(m, "Issue1561Inner")
-        .def(py::init<std::string>())
-        .def_readwrite("data", &Issue1561Inner::data);
-
-    py::class_<Issue1561Outer>(m, "Issue1561Outer")
-        .def(py::init<>())
-        .def_readwrite("list", &Issue1561Outer::list);
-}
diff --git a/spaces/CVPR/LIVE/pydiffvg_tensorflow/image.py b/spaces/CVPR/LIVE/pydiffvg_tensorflow/image.py
deleted file mode 100644
index 18eb1e6b66ae077b1c9d4b534a5fce250fe3958a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pydiffvg_tensorflow/image.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import numpy as np
-import skimage
-import skimage.io
-import os
-
-def imwrite(img, filename, gamma = 2.2, normalize = False):
-    directory = os.path.dirname(filename)
-    if directory != '' and not os.path.exists(directory):
-        os.makedirs(directory)
-
-    if not isinstance(img, np.ndarray):
-        img = img.numpy()
-    if normalize:
-        img_rng = np.max(img) - np.min(img)
-        if img_rng > 0:
-            img = (img - np.min(img)) / img_rng
-    img = np.clip(img, 0.0, 1.0)
-    if img.ndim==2:
-        #repeat along the third dimension
-        img=np.expand_dims(img,2)
-    img[:, :, :3] = np.power(img[:, :, :3], 1.0/gamma)
-    skimage.io.imsave(filename, (img * 255).astype(np.uint8))
\ No newline at end of file
diff --git a/spaces/CVPR/regionclip-demo/detectron2/modeling/backbone/build.py b/spaces/CVPR/regionclip-demo/detectron2/modeling/backbone/build.py
deleted file mode 100644
index 3f407420c13b3c28878aaea98e6958093b9d2caf..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/modeling/backbone/build.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.layers import ShapeSpec
-from detectron2.utils.registry import Registry
-
-from .backbone import Backbone
-
-BACKBONE_REGISTRY = Registry("BACKBONE")
-BACKBONE_REGISTRY.__doc__ = """
-Registry for backbones, which extract feature maps from images
-
-The registered object must be a callable that accepts two arguments:
-
-1. A :class:`detectron2.config.CfgNode`
-2. A :class:`detectron2.layers.ShapeSpec`, which contains the input shape specification.
-
-Registered object must return instance of :class:`Backbone`.
-"""
-
-
-def build_backbone(cfg, input_shape=None):
-    """
-    Build a backbone from `cfg.MODEL.BACKBONE.NAME`.
-
-    Returns:
-        an instance of :class:`Backbone`
-    """
-    if input_shape is None:
-        input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))
-
-    backbone_name = cfg.MODEL.BACKBONE.NAME
-    backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)
-    assert isinstance(backbone, Backbone)
-    return backbone
-
-def build_text_backbone(cfg, input_shape=None):
-    """
-    Build a backbone from `cfg.MODEL.BACKBONE.NAME`.
-
-    Returns:
-        an instance of :class:`Backbone`
-    """
-    if input_shape is None:
-        input_shape = ShapeSpec(channels=len(cfg.MODEL.PIXEL_MEAN))
-
-    backbone_name = cfg.MODEL.BACKBONE.NAME
-    backbone = BACKBONE_REGISTRY.get(backbone_name)(cfg, input_shape)
-    assert isinstance(backbone, Backbone)
-    return backbone
\ No newline at end of file
diff --git a/spaces/ChallengeHub/Chinese-LangChain/corpus/zh_wikipedia/README.md b/spaces/ChallengeHub/Chinese-LangChain/corpus/zh_wikipedia/README.md
deleted file mode 100644
index 63d61d9b98690f306fc3d3fbdb4fda6c18d65164..0000000000000000000000000000000000000000
--- a/spaces/ChallengeHub/Chinese-LangChain/corpus/zh_wikipedia/README.md
+++ /dev/null
@@ -1,114 +0,0 @@
-## 知识库构建
-
-
-###  1 Wikipedia构建
-
-参考教程：https://blog.51cto.com/u_15127535/2697309
-
-
-一、维基百科
-
-维基百科（Wikipedia），是一个基于维基技术的多语言百科全书协作计划，也是一部用不同语言写成的网络百科全书。维基百科是由吉米·威尔士与拉里·桑格两人合作创建的，于2001年1月13日在互联网上推出网站服务，并在2001年1月15日正式展开网络百科全书的项目。
-
-
-
-二、维基百科处理
-
-1 环境配置（1）编程语言采用 python3（2）Gensim第三方库，Gensim是一个Python的工具包，其中有包含了中文维基百科数据处理的类，使用方便。
-Gensim : https://github.com/RaRe-Technologies/gensim
-
-使用 pip install gensim 安装gensim。
-
-（3）OpenCC第三方库，是中文字符转换，包括中文简体繁体相互转换等。
-
-OpenCC：https://github.com/BYVoid/OpenCC，OpenCC源码采用c++实现，如果会用c++的可以使用根据介绍，make编译源码。
-
-OpenCC也有python版本实现，可以通过pip安装（pip install opencc-python），速度要比c++版慢，但是使用方便，安装简单，推荐使用pip安装。
-
-
-
-2 数据下载
-
-中文维基百科数据按月进行更新备份，一般情况下，下载当前最新的数据，下载地址（https://dumps.wikimedia.org/zhwiki/latest/），我们下载的数据是：zhwiki-latest-pages-articles.xml.bz2。
-
-中文维基百科数据一般包含如下几个部分：
-
-
-
-训练词向量采用的数据是正文数据，下面我们将对正文数据进行处理。
-
-
-
-3 数据抽取
-
-下载下来的数据是压缩文件（bz2，gz），不需要解压，这里已经写好了一份利用gensim处理维基百科数据的脚本
-
-wikidata_processhttps://github.com/bamtercelboo/corpus_process_script/tree/master/wikidata_process
-
-使用：
-
-python wiki_process.py zhwiki-latest-pages-articles.xml.bz2 zhwiki-latest.txt
-
-这部分需要一些的时间，处理过后的得到一份中文维基百科正文数据（zhwiki-latest.txt）。
-
-输出文件类似于：
-
-歐幾里得 西元前三世紀的古希臘數學家 現在被認為是幾何之父 此畫為拉斐爾的作品 雅典學院 数学 是利用符号语言研究數量 结构 变化以及空间等概念的一門学科
-
-
-
-4 中文繁体转简体
-
-经过上述脚本得到的文件包含了大量的中文繁体字，我们需要将其转换成中文简体字。
-
-我们利用OpenCC进行繁体转简体的操作，这里已经写好了一份python版本的脚本来进行处理
-
-chinese_t2s
-
-https://github.com/bamtercelboo/corpus_process_script/tree/master/chinese_t2s
-
-使用：
-
-python chinese_t2s.py –input input_file –output output_file
-
-like:
-
-python chinese_t2s.py –input zhwiki-latest.txt –output zhwiki-latest-simplified.txt
-
-输出文件类似于
-
-欧几里得 西元前三世纪的古希腊数学家 现在被认为是几何之父 此画为拉斐尔的作品 雅典学院 数学 是利用符号语言研究数量 结构 变化以及空间等概念的一门学科
-
-      5.清洗语料
-
-上述处理已经得到了我们想要的数据，但是在其他的一些任务中，还需要对这份数据进行简单的处理，像词向量任务，在这得到的数据里，还包含很多的英文，日文，德语，中文标点，乱码等一些字符，我们要把这些字符清洗掉，只留下中文字符，仅仅留下中文字符只是一种处理方案，不同的任务需要不同的处理，这里已经写好了一份脚本
-
-clean
-
-https://github.com/bamtercelboo/corpus_process_script/tree/master/clean
-
-使用：
-
-python clean_corpus.py –input input_file –output output_file
-
-like：
-
-python clean_corpus.py –input zhwiki-latest-simplified.txt –output zhwiki-latest-simplified_cleaned.txt
-
-效果：
-
-input:
-
-哲学	哲学（英语：philosophy）是对普遍的和基本的问题的研究，这些问题通常和存在、知识、价值、理性、心灵、语言等有关。
-
-output:
-
-哲学哲学英语是对普遍的和基本的问题的研究这些问题通常和存在知识价值理性心灵语言等有关
-
-
-
-三、数据处理脚本
-
-近在github上新开了一个Repositorycorpus-process-scripthttps://github.com/bamtercelboo/corpus_process_script在这个repo，将存放中英文数据处理脚本，语言不限，会有详细的README，希望对大家能有一些帮助。
-References
-
diff --git a/spaces/ChengZ/DeepDanbooru_string0/app.py b/spaces/ChengZ/DeepDanbooru_string0/app.py
deleted file mode 100644
index 49019837c9207cc68cb37be0342f3bc44fd0decb..0000000000000000000000000000000000000000
--- a/spaces/ChengZ/DeepDanbooru_string0/app.py
+++ /dev/null
@@ -1,185 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import functools
-import os
-import html
-import pathlib
-import tarfile
-
-import deepdanbooru as dd
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import PIL.Image
-import tensorflow as tf
-import piexif
-import piexif.helper
-
-TITLE = 'DeepDanbooru String'
-
-TOKEN = os.environ['TOKEN']
-MODEL_REPO = 'CikeyQI/DeepDanbooru_string'
-MODEL_FILENAME = 'model-resnet_custom_v3.h5'
-LABEL_FILENAME = 'tags.txt'
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--score-slider-step', type=float, default=0.05)
-    parser.add_argument('--score-threshold', type=float, default=0.5)
-    parser.add_argument('--theme', type=str, default='dark-grass')
-    parser.add_argument('--live', action='store_true')
-    parser.add_argument('--share', action='store_true')
-    parser.add_argument('--port', type=int)
-    parser.add_argument('--disable-queue',
-                        dest='enable_queue',
-                        action='store_false')
-    parser.add_argument('--allow-flagging', type=str, default='never')
-    return parser.parse_args()
-
-
-def load_sample_image_paths() -> list[pathlib.Path]:
-    image_dir = pathlib.Path('images')
-    if not image_dir.exists():
-        dataset_repo = 'hysts/sample-images-TADNE'
-        path = huggingface_hub.hf_hub_download(dataset_repo,
-                                               'images.tar.gz',
-                                               repo_type='dataset',
-                                               use_auth_token=TOKEN)
-        with tarfile.open(path) as f:
-            f.extractall()
-    return sorted(image_dir.glob('*'))
-
-
-def load_model() -> tf.keras.Model:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           MODEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    model = tf.keras.models.load_model(path)
-    return model
-
-
-def load_labels() -> list[str]:
-    path = huggingface_hub.hf_hub_download(MODEL_REPO,
-                                           LABEL_FILENAME,
-                                           use_auth_token=TOKEN)
-    with open(path) as f:
-        labels = [line.strip() for line in f.readlines()]
-    return labels
-
-def plaintext_to_html(text):
-    text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
-    return text
-
-def predict(image: PIL.Image.Image, score_threshold: float,
-            model: tf.keras.Model, labels: list[str]) -> dict[str, float]:
-    rawimage = image
-    _, height, width, _ = model.input_shape
-    image = np.asarray(image)
-    image = tf.image.resize(image,
-                            size=(height, width),
-                            method=tf.image.ResizeMethod.AREA,
-                            preserve_aspect_ratio=True)
-    image = image.numpy()
-    image = dd.image.transform_and_pad_image(image, width, height)
-    image = image / 255.
-    probs = model.predict(image[None, ...])[0]
-    probs = probs.astype(float)
-    res = dict()
-    for prob, label in zip(probs.tolist(), labels):
-        if prob < score_threshold:
-            continue
-        res[label] = prob
-    b = dict(sorted(res.items(),key=lambda item:item[1], reverse=True))
-    a = ', '.join(list(b.keys())).replace('_',' ').replace('(','\(').replace(')','\)')
-    c = ', '.join(list(b.keys()))
-    
-    items = rawimage.info
-    geninfo = ''
-    
-    if "exif" in rawimage.info:
-        exif = piexif.load(rawimage.info["exif"])
-        exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'')
-        try:
-            exif_comment = piexif.helper.UserComment.load(exif_comment)
-        except ValueError:
-            exif_comment = exif_comment.decode('utf8', errors="ignore")
-    
-        items['exif comment'] = exif_comment
-        geninfo = exif_comment
-    
-        for field in ['jfif', 'jfif_version', 'jfif_unit', 'jfif_density', 'dpi', 'exif',
-                      'loop', 'background', 'timestamp', 'duration']:
-            items.pop(field, None)
-    
-    geninfo = items.get('parameters', geninfo)
-    
-    info = f"""
-<p><h4>PNG Info</h4></p>    
-"""
-    for key, text in items.items():
-        info += f"""
-<div>
-<p><b>{plaintext_to_html(str(key))}</b></p>
-<p>{plaintext_to_html(str(text))}</p>
-</div>
-""".strip()+"\n"
-    
-    if len(info) == 0:
-        message = "Nothing found in the image."
-        info = f"<div><p>{message}<p></div>"
-    
-    return (a,c,res,info)
-
-
-def main():
-    args = parse_args()
-    model = load_model()
-    labels = load_labels()
-
-    func = functools.partial(predict, model=model, labels=labels)
-    func = functools.update_wrapper(func, predict)
-
-    gr.Interface(
-        func,
-        [
-            gr.inputs.Image(type='pil', label='Input'),
-            gr.inputs.Slider(0,
-                             1,
-                             step=args.score_slider_step,
-                             default=args.score_threshold,
-                             label='Score Threshold'),
-        ],
-        [
-            gr.outputs.Textbox(label='Output (string)'), 
-            gr.outputs.Textbox(label='Output (raw string)'), 
-            gr.outputs.Label(label='Output (label)'),
-            gr.outputs.HTML()
-        ],
-        examples=[
-        ['miku.jpg',0.5],
-        ['miku2.jpg',0.5]
-        ],
-        title=TITLE,
-        description='''
-Demo for [KichangKim/DeepDanbooru](https://github.com/KichangKim/DeepDanbooru) with "ready to copy" prompt and a prompt analyzer.
-
-Modified from [hysts/DeepDanbooru](https://huggingface.co/spaces/hysts/DeepDanbooru)
-
-PNG Info code forked from [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-        ''',
-        theme=args.theme,
-        allow_flagging=args.allow_flagging,
-        live=args.live,
-    ).launch(
-        enable_queue=args.enable_queue,
-        server_port=args.port,
-        share=args.share,
-    )
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n/locale_diff.py b/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n/locale_diff.py
deleted file mode 100644
index 257277965e0866a86d0361863a8f1b408c4f71ab..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/i18n/locale_diff.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import json
-import os
-from collections import OrderedDict
-
-# Define the standard file name
-standard_file = "zh_CN.json"
-
-# Find all JSON files in the directory
-dir_path = "./"
-languages = [
-    f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file
-]
-
-# Load the standard file
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-
-# Loop through each language file
-for lang_file in languages:
-    # Load the language file
-    with open(lang_file, "r", encoding="utf-8") as f:
-        lang_data = json.load(f, object_pairs_hook=OrderedDict)
-
-    # Find the difference between the language file and the standard file
-    diff = set(standard_data.keys()) - set(lang_data.keys())
-
-    miss = set(lang_data.keys()) - set(standard_data.keys())
-
-    # Add any missing keys to the language file
-    for key in diff:
-        lang_data[key] = key
-
-    # Del any extra keys to the language file
-    for key in miss:
-        del lang_data[key]
-
-    # Sort the keys of the language file to match the order of the standard file
-    lang_data = OrderedDict(
-        sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0]))
-    )
-
-    # Save the updated language file
-    with open(lang_file, "w", encoding="utf-8") as f:
-        json.dump(lang_data, f, ensure_ascii=False, indent=4)
-        f.write("\n")
diff --git "a/spaces/Cong723/gpt-academic-public/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py" "b/spaces/Cong723/gpt-academic-public/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
deleted file mode 100644
index 505086455af8d2676055ab084cf97058b954c7d5..0000000000000000000000000000000000000000
--- "a/spaces/Cong723/gpt-academic-public/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
+++ /dev/null
@@ -1,112 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import tiktoken
-    print('begin analysis on:', file_name)
-
-    ############################## <第 0 步，切割PDF> ##################################
-    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-    # 的长度必须小于 2500 个 Token
-    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-    
-    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-    final_results = []
-    final_results.append(paper_meta)
-
-    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-    iteration_results = []
-    last_iteration_result = paper_meta  # 初始值是摘要
-    MAX_WORD_TOTAL = 4096
-    n_fragment = len(paper_fragments)
-    if n_fragment >= 20: print('文章极长，不能达到预期效果')
-    for i in range(n_fragment):
-        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
-        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                           llm_kwargs, chatbot, 
-                                                                           history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                           sys_prompt="Extract the main idea of this section."  # 提示
-                                                                        ) 
-        iteration_results.append(gpt_say)
-        last_iteration_result = gpt_say
-
-    ############################## <第 3 步，整理history> ##################################
-    final_results.extend(iteration_results)
-    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
-    # 接下来两句话只显示在界面上，不起实际作用
-    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
-    chatbot.append([i_say_show_user, gpt_say])
-
-    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
-    from .crazy_utils import input_clipping
-    _, final_results = input_clipping("", final_results, max_token_limit=3200)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-
-@CatchException
-def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    txt = file_manifest[0]
-    # 开始正式执行任务
-    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/cliTools.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/cliTools.py
deleted file mode 100644
index 8322ea9ebb7cd1dd907829a985b9833058bc54c1..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/cliTools.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""Collection of utilities for command-line interfaces and console scripts."""
-import os
-import re
-
-
-numberAddedRE = re.compile(r"#\d+$")
-
-
-def makeOutputFileName(
-    input, outputDir=None, extension=None, overWrite=False, suffix=""
-):
-    """Generates a suitable file name for writing output.
-
-    Often tools will want to take a file, do some kind of transformation to it,
-    and write it out again. This function determines an appropriate name for the
-    output file, through one or more of the following steps:
-
-    - changing the output directory
-    - appending suffix before file extension
-    - replacing the file extension
-    - suffixing the filename with a number (``#1``, ``#2``, etc.) to avoid
-      overwriting an existing file.
-
-    Args:
-        input: Name of input file.
-        outputDir: Optionally, a new directory to write the file into.
-        suffix: Optionally, a string suffix is appended to file name before
-            the extension.
-        extension: Optionally, a replacement for the current file extension.
-        overWrite: Overwriting an existing file is permitted if true; if false
-            and the proposed filename exists, a new name will be generated by
-            adding an appropriate number suffix.
-
-    Returns:
-        str: Suitable output filename
-    """
-    dirName, fileName = os.path.split(input)
-    fileName, ext = os.path.splitext(fileName)
-    if outputDir:
-        dirName = outputDir
-    fileName = numberAddedRE.split(fileName)[0]
-    if extension is None:
-        extension = os.path.splitext(input)[1]
-    output = os.path.join(dirName, fileName + suffix + extension)
-    n = 1
-    if not overWrite:
-        while os.path.exists(output):
-            output = os.path.join(
-                dirName, fileName + suffix + "#" + repr(n) + extension
-            )
-            n += 1
-    return output
diff --git a/spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/languages.py b/spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/languages.py
deleted file mode 100644
index 0551511f86eb917fd0668d971b21e177c387b7ef..0000000000000000000000000000000000000000
--- a/spaces/Daextream/Whisper-Auto-Subtitled-Video-Generator/languages.py
+++ /dev/null
@@ -1,101 +0,0 @@
-LANGUAGES = {
-    "en": "eng",
-    "zh": "zho",
-    "de": "deu",
-    "es": "spa",
-    "ru": "rus",
-    "ko": "kor",
-    "fr": "fra",
-    "ja": "jpn",
-    "pt": "por",
-    "tr": "tur",
-    "pl": "pol",
-    "ca": "cat",
-    "nl": "nld",
-    "ar": "ara",
-    "sv": "swe",
-    "it": "ita",
-    "id": "ind",
-    "hi": "hin",
-    "fi": "fin",
-    "vi": "vie",
-    "iw": "heb",
-    "uk": "ukr",
-    "el": "ell",
-    "ms": "msa",
-    "cs": "ces",
-    "ro": "ron",
-    "da": "dan",
-    "hu": "hun",
-    "ta": "tam",
-    "no": "nor",
-    "th": "tha",
-    "ur": "urd",
-    "hr": "hrv",
-    "bg": "bul",
-    "lt": "lit",
-    "la": "lat",
-    "mi": "mri",
-    "ml": "mal",
-    "cy": "cym",
-    "sk": "slk",
-    "te": "tel",
-    "fa": "fas",
-    "lv": "lav",
-    "bn": "ben",
-    "sr": "srp",
-    "az": "aze",
-    "sl": "slv",
-    "kn": "kan",
-    "et": "est",
-    "mk": "mkd",
-    "br": "bre",
-    "eu": "eus",
-    "is": "isl",
-    "hy": "hye",
-    "ne": "nep",
-    "mn": "mon",
-    "bs": "bos",
-    "kk": "kaz",
-    "sq": "sqi",
-    "sw": "swa",
-    "gl": "glg",
-    "mr": "mar",
-    "pa": "pan",
-    "si": "sin",
-    "km": "khm",
-    "sn": "sna",
-    "yo": "yor",
-    "so": "som",
-    "af": "afr",
-    "oc": "oci",
-    "ka": "kat",
-    "be": "bel",
-    "tg": "tgk",
-    "sd": "snd",
-    "gu": "guj",
-    "am": "amh",
-    "yi": "yid",
-    "lo": "lao",
-    "uz": "uzb",
-    "fo": "fao",
-    "ht": "hat",
-    "ps": "pus",
-    "tk": "tuk",
-    "nn": "nno",
-    "mt": "mlt",
-    "sa": "san",
-    "lb": "ltz",
-    "my": "mya",
-    "bo": "bod",
-    "tl": "tgl",
-    "mg": "mlg",
-    "as": "asm",
-    "tt": "tat",
-    "haw": "haw",
-    "ln": "lin",
-    "ha": "hau",
-    "ba": "bak",
-    "jw": "jav",
-    "su": "sun",
-}
\ No newline at end of file
diff --git a/spaces/Dauzy/whisper-webui/app-local.py b/spaces/Dauzy/whisper-webui/app-local.py
deleted file mode 100644
index c7717d096ca5f95177f0dba03cd62ca729bae9f3..0000000000000000000000000000000000000000
--- a/spaces/Dauzy/whisper-webui/app-local.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Run the app with no audio file restrictions
-from app import create_ui
-from src.config import ApplicationConfig
-
-create_ui(ApplicationConfig.create_default(input_audio_max_duration=-1))
\ No newline at end of file
diff --git a/spaces/Demi2809/rvc-models/infer_pack/attentions.py b/spaces/Demi2809/rvc-models/infer_pack/attentions.py
deleted file mode 100644
index 77cb63ffccf3e33badf22d50862a64ba517b487f..0000000000000000000000000000000000000000
--- a/spaces/Demi2809/rvc-models/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer_pack import commons
-from infer_pack import modules
-from infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/Dormin22/Proxy/greeting.md b/spaces/Dormin22/Proxy/greeting.md
deleted file mode 100644
index 9df71687dc75de4b8afd93c54cb779c720ff2ed2..0000000000000000000000000000000000000000
--- a/spaces/Dormin22/Proxy/greeting.md
+++ /dev/null
@@ -1 +0,0 @@
-Hope is the last thing ever lost.
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/README.md b/spaces/DragGan/DragGan-Inversion/README.md
deleted file mode 100644
index 7ea7dcbb011aebb97612f542ec5a20a8e412cb0d..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/README.md
+++ /dev/null
@@ -1,77 +0,0 @@
----
-title: DragGan - Drag Your GAN - Inversion
-emoji: 🔄🐉
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-python_version: 3.8.17
-sdk_version: 3.36.1
-app_file: visualizer_drag_gradio_inversion.py
-pinned: false
----
-
-
-# Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
-
-https://arxiv.org/abs/2305.10973
-https://huggingface.co/DragGan/DragGan-Models
-
-<p align="center">
-    <img src="DragGAN.gif", width="700">
-</p>
-
-**Figure:** *Drag your GAN.*
-
-> **Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold** <br>
-> Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, Christian Theobalt<br>
-> *SIGGRAPH 2023 Conference Proceedings*
-
-## Requirements
-
-Please follow the requirements of [https://github.com/NVlabs/stylegan3](https://github.com/NVlabs/stylegan3).
-
-## Download pre-trained StyleGAN2 weights
-
-To download pre-trained weights, simply run:
-```sh
-sh scripts/download_model.sh
-```
-If you want to try StyleGAN-Human and the Landscapes HQ (LHQ) dataset, please download weights from these links: [StyleGAN-Human](https://drive.google.com/file/d/1dlFEHbu-WzQWJl7nBBZYcTyo000H9hVm/view?usp=sharing), [LHQ](https://drive.google.com/file/d/16twEf0T9QINAEoMsWefoWiyhcTd-aiWc/view?usp=sharing), and put them under `./checkpoints`.
-
-Feel free to try other pretrained StyleGAN.
-
-## Run DragGAN GUI
-
-To start the DragGAN GUI, simply run:
-```sh
-sh scripts/gui.sh
-```
-
-This GUI supports editing GAN-generated images. To edit a real image, you need to first perform GAN inversion using tools like [PTI](https://github.com/danielroich/PTI). Then load the new latent code and model weights to the GUI.
-
-You can run DragGAN Gradio demo as well:
-```sh
-python visualizer_drag_gradio.py
-```
-
-## Acknowledgement
-
-This code is developed based on [StyleGAN3](https://github.com/NVlabs/stylegan3). Part of the code is borrowed from [StyleGAN-Human](https://github.com/stylegan-human/StyleGAN-Human).
-
-## License
-
-The code related to the DragGAN algorithm is licensed under [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/).
-However, most of this project are available under a separate license terms: all codes used or modified from [StyleGAN3](https://github.com/NVlabs/stylegan3) is under the [Nvidia Source Code License](https://github.com/NVlabs/stylegan3/blob/main/LICENSE.txt).
-
-Any form of use and derivative of this code must preserve the watermarking functionality.
-
-## BibTeX
-
-```bibtex
-@inproceedings{pan2023draggan,
-    title={Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold},
-    author={Pan, Xingang and Tewari, Ayush, and Leimk{\"u}hler, Thomas and Liu, Lingjie and Meka, Abhimitra and Theobalt, Christian},
-    booktitle = {ACM SIGGRAPH 2023 Conference Proceedings},
-    year={2023}
-}
-```
diff --git a/spaces/DragGan/DragGan/stylegan_human/training/dataset.py b/spaces/DragGan/DragGan/stylegan_human/training/dataset.py
deleted file mode 100644
index 68c356e3b89b63211e0b4bdde88babcffd26d59e..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan/stylegan_human/training/dataset.py
+++ /dev/null
@@ -1,238 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Streaming images and labels from datasets created with dataset_tool.py."""
-
-import os
-import numpy as np
-import zipfile
-import PIL.Image
-import json
-import torch
-import dnnlib
-
-try:
-    import pyspng
-except ImportError:
-    pyspng = None
-
-#----------------------------------------------------------------------------
-
-class Dataset(torch.utils.data.Dataset):
-    def __init__(self,
-        name,                   # Name of the dataset.
-        raw_shape,              # Shape of the raw image data (NCHW).
-        max_size    = None,     # Artificially limit the size of the dataset. None = no limit. Applied before xflip.
-        use_labels  = False,    # Enable conditioning labels? False = label dimension is zero.
-        xflip       = False,    # Artificially double the size of the dataset via x-flips. Applied after max_size.
-        random_seed = 0,        # Random seed to use when applying max_size.
-    ):
-        self._name = name
-        self._raw_shape = list(raw_shape)
-        self._use_labels = use_labels
-        self._raw_labels = None
-        self._label_shape = None
-
-        # Apply max_size.
-        self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64)
-        if (max_size is not None) and (self._raw_idx.size > max_size):
-            np.random.RandomState(random_seed).shuffle(self._raw_idx)
-            self._raw_idx = np.sort(self._raw_idx[:max_size])
-
-        # Apply xflip.
-        self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8)
-        if xflip:
-            self._raw_idx = np.tile(self._raw_idx, 2)
-            self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)])
-
-    def _get_raw_labels(self):
-        if self._raw_labels is None:
-            self._raw_labels = self._load_raw_labels() if self._use_labels else None
-            if self._raw_labels is None:
-                self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32)
-            assert isinstance(self._raw_labels, np.ndarray)
-            assert self._raw_labels.shape[0] == self._raw_shape[0]
-            assert self._raw_labels.dtype in [np.float32, np.int64]
-            if self._raw_labels.dtype == np.int64:
-                assert self._raw_labels.ndim == 1
-                assert np.all(self._raw_labels >= 0)
-        return self._raw_labels
-
-    def close(self): # to be overridden by subclass
-        pass
-
-    def _load_raw_image(self, raw_idx): # to be overridden by subclass
-        raise NotImplementedError
-
-    def _load_raw_labels(self): # to be overridden by subclass
-        raise NotImplementedError
-
-    def __getstate__(self):
-        return dict(self.__dict__, _raw_labels=None)
-
-    def __del__(self):
-        try:
-            self.close()
-        except:
-            pass
-
-    def __len__(self):
-        return self._raw_idx.size
-
-    def __getitem__(self, idx):
-        image = self._load_raw_image(self._raw_idx[idx])
-        assert isinstance(image, np.ndarray)
-        assert list(image.shape) == self.image_shape
-        assert image.dtype == np.uint8
-        if self._xflip[idx]:
-            assert image.ndim == 3 # CHW
-            image = image[:, :, ::-1]
-        return image.copy(), self.get_label(idx)
-
-    def get_label(self, idx):
-        label = self._get_raw_labels()[self._raw_idx[idx]]
-        if label.dtype == np.int64:
-            onehot = np.zeros(self.label_shape, dtype=np.float32)
-            onehot[label] = 1
-            label = onehot
-        return label.copy()
-
-    def get_details(self, idx):
-        d = dnnlib.EasyDict()
-        d.raw_idx = int(self._raw_idx[idx])
-        d.xflip = (int(self._xflip[idx]) != 0)
-        d.raw_label = self._get_raw_labels()[d.raw_idx].copy()
-        return d
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def image_shape(self):
-        return list(self._raw_shape[1:])
-
-    @property
-    def num_channels(self):
-        assert len(self.image_shape) == 3 # CHW
-        return self.image_shape[0]
-
-    @property
-    def resolution(self):
-        assert len(self.image_shape) == 3 # CHW
-        assert self.image_shape[1] == self.image_shape[2]
-        return self.image_shape[1]
-
-    @property
-    def label_shape(self):
-        if self._label_shape is None:
-            raw_labels = self._get_raw_labels()
-            if raw_labels.dtype == np.int64:
-                self._label_shape = [int(np.max(raw_labels)) + 1]
-            else:
-                self._label_shape = raw_labels.shape[1:]
-        return list(self._label_shape)
-
-    @property
-    def label_dim(self):
-        assert len(self.label_shape) == 1
-        return self.label_shape[0]
-
-    @property
-    def has_labels(self):
-        return any(x != 0 for x in self.label_shape)
-
-    @property
-    def has_onehot_labels(self):
-        return self._get_raw_labels().dtype == np.int64
-
-#----------------------------------------------------------------------------
-
-class ImageFolderDataset(Dataset):
-    def __init__(self,
-        path,                   # Path to directory or zip.
-        resolution      = None, # Ensure specific resolution, None = highest available.
-        **super_kwargs,         # Additional arguments for the Dataset base class.
-    ):
-        self._path = path
-        self._zipfile = None
-
-        if os.path.isdir(self._path):
-            self._type = 'dir'
-            self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files}
-        elif self._file_ext(self._path) == '.zip':
-            self._type = 'zip'
-            self._all_fnames = set(self._get_zipfile().namelist())
-        else:
-            raise IOError('Path must point to a directory or zip')
-
-        PIL.Image.init()
-        self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION)
-        if len(self._image_fnames) == 0:
-            raise IOError('No image files found in the specified path')
-
-        name = os.path.splitext(os.path.basename(self._path))[0]
-        raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape)
-        if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution):
-            raise IOError('Image files do not match the specified resolution')
-        super().__init__(name=name, raw_shape=raw_shape, **super_kwargs)
-
-    @staticmethod
-    def _file_ext(fname):
-        return os.path.splitext(fname)[1].lower()
-
-    def _get_zipfile(self):
-        assert self._type == 'zip'
-        if self._zipfile is None:
-            self._zipfile = zipfile.ZipFile(self._path)
-        return self._zipfile
-
-    def _open_file(self, fname):
-        if self._type == 'dir':
-            return open(os.path.join(self._path, fname), 'rb')
-        if self._type == 'zip':
-            return self._get_zipfile().open(fname, 'r')
-        return None
-
-    def close(self):
-        try:
-            if self._zipfile is not None:
-                self._zipfile.close()
-        finally:
-            self._zipfile = None
-
-    def __getstate__(self):
-        return dict(super().__getstate__(), _zipfile=None)
-
-    def _load_raw_image(self, raw_idx):
-        fname = self._image_fnames[raw_idx]
-        with self._open_file(fname) as f:
-            if pyspng is not None and self._file_ext(fname) == '.png':
-                image = pyspng.load(f.read())
-            else:
-                image = np.array(PIL.Image.open(f))
-        if image.ndim == 2:
-            image = image[:, :, np.newaxis] # HW => HWC
-        image = image.transpose(2, 0, 1) # HWC => CHW
-        return image
-
-    def _load_raw_labels(self):
-        fname = 'dataset.json'
-        if fname not in self._all_fnames:
-            return None
-        with self._open_file(fname) as f:
-            labels = json.load(f)['labels']
-        if labels is None:
-            return None
-        labels = dict(labels)
-        labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames]
-        labels = np.array(labels)
-        labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim])
-        return labels
-
-#----------------------------------------------------------------------------
diff --git a/spaces/EDGAhab/VITS-Aatrox-AI/utils.py b/spaces/EDGAhab/VITS-Aatrox-AI/utils.py
deleted file mode 100644
index b445fb65836a0b97e46426300eea9a820179797a..0000000000000000000000000000000000000000
--- a/spaces/EDGAhab/VITS-Aatrox-AI/utils.py
+++ /dev/null
@@ -1,258 +0,0 @@
-import os
-import glob
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-  logger.info("Saving model and optimizer state at iteration {} to {}".format(
-    iteration, checkpoint_path))
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  torch.save({'model': state_dict,
-              'iteration': iteration,
-              'optimizer': optimizer.state_dict(),
-              'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-  for k, v in scalars.items():
-    writer.add_scalar(k, v, global_step)
-  for k, v in histograms.items():
-    writer.add_histogram(k, v, global_step)
-  for k, v in images.items():
-    writer.add_image(k, v, global_step, dataformats='HWC')
-  for k, v in audios.items():
-    writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-  f_list = glob.glob(os.path.join(dir_path, regex))
-  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-  x = f_list[-1]
-  print(x)
-  return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-  
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_wav_to_torch(full_path):
-  sampling_rate, data = read(full_path)
-  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-  
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-  
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-  
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-    
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()
diff --git a/spaces/Eddycrack864/Applio-Inference/lib/infer_pack/attentions.py b/spaces/Eddycrack864/Applio-Inference/lib/infer_pack/attentions.py
deleted file mode 100644
index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/lib/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer_pack import commons
-from lib.infer_pack import modules
-from lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
diff --git a/spaces/Egrt/GCycleGAN/nets/resnest/resnest.py b/spaces/Egrt/GCycleGAN/nets/resnest/resnest.py
deleted file mode 100644
index a9fe5f79d347349aeb1db8ed5af5f2f8415a8b4d..0000000000000000000000000000000000000000
--- a/spaces/Egrt/GCycleGAN/nets/resnest/resnest.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""
-@author: Jun Wang
-@date: 20210301
-@contact: jun21wangustc@gmail.com
-"""
-
-# based on:
-# https://github.com/zhanghang1989/ResNeSt/blob/master/resnest/torch/resnest.py
-
-import torch
-import torch.nn as nn
-from .resnet import ResNet, Bottleneck
-
-class Flatten(nn.Module):
-    def forward(self, input):
-        return input.view(input.size(0), -1)
-
-def l2_norm(input,axis=1):
-    norm = torch.norm(input,2,axis,True)
-    output = torch.div(input, norm)
-    return output
-                        
-class ResNeSt(nn.Module):
-    def __init__(self, num_layers=50, drop_ratio=0.4, feat_dim=512, out_h=7, out_w=7):
-        super(ResNeSt, self).__init__()
-        self.input_layer = nn.Sequential(nn.Conv2d(3, 64, (3, 3), 1, 1 ,bias=False),
-                                      nn.BatchNorm2d(64),
-                                      nn.PReLU(64))
-        self.output_layer = nn.Sequential(nn.BatchNorm2d(2048),
-                                       nn.Dropout(drop_ratio),
-                                       Flatten(),
-                                       nn.Linear(2048 * out_h * out_w, feat_dim),
-                                       nn.BatchNorm1d(feat_dim))
-        if num_layers == 50:
-            self.body = ResNet(Bottleneck, [3, 4, 6, 3],
-                                       radix=2, groups=1, bottleneck_width=64,
-                                       deep_stem=True, stem_width=32, avg_down=True,
-                                       avd=True, avd_first=False)
-        elif num_layers == 101:
-            self.body = ResNet(Bottleneck, [3, 4, 23, 3],
-                               radix=2, groups=1, bottleneck_width=64,
-                               deep_stem=True, stem_width=64, avg_down=True,
-                               avd=True, avd_first=False)
-        elif num_layers == 200:
-            self.body = ResNet(Bottleneck, [3, 24, 36, 3],
-                               radix=2, groups=1, bottleneck_width=64,
-                               deep_stem=True, stem_width=64, avg_down=True,
-                               avd=True, avd_first=False)
-        elif num_layers == 269:
-            self.body = ResNet(Bottleneck, [3, 30, 48, 8],
-                               radix=2, groups=1, bottleneck_width=64,
-                               deep_stem=True, stem_width=64, avg_down=True,
-                               avd=True, avd_first=False)
-        else:
-            pass
-    def forward(self, x):
-        x = self.input_layer(x)
-        x = self.body(x)
-        x = self.output_layer(x)
-        return l2_norm(x)
diff --git a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/train.py b/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/train.py
deleted file mode 100644
index a01c0dfccdb8b02283100ec5b792c33afaf22f5e..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/codeformer/CodeFormer/basicsr/train.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import argparse
-import datetime
-import logging
-import math
-import copy
-import random
-import time
-import torch
-from os import path as osp
-
-from basicsr.data import build_dataloader, build_dataset
-from basicsr.data.data_sampler import EnlargedSampler
-from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
-from basicsr.models import build_model
-from basicsr.utils import (MessageLogger, check_resume, get_env_info, get_root_logger, init_tb_logger,
-                           init_wandb_logger, make_exp_dirs, mkdir_and_rename, set_random_seed)
-from basicsr.utils.dist_util import get_dist_info, init_dist
-from basicsr.utils.options import dict2str, parse
-
-import warnings
-# ignore UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`.
-warnings.filterwarnings("ignore", category=UserWarning)
-
-def parse_options(root_path, is_train=True):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, required=True, help='Path to option YAML file.')
-    parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none', help='job launcher')
-    parser.add_argument('--local_rank', type=int, default=0)
-    args = parser.parse_args()
-    opt = parse(args.opt, root_path, is_train=is_train)
-
-    # distributed settings
-    if args.launcher == 'none':
-        opt['dist'] = False
-        print('Disable distributed.', flush=True)
-    else:
-        opt['dist'] = True
-        if args.launcher == 'slurm' and 'dist_params' in opt:
-            init_dist(args.launcher, **opt['dist_params'])
-        else:
-            init_dist(args.launcher)
-
-    opt['rank'], opt['world_size'] = get_dist_info()
-
-    # random seed
-    seed = opt.get('manual_seed')
-    if seed is None:
-        seed = random.randint(1, 10000)
-        opt['manual_seed'] = seed
-    set_random_seed(seed + opt['rank'])
-
-    return opt
-
-
-def init_loggers(opt):
-    log_file = osp.join(opt['path']['log'], f"train_{opt['name']}.log")
-    logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=log_file)
-    logger.info(get_env_info())
-    logger.info(dict2str(opt))
-
-    # initialize wandb logger before tensorboard logger to allow proper sync:
-    if (opt['logger'].get('wandb') is not None) and (opt['logger']['wandb'].get('project') is not None):
-        assert opt['logger'].get('use_tb_logger') is True, ('should turn on tensorboard when using wandb')
-        init_wandb_logger(opt)
-    tb_logger = None
-    if opt['logger'].get('use_tb_logger'):
-        tb_logger = init_tb_logger(log_dir=osp.join('tb_logger', opt['name']))
-    return logger, tb_logger
-
-
-def create_train_val_dataloader(opt, logger):
-    # create train and val dataloaders
-    train_loader, val_loader = None, None
-    for phase, dataset_opt in opt['datasets'].items():
-        if phase == 'train':
-            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
-            train_set = build_dataset(dataset_opt)
-            train_sampler = EnlargedSampler(train_set, opt['world_size'], opt['rank'], dataset_enlarge_ratio)
-            train_loader = build_dataloader(
-                train_set,
-                dataset_opt,
-                num_gpu=opt['num_gpu'],
-                dist=opt['dist'],
-                sampler=train_sampler,
-                seed=opt['manual_seed'])
-
-            num_iter_per_epoch = math.ceil(
-                len(train_set) * dataset_enlarge_ratio / (dataset_opt['batch_size_per_gpu'] * opt['world_size']))
-            total_iters = int(opt['train']['total_iter'])
-            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
-            logger.info('Training statistics:'
-                        f'\n\tNumber of train images: {len(train_set)}'
-                        f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
-                        f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
-                        f'\n\tWorld size (gpu number): {opt["world_size"]}'
-                        f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
-                        f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')
-
-        elif phase == 'val':
-            val_set = build_dataset(dataset_opt)
-            val_loader = build_dataloader(
-                val_set, dataset_opt, num_gpu=opt['num_gpu'], dist=opt['dist'], sampler=None, seed=opt['manual_seed'])
-            logger.info(f'Number of val images/folders in {dataset_opt["name"]}: ' f'{len(val_set)}')
-        else:
-            raise ValueError(f'Dataset phase {phase} is not recognized.')
-
-    return train_loader, train_sampler, val_loader, total_epochs, total_iters
-
-
-def train_pipeline(root_path):
-    # parse options, set distributed setting, set ramdom seed
-    opt = parse_options(root_path, is_train=True)
-
-    torch.backends.cudnn.benchmark = True
-    # torch.backends.cudnn.deterministic = True
-
-    # load resume states if necessary
-    if opt['path'].get('resume_state'):
-        device_id = torch.cuda.current_device()
-        resume_state = torch.load(
-            opt['path']['resume_state'], map_location=lambda storage, loc: storage.cuda(device_id))
-    else:
-        resume_state = None
-
-    # mkdir for experiments and logger
-    if resume_state is None:
-        make_exp_dirs(opt)
-        if opt['logger'].get('use_tb_logger') and opt['rank'] == 0:
-            mkdir_and_rename(osp.join('tb_logger', opt['name']))
-
-    # initialize loggers
-    logger, tb_logger = init_loggers(opt)
-    
-    # create train and validation dataloaders
-    result = create_train_val_dataloader(opt, logger)
-    train_loader, train_sampler, val_loader, total_epochs, total_iters = result
-
-    # create model
-    if resume_state:  # resume training
-        check_resume(opt, resume_state['iter'])
-        model = build_model(opt)
-        model.resume_training(resume_state)  # handle optimizers and schedulers
-        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, " f"iter: {resume_state['iter']}.")
-        start_epoch = resume_state['epoch']
-        current_iter = resume_state['iter']
-    else:
-        model = build_model(opt)
-        start_epoch = 0
-        current_iter = 0
-
-    # create message logger (formatted outputs)
-    msg_logger = MessageLogger(opt, current_iter, tb_logger)
-
-    # dataloader prefetcher
-    prefetch_mode = opt['datasets']['train'].get('prefetch_mode')
-    if prefetch_mode is None or prefetch_mode == 'cpu':
-        prefetcher = CPUPrefetcher(train_loader)
-    elif prefetch_mode == 'cuda':
-        prefetcher = CUDAPrefetcher(train_loader, opt)
-        logger.info(f'Use {prefetch_mode} prefetch dataloader')
-        if opt['datasets']['train'].get('pin_memory') is not True:
-            raise ValueError('Please set pin_memory=True for CUDAPrefetcher.')
-    else:
-        raise ValueError(f'Wrong prefetch_mode {prefetch_mode}.' "Supported ones are: None, 'cuda', 'cpu'.")
-
-    # training
-    logger.info(f'Start training from epoch: {start_epoch}, iter: {current_iter+1}')
-    data_time, iter_time = time.time(), time.time()
-    start_time = time.time()
-
-    for epoch in range(start_epoch, total_epochs + 1):
-        train_sampler.set_epoch(epoch)
-        prefetcher.reset()
-        train_data = prefetcher.next()
-
-        while train_data is not None:
-            data_time = time.time() - data_time
-
-            current_iter += 1
-            if current_iter > total_iters:
-                break
-            # update learning rate
-            model.update_learning_rate(current_iter, warmup_iter=opt['train'].get('warmup_iter', -1))
-            # training
-            model.feed_data(train_data)
-            model.optimize_parameters(current_iter)
-            iter_time = time.time() - iter_time
-            # log
-            if current_iter % opt['logger']['print_freq'] == 0:
-                log_vars = {'epoch': epoch, 'iter': current_iter}
-                log_vars.update({'lrs': model.get_current_learning_rate()})
-                log_vars.update({'time': iter_time, 'data_time': data_time})
-                log_vars.update(model.get_current_log())
-                msg_logger(log_vars)
-
-            # save models and training states
-            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
-                logger.info('Saving models and training states.')
-                model.save(epoch, current_iter)
-
-            # validation
-            if opt.get('val') is not None and opt['datasets'].get('val') is not None \
-                and (current_iter % opt['val']['val_freq'] == 0):
-                model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
-
-            data_time = time.time()
-            iter_time = time.time()
-            train_data = prefetcher.next()
-        # end of iter
-
-    # end of epoch
-
-    consumed_time = str(datetime.timedelta(seconds=int(time.time() - start_time)))
-    logger.info(f'End of training. Time consumed: {consumed_time}')
-    logger.info('Save the latest model.')
-    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
-    if opt.get('val') is not None and opt['datasets'].get('val'):
-        model.validation(val_loader, current_iter, tb_logger, opt['val']['save_img'])
-    if tb_logger:
-        tb_logger.close()
-
-
-if __name__ == '__main__':
-    root_path = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir))
-    train_pipeline(root_path)
diff --git a/spaces/FelixLuoX/stable_diffusion_test/app.py b/spaces/FelixLuoX/stable_diffusion_test/app.py
deleted file mode 100644
index b6f79bfcd44575ec329b81661e66b911cf660983..0000000000000000000000000000000000000000
--- a/spaces/FelixLuoX/stable_diffusion_test/app.py
+++ /dev/null
@@ -1,494 +0,0 @@
-import gradio as gr
-import io 
-from PIL import Image, PngImagePlugin
-import base64
-import requests
-import json 
-import ui_functions as uifn
-
-txt2img_defaults = {
-    'prompt': '',
-    'ddim_steps': 50,
-    'toggles': [1, 2, 3],
-    'sampler_name': 'k_lms',
-    'ddim_eta': 0.0,
-    'n_iter': 1,
-    'batch_size': 1,
-    'cfg_scale': 7.5,
-    'seed': '',
-    'height': 512,
-    'width': 512,
-    'fp': None,
-    'variant_amount': 0.0,
-    'variant_seed': '',
-    'submit_on_enter': 'Yes',
-}
-
-img2img_defaults = {
-    'prompt': '',
-    'ddim_steps': 50,
-    'toggles': [1, 4, 5],
-    'sampler_name': 'k_lms',
-    'ddim_eta': 0.0,
-    'n_iter': 1,
-    'batch_size': 1,
-    'cfg_scale': 5.0,
-    'denoising_strength': 0.75,
-    'mask_mode': 1,
-    'resize_mode': 0,
-    'seed': '',
-    'height': 512,
-    'width': 512,
-    'fp': None,
-}
-sample_img2img = None
-job_manager = None
-RealESRGAN = True
-show_embeddings = False
-
-img2img_resize_modes = [
-    "Just resize",
-    "Crop and resize",
-    "Resize and fill",
-]
-
-img2img_toggles = [
-    'Create prompt matrix (separate multiple prompts using |, and get all combinations of them)',
-    'Normalize Prompt Weights (ensure sum of weights add up to 1.0)',
-    'Loopback (use images from previous batch when creating next batch)',
-    'Random loopback seed',
-    'Save individual images',
-    'Save grid',
-    'Sort samples by prompt',
-    'Write sample info files',
-    'Write sample info to one file',
-    'jpg samples',
-]
-
-img2img_toggle_defaults = [img2img_toggles[i] for i in img2img_defaults['toggles']]
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-def base2picture(resbase64):
-    res=resbase64.split(',')[1]
-    img_b64decode = base64.b64decode(res)
-    image = io.BytesIO(img_b64decode)
-    img = Image.open(image)
-    return img 
-
-def filter_content(raw_style: str):
-    if "(" in raw_style:
-        i = raw_style.index("(")
-    else :
-        i = -1
-
-    if i == -1:
-        return raw_style
-    else :
-        return raw_style[:i]
-
-def request_images(raw_text, class_draw, style_draw, batch_size):
-    if filter_content(class_draw) != "国画":
-        if filter_content(class_draw) != "通用":
-            raw_text = raw_text + f"，{filter_content(class_draw)}"
-
-        for sty in style_draw:
-            raw_text = raw_text + f"，{filter_content(sty)}"
-        print(f"raw text is {raw_text}")
-        url = "http://flagart.baai.ac.cn/api/general/"
-    elif filter_content(class_draw) == "国画":
-        if raw_text.endswith("国画"):
-            pass 
-        else :
-            raw_text = raw_text + "，国画"
-        url = "http://flagart.baai.ac.cn/api/guohua/"
-
-    d = {"data":[raw_text, batch_size]}
-    r = requests.post(url, json=d, headers={"Content-Type": "application/json", "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Connection": "keep-alive"})
-    result_text = r.text
-    content = json.loads(result_text)["data"][0]
-    images = []
-    for i in range(batch_size):
-        # print(content[i])
-        images.append(base2picture(content[i]))        
-
-    return images 
-
-def call_JS(sd_method, **kwargs):
-    param_str = json.dumps(kwargs)
-    return f"async (...x) => {{ return await SD.{sd_method}({{ x, ...{param_str} }}) ?? []; }}"
-
-def encode_pil_to_base64(pil_image):
-    with io.BytesIO() as output_bytes:
-
-        # Copy any text-only metadata
-        use_metadata = False
-        metadata = PngImagePlugin.PngInfo()
-        for key, value in pil_image.info.items():
-            if isinstance(key, str) and isinstance(value, str):
-                metadata.add_text(key, value)
-                use_metadata = True
-
-        pil_image.save(
-            output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
-        )
-        bytes_data = output_bytes.getvalue()
-    base64_str = str(base64.b64encode(bytes_data), "utf-8")
-    return "data:image/png;base64," + base64_str
-
-def img2img(*args):
-    
-    # 处理image
-    for i, item in enumerate(args):
-        # print(type(item))
-        if type(item) == dict:
-            args[i]['image']  = encode_pil_to_base64(item['image'])
-            args[i]['mask'] = encode_pil_to_base64(item['mask'])
-        else:
-            print(i,type(item))
-            print(item)
-    
-    batch_size = args[8]
-
-    url = "http://flagart.baai.ac.cn/api/img2img/"
-    d = {"data":args}
-    r = requests.post(url, json=d, headers={"Content-Type": "application/json", "Accept": "*/*", "Accept-Encoding": "gzip, deflate, br", "Connection": "keep-alive"})
-    print(r)
-    result_text = r.text
-    content = json.loads(result_text)["data"][0]
-    images = []
-    for i in range(batch_size):
-        # print(content[i])
-        images.append(base2picture(content[i]))   
-    # content = json.loads(result_text)
-    # print(result_text)
-    # print("服务器已经把东西返回来啦！！！！！！！乌拉乌拉！！！！！")
-    return images
-
-
-examples = [
-    '水墨蝴蝶和牡丹花，国画',
-    '苍劲有力的墨竹，国画',
-    '暴风雨中的灯塔',
-    '机械小松鼠，科学幻想',
-    '中国水墨山水画，国画',
-    "Lighthouse in the storm",
-    "A dog",
-    "Landscape by 张大千",
-    "A tiger 长了兔子耳朵",
-    "A baby bird 铅笔素描",
-
-]
-
-if __name__ == "__main__":
-    block = gr.Blocks(css=read_content('style.css'))
-
-    with block:
-        # gr.HTML(read_content("header.html"))
-
-        with gr.Tabs() as tabs:
-
-            with gr.TabItem("文生图(Text-to-img)"):
-
-                with gr.Group():
-                    with gr.Box():
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            text = gr.Textbox(
-                                label="Prompt",
-                                show_label=False,
-                                max_lines=1,
-                                placeholder="Input text(输入文字)",
-                                interactive=True,
-                            ).style(
-                                border=(True, False, True, True),
-                                rounded=(True, False, False, True),
-                                container=False,
-                            )
-                            
-                            btn = gr.Button("Generate image").style(
-                                margin=False,
-                                rounded=(True, True, True, True),
-                            )
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            class_draw = gr.Dropdown(["通用(general)", "国画(traditional Chinese painting)", 
-                                                        "照片，摄影(picture photography)", "油画(oil painting)", 
-                                                        "铅笔素描(pencil sketch)", "CG", 
-                                                        "水彩画(watercolor painting)", "水墨画(ink and wash)",
-                                                        "插画(illustrations)", "3D", "图生图(img2img)"],
-                                                    label="生成类型(type)",
-                                                    show_label=True,
-                                                    value="通用(general)")
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            style_draw = gr.CheckboxGroup(["蒸汽朋克(steampunk)", "电影摄影风格(film photography)", 
-                                                        "概念艺术(concept art)", "Warming lighting", 
-                                                        "Dramatic lighting", "Natural lighting", 
-                                                        "虚幻引擎(unreal engine)", "4k", "8k",
-                                                        "充满细节(full details)"],
-                                                    label="画面风格(style)",
-                                                    show_label=True,
-                                                    )
-                        with gr.Row().style(mobile_collapse=False, equal_height=True):
-                            sample_size = gr.Slider(minimum=1,
-                                                    maximum=4,
-                                                    step=1,
-                                                    label="生成数量(number)",
-                                                    show_label=True,
-                                                    interactive=True,
-                                                    )
-
-                    gallery = gr.Gallery(
-                        label="Generated images", show_label=False, elem_id="gallery"
-                    ).style(grid=[2], height="auto")
-
-                    gr.Examples(examples=examples, fn=request_images, inputs=text, outputs=gallery, examples_per_page=100)
-                    text.submit(request_images, inputs=[text, class_draw, style_draw, sample_size], outputs=gallery)
-                    btn.click(request_images, inputs=[text, class_draw, style_draw, sample_size], outputs=gallery)
-
-            with gr.TabItem("图生图(Img-to-Img)", id="img2img_tab"):
-                with gr.Row(elem_id="prompt_row"):
-                    img2img_prompt = gr.Textbox(label="Prompt",
-                                                elem_id='img2img_prompt_input',
-                                                placeholder="神奇的森林，流淌的河流.",
-                                                lines=1,
-                                                max_lines=1 if txt2img_defaults['submit_on_enter'] == 'Yes' else 25,
-                                                value=img2img_defaults['prompt'],
-                                                show_label=False).style()
-
-                    img2img_btn_mask = gr.Button("Generate", variant="primary", visible=False,
-                                                 elem_id="img2img_mask_btn")
-                    img2img_btn_editor = gr.Button("Generate", variant="primary", elem_id="img2img_edit_btn")
-                with gr.Row().style(equal_height=False):
-                    with gr.Column():
-                        gr.Markdown('#### 输入图像')
-                        img2img_image_mask = gr.Image(
-                            value=sample_img2img,
-                            source="upload",
-                            interactive=True,
-                            tool="sketch",
-                            type='pil',
-                            elem_id="img2img_mask",
-                            image_mode="RGBA"
-                        )
-                        img2img_image_editor = gr.Image(
-                            value=sample_img2img,
-                            source="upload",
-                            interactive=True,
-                            tool="select",
-                            type='pil',
-                            visible=False,
-                            image_mode="RGBA",
-                            elem_id="img2img_editor"
-                        )
-
-                        with gr.Tabs():
-                            with gr.TabItem("编辑设置"):
-                                with gr.Row():
-                                    # disable Uncrop for now
-                                    choices=["Mask", "Crop", "Uncrop"]
-                                    img2img_image_editor_mode = gr.Radio(choices=["Mask"],
-                                                                         label="编辑模式",
-                                                                         value="Mask", elem_id='edit_mode_select',
-                                                                         visible=True)
-                                    img2img_mask = gr.Radio(choices=["保留mask区域", "生成mask区域"],
-                                                            label="Mask 方式",
-                                                            #value=img2img_mask_modes[img2img_defaults['mask_mode']],
-                                                            value = "生成mask区域",
-                                                            visible=True)
-
-                                    img2img_mask_blur_strength = gr.Slider(minimum=1, maximum=10, step=1,
-                                                                           label="How much blurry should the mask be? (to avoid hard edges)",
-                                                                           value=3, visible=False)
-
-                                    img2img_resize = gr.Radio(label="Resize mode",
-                                                              choices=["Just resize", "Crop and resize",
-                                                                       "Resize and fill"],
-                                                              value=img2img_resize_modes[
-                                                                  img2img_defaults['resize_mode']], visible=False)
-
-                                img2img_painterro_btn = gr.Button("Advanced Editor",visible=False)
-                            # with gr.TabItem("Hints",visible=False):
-                            #     img2img_help = gr.Markdown(visible=False, value=uifn.help_text)
-
-                    with gr.Column():
-                        gr.Markdown('#### 编辑后的图片')
-                        output_img2img_gallery = gr.Gallery(label="Images", elem_id="img2img_gallery_output").style(
-                            grid=[4, 4, 4])
-                        img2img_job_ui = job_manager.draw_gradio_ui() if job_manager else None
-                        with gr.Column(visible=False):
-                            with gr.Tabs(visible=False):
-                                with gr.TabItem("", id="img2img_actions_tab",visible=False):
-                                    gr.Markdown("Select an image, then press one of the buttons below")
-                                    with gr.Row():
-                                        output_img2img_copy_to_clipboard_btn = gr.Button("Copy to clipboard")
-                                        output_img2img_copy_to_input_btn = gr.Button("Push to img2img input")
-                                        output_img2img_copy_to_mask_btn = gr.Button("Push to img2img input mask")
-
-                                    gr.Markdown("Warning: This will clear your current image and mask settings!")
-                                with gr.TabItem("", id="img2img_output_info_tab",visible=False):
-                                    output_img2img_params = gr.Textbox(label="Generation parameters")
-                                    with gr.Row():
-                                        output_img2img_copy_params = gr.Button("Copy full parameters").click(
-                                            inputs=output_img2img_params, outputs=[],
-                                            _js='(x) => {navigator.clipboard.writeText(x.replace(": ",":"))}', fn=None,
-                                            show_progress=False)
-                                        output_img2img_seed = gr.Number(label='Seed', interactive=False, visible=False)
-                                        output_img2img_copy_seed = gr.Button("Copy only seed").click(
-                                            inputs=output_img2img_seed, outputs=[],
-                                            _js=call_JS("gradioInputToClipboard"), fn=None, show_progress=False)
-                                    output_img2img_stats = gr.HTML(label='Stats')
-
-                gr.Markdown('# 编辑设置')
-
-                with gr.Row():
-                    with gr.Column():
-                        img2img_width = gr.Slider(minimum=64, maximum=2048, step=64, label="图片宽度",
-                                                  value=img2img_defaults["width"])
-                        img2img_height = gr.Slider(minimum=64, maximum=2048, step=64, label="图片高度",
-                                                   value=img2img_defaults["height"])
-                        img2img_cfg = gr.Slider(minimum=-40.0, maximum=30.0, step=0.5,
-                                                label='文本引导强度',
-                                                value=img2img_defaults['cfg_scale'], elem_id='cfg_slider')
-                        img2img_seed = gr.Textbox(label="随机种子", lines=1, max_lines=1,
-                                                  value=img2img_defaults["seed"])
-                        img2img_batch_count = gr.Slider(minimum=1, maximum=50, step=1,
-                                                        label='生成数量',
-                                                        value=img2img_defaults['n_iter'])
-                        img2img_dimensions_info_text_box = gr.Textbox(
-                            label="长宽比设置")
-                    with gr.Column():
-                        img2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="采样步数",
-                                                  value=img2img_defaults['ddim_steps'])
-
-                        img2img_sampling = gr.Dropdown(label='采样方式',
-                                                       choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler',
-                                                                'k_heun', 'k_lms'],
-                                                       value=img2img_defaults['sampler_name'])
-
-                        img2img_denoising = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising Strength',
-                                                      value=img2img_defaults['denoising_strength'],visible=False)
-
-                        img2img_toggles = gr.CheckboxGroup(label='', choices=img2img_toggles,
-                                                           value=img2img_toggle_defaults,visible=False)
-
-                        img2img_realesrgan_model_name = gr.Dropdown(label='RealESRGAN model',
-                                                                    choices=['RealESRGAN_x4plus',
-                                                                             'RealESRGAN_x4plus_anime_6B'],
-                                                                    value='RealESRGAN_x4plus',
-                                                                    visible=RealESRGAN is not None)  # TODO: Feels like I shouldnt slot it in here.
-
-                        img2img_embeddings = gr.File(label="Embeddings file for textual inversion",
-                                                     visible=show_embeddings)
-
-                img2img_image_editor_mode.change(
-                    uifn.change_image_editor_mode,
-                    [img2img_image_editor_mode,
-                     img2img_image_editor,
-                     img2img_image_mask,
-                     img2img_resize,
-                     img2img_width,
-                     img2img_height
-                     ],
-                    [img2img_image_editor, img2img_image_mask, img2img_btn_editor, img2img_btn_mask,
-                     img2img_painterro_btn, img2img_mask, img2img_mask_blur_strength]
-                )
-
-                # img2img_image_editor_mode.change(
-                #     uifn.update_image_mask,
-                #     [img2img_image_editor, img2img_resize, img2img_width, img2img_height],
-                #     img2img_image_mask
-                # )
-
-                # output_txt2img_copy_to_input_btn.click(
-                #     uifn.copy_img_to_input,
-                #     [output_txt2img_gallery],
-                #     [img2img_image_editor, img2img_image_mask, tabs],
-                #     _js=call_JS("moveImageFromGallery",
-                #                 fromId="txt2img_gallery_output",
-                #                 toId="img2img_editor")
-                # )
-
-                output_img2img_copy_to_input_btn.click(
-                    uifn.copy_img_to_edit,
-                    [output_img2img_gallery],
-                    [img2img_image_editor, tabs, img2img_image_editor_mode],
-                    _js=call_JS("moveImageFromGallery",
-                                fromId="img2img_gallery_output",
-                                toId="img2img_editor")
-                )
-                output_img2img_copy_to_mask_btn.click(
-                    uifn.copy_img_to_mask,
-                    [output_img2img_gallery],
-                    [img2img_image_mask, tabs, img2img_image_editor_mode],
-                    _js=call_JS("moveImageFromGallery",
-                                fromId="img2img_gallery_output",
-                                toId="img2img_editor")
-                )
-
-                output_img2img_copy_to_clipboard_btn.click(fn=None, inputs=output_img2img_gallery, outputs=[],
-                                                           _js=call_JS("copyImageFromGalleryToClipboard",
-                                                                       fromId="img2img_gallery_output")
-                                                           )
-
-                img2img_func = img2img
-                img2img_inputs = [img2img_prompt, img2img_image_editor_mode, img2img_mask,
-                                  img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
-                                  img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
-                                  img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
-                                  img2img_image_mask]
-                # img2img_outputs = [output_img2img_gallery, output_img2img_seed, output_img2img_params,
-                #                    output_img2img_stats]
-
-                img2img_outputs = [output_img2img_gallery]
-
-                # If a JobManager was passed in then wrap the Generate functions
-                if img2img_job_ui:
-                    img2img_func, img2img_inputs, img2img_outputs = img2img_job_ui.wrap_func(
-                        func=img2img_func,
-                        inputs=img2img_inputs,
-                        outputs=img2img_outputs,
-                    )
-
-                img2img_btn_mask.click(
-                    img2img_func,
-                    img2img_inputs,
-                    img2img_outputs
-                )
-
-                def img2img_submit_params():
-                    # print([img2img_prompt, img2img_image_editor_mode, img2img_mask,
-                    #              img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles,
-                    #              img2img_realesrgan_model_name, img2img_batch_count, img2img_cfg,
-                    #              img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize,
-                    #              img2img_image_editor, img2img_image_mask, img2img_embeddings])
-                    return (img2img_func,
-                            img2img_inputs,
-                            img2img_outputs)
-
-                img2img_btn_editor.click(*img2img_submit_params())
-
-                # GENERATE ON ENTER
-                img2img_prompt.submit(None, None, None,
-                                      _js=call_JS("clickFirstVisibleButton",
-                                                  rowId="prompt_row"))
-
-                img2img_painterro_btn.click(None,
-                                            [img2img_image_editor, img2img_image_mask, img2img_image_editor_mode],
-                                            [img2img_image_editor, img2img_image_mask],
-                                            _js=call_JS("Painterro.init", toId="img2img_editor")
-                                            )
-
-                img2img_width.change(fn=uifn.update_dimensions_info, inputs=[img2img_width, img2img_height],
-                                     outputs=img2img_dimensions_info_text_box)
-                img2img_height.change(fn=uifn.update_dimensions_info, inputs=[img2img_width, img2img_height],
-                                      outputs=img2img_dimensions_info_text_box)
-
-        # gr.HTML(read_content("footer.html"))
-        # gr.Image('./contributors.png')
-
-    block.queue(max_size=50, concurrency_count=20).launch()
\ No newline at end of file
diff --git a/spaces/Fernando22/freegpt-webui/README.md b/spaces/Fernando22/freegpt-webui/README.md
deleted file mode 100644
index 35279f351cdd06266746ff798cdb9bac48681082..0000000000000000000000000000000000000000
--- a/spaces/Fernando22/freegpt-webui/README.md
+++ /dev/null
@@ -1,195 +0,0 @@
----
-title: FreeGPT WebUI
-emoji: 🚀
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-sdk_version: 1.24.0
-app_file: run.py
-pinned: true
-app_port: 1338
-duplicated_from: monra/freegpt-webui
----
-
-# FreeGPT WebUI 
-## GPT 3.5/4
-
-<strong>NOT REQUIRE ANY API KEY</strong> ❌🔑 
-
-This project features a WebUI utilizing the [G4F API](https://github.com/xtekky/gpt4free). <br>
-Experience the power of ChatGPT with a user-friendly interface, enhanced jailbreaks, and completely free.
-
-## Known bugs 🚧
-- Stream mode not working properly.
-
-## News 📢
-I have created a new version of FreeGPT WebUI using the [ChimeraGPT API](https://chimeragpt.adventblocks.cc/). 
-<br>
-<br>
-This free API allows you to use various AI chat models, including <strong>GPT-4, GPT-4-32k, Claude-2, Claude-2-100k, and more.</strong> <br>
-Check out the project here: [FreeGPT WebUI - Chimera Version](https://github.com/ramonvc/freegpt-webui/tree/chimeragpt-version).
-
-## Project Hosting and Demonstration 🌐🚀
-The project is hosted on multiple platforms to be tested and modified.
-|Plataform|Status|API Key|Free|Repo|Demo|
-|--|--|--|--|--|--|
-|[replit](https://replit.com/)|![Active](https://img.shields.io/badge/Active-brightgreen)|◼️|☑️|[FreeGPT WebUI](https://replit.com/@ramonvc/freegpt-webui)|[Chat](https://freegpt-webui.ramonvc.repl.co/chat/)
-|[hugging face](https://huggingface.co)|![Active](https://img.shields.io/badge/Active-brightgreen)|◼️|☑️|[FreeGPT WebUI](https://huggingface.co/spaces/monra/freegpt-webui/tree/main)|[Chat](https://huggingface.co/spaces/monra/freegpt-webui)
-|[replit](https://replit.com/)|![Active](https://img.shields.io/badge/Active-brightgreen)|☑️|☑️|[FreeGPT WebUI - Chimera Version](https://replit.com/@ramonvc/freegpt-webui-chimera)|[Chat](https://freegpt-webui-chimera.ramonvc.repl.co/chat/)
-
-## Note ℹ️ 
-<p>
-  FreeGPT is a project that utilizes various free AI conversation API Providers. Each Provider is an API that provides responses generated by different AI models. The source code related to these services is available in <a href="https://github.com/ramonvc/freegpt-webui/tree/main/g4f">G4F folder</a>.
-
-It is important to note that, due to the extensive reach of this project, the free services registered here may receive a significant number of requests, which can result in temporary unavailability or access limitations. Therefore, it is common to encounter these services being offline or unstable.
-
-We recommend that you search for your own Providers and add them to your personal projects to avoid service instability and unavailability. Within the project, in the <a href="https://github.com/ramonvc/freegpt-webui/tree/main/g4f/Provider/Providers">Providers folder</a>, you will find several examples of Providers that have worked in the past or are still functioning. It is easy to follow the logic of these examples to find free GPT services and incorporate the requests into your specific FreeGPT project.
-
-Please note that the choice and integration of additional Providers are the user's responsibility and are not directly related to the FreeGPT project, as the project serves as an example of how to combine the <a href="https://github.com/xtekky/gpt4free">G4F API</a> with a web interface.
-</p>
-
-## Table of Contents  
-- [To-Do List](#to-do-list-%EF%B8%8F)  
-- [Getting Started](#getting-started-white_check_mark)  
-  - [Cloning the Repository](#cloning-the-repository-inbox_tray)  
-  - [Install Dependencies](#install-dependencies-wrench)  
-- [Running the Application](#running-the-application-rocket)  
-- [Docker](#docker-)  
-  - [Prerequisites](#prerequisites)  
-  - [Running the Docker](#running-the-docker)
-- [Incorporated Projects](#incorporated-projects-busts_in_silhouette)
-  - [WebUI](#webui) 
-  - [API FreeGPT](#api-g4f)
-- [Star History](#star-history)
-- [Legal Notice](#legal-notice) 
-
-##
-
-## To-Do List ✔️
-
-- [x] Integrate the free GPT API into the WebUI
-- [x] Create Docker support
-- [x] Improve the Jailbreak functionality
-- [x] Add the GPT-4 model
-- [x] Enhance the user interface
-- [ ] Check status of API Providers (online/offline)
-- [ ] Enable editing and creating Jailbreaks/Roles in the WebUI
-- [ ] Refactor web client
-
-## Getting Started :white_check_mark:  
-To get started with this project, you'll need to clone the repository and have [Python](https://www.python.org/downloads/) installed on your system.  
-  
-### Cloning the Repository :inbox_tray:
-Run the following command to clone the repository:  
-
-```
-git clone https://github.com/ramonvc/freegpt-webui.git
-```
-
-### Install Dependencies :wrench: 
-Navigate to the project directory:
-```
-cd freegpt-webui
-```
-
-Install the dependencies:
-```
-pip install -r requirements.txt
-```
-## Running the Application :rocket:
-To run the application, run the following command:
-```
-python run.py
-```
-
-Access the application in your browser using the URL:
-```
-http://127.0.0.1:1338
-```
-or
-```
-http://localhost:1338
-```
-
-
-## Docker 🐳
-### Prerequisites
-Before you start, make sure you have installed [Docker](https://www.docker.com/get-started) on your machine.
-
-### Running the Docker
-Pull the Docker image from Docker Hub:
-```
-docker pull ramonvc/freegpt-webui
-```
-
-Run the application using Docker:
-```
-docker run -p 1338:1338 ramonvc/freegpt-webui
-```
-
-Access the application in your browser using the URL:
-```
-http://127.0.0.1:1338
-```
-or
-```
-http://localhost:1338
-```
-
-When you're done using the application, stop the Docker containers using the following command:
-```
-docker stop <container-id>
-```
-
-## Incorporated Projects :busts_in_silhouette:
-I highly recommend visiting and supporting both projects.
-
-### WebUI
-The application interface was incorporated from the [chatgpt-clone](https://github.com/xtekky/chatgpt-clone) repository.
-
-### API G4F
-The free GPT-4 API was incorporated from the [GPT4Free](https://github.com/xtekky/gpt4free) repository.
-
-<br>
-
-## Star History
-[![Star History Chart](https://api.star-history.com/svg?repos=ramonvc/freegpt-webui&type=Timeline)](https://star-history.com/#ramonvc/freegpt-webui&Timeline)
-
-<br>
-
-## Legal Notice
-This repository is _not_ associated with or endorsed by providers of the APIs contained in this GitHub repository. This
-project is intended **for educational purposes only**. This is just a little personal project. Sites may contact me to
-improve their security or request the removal of their site from this repository.
-
-Please note the following:
-
-1. **Disclaimer**: The APIs, services, and trademarks mentioned in this repository belong to their respective owners.
-   This project is _not_ claiming any right over them nor is it affiliated with or endorsed by any of the providers
-   mentioned.
-
-2. **Responsibility**: The author of this repository is _not_ responsible for any consequences, damages, or losses
-   arising from the use or misuse of this repository or the content provided by the third-party APIs. Users are solely
-   responsible for their actions and any repercussions that may follow. We strongly recommend the users to follow the
-   TOS of the each Website.
-
-3. **Educational Purposes Only**: This repository and its content are provided strictly for educational purposes. By
-   using the information and code provided, users acknowledge that they are using the APIs and models at their own risk
-   and agree to comply with any applicable laws and regulations.
-
-4. **Copyright**: All content in this repository, including but not limited to code, images, and documentation, is the
-   intellectual property of the repository author, unless otherwise stated. Unauthorized copying, distribution, or use
-   of any content in this repository is strictly prohibited without the express written consent of the repository
-   author.
-
-5. **Indemnification**: Users agree to indemnify, defend, and hold harmless the author of this repository from and
-   against any and all claims, liabilities, damages, losses, or expenses, including legal fees and costs, arising out of
-   or in any way connected with their use or misuse of this repository, its content, or related third-party APIs.
-
-6. **Updates and Changes**: The author reserves the right to modify, update, or remove any content, information, or
-   features in this repository at any time without prior notice. Users are responsible for regularly reviewing the
-   content and any changes made to this repository.
-
-By using this repository or any code related to it, you agree to these terms. The author is not responsible for any
-copies, forks, or reuploads made by other users. This is the author's only account and repository. To prevent
-impersonation or irresponsible actions, you may comply with the GNU GPL license this Repository uses.
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/__init__.py b/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/__init__.py
deleted file mode 100644
index 11e5586c347c3071a9d1aca0425d112f45402e85..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-pcr/text/__init__.py
+++ /dev/null
@@ -1,60 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  clean_text = _clean_text(text, cleaner_names)
-  print(clean_text)
-  print(f" length:{len(clean_text)}")
-  for symbol in clean_text:
-    if symbol not in symbol_to_id.keys():
-      continue
-    symbol_id = symbol_to_id[symbol]
-    sequence += [symbol_id]
-  print(f" length:{len(sequence)}")
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text, symbols):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  sequence = [symbol_to_id[symbol] for symbol in cleaned_text if symbol in symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/FridaZuley/RVC_HFKawaii/i18n/scan_i18n.py b/spaces/FridaZuley/RVC_HFKawaii/i18n/scan_i18n.py
deleted file mode 100644
index f3e52cf4f9f06d78877d77d2353f666aa759e36f..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/i18n/scan_i18n.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import ast
-import glob
-import json
-from collections import OrderedDict
-
-
-def extract_i18n_strings(node):
-    i18n_strings = []
-
-    if (
-        isinstance(node, ast.Call)
-        and isinstance(node.func, ast.Name)
-        and node.func.id == "i18n"
-    ):
-        for arg in node.args:
-            if isinstance(arg, ast.Str):
-                i18n_strings.append(arg.s)
-
-    for child_node in ast.iter_child_nodes(node):
-        i18n_strings.extend(extract_i18n_strings(child_node))
-
-    return i18n_strings
-
-
-# scan the directory for all .py files (recursively)
-# for each file, parse the code into an AST
-# for each AST, extract the i18n strings
-
-strings = []
-for filename in glob.iglob("**/*.py", recursive=True):
-    with open(filename, "r") as f:
-        code = f.read()
-        if "I18nAuto" in code:
-            tree = ast.parse(code)
-            i18n_strings = extract_i18n_strings(tree)
-            print(filename, len(i18n_strings))
-            strings.extend(i18n_strings)
-code_keys = set(strings)
-"""
-n_i18n.py
-gui_v1.py 26
-app.py 16
-infer-web.py 147
-scan_i18n.py 0
-i18n.py 0
-lib/train/process_ckpt.py 1
-"""
-print()
-print("Total unique:", len(code_keys))
-
-
-standard_file = "i18n/locale/zh_CN.json"
-with open(standard_file, "r", encoding="utf-8") as f:
-    standard_data = json.load(f, object_pairs_hook=OrderedDict)
-standard_keys = set(standard_data.keys())
-
-# Define the standard file name
-unused_keys = standard_keys - code_keys
-print("Unused keys:", len(unused_keys))
-for unused_key in unused_keys:
-    print("\t", unused_key)
-
-missing_keys = code_keys - standard_keys
-print("Missing keys:", len(missing_keys))
-for missing_key in missing_keys:
-    print("\t", missing_key)
-
-code_keys_dict = OrderedDict()
-for s in strings:
-    code_keys_dict[s] = s
-
-# write back
-with open(standard_file, "w", encoding="utf-8") as f:
-    json.dump(code_keys_dict, f, ensure_ascii=False, indent=4, sort_keys=True)
-    f.write("\n")
diff --git a/spaces/GEM/DatasetCardForm/formatting/README.md b/spaces/GEM/DatasetCardForm/formatting/README.md
deleted file mode 100644
index e63fb61691eda571a3df1f89fe45eff76f579dfb..0000000000000000000000000000000000000000
--- a/spaces/GEM/DatasetCardForm/formatting/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
-# Formatting Utilities
-
-The resources in this folder are used to format the saved dataset card in a readable format (JSON, Markdown).
-
-File | Description
---- | ---
-[`key_to_question.json`](https://huggingface.co/spaces/GEM/DatasetCardForm/blob/main/formatting/key_to_question.json) | Maps from the saved key back to the original question, which can then be parsed into question/answer pairs.
-[`reformat_json.py`](https://huggingface.co/spaces/GEM/DatasetCardForm/blob/main/formatting/reformat_json.py) | Uses `key_to_question.json` to transform dataset cards into a JSON organized by Data Card standards (e.g., sections, subsections, scopes). Everything labeled `N/A` in the original card will be rendered; empty fields will be completely omitted.
-[`json_to_md.py`](https://huggingface.co/spaces/GEM/DatasetCardForm/blob/main/formatting/json_to_md.py) | Transforms output from `reformat_json.py` into Markdown that is compatible with Data Cards Labs (e.g., special comment syntax).
\ No newline at end of file
diff --git a/spaces/GTR-32X/uboa/README.md b/spaces/GTR-32X/uboa/README.md
deleted file mode 100644
index cfca43b0aa887aacbb1ba57f63c34d0da25b0b90..0000000000000000000000000000000000000000
--- a/spaces/GTR-32X/uboa/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Uboa Proxy
-emoji: 📉
-colorFrom: yellow
-colorTo: pink
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GaenKoki/voicevox/test/test_connect_base64_waves.py b/spaces/GaenKoki/voicevox/test/test_connect_base64_waves.py
deleted file mode 100644
index e50c8f517e64e178f180abab0ed2372878848f86..0000000000000000000000000000000000000000
--- a/spaces/GaenKoki/voicevox/test/test_connect_base64_waves.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import base64
-import io
-from unittest import TestCase
-
-import numpy as np
-import numpy.testing
-import soundfile
-from scipy.signal import resample
-
-from voicevox_engine.utility import ConnectBase64WavesException, connect_base64_waves
-
-
-def generate_sine_wave_ndarray(
-    seconds: float, samplerate: int, frequency: float
-) -> np.ndarray:
-    x = np.linspace(0, seconds, int(seconds * samplerate), endpoint=False)
-    wave = np.sin(2 * np.pi * frequency * x).astype(np.float32)
-
-    return wave
-
-
-def encode_bytes(wave_ndarray: np.ndarray, samplerate: int) -> bytes:
-    wave_bio = io.BytesIO()
-    soundfile.write(
-        file=wave_bio,
-        data=wave_ndarray,
-        samplerate=samplerate,
-        format="WAV",
-        subtype="FLOAT",
-    )
-    wave_bio.seek(0)
-
-    return wave_bio.getvalue()
-
-
-def generate_sine_wave_bytes(
-    seconds: float, samplerate: int, frequency: float
-) -> bytes:
-    wave_ndarray = generate_sine_wave_ndarray(seconds, samplerate, frequency)
-    return encode_bytes(wave_ndarray, samplerate)
-
-
-def encode_base64(wave_bytes: bytes) -> str:
-    return base64.standard_b64encode(wave_bytes).decode("utf-8")
-
-
-def generate_sine_wave_base64(seconds: float, samplerate: int, frequency: float) -> str:
-    wave_bytes = generate_sine_wave_bytes(seconds, samplerate, frequency)
-    wave_base64 = encode_base64(wave_bytes)
-    return wave_base64
-
-
-class TestConnectBase64Waves(TestCase):
-    def test_connect(self):
-        samplerate = 1000
-        wave = generate_sine_wave_ndarray(
-            seconds=2, samplerate=samplerate, frequency=10
-        )
-        wave_base64 = encode_base64(encode_bytes(wave, samplerate=samplerate))
-
-        wave_x2_ref = np.concatenate([wave, wave])
-
-        wave_x2, _ = connect_base64_waves(waves=[wave_base64, wave_base64])
-
-        self.assertEqual(wave_x2_ref.shape, wave_x2.shape)
-
-        self.assertTrue((wave_x2_ref == wave_x2).all())
-
-    def test_no_wave_error(self):
-        self.assertRaises(ConnectBase64WavesException, connect_base64_waves, waves=[])
-
-    def test_invalid_base64_error(self):
-        wave_1000hz = generate_sine_wave_base64(
-            seconds=2, samplerate=1000, frequency=10
-        )
-        wave_1000hz_broken = wave_1000hz[1:]  # remove head 1 char
-
-        self.assertRaises(
-            ConnectBase64WavesException,
-            connect_base64_waves,
-            waves=[
-                wave_1000hz_broken,
-            ],
-        )
-
-    def test_invalid_wave_file_error(self):
-        wave_1000hz = generate_sine_wave_bytes(seconds=2, samplerate=1000, frequency=10)
-        wave_1000hz_broken_bytes = wave_1000hz[1:]  # remove head 1 byte
-        wave_1000hz_broken = encode_base64(wave_1000hz_broken_bytes)
-
-        self.assertRaises(
-            ConnectBase64WavesException,
-            connect_base64_waves,
-            waves=[
-                wave_1000hz_broken,
-            ],
-        )
-
-    def test_different_frequency(self):
-        wave_24000hz = generate_sine_wave_ndarray(
-            seconds=1, samplerate=24000, frequency=10
-        )
-        wave_1000hz = generate_sine_wave_ndarray(
-            seconds=2, samplerate=1000, frequency=10
-        )
-        wave_24000_base64 = encode_base64(encode_bytes(wave_24000hz, samplerate=24000))
-        wave_1000_base64 = encode_base64(encode_bytes(wave_1000hz, samplerate=1000))
-
-        wave_1000hz_to2400hz = resample(wave_1000hz, 24000 * len(wave_1000hz) // 1000)
-        wave_x2_ref = np.concatenate([wave_24000hz, wave_1000hz_to2400hz])
-
-        wave_x2, _ = connect_base64_waves(waves=[wave_24000_base64, wave_1000_base64])
-
-        self.assertEqual(wave_x2_ref.shape, wave_x2.shape)
-        numpy.testing.assert_array_almost_equal(wave_x2_ref, wave_x2)
-
-    def test_different_channels(self):
-        wave_1000hz = generate_sine_wave_ndarray(
-            seconds=2, samplerate=1000, frequency=10
-        )
-        wave_2ch_1000hz = np.array([wave_1000hz, wave_1000hz]).T
-        wave_1ch_base64 = encode_base64(encode_bytes(wave_1000hz, samplerate=1000))
-        wave_2ch_base64 = encode_base64(encode_bytes(wave_2ch_1000hz, samplerate=1000))
-
-        wave_x2_ref = np.concatenate([wave_2ch_1000hz, wave_2ch_1000hz])
-
-        wave_x2, _ = connect_base64_waves(waves=[wave_1ch_base64, wave_2ch_base64])
-
-        self.assertEqual(wave_x2_ref.shape, wave_x2.shape)
-        self.assertTrue((wave_x2_ref == wave_x2).all())
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
deleted file mode 100644
index dd5153e6ef0ef16b8607279634ce6f1593bd3c1c..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
-model = dict(
-    pretrained='open-mmlab://regnetx_3.2gf',
-    backbone=dict(
-        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
-        stage_with_dcn=(False, True, True, True)))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/mmcv_custom/checkpoint.py b/spaces/Gradio-Blocks/uniformer_image_detection/mmcv_custom/checkpoint.py
deleted file mode 100644
index 51322c1c3802f357481065a70dc5152469d80eb8..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/mmcv_custom/checkpoint.py
+++ /dev/null
@@ -1,500 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-import io
-import os
-import os.path as osp
-import pkgutil
-import time
-import warnings
-from collections import OrderedDict
-from importlib import import_module
-from tempfile import TemporaryDirectory
-
-import torch
-import torchvision
-from torch.optim import Optimizer
-from torch.utils import model_zoo
-from torch.nn import functional as F
-
-import mmcv
-from mmcv.fileio import FileClient
-from mmcv.fileio import load as load_file
-from mmcv.parallel import is_module_wrapper
-from mmcv.utils import mkdir_or_exist
-from mmcv.runner import get_dist_info
-
-ENV_MMCV_HOME = 'MMCV_HOME'
-ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
-DEFAULT_CACHE_DIR = '~/.cache'
-
-
-def _get_mmcv_home():
-    mmcv_home = os.path.expanduser(
-        os.getenv(
-            ENV_MMCV_HOME,
-            os.path.join(
-                os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
-
-    mkdir_or_exist(mmcv_home)
-    return mmcv_home
-
-
-def load_state_dict(module, state_dict, strict=False, logger=None):
-    """Load state_dict to a module.
-
-    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
-    Default value for ``strict`` is set to ``False`` and the message for
-    param mismatch will be shown even if strict is False.
-
-    Args:
-        module (Module): Module that receives the state_dict.
-        state_dict (OrderedDict): Weights.
-        strict (bool): whether to strictly enforce that the keys
-            in :attr:`state_dict` match the keys returned by this module's
-            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
-        logger (:obj:`logging.Logger`, optional): Logger to log the error
-            message. If not specified, print function will be used.
-    """
-    unexpected_keys = []
-    all_missing_keys = []
-    err_msg = []
-
-    metadata = getattr(state_dict, '_metadata', None)
-    state_dict = state_dict.copy()
-    if metadata is not None:
-        state_dict._metadata = metadata
-
-    # use _load_from_state_dict to enable checkpoint version control
-    def load(module, prefix=''):
-        # recursively check parallel module in case that the model has a
-        # complicated structure, e.g., nn.Module(nn.Module(DDP))
-        if is_module_wrapper(module):
-            module = module.module
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
-        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
-                                     all_missing_keys, unexpected_keys,
-                                     err_msg)
-        for name, child in module._modules.items():
-            if child is not None:
-                load(child, prefix + name + '.')
-
-    load(module)
-    load = None  # break load->load reference cycle
-
-    # ignore "num_batches_tracked" of BN layers
-    missing_keys = [
-        key for key in all_missing_keys if 'num_batches_tracked' not in key
-    ]
-
-    if unexpected_keys:
-        err_msg.append('unexpected key in source '
-                       f'state_dict: {", ".join(unexpected_keys)}\n')
-    if missing_keys:
-        err_msg.append(
-            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
-
-    rank, _ = get_dist_info()
-    if len(err_msg) > 0 and rank == 0:
-        err_msg.insert(
-            0, 'The model and loaded state dict do not match exactly\n')
-        err_msg = '\n'.join(err_msg)
-        if strict:
-            raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
-        else:
-            print(err_msg)
-
-
-def load_url_dist(url, model_dir=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    return checkpoint
-
-
-def load_pavimodel_dist(model_path, map_location=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    try:
-        from pavi import modelcloud
-    except ImportError:
-        raise ImportError(
-            'Please install pavi to load checkpoint from modelcloud.')
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        model = modelcloud.get(model_path)
-        with TemporaryDirectory() as tmp_dir:
-            downloaded_file = osp.join(tmp_dir, model.name)
-            model.download(downloaded_file)
-            checkpoint = torch.load(downloaded_file, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            model = modelcloud.get(model_path)
-            with TemporaryDirectory() as tmp_dir:
-                downloaded_file = osp.join(tmp_dir, model.name)
-                model.download(downloaded_file)
-                checkpoint = torch.load(
-                    downloaded_file, map_location=map_location)
-    return checkpoint
-
-
-def load_fileclient_dist(filename, backend, map_location):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    allowed_backends = ['ceph']
-    if backend not in allowed_backends:
-        raise ValueError(f'Load from Backend {backend} is not supported.')
-    if rank == 0:
-        fileclient = FileClient(backend=backend)
-        buffer = io.BytesIO(fileclient.get(filename))
-        checkpoint = torch.load(buffer, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            fileclient = FileClient(backend=backend)
-            buffer = io.BytesIO(fileclient.get(filename))
-            checkpoint = torch.load(buffer, map_location=map_location)
-    return checkpoint
-
-
-def get_torchvision_models():
-    model_urls = dict()
-    for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
-        if ispkg:
-            continue
-        _zoo = import_module(f'torchvision.models.{name}')
-        if hasattr(_zoo, 'model_urls'):
-            _urls = getattr(_zoo, 'model_urls')
-            model_urls.update(_urls)
-    return model_urls
-
-
-def get_external_models():
-    mmcv_home = _get_mmcv_home()
-    default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
-    default_urls = load_file(default_json_path)
-    assert isinstance(default_urls, dict)
-    external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
-    if osp.exists(external_json_path):
-        external_urls = load_file(external_json_path)
-        assert isinstance(external_urls, dict)
-        default_urls.update(external_urls)
-
-    return default_urls
-
-
-def get_mmcls_models():
-    mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
-    mmcls_urls = load_file(mmcls_json_path)
-
-    return mmcls_urls
-
-
-def get_deprecated_model_names():
-    deprecate_json_path = osp.join(mmcv.__path__[0],
-                                   'model_zoo/deprecated.json')
-    deprecate_urls = load_file(deprecate_json_path)
-    assert isinstance(deprecate_urls, dict)
-
-    return deprecate_urls
-
-
-def _process_mmcls_checkpoint(checkpoint):
-    state_dict = checkpoint['state_dict']
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k.startswith('backbone.'):
-            new_state_dict[k[9:]] = v
-    new_checkpoint = dict(state_dict=new_state_dict)
-
-    return new_checkpoint
-
-
-def _load_checkpoint(filename, map_location=None):
-    """Load checkpoint from somewhere (modelzoo, file, url).
-
-    Args:
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str | None): Same as :func:`torch.load`. Default: None.
-
-    Returns:
-        dict | OrderedDict: The loaded checkpoint. It can be either an
-            OrderedDict storing model weights or a dict containing other
-            information, which depends on the checkpoint.
-    """
-    if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
-                      'use "torchvision://" instead')
-        model_urls = get_torchvision_models()
-        model_name = filename[11:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('torchvision://'):
-        model_urls = get_torchvision_models()
-        model_name = filename[14:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('open-mmlab://'):
-        model_urls = get_external_models()
-        model_name = filename[13:]
-        deprecated_urls = get_deprecated_model_names()
-        if model_name in deprecated_urls:
-            warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '
-                          f'of open-mmlab://{deprecated_urls[model_name]}')
-            model_name = deprecated_urls[model_name]
-        model_url = model_urls[model_name]
-        # check if is url
-        if model_url.startswith(('http://', 'https://')):
-            checkpoint = load_url_dist(model_url)
-        else:
-            filename = osp.join(_get_mmcv_home(), model_url)
-            if not osp.isfile(filename):
-                raise IOError(f'{filename} is not a checkpoint file')
-            checkpoint = torch.load(filename, map_location=map_location)
-    elif filename.startswith('mmcls://'):
-        model_urls = get_mmcls_models()
-        model_name = filename[8:]
-        checkpoint = load_url_dist(model_urls[model_name])
-        checkpoint = _process_mmcls_checkpoint(checkpoint)
-    elif filename.startswith(('http://', 'https://')):
-        checkpoint = load_url_dist(filename)
-    elif filename.startswith('pavi://'):
-        model_path = filename[7:]
-        checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
-    elif filename.startswith('s3://'):
-        checkpoint = load_fileclient_dist(
-            filename, backend='ceph', map_location=map_location)
-    else:
-        if not osp.isfile(filename):
-            raise IOError(f'{filename} is not a checkpoint file')
-        checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-def load_checkpoint(model,
-                    filename,
-                    map_location='cpu',
-                    strict=False,
-                    logger=None):
-    """Load checkpoint from a file or URI.
-
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = _load_checkpoint(filename, map_location)
-    # OrderedDict is a subclass of dict
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    # get state_dict from checkpoint
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    elif 'model' in checkpoint:
-        state_dict = checkpoint['model']
-    else:
-        state_dict = checkpoint
-    # strip prefix of state_dict
-    if list(state_dict.keys())[0].startswith('module.'):
-        state_dict = {k[7:]: v for k, v in state_dict.items()}
-
-    # for MoBY, load model of online branch
-    if sorted(list(state_dict.keys()))[0].startswith('encoder'):
-        state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
-
-    # reshape absolute position embedding
-    if state_dict.get('absolute_pos_embed') is not None:
-        absolute_pos_embed = state_dict['absolute_pos_embed']
-        N1, L, C1 = absolute_pos_embed.size()
-        N2, C2, H, W = model.absolute_pos_embed.size()
-        if N1 != N2 or C1 != C2 or L != H*W:
-            logger.warning("Error in loading absolute_pos_embed, pass")
-        else:
-            state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
-
-    # interpolate position bias table if needed
-    relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
-    for table_key in relative_position_bias_table_keys:
-        table_pretrained = state_dict[table_key]
-        table_current = model.state_dict()[table_key]
-        L1, nH1 = table_pretrained.size()
-        L2, nH2 = table_current.size()
-        if nH1 != nH2:
-            logger.warning(f"Error in loading {table_key}, pass")
-        else:
-            if L1 != L2:
-                S1 = int(L1 ** 0.5)
-                S2 = int(L2 ** 0.5)
-                table_pretrained_resized = F.interpolate(
-                     table_pretrained.permute(1, 0).view(1, nH1, S1, S1),
-                     size=(S2, S2), mode='bicubic')
-                state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
-
-    # load state_dict
-    load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-
-
-def weights_to_cpu(state_dict):
-    """Copy a model state_dict to cpu.
-
-    Args:
-        state_dict (OrderedDict): Model weights on GPU.
-
-    Returns:
-        OrderedDict: Model weights on GPU.
-    """
-    state_dict_cpu = OrderedDict()
-    for key, val in state_dict.items():
-        state_dict_cpu[key] = val.cpu()
-    return state_dict_cpu
-
-
-def _save_to_state_dict(module, destination, prefix, keep_vars):
-    """Saves module state to `destination` dictionary.
-
-    This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (dict): A dict where state will be stored.
-        prefix (str): The prefix for parameters and buffers used in this
-            module.
-    """
-    for name, param in module._parameters.items():
-        if param is not None:
-            destination[prefix + name] = param if keep_vars else param.detach()
-    for name, buf in module._buffers.items():
-        # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d
-        if buf is not None:
-            destination[prefix + name] = buf if keep_vars else buf.detach()
-
-
-def get_state_dict(module, destination=None, prefix='', keep_vars=False):
-    """Returns a dictionary containing a whole state of the module.
-
-    Both parameters and persistent buffers (e.g. running averages) are
-    included. Keys are corresponding parameter and buffer names.
-
-    This method is modified from :meth:`torch.nn.Module.state_dict` to
-    recursively check parallel module in case that the model has a complicated
-    structure, e.g., nn.Module(nn.Module(DDP)).
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (OrderedDict): Returned dict for the state of the
-            module.
-        prefix (str): Prefix of the key.
-        keep_vars (bool): Whether to keep the variable property of the
-            parameters. Default: False.
-
-    Returns:
-        dict: A dictionary containing a whole state of the module.
-    """
-    # recursively check parallel module in case that the model has a
-    # complicated structure, e.g., nn.Module(nn.Module(DDP))
-    if is_module_wrapper(module):
-        module = module.module
-
-    # below is the same as torch.nn.Module.state_dict()
-    if destination is None:
-        destination = OrderedDict()
-        destination._metadata = OrderedDict()
-    destination._metadata[prefix[:-1]] = local_metadata = dict(
-        version=module._version)
-    _save_to_state_dict(module, destination, prefix, keep_vars)
-    for name, child in module._modules.items():
-        if child is not None:
-            get_state_dict(
-                child, destination, prefix + name + '.', keep_vars=keep_vars)
-    for hook in module._state_dict_hooks.values():
-        hook_result = hook(module, destination, prefix, local_metadata)
-        if hook_result is not None:
-            destination = hook_result
-    return destination
-
-
-def save_checkpoint(model, filename, optimizer=None, meta=None):
-    """Save checkpoint to file.
-
-    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
-    ``optimizer``. By default ``meta`` will contain version and time info.
-
-    Args:
-        model (Module): Module whose params are to be saved.
-        filename (str): Checkpoint filename.
-        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
-        meta (dict, optional): Metadata to be saved in checkpoint.
-    """
-    if meta is None:
-        meta = {}
-    elif not isinstance(meta, dict):
-        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
-    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
-
-    if is_module_wrapper(model):
-        model = model.module
-
-    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
-        # save class name to the meta
-        meta.update(CLASSES=model.CLASSES)
-
-    checkpoint = {
-        'meta': meta,
-        'state_dict': weights_to_cpu(get_state_dict(model))
-    }
-    # save optimizer state dict in the checkpoint
-    if isinstance(optimizer, Optimizer):
-        checkpoint['optimizer'] = optimizer.state_dict()
-    elif isinstance(optimizer, dict):
-        checkpoint['optimizer'] = {}
-        for name, optim in optimizer.items():
-            checkpoint['optimizer'][name] = optim.state_dict()
-
-    if filename.startswith('pavi://'):
-        try:
-            from pavi import modelcloud
-            from pavi.exception import NodeNotFoundError
-        except ImportError:
-            raise ImportError(
-                'Please install pavi to load checkpoint from modelcloud.')
-        model_path = filename[7:]
-        root = modelcloud.Folder()
-        model_dir, model_name = osp.split(model_path)
-        try:
-            model = modelcloud.get(model_dir)
-        except NodeNotFoundError:
-            model = root.create_training_model(model_dir)
-        with TemporaryDirectory() as tmp_dir:
-            checkpoint_file = osp.join(tmp_dir, model_name)
-            with open(checkpoint_file, 'wb') as f:
-                torch.save(checkpoint, f)
-                f.flush()
-            model.create_file(checkpoint_file, name=model_name)
-    else:
-        mmcv.mkdir_or_exist(osp.dirname(filename))
-        # immediately flush buffer
-        with open(filename, 'wb') as f:
-            torch.save(checkpoint, f)
-            f.flush()
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/fcn_unet_s5-d16.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/fcn_unet_s5-d16.py
deleted file mode 100644
index a33e7972877f902d0e7d18401ca675e3e4e60a18..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/_base_/models/fcn_unet_s5-d16.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained=None,
-    backbone=dict(
-        type='UNet',
-        in_channels=3,
-        base_channels=64,
-        num_stages=5,
-        strides=(1, 1, 1, 1, 1),
-        enc_num_convs=(2, 2, 2, 2, 2),
-        dec_num_convs=(2, 2, 2, 2),
-        downsamples=(True, True, True, True),
-        enc_dilations=(1, 1, 1, 1, 1),
-        dec_dilations=(1, 1, 1, 1),
-        with_cp=False,
-        conv_cfg=None,
-        norm_cfg=norm_cfg,
-        act_cfg=dict(type='ReLU'),
-        upsample_cfg=dict(type='InterpConv'),
-        norm_eval=False),
-    decode_head=dict(
-        type='FCNHead',
-        in_channels=64,
-        in_index=4,
-        channels=64,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=2,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=128,
-        in_index=3,
-        channels=64,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=2,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='slide', crop_size=256, stride=170))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
deleted file mode 100644
index e4bda3eded693bfd44a8c86ced7ae6ee9963c583..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r50-d8_769x769_40k_cityscapes.py
+++ /dev/null
@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/deeplabv3plus_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_40k.py'
-]
-model = dict(
-    decode_head=dict(align_corners=True),
-    auxiliary_head=dict(align_corners=True),
-    test_cfg=dict(mode='slide', crop_size=(769, 769), stride=(513, 513)))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/losses/accuracy.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/losses/accuracy.py
deleted file mode 100644
index c0fd2e7e74a0f721c4a814c09d6e453e5956bb38..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/losses/accuracy.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import torch.nn as nn
-
-
-def accuracy(pred, target, topk=1, thresh=None):
-    """Calculate accuracy according to the prediction and target.
-
-    Args:
-        pred (torch.Tensor): The model prediction, shape (N, num_class, ...)
-        target (torch.Tensor): The target of each prediction, shape (N, , ...)
-        topk (int | tuple[int], optional): If the predictions in ``topk``
-            matches the target, the predictions will be regarded as
-            correct ones. Defaults to 1.
-        thresh (float, optional): If not None, predictions with scores under
-            this threshold are considered incorrect. Default to None.
-
-    Returns:
-        float | tuple[float]: If the input ``topk`` is a single integer,
-            the function will return a single float as accuracy. If
-            ``topk`` is a tuple containing multiple integers, the
-            function will return a tuple containing accuracies of
-            each ``topk`` number.
-    """
-    assert isinstance(topk, (int, tuple))
-    if isinstance(topk, int):
-        topk = (topk, )
-        return_single = True
-    else:
-        return_single = False
-
-    maxk = max(topk)
-    if pred.size(0) == 0:
-        accu = [pred.new_tensor(0.) for i in range(len(topk))]
-        return accu[0] if return_single else accu
-    assert pred.ndim == target.ndim + 1
-    assert pred.size(0) == target.size(0)
-    assert maxk <= pred.size(1), \
-        f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
-    pred_value, pred_label = pred.topk(maxk, dim=1)
-    # transpose to shape (maxk, N, ...)
-    pred_label = pred_label.transpose(0, 1)
-    correct = pred_label.eq(target.unsqueeze(0).expand_as(pred_label))
-    if thresh is not None:
-        # Only prediction values larger than thresh are counted as correct
-        correct = correct & (pred_value > thresh).t()
-    res = []
-    for k in topk:
-        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
-        res.append(correct_k.mul_(100.0 / target.numel()))
-    return res[0] if return_single else res
-
-
-class Accuracy(nn.Module):
-    """Accuracy calculation module."""
-
-    def __init__(self, topk=(1, ), thresh=None):
-        """Module to calculate the accuracy.
-
-        Args:
-            topk (tuple, optional): The criterion used to calculate the
-                accuracy. Defaults to (1,).
-            thresh (float, optional): If not None, predictions with scores
-                under this threshold are considered incorrect. Default to None.
-        """
-        super().__init__()
-        self.topk = topk
-        self.thresh = thresh
-
-    def forward(self, pred, target):
-        """Forward function to calculate accuracy.
-
-        Args:
-            pred (torch.Tensor): Prediction of models.
-            target (torch.Tensor): Target for each prediction.
-
-        Returns:
-            tuple[float]: The accuracies under different topk criterions.
-        """
-        return accuracy(pred, target, self.topk, self.thresh)
diff --git a/spaces/GrantC/learning_goals_bloom/app.py b/spaces/GrantC/learning_goals_bloom/app.py
deleted file mode 100644
index e91d4bd9877d2208d72ca1bf5ccb2c7201cd32fe..0000000000000000000000000000000000000000
--- a/spaces/GrantC/learning_goals_bloom/app.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import gradio as gr
-import requests
-import os
-
-API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
-HF_TOKEN = os.environ["HF_TOKEN"]
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
-
-def get_results(prompt):
-    json_ = {"inputs": prompt,
-            "parameters":
-            {
-            "top_p": 0.9,
-            "typical_p":0.2,
-          "temperature": 0.8,
-          "max_new_tokens": 250,
-          "return_full_text": True
-          }, "options": 
-              {
-              "use_cache": True,
-              "wait_for_model":True
-              },}
-    try:
-        response = requests.post(API_URL, headers=headers, json=json_)
-        output = response.json()
-        output_tmp = output[0]['generated_text']
-    except:
-        output_tmp = "Not able to work"
-    return output_tmp     
-
-
-def learn_goals(topic, text):
-    prompt = "Topic: " + topic + '\n'
-    prompt = prompt + text + '\n'
-    prompt = prompt + """Some examples of learning goals from above are:
-    1. The student will be able to"""
-    
-    return get_results(prompt)
-     
-
-topic = gr.Textbox(lines = 1, placeholder="Topic")
-text = gr.Textbox(lines = 5, placeholder="Text")
-
-iface = gr.Interface(fn=learn_goals, inputs=[topic, text], outputs="text")
-iface.launch()
\ No newline at end of file
diff --git a/spaces/Grezz/generate_human_motion/pyrender/examples/example.py b/spaces/Grezz/generate_human_motion/pyrender/examples/example.py
deleted file mode 100644
index 599a4850a5899cdeb1a76db1c5cf1c91c263cd41..0000000000000000000000000000000000000000
--- a/spaces/Grezz/generate_human_motion/pyrender/examples/example.py
+++ /dev/null
@@ -1,157 +0,0 @@
-"""Examples of using pyrender for viewing and offscreen rendering.
-"""
-import pyglet
-pyglet.options['shadow_window'] = False
-import os
-import numpy as np
-import trimesh
-
-from pyrender import PerspectiveCamera,\
-                     DirectionalLight, SpotLight, PointLight,\
-                     MetallicRoughnessMaterial,\
-                     Primitive, Mesh, Node, Scene,\
-                     Viewer, OffscreenRenderer, RenderFlags
-
-#==============================================================================
-# Mesh creation
-#==============================================================================
-
-#------------------------------------------------------------------------------
-# Creating textured meshes from trimeshes
-#------------------------------------------------------------------------------
-
-# Fuze trimesh
-fuze_trimesh = trimesh.load('./models/fuze.obj')
-fuze_mesh = Mesh.from_trimesh(fuze_trimesh)
-
-# Drill trimesh
-drill_trimesh = trimesh.load('./models/drill.obj')
-drill_mesh = Mesh.from_trimesh(drill_trimesh)
-drill_pose = np.eye(4)
-drill_pose[0,3] = 0.1
-drill_pose[2,3] = -np.min(drill_trimesh.vertices[:,2])
-
-# Wood trimesh
-wood_trimesh = trimesh.load('./models/wood.obj')
-wood_mesh = Mesh.from_trimesh(wood_trimesh)
-
-# Water bottle trimesh
-bottle_gltf = trimesh.load('./models/WaterBottle.glb')
-bottle_trimesh = bottle_gltf.geometry[list(bottle_gltf.geometry.keys())[0]]
-bottle_mesh = Mesh.from_trimesh(bottle_trimesh)
-bottle_pose = np.array([
-    [1.0, 0.0,  0.0, 0.1],
-    [0.0, 0.0, -1.0, -0.16],
-    [0.0, 1.0,  0.0, 0.13],
-    [0.0, 0.0,  0.0, 1.0],
-])
-
-#------------------------------------------------------------------------------
-# Creating meshes with per-vertex colors
-#------------------------------------------------------------------------------
-boxv_trimesh = trimesh.creation.box(extents=0.1*np.ones(3))
-boxv_vertex_colors = np.random.uniform(size=(boxv_trimesh.vertices.shape))
-boxv_trimesh.visual.vertex_colors = boxv_vertex_colors
-boxv_mesh = Mesh.from_trimesh(boxv_trimesh, smooth=False)
-
-#------------------------------------------------------------------------------
-# Creating meshes with per-face colors
-#------------------------------------------------------------------------------
-boxf_trimesh = trimesh.creation.box(extents=0.1*np.ones(3))
-boxf_face_colors = np.random.uniform(size=boxf_trimesh.faces.shape)
-boxf_trimesh.visual.face_colors = boxf_face_colors
-boxf_mesh = Mesh.from_trimesh(boxf_trimesh, smooth=False)
-
-#------------------------------------------------------------------------------
-# Creating meshes from point clouds
-#------------------------------------------------------------------------------
-points = trimesh.creation.icosphere(radius=0.05).vertices
-point_colors = np.random.uniform(size=points.shape)
-points_mesh = Mesh.from_points(points, colors=point_colors)
-
-#==============================================================================
-# Light creation
-#==============================================================================
-
-direc_l = DirectionalLight(color=np.ones(3), intensity=1.0)
-spot_l = SpotLight(color=np.ones(3), intensity=10.0,
-                   innerConeAngle=np.pi/16, outerConeAngle=np.pi/6)
-point_l = PointLight(color=np.ones(3), intensity=10.0)
-
-#==============================================================================
-# Camera creation
-#==============================================================================
-
-cam = PerspectiveCamera(yfov=(np.pi / 3.0))
-cam_pose = np.array([
-    [0.0,  -np.sqrt(2)/2, np.sqrt(2)/2, 0.5],
-    [1.0, 0.0,           0.0,           0.0],
-    [0.0,  np.sqrt(2)/2,  np.sqrt(2)/2, 0.4],
-    [0.0,  0.0,           0.0,          1.0]
-])
-
-#==============================================================================
-# Scene creation
-#==============================================================================
-
-scene = Scene(ambient_light=np.array([0.02, 0.02, 0.02, 1.0]))
-
-#==============================================================================
-# Adding objects to the scene
-#==============================================================================
-
-#------------------------------------------------------------------------------
-# By manually creating nodes
-#------------------------------------------------------------------------------
-fuze_node = Node(mesh=fuze_mesh, translation=np.array([0.1, 0.15, -np.min(fuze_trimesh.vertices[:,2])]))
-scene.add_node(fuze_node)
-boxv_node = Node(mesh=boxv_mesh, translation=np.array([-0.1, 0.10, 0.05]))
-scene.add_node(boxv_node)
-boxf_node = Node(mesh=boxf_mesh, translation=np.array([-0.1, -0.10, 0.05]))
-scene.add_node(boxf_node)
-
-#------------------------------------------------------------------------------
-# By using the add() utility function
-#------------------------------------------------------------------------------
-drill_node = scene.add(drill_mesh, pose=drill_pose)
-bottle_node = scene.add(bottle_mesh, pose=bottle_pose)
-wood_node = scene.add(wood_mesh)
-direc_l_node = scene.add(direc_l, pose=cam_pose)
-spot_l_node = scene.add(spot_l, pose=cam_pose)
-
-#==============================================================================
-# Using the viewer with a default camera
-#==============================================================================
-
-v = Viewer(scene, shadows=True)
-
-#==============================================================================
-# Using the viewer with a pre-specified camera
-#==============================================================================
-cam_node = scene.add(cam, pose=cam_pose)
-v = Viewer(scene, central_node=drill_node)
-
-#==============================================================================
-# Rendering offscreen from that camera
-#==============================================================================
-
-r = OffscreenRenderer(viewport_width=640*2, viewport_height=480*2)
-color, depth = r.render(scene)
-
-import matplotlib.pyplot as plt
-plt.figure()
-plt.imshow(color)
-plt.show()
-
-#==============================================================================
-# Segmask rendering
-#==============================================================================
-
-nm = {node: 20*(i + 1) for i, node in enumerate(scene.mesh_nodes)}
-seg = r.render(scene, RenderFlags.SEG, nm)[0]
-plt.figure()
-plt.imshow(seg)
-plt.show()
-
-r.delete()
-
diff --git a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/taskonomy/__init__.py b/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/taskonomy/__init__.py
deleted file mode 100644
index 625719fab6ac4260fc85d153d7c1c49a6f3016ba..0000000000000000000000000000000000000000
--- a/spaces/HCMUT-GraduateThesis-HNTThinh/rgbdsod-multimae-demo/utils/taskonomy/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .taskonomy_dataset import TaskonomyDataset
\ No newline at end of file
diff --git a/spaces/Hallucinate/demo/k_diffusion/__init__.py b/spaces/Hallucinate/demo/k_diffusion/__init__.py
deleted file mode 100644
index 5de9decab9fef99f2dd152f16b82b5806508ffdf..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/k_diffusion/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-from . import augmentation, config, evaluation, external, gns, layers, models, sampling, utils
-from .layers import Denoiser
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py
deleted file mode 100644
index 23869ebcd0c438f36e310c8ccddd3b5c07a71182..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/fast_noisy_channel/noisy_channel_beam_search.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-from fairseq.search import Search
-
-
-class NoisyChannelBeamSearch(Search):
-
-    def __init__(self, tgt_dict):
-        super().__init__(tgt_dict)
-        self.fw_scores_buf = None
-        self.lm_scores_buf = None
-
-    def _init_buffers(self, t):
-        # super()._init_buffers(t)
-        if self.fw_scores_buf is None:
-            self.scores_buf = t.new()
-            self.indices_buf = torch.LongTensor().to(device=t.device)
-            self.beams_buf = torch.LongTensor().to(device=t.device)
-            self.fw_scores_buf = t.new()
-            self.lm_scores_buf = t.new()
-
-    def combine_fw_bw(self, combine_method, fw_cum, bw, step):
-        if combine_method == "noisy_channel":
-            fw_norm = fw_cum.div(step + 1)
-            lprobs = bw + fw_norm
-        elif combine_method == "lm_only":
-            lprobs = bw + fw_cum
-
-        return lprobs
-
-    def step(self, step, fw_lprobs, scores, bw_lprobs, lm_lprobs, combine_method):
-        self._init_buffers(fw_lprobs)
-        bsz, beam_size, vocab_size = fw_lprobs.size()
-
-        if step == 0:
-            # at the first step all hypotheses are equally likely, so use
-            # only the first beam
-            fw_lprobs = fw_lprobs[:, ::beam_size, :].contiguous()
-            bw_lprobs = bw_lprobs[:, ::beam_size, :].contiguous()
-            # nothing to add since we are at the first step
-            fw_lprobs_cum = fw_lprobs
-
-        else:
-            # make probs contain cumulative scores for each hypothesis
-            raw_scores = (scores[:, :, step - 1].unsqueeze(-1))
-            fw_lprobs_cum = (fw_lprobs.add(raw_scores))
-
-        combined_lprobs = self.combine_fw_bw(combine_method, fw_lprobs_cum, bw_lprobs, step)
-
-        # choose the top k according to the combined noisy channel model score
-        torch.topk(
-            combined_lprobs.view(bsz, -1),
-            k=min(
-                # Take the best 2 x beam_size predictions. We'll choose the first
-                # beam_size of these which don't predict eos to continue with.
-                beam_size * 2,
-                combined_lprobs.view(bsz, -1).size(1) - 1,  # -1 so we never select pad
-            ),
-            out=(self.scores_buf, self.indices_buf),
-        )
-        # save corresponding fw and lm scores
-        self.fw_scores_buf = torch.gather(fw_lprobs_cum.view(bsz, -1), 1, self.indices_buf)
-        self.lm_scores_buf = torch.gather(lm_lprobs.view(bsz, -1), 1, self.indices_buf)
-        # Project back into relative indices and beams
-        self.beams_buf = self.indices_buf // vocab_size
-        self.indices_buf.fmod_(vocab_size)
-        return self.scores_buf, self.fw_scores_buf, self.lm_scores_buf, self.indices_buf, self.beams_buf
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/tasks/mm_tasks/__init__.py b/spaces/HarryLee/eCommerceImageCaptioning/tasks/mm_tasks/__init__.py
deleted file mode 100644
index 0190ffc87bc9463f20928d57501133839dc988d7..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/tasks/mm_tasks/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .caption import CaptionTask
diff --git a/spaces/Hise/rvc-hololive-models/vc_infer_pipeline.py b/spaces/Hise/rvc-hololive-models/vc_infer_pipeline.py
deleted file mode 100644
index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000
--- a/spaces/Hise/rvc-hololive-models/vc_infer_pipeline.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import numpy as np, parselmouth, torch, pdb
-from time import time as ttime
-import torch.nn.functional as F
-from config import x_pad, x_query, x_center, x_max
-import scipy.signal as signal
-import pyworld, os, traceback, faiss
-from scipy import signal
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-
-class VC(object):
-    def __init__(self, tgt_sr, device, is_half):
-        self.sr = 16000  # hubert输入采样率
-        self.window = 160  # 每帧点数
-        self.t_pad = self.sr * x_pad  # 每条前后pad时间
-        self.t_pad_tgt = tgt_sr * x_pad
-        self.t_pad2 = self.t_pad * 2
-        self.t_query = self.sr * x_query  # 查询切点前后查询时间
-        self.t_center = self.sr * x_center  # 查询切点位置
-        self.t_max = self.sr * x_max  # 免查询时长阈值
-        self.device = device
-        self.is_half = is_half
-
-    def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None):
-        time_step = self.window / self.sr * 1000
-        f0_min = 50
-        f0_max = 1100
-        f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-        f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-        if f0_method == "pm":
-            f0 = (
-                parselmouth.Sound(x, self.sr)
-                .to_pitch_ac(
-                    time_step=time_step / 1000,
-                    voicing_threshold=0.6,
-                    pitch_floor=f0_min,
-                    pitch_ceiling=f0_max,
-                )
-                .selected_array["frequency"]
-            )
-            pad_size = (p_len - len(f0) + 1) // 2
-            if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-                f0 = np.pad(
-                    f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
-                )
-        elif f0_method == "harvest":
-            f0, t = pyworld.harvest(
-                x.astype(np.double),
-                fs=self.sr,
-                f0_ceil=f0_max,
-                f0_floor=f0_min,
-                frame_period=10,
-            )
-            f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
-            f0 = signal.medfilt(f0, 3)
-        f0 *= pow(2, f0_up_key / 12)
-        # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        tf0 = self.sr // self.window  # 每秒f0点数
-        if inp_f0 is not None:
-            delta_t = np.round(
-                (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
-            ).astype("int16")
-            replace_f0 = np.interp(
-                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
-            )
-            shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
-        # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
-        f0bak = f0.copy()
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
-            f0_mel_max - f0_mel_min
-        ) + 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > 255] = 255
-        f0_coarse = np.rint(f0_mel).astype(np.int)
-        return f0_coarse, f0bak  # 1-0
-
-    def vc(
-        self,
-        model,
-        net_g,
-        sid,
-        audio0,
-        pitch,
-        pitchf,
-        times,
-        index,
-        big_npy,
-        index_rate,
-    ):  # ,file_index,file_big_npy
-        feats = torch.from_numpy(audio0)
-        if self.is_half:
-            feats = feats.half()
-        else:
-            feats = feats.float()
-        if feats.dim() == 2:  # double channels
-            feats = feats.mean(-1)
-        assert feats.dim() == 1, feats.dim()
-        feats = feats.view(1, -1)
-        padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
-        inputs = {
-            "source": feats.to(self.device),
-            "padding_mask": padding_mask,
-            "output_layer": 9,  # layer 9
-        }
-        t0 = ttime()
-        with torch.no_grad():
-            logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])
-
-        if (
-            isinstance(index, type(None)) == False
-            and isinstance(big_npy, type(None)) == False
-            and index_rate != 0
-        ):
-            npy = feats[0].cpu().numpy()
-            if self.is_half:
-                npy = npy.astype("float32")
-            _, I = index.search(npy, 1)
-            npy = big_npy[I.squeeze()]
-            if self.is_half:
-                npy = npy.astype("float16")
-            feats = (
-                torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
-                + (1 - index_rate) * feats
-            )
-
-        feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
-        t1 = ttime()
-        p_len = audio0.shape[0] // self.window
-        if feats.shape[1] < p_len:
-            p_len = feats.shape[1]
-            if pitch != None and pitchf != None:
-                pitch = pitch[:, :p_len]
-                pitchf = pitchf[:, :p_len]
-        p_len = torch.tensor([p_len], device=self.device).long()
-        with torch.no_grad():
-            if pitch != None and pitchf != None:
-                audio1 = (
-                    (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-            else:
-                audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
-                    .data.cpu()
-                    .float()
-                    .numpy()
-                    .astype(np.int16)
-                )
-        del feats, p_len, padding_mask
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        t2 = ttime()
-        times[0] += t1 - t0
-        times[2] += t2 - t1
-        return audio1
-
-    def pipeline(
-        self,
-        model,
-        net_g,
-        sid,
-        audio,
-        times,
-        f0_up_key,
-        f0_method,
-        file_index,
-        file_big_npy,
-        index_rate,
-        if_f0,
-        f0_file=None,
-    ):
-        if (
-            file_big_npy != ""
-            and file_index != ""
-            and os.path.exists(file_big_npy) == True
-            and os.path.exists(file_index) == True
-            and index_rate != 0
-        ):
-            try:
-                index = faiss.read_index(file_index)
-                big_npy = np.load(file_big_npy)
-            except:
-                traceback.print_exc()
-                index = big_npy = None
-        else:
-            index = big_npy = None
-            print("Feature retrieval library doesn't exist or ratio is 0")
-        audio = signal.filtfilt(bh, ah, audio)
-        audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
-        opt_ts = []
-        if audio_pad.shape[0] > self.t_max:
-            audio_sum = np.zeros_like(audio)
-            for i in range(self.window):
-                audio_sum += audio_pad[i : i - self.window]
-            for t in range(self.t_center, audio.shape[0], self.t_center):
-                opt_ts.append(
-                    t
-                    - self.t_query
-                    + np.where(
-                        np.abs(audio_sum[t - self.t_query : t + self.t_query])
-                        == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
-                    )[0][0]
-                )
-        s = 0
-        audio_opt = []
-        t = None
-        t1 = ttime()
-        audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
-        p_len = audio_pad.shape[0] // self.window
-        inp_f0 = None
-        if hasattr(f0_file, "name") == True:
-            try:
-                with open(f0_file.name, "r") as f:
-                    lines = f.read().strip("\n").split("\n")
-                inp_f0 = []
-                for line in lines:
-                    inp_f0.append([float(i) for i in line.split(",")])
-                inp_f0 = np.array(inp_f0, dtype="float32")
-            except:
-                traceback.print_exc()
-        sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
-        pitch, pitchf = None, None
-        if if_f0 == 1:
-            pitch, pitchf = self.get_f0(audio_pad, p_len, f0_up_key, f0_method, inp_f0)
-            pitch = pitch[:p_len]
-            pitchf = pitchf[:p_len]
-            pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
-            pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
-        t2 = ttime()
-        times[1] += t2 - t1
-        for t in opt_ts:
-            t = t // self.window * self.window
-            if if_f0 == 1:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        pitch[:, s // self.window : (t + self.t_pad2) // self.window],
-                        pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            else:
-                audio_opt.append(
-                    self.vc(
-                        model,
-                        net_g,
-                        sid,
-                        audio_pad[s : t + self.t_pad2 + self.window],
-                        None,
-                        None,
-                        times,
-                        index,
-                        big_npy,
-                        index_rate,
-                    )[self.t_pad_tgt : -self.t_pad_tgt]
-                )
-            s = t
-        if if_f0 == 1:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    pitch[:, t // self.window :] if t is not None else pitch,
-                    pitchf[:, t // self.window :] if t is not None else pitchf,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        else:
-            audio_opt.append(
-                self.vc(
-                    model,
-                    net_g,
-                    sid,
-                    audio_pad[t:],
-                    None,
-                    None,
-                    times,
-                    index,
-                    big_npy,
-                    index_rate,
-                )[self.t_pad_tgt : -self.t_pad_tgt]
-            )
-        audio_opt = np.concatenate(audio_opt)
-        del pitch, pitchf, sid
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        return audio_opt
diff --git a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/widgets/label_distribution.py b/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/widgets/label_distribution.py
deleted file mode 100644
index 7fd7f39e45e74935f2c0fe634d44a9ea86e2b9a2..0000000000000000000000000000000000000000
--- a/spaces/HuggingFaceM4/IDEFICS_Data_Measurement_Tool/widgets/label_distribution.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import gradio as gr
-
-from widgets.widget_base import Widget
-from data_measurements.dataset_statistics import DatasetStatisticsCacheClass as dmt_cls
-import utils
-
-logs = utils.prepare_logging(__file__)
-
-
-class LabelDistribution(Widget):
-    def __init__(self):
-        self.label_dist_plot = gr.Plot(render=False, visible=False)
-        self.label_dist_no_label_text = gr.Markdown(
-            value="No labels were found in the dataset", render=False, visible=False
-        )
-        self.label_dist_accordion = gr.Accordion(render=False, label="", open=False)
-
-    def render(self):
-        with gr.TabItem(label="Label Distribution"):
-            gr.Markdown(
-                "Use this widget to see how balanced the labels in your dataset are."
-            )
-            self.label_dist_plot.render()
-            self.label_dist_no_label_text.render()
-
-    def update(self, dstats: dmt_cls):
-        logs.info(f"FIGS labels: {bool(dstats.fig_labels)}")
-        if dstats.fig_labels:
-            output = {
-                self.label_dist_plot: gr.Plot.update(
-                    value=dstats.fig_labels, visible=True
-                ),
-                self.label_dist_no_label_text: gr.Markdown.update(visible=False),
-            }
-        else:
-            output = {
-                self.label_dist_plot: gr.Plot.update(visible=False),
-                self.label_dist_no_label_text: gr.Markdown.update(visible=True),
-            }
-        return output
-
-    @property
-    def output_components(self):
-        return [self.label_dist_plot, self.label_dist_no_label_text]
-
-    def add_events(self, state: gr.State):
-        pass
diff --git a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py b/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py
deleted file mode 100644
index 72b92a341dcd1b82035af72b8a6b4edc65783ecc..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/textless_nlp/gslm/metrics/asr_metrics/continuation_eval.py
+++ /dev/null
@@ -1,99 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-
-from collections import defaultdict
-import numpy as np
-from misc.bleu_utils import sentence_bleu
-import json
-import warnings
-
-
-def get_args():
-    import argparse
-
-    parser = argparse.ArgumentParser("Tool to calculate Continuation-BLEU2")
-    parser.add_argument('--asr-transcript', type=str,
-                        help='Path to the transcript file.')
-    parser.add_argument('--prompts-description', type=str,
-                        help='Path to the ground-truth continuation')
-    parser.add_argument('--manifest', type=str, required=True)
-    parser.add_argument('--take-shortest', type=int, default=1000)
-
-    args = parser.parse_args()
-
-    return args
-
-
-def main():
-    # NLTK produces warnings
-    warnings.filterwarnings("ignore")
-
-    args = get_args()
-
-    with open(args.prompts_description, 'r') as fin:
-        original_continuations = json.loads(fin.read())
-
-    sequence2length = [(k, v[0]) for k, v in original_continuations.items()]
-    assert all(float(v) >= 6.0 for (_, v) in sequence2length)  # 6 seconds
-
-    sequence2length.sort(key=lambda x: x[1])
-    to_take = set(v[0] for v in sequence2length[:args.take_shortest])
-
-    with open(args.manifest, 'r') as fin:
-        fin.readline()
-
-        linenum2file = dict([
-            (i, l.split("__")[0]) for (i, l) in enumerate(fin)
-        ])
-
-    max_files = max(linenum2file.keys())
-    continuations = defaultdict(list)
-
-    mean_length_after = 0
-    n_examples = 0
-
-    with open(args.asr_transcript, 'r') as fin:
-        for line in fin:
-            n_examples += 1
-            line = line.split()
-            sequence_id = int(line[-1].split('-')[1][:-1])
-
-            assert sequence_id <= max_files
-
-            sequence_name = linenum2file[sequence_id]
-
-            continuations[sequence_name].append(line[:-1])
-            mean_length_after += len(line)
-
-    mean_length_after /= n_examples
-    print(f'Mean length of continuations, in words: {mean_length_after}')
-    metric_values = []
-
-    mean_ground_truth_words = 0
-    n_examples = 0
-    n_candidates = 0
-
-    for k, candidates in continuations.items():
-        if k not in to_take:
-            continue
-
-        n_examples += 1
-
-        ground_truth = original_continuations[k][1].split()
-        n_candidates += len(candidates)
-        bleu = sentence_bleu(candidates, ground_truth, weights=(
-            0.5, 0.5), no_length_penalty=True, averaging_mode="geometric")
-        mean_ground_truth_words += len(ground_truth)
-
-        metric_values.append(bleu)
-
-    n = len(metric_values)
-    print(
-        f'Median BLEU over {n} examples: {np.median(metric_values)} +- {np.std(metric_values) / np.sqrt(n)}')
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/roberta/model.py b/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/roberta/model.py
deleted file mode 100644
index 77a80ef72057219110b34678a38705549910edd3..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/model_parallel/models/roberta/model.py
+++ /dev/null
@@ -1,225 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-RoBERTa: A Robustly Optimized BERT Pretraining Approach.
-"""
-
-import logging
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.model_parallel.models.transformer import ModelParallelTransformerEncoder
-from fairseq.models import register_model, register_model_architecture
-from fairseq.models.roberta import (
-    roberta_base_architecture,
-    roberta_prenorm_architecture,
-    RobertaEncoder,
-    RobertaModel,
-)
-from fairseq.modules import LayerNorm
-
-
-try:
-    from fairseq.model_parallel.megatron.mpu import (
-        copy_to_model_parallel_region,
-        gather_from_model_parallel_region,
-        ColumnParallelLinear,
-        VocabParallelEmbedding,
-    )
-
-    has_megatron_submodule = True
-except (ImportError, ModuleNotFoundError):
-    has_megatron_submodule = False
-
-logger = logging.getLogger(__name__)
-
-
-@register_model("model_parallel_roberta")
-class ModelParallelRobertaModel(RobertaModel):
-    def __init__(self, args, encoder):
-        super().__init__(args, encoder)
-
-        self.classification_heads = nn.ModuleDict()
-
-    @staticmethod
-    def add_args(parser):
-        RobertaModel.add_args(parser)
-        parser.add_argument(
-            "--no-final-layer-norm",
-            action="store_true",
-            help=(
-                "don't add final layernorm (only applicable when "
-                "--encoder-normalize-before=True"
-            ),
-        )
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present
-        base_architecture(args)
-
-        task.source_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
-        task.target_dictionary.pad_to_multiple_(args.model_parallel_size * 8)
-
-        if not hasattr(args, "max_positions"):
-            args.max_positions = args.tokens_per_sample
-
-        if getattr(args, "untie_weights_roberta", False):
-            raise NotImplementedError(
-                "--untie-weights-roberta is not supported in model parallel mode"
-            )
-
-        encoder = ModelParallelRobertaEncoder(args, task.source_dictionary)
-        return cls(args, encoder)
-
-    def forward(
-        self,
-        src_tokens,
-        features_only=False,
-        return_all_hiddens=False,
-        classification_head_name=None,
-        **kwargs
-    ):
-        if classification_head_name is not None:
-            features_only = True
-
-        x, extra = self.encoder(src_tokens, features_only, return_all_hiddens, **kwargs)
-
-        if classification_head_name is not None:
-            x = self.classification_heads[classification_head_name](x)
-        return x, extra
-
-    def register_classification_head(
-        self, name, num_classes=None, inner_dim=None, **kwargs
-    ):
-        """Register a classification head."""
-        if name in self.classification_heads:
-            prev_num_classes = self.classification_heads[name].out_proj.out_features
-            prev_inner_dim = self.classification_heads[name].dense.out_features
-            if num_classes != prev_num_classes or inner_dim != prev_inner_dim:
-                logger.warning(
-                    're-registering head "{}" with num_classes {} (prev: {}) '
-                    "and inner_dim {} (prev: {})".format(
-                        name, num_classes, prev_num_classes, inner_dim, prev_inner_dim
-                    )
-                )
-        self.classification_heads[name] = ModelParallelRobertaClassificationHead(
-            self.args.encoder_embed_dim,
-            inner_dim or self.args.encoder_embed_dim,
-            num_classes,
-            self.args.pooler_activation_fn,
-            self.args.pooler_dropout,
-        )
-
-
-class ModelParallelRobertaLMHead(nn.Module):
-    """Head for masked language modeling."""
-
-    def __init__(self, embed_dim, output_dim, activation_fn, weight=None):
-        super().__init__()
-        self.dense = ColumnParallelLinear(embed_dim, embed_dim, gather_output=True)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.layer_norm = LayerNorm(embed_dim)
-
-        if weight is None:
-            weight = nn.Linear(embed_dim, output_dim, bias=False).weight
-        self.weight = weight
-        self.bias = nn.Parameter(torch.zeros(output_dim))
-
-    def forward(self, features, masked_tokens=None, **kwargs):
-        # Only project the unmasked tokens while training,
-        # saves both memory and computation
-        if masked_tokens is not None:
-            features = features[masked_tokens, :]
-
-        x = self.dense(features)
-        x = self.activation_fn(x)
-        x = self.layer_norm(x)
-
-        x = copy_to_model_parallel_region(x)
-        # project back to size of vocabulary with bias
-        x = F.linear(x, self.weight)
-        x = gather_from_model_parallel_region(x).contiguous()
-        x = x + self.bias
-        return x
-
-
-class ModelParallelRobertaClassificationHead(nn.Module):
-    """Head for sentence-level classification tasks."""
-
-    def __init__(
-        self, input_dim, inner_dim, num_classes, activation_fn, pooler_dropout
-    ):
-        super().__init__()
-        self.dense = ColumnParallelLinear(input_dim, inner_dim, gather_output=True)
-        self.activation_fn = utils.get_activation_fn(activation_fn)
-        self.dropout = nn.Dropout(p=pooler_dropout)
-        self.out_proj = nn.Linear(inner_dim, num_classes)
-
-    def forward(self, features, **kwargs):
-        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
-        x = self.dropout(x)
-        x = self.dense(x)
-        x = self.activation_fn(x)
-        x = self.dropout(x)
-        x = self.out_proj(x)
-        return x
-
-
-class ModelParallelRobertaEncoder(RobertaEncoder):
-    """RoBERTa encoder."""
-
-    def __init__(self, args, dictionary):
-        super().__init__(args, dictionary)
-        assert not self.args.untie_weights_roberta
-
-    def build_embedding(self, vocab_size, embedding_dim, padding_idx):
-        return VocabParallelEmbedding(vocab_size, embedding_dim, padding_idx)
-
-    def build_encoder(self, args, dictionary, embed_tokens):
-        return ModelParallelTransformerEncoder(args, dictionary, embed_tokens)
-
-    def build_lm_head(self, embed_dim, output_dim, activation_fn, weight):
-        return ModelParallelRobertaLMHead(embed_dim, output_dim, activation_fn, weight)
-
-
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta")
-def base_architecture(args):
-    args.no_final_layer_norm = getattr(args, "no_final_layer_norm", False)
-    # model parallel RoBERTa defaults to "Pre-LN" formulation
-    roberta_prenorm_architecture(args)
-
-
-# earlier versions of model parallel RoBERTa removed the final layer norm
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_v1")
-def model_parallel_roberta_v1_architecture(args):
-    args.no_final_layer_norm = getattr(args, "no_final_layer_norm", True)
-    base_architecture(args)
-
-
-@register_model_architecture(
-    "model_parallel_roberta", "model_parallel_roberta_postnorm"
-)
-def model_parallel_roberta_postnorm_architecture(args):
-    # the original BERT/RoBERTa uses the "Post-LN" formulation
-    roberta_base_architecture(args)
-
-
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_base")
-def model_parallel_roberta_base_architecture(args):
-    base_architecture(args)
-
-
-@register_model_architecture("model_parallel_roberta", "model_parallel_roberta_large")
-def model_parallel_roberta_large_architecture(args):
-    args.encoder_layers = getattr(args, "encoder_layers", 24)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    base_architecture(args)
diff --git a/spaces/ICML2022/resefa/models/stylegan_generator.py b/spaces/ICML2022/resefa/models/stylegan_generator.py
deleted file mode 100644
index c0034b34a5b72bfe6b305a9f6ff8d772b391c4f5..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/models/stylegan_generator.py
+++ /dev/null
@@ -1,999 +0,0 @@
-# python3.7
-"""Contains the implementation of generator described in StyleGAN.
-
-Paper: https://arxiv.org/pdf/1812.04948.pdf
-
-Official TensorFlow implementation: https://github.com/NVlabs/stylegan
-"""
-
-import numpy as np
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.cuda.amp import autocast
-
-from .utils.ops import all_gather
-
-__all__ = ['StyleGANGenerator']
-
-# Resolutions allowed.
-_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
-
-# Fused-scale options allowed.
-_FUSED_SCALE_ALLOWED = [True, False, 'auto']
-
-# pylint: disable=missing-function-docstring
-
-class StyleGANGenerator(nn.Module):
-    """Defines the generator network in StyleGAN.
-
-    NOTE: The synthesized images are with `RGB` channel order and pixel range
-    [-1, 1].
-
-    Settings for the mapping network:
-
-    (1) z_dim: Dimension of the input latent space, Z. (default: 512)
-    (2) w_dim: Dimension of the output latent space, W. (default: 512)
-    (3) repeat_w: Repeat w-code for different layers. (default: True)
-    (4) normalize_z: Whether to normalize the z-code. (default: True)
-    (5) mapping_layers: Number of layers of the mapping network. (default: 8)
-    (6) mapping_fmaps: Number of hidden channels of the mapping network.
-        (default: 512)
-    (7) mapping_use_wscale: Whether to use weight scaling for the mapping
-        network. (default: True)
-    (8) mapping_wscale_gain: The factor to control weight scaling for the
-        mapping network (default: sqrt(2.0))
-    (9) mapping_lr_mul: Learning rate multiplier for the mapping network.
-        (default: 0.01)
-
-    Settings for conditional generation:
-
-    (1) label_dim: Dimension of the additional label for conditional generation.
-        In one-hot conditioning case, it is equal to the number of classes. If
-        set to 0, conditioning training will be disabled. (default: 0)
-    (2) embedding_dim: Dimension of the embedding space, if needed.
-        (default: 512)
-
-    Settings for the synthesis network:
-
-    (1) resolution: The resolution of the output image. (default: -1)
-    (2) init_res: The initial resolution to start with convolution. (default: 4)
-    (3) image_channels: Number of channels of the output image. (default: 3)
-    (4) final_tanh: Whether to use `tanh` to control the final pixel range.
-        (default: False)
-    (5) fused_scale:  The strategy of fusing `upsample` and `conv2d` as one
-        operator. `True` means blocks from all resolutions will fuse. `False`
-        means blocks from all resolutions will not fuse. `auto` means blocks
-        from resolutions higher than (or equal to) `fused_scale_res` will fuse.
-        (default: `auto`)
-    (6) fused_scale_res: Minimum resolution to fuse `conv2d` and `downsample`
-        as one operator. This field only takes effect if `fused_scale` is set
-        as `auto`. (default: 128)
-    (7) use_wscale: Whether to use weight scaling. (default: True)
-    (8) wscale_gain: The factor to control weight scaling. (default: sqrt(2.0))
-    (9) lr_mul: Learning rate multiplier for the synthesis network.
-        (default: 1.0)
-    (10) noise_type: Type of noise added to the convolutional results at each
-         layer. (default: `spatial`)
-    (11) fmaps_base: Factor to control number of feature maps for each layer.
-         (default: 16 << 10)
-    (12) fmaps_max: Maximum number of feature maps in each layer. (default: 512)
-    (13) filter_kernel: Kernel used for filtering (e.g., downsampling).
-         (default: (1, 2, 1))
-    (14) eps: A small value to avoid divide overflow. (default: 1e-8)
-
-    Runtime settings:
-
-    (1) w_moving_decay: Decay factor for updating `w_avg`, which is used for
-        training only. Set `None` to disable. (default: None)
-    (2) sync_w_avg: Synchronizing the stats of `w_avg` across replicas. If set
-        as `True`, the stats will be more accurate, yet the speed maybe a little
-        bit slower. (default: False)
-    (3) style_mixing_prob: Probability to perform style mixing as a training
-        regularization. Set `None` to disable. (default: None)
-    (4) trunc_psi: Truncation psi, set `None` to disable. (default: None)
-    (5) trunc_layers: Number of layers to perform truncation. (default: None)
-    (6) noise_mode: Mode of the layer-wise noise. Support `none`, `random`,
-        `const`. (default: `const`)
-    (7) enable_amp: Whether to enable automatic mixed precision training.
-        (default: False)
-    """
-
-    def __init__(self,
-                 # Settings for mapping network.
-                 z_dim=512,
-                 w_dim=512,
-                 repeat_w=True,
-                 normalize_z=True,
-                 mapping_layers=8,
-                 mapping_fmaps=512,
-                 mapping_use_wscale=True,
-                 mapping_wscale_gain=np.sqrt(2.0),
-                 mapping_lr_mul=0.01,
-                 # Settings for conditional generation.
-                 label_dim=0,
-                 embedding_dim=512,
-                 # Settings for synthesis network.
-                 resolution=-1,
-                 init_res=4,
-                 image_channels=3,
-                 final_tanh=False,
-                 fused_scale='auto',
-                 fused_scale_res=128,
-                 use_wscale=True,
-                 wscale_gain=np.sqrt(2.0),
-                 lr_mul=1.0,
-                 noise_type='spatial',
-                 fmaps_base=16 << 10,
-                 fmaps_max=512,
-                 filter_kernel=(1, 2, 1),
-                 eps=1e-8):
-        """Initializes with basic settings.
-
-        Raises:
-            ValueError: If the `resolution` is not supported, or `fused_scale`
-                is not supported.
-        """
-        super().__init__()
-
-        if resolution not in _RESOLUTIONS_ALLOWED:
-            raise ValueError(f'Invalid resolution: `{resolution}`!\n'
-                             f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
-        if fused_scale not in _FUSED_SCALE_ALLOWED:
-            raise ValueError(f'Invalid fused-scale option: `{fused_scale}`!\n'
-                             f'Options allowed: {_FUSED_SCALE_ALLOWED}.')
-
-        self.z_dim = z_dim
-        self.w_dim = w_dim
-        self.repeat_w = repeat_w
-        self.normalize_z = normalize_z
-        self.mapping_layers = mapping_layers
-        self.mapping_fmaps = mapping_fmaps
-        self.mapping_use_wscale = mapping_use_wscale
-        self.mapping_wscale_gain = mapping_wscale_gain
-        self.mapping_lr_mul = mapping_lr_mul
-
-        self.label_dim = label_dim
-        self.embedding_dim = embedding_dim
-
-        self.resolution = resolution
-        self.init_res = init_res
-        self.image_channels = image_channels
-        self.final_tanh = final_tanh
-        self.fused_scale = fused_scale
-        self.fused_scale_res = fused_scale_res
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.noise_type = noise_type.lower()
-        self.fmaps_base = fmaps_base
-        self.fmaps_max = fmaps_max
-        self.filter_kernel = filter_kernel
-        self.eps = eps
-
-        # Dimension of latent space, which is convenient for sampling.
-        self.latent_dim = (z_dim,)
-
-        # Number of synthesis (convolutional) layers.
-        self.num_layers = int(np.log2(resolution // init_res * 2)) * 2
-
-        self.mapping = MappingNetwork(input_dim=z_dim,
-                                      output_dim=w_dim,
-                                      num_outputs=self.num_layers,
-                                      repeat_output=repeat_w,
-                                      normalize_input=normalize_z,
-                                      num_layers=mapping_layers,
-                                      hidden_dim=mapping_fmaps,
-                                      use_wscale=mapping_use_wscale,
-                                      wscale_gain=mapping_wscale_gain,
-                                      lr_mul=mapping_lr_mul,
-                                      label_dim=label_dim,
-                                      embedding_dim=embedding_dim,
-                                      eps=eps)
-
-        # This is used for truncation trick.
-        if self.repeat_w:
-            self.register_buffer('w_avg', torch.zeros(w_dim))
-        else:
-            self.register_buffer('w_avg', torch.zeros(self.num_layers * w_dim))
-
-        self.synthesis = SynthesisNetwork(resolution=resolution,
-                                          init_res=init_res,
-                                          w_dim=w_dim,
-                                          image_channels=image_channels,
-                                          final_tanh=final_tanh,
-                                          fused_scale=fused_scale,
-                                          fused_scale_res=fused_scale_res,
-                                          use_wscale=use_wscale,
-                                          wscale_gain=wscale_gain,
-                                          lr_mul=lr_mul,
-                                          noise_type=noise_type,
-                                          fmaps_base=fmaps_base,
-                                          fmaps_max=fmaps_max,
-                                          filter_kernel=filter_kernel,
-                                          eps=eps)
-
-        self.pth_to_tf_var_mapping = {'w_avg': 'dlatent_avg'}
-        for key, val in self.mapping.pth_to_tf_var_mapping.items():
-            self.pth_to_tf_var_mapping[f'mapping.{key}'] = val
-        for key, val in self.synthesis.pth_to_tf_var_mapping.items():
-            self.pth_to_tf_var_mapping[f'synthesis.{key}'] = val
-
-    def set_space_of_latent(self, space_of_latent):
-        """Sets the space to which the latent code belong.
-
-        See `SynthesisNetwork` for more details.
-        """
-        self.synthesis.set_space_of_latent(space_of_latent)
-
-    def forward(self,
-                z,
-                label=None,
-                lod=None,
-                w_moving_decay=None,
-                sync_w_avg=False,
-                style_mixing_prob=None,
-                trunc_psi=None,
-                trunc_layers=None,
-                noise_mode='const',
-                enable_amp=False):
-        mapping_results = self.mapping(z, label)
-
-        w = mapping_results['w']
-        if self.training and w_moving_decay is not None:
-            if sync_w_avg:
-                batch_w_avg = all_gather(w.detach()).mean(dim=0)
-            else:
-                batch_w_avg = w.detach().mean(dim=0)
-            self.w_avg.copy_(batch_w_avg.lerp(self.w_avg, w_moving_decay))
-
-        wp = mapping_results.pop('wp')
-        if self.training and style_mixing_prob is not None:
-            if np.random.uniform() < style_mixing_prob:
-                new_z = torch.randn_like(z)
-                new_wp = self.mapping(new_z, label)['wp']
-                lod = self.synthesis.lod.item() if lod is None else lod
-                current_layers = self.num_layers - int(lod) * 2
-                mixing_cutoff = np.random.randint(1, current_layers)
-                wp[:, mixing_cutoff:] = new_wp[:, mixing_cutoff:]
-
-        if not self.training:
-            trunc_psi = 1.0 if trunc_psi is None else trunc_psi
-            trunc_layers = 0 if trunc_layers is None else trunc_layers
-            if trunc_psi < 1.0 and trunc_layers > 0:
-                w_avg = self.w_avg.reshape(1, -1, self.w_dim)[:, :trunc_layers]
-                wp[:, :trunc_layers] = w_avg.lerp(
-                    wp[:, :trunc_layers], trunc_psi)
-
-        with autocast(enabled=enable_amp):
-            synthesis_results = self.synthesis(wp,
-                                               lod=lod,
-                                               noise_mode=noise_mode)
-
-        return {**mapping_results, **synthesis_results}
-
-
-class MappingNetwork(nn.Module):
-    """Implements the latent space mapping module.
-
-    Basically, this module executes several dense layers in sequence, and the
-    label embedding if needed.
-    """
-
-    def __init__(self,
-                 input_dim,
-                 output_dim,
-                 num_outputs,
-                 repeat_output,
-                 normalize_input,
-                 num_layers,
-                 hidden_dim,
-                 use_wscale,
-                 wscale_gain,
-                 lr_mul,
-                 label_dim,
-                 embedding_dim,
-                 eps):
-        super().__init__()
-
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.num_outputs = num_outputs
-        self.repeat_output = repeat_output
-        self.normalize_input = normalize_input
-        self.num_layers = num_layers
-        self.hidden_dim = hidden_dim
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.label_dim = label_dim
-        self.embedding_dim = embedding_dim
-        self.eps = eps
-
-        self.pth_to_tf_var_mapping = {}
-
-        if normalize_input:
-            self.norm = PixelNormLayer(dim=1, eps=eps)
-
-        if self.label_dim > 0:
-            input_dim = input_dim + embedding_dim
-            self.embedding = nn.Parameter(
-                torch.randn(label_dim, embedding_dim))
-            self.pth_to_tf_var_mapping['embedding'] = 'LabelConcat/weight'
-
-        if num_outputs is not None and not repeat_output:
-            output_dim = output_dim * num_outputs
-        for i in range(num_layers):
-            in_channels = (input_dim if i == 0 else hidden_dim)
-            out_channels = (output_dim if i == (num_layers - 1) else hidden_dim)
-            self.add_module(f'dense{i}',
-                            DenseLayer(in_channels=in_channels,
-                                       out_channels=out_channels,
-                                       add_bias=True,
-                                       use_wscale=use_wscale,
-                                       wscale_gain=wscale_gain,
-                                       lr_mul=lr_mul,
-                                       activation_type='lrelu'))
-            self.pth_to_tf_var_mapping[f'dense{i}.weight'] = f'Dense{i}/weight'
-            self.pth_to_tf_var_mapping[f'dense{i}.bias'] = f'Dense{i}/bias'
-
-    def forward(self, z, label=None):
-        if z.ndim != 2 or z.shape[1] != self.input_dim:
-            raise ValueError(f'Input latent code should be with shape '
-                             f'[batch_size, input_dim], where '
-                             f'`input_dim` equals to {self.input_dim}!\n'
-                             f'But `{z.shape}` is received!')
-
-        if self.label_dim > 0:
-            if label is None:
-                raise ValueError(f'Model requires an additional label '
-                                 f'(with dimension {self.label_dim}) as input, '
-                                 f'but no label is received!')
-            if label.ndim != 2 or label.shape != (z.shape[0], self.label_dim):
-                raise ValueError(f'Input label should be with shape '
-                                 f'[batch_size, label_dim], where '
-                                 f'`batch_size` equals to that of '
-                                 f'latent codes ({z.shape[0]}) and '
-                                 f'`label_dim` equals to {self.label_dim}!\n'
-                                 f'But `{label.shape}` is received!')
-            label = label.to(dtype=torch.float32)
-            embedding = torch.matmul(label, self.embedding)
-            z = torch.cat((z, embedding), dim=1)
-
-        if self.normalize_input:
-            w = self.norm(z)
-        else:
-            w = z
-
-        for i in range(self.num_layers):
-            w = getattr(self, f'dense{i}')(w)
-
-        wp = None
-        if self.num_outputs is not None:
-            if self.repeat_output:
-                wp = w.unsqueeze(1).repeat((1, self.num_outputs, 1))
-            else:
-                wp = w.reshape(-1, self.num_outputs, self.output_dim)
-
-        results = {
-            'z': z,
-            'label': label,
-            'w': w,
-            'wp': wp,
-        }
-        if self.label_dim > 0:
-            results['embedding'] = embedding
-        return results
-
-
-class SynthesisNetwork(nn.Module):
-    """Implements the image synthesis module.
-
-    Basically, this module executes several convolutional layers in sequence.
-    """
-
-    def __init__(self,
-                 resolution,
-                 init_res,
-                 w_dim,
-                 image_channels,
-                 final_tanh,
-                 fused_scale,
-                 fused_scale_res,
-                 use_wscale,
-                 wscale_gain,
-                 lr_mul,
-                 noise_type,
-                 fmaps_base,
-                 fmaps_max,
-                 filter_kernel,
-                 eps):
-        super().__init__()
-
-        self.init_res = init_res
-        self.init_res_log2 = int(np.log2(init_res))
-        self.resolution = resolution
-        self.final_res_log2 = int(np.log2(resolution))
-        self.w_dim = w_dim
-        self.image_channels = image_channels
-        self.final_tanh = final_tanh
-        self.fused_scale = fused_scale
-        self.fused_scale_res = fused_scale_res
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.noise_type = noise_type.lower()
-        self.fmaps_base = fmaps_base
-        self.fmaps_max = fmaps_max
-        self.eps = eps
-
-        self.num_layers = (self.final_res_log2 - self.init_res_log2 + 1) * 2
-
-        # Level-of-details (used for progressive training).
-        self.register_buffer('lod', torch.zeros(()))
-        self.pth_to_tf_var_mapping = {'lod': 'lod'}
-
-        for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1):
-            res = 2 ** res_log2
-            in_channels = self.get_nf(res // 2)
-            out_channels = self.get_nf(res)
-            block_idx = res_log2 - self.init_res_log2
-
-            # First layer (kernel 3x3) with upsampling
-            layer_name = f'layer{2 * block_idx}'
-            if res == self.init_res:
-                self.add_module(layer_name,
-                                ModulateConvLayer(in_channels=0,
-                                                  out_channels=out_channels,
-                                                  resolution=res,
-                                                  w_dim=w_dim,
-                                                  kernel_size=None,
-                                                  add_bias=True,
-                                                  scale_factor=None,
-                                                  fused_scale=None,
-                                                  filter_kernel=None,
-                                                  use_wscale=use_wscale,
-                                                  wscale_gain=wscale_gain,
-                                                  lr_mul=lr_mul,
-                                                  noise_type=noise_type,
-                                                  activation_type='lrelu',
-                                                  use_style=True,
-                                                  eps=eps))
-                tf_layer_name = 'Const'
-                self.pth_to_tf_var_mapping[f'{layer_name}.const'] = (
-                    f'{res}x{res}/{tf_layer_name}/const')
-            else:
-                self.add_module(
-                    layer_name,
-                    ModulateConvLayer(in_channels=in_channels,
-                                      out_channels=out_channels,
-                                      resolution=res,
-                                      w_dim=w_dim,
-                                      kernel_size=3,
-                                      add_bias=True,
-                                      scale_factor=2,
-                                      fused_scale=(res >= fused_scale_res
-                                                   if fused_scale == 'auto'
-                                                   else fused_scale),
-                                      filter_kernel=filter_kernel,
-                                      use_wscale=use_wscale,
-                                      wscale_gain=wscale_gain,
-                                      lr_mul=lr_mul,
-                                      noise_type=noise_type,
-                                      activation_type='lrelu',
-                                      use_style=True,
-                                      eps=eps))
-                tf_layer_name = 'Conv0_up'
-                self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                    f'{res}x{res}/{tf_layer_name}/weight')
-            self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                f'{res}x{res}/{tf_layer_name}/bias')
-            self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = (
-                f'{res}x{res}/{tf_layer_name}/StyleMod/weight')
-            self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = (
-                f'{res}x{res}/{tf_layer_name}/StyleMod/bias')
-            self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = (
-                f'{res}x{res}/{tf_layer_name}/Noise/weight')
-            self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = (
-                f'noise{2 * block_idx}')
-
-            # Second layer (kernel 3x3) without upsampling.
-            layer_name = f'layer{2 * block_idx + 1}'
-            self.add_module(layer_name,
-                            ModulateConvLayer(in_channels=out_channels,
-                                              out_channels=out_channels,
-                                              resolution=res,
-                                              w_dim=w_dim,
-                                              kernel_size=3,
-                                              add_bias=True,
-                                              scale_factor=1,
-                                              fused_scale=False,
-                                              filter_kernel=None,
-                                              use_wscale=use_wscale,
-                                              wscale_gain=wscale_gain,
-                                              lr_mul=lr_mul,
-                                              noise_type=noise_type,
-                                              activation_type='lrelu',
-                                              use_style=True,
-                                              eps=eps))
-            tf_layer_name = 'Conv' if res == self.init_res else 'Conv1'
-            self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
-                f'{res}x{res}/{tf_layer_name}/weight')
-            self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
-                f'{res}x{res}/{tf_layer_name}/bias')
-            self.pth_to_tf_var_mapping[f'{layer_name}.style.weight'] = (
-                f'{res}x{res}/{tf_layer_name}/StyleMod/weight')
-            self.pth_to_tf_var_mapping[f'{layer_name}.style.bias'] = (
-                f'{res}x{res}/{tf_layer_name}/StyleMod/bias')
-            self.pth_to_tf_var_mapping[f'{layer_name}.noise_strength'] = (
-                f'{res}x{res}/{tf_layer_name}/Noise/weight')
-            self.pth_to_tf_var_mapping[f'{layer_name}.noise'] = (
-                f'noise{2 * block_idx + 1}')
-
-            # Output convolution layer for each resolution.
-            self.add_module(f'output{block_idx}',
-                            ModulateConvLayer(in_channels=out_channels,
-                                              out_channels=image_channels,
-                                              resolution=res,
-                                              w_dim=w_dim,
-                                              kernel_size=1,
-                                              add_bias=True,
-                                              scale_factor=1,
-                                              fused_scale=False,
-                                              filter_kernel=None,
-                                              use_wscale=use_wscale,
-                                              wscale_gain=1.0,
-                                              lr_mul=lr_mul,
-                                              noise_type='none',
-                                              activation_type='linear',
-                                              use_style=False,
-                                              eps=eps))
-            self.pth_to_tf_var_mapping[f'output{block_idx}.weight'] = (
-                f'ToRGB_lod{self.final_res_log2 - res_log2}/weight')
-            self.pth_to_tf_var_mapping[f'output{block_idx}.bias'] = (
-                f'ToRGB_lod{self.final_res_log2 - res_log2}/bias')
-
-    def get_nf(self, res):
-        """Gets number of feature maps according to the given resolution."""
-        return min(self.fmaps_base // res, self.fmaps_max)
-
-    def set_space_of_latent(self, space_of_latent):
-        """Sets the space to which the latent code belong.
-
-        This function is particularly used for choosing how to inject the latent
-        code into the convolutional layers. The original generator will take a
-        W-Space code and apply it for style modulation after an affine
-        transformation. But, sometimes, it may need to directly feed an already
-        affine-transformed code into the convolutional layer, e.g., when
-        training an encoder for GAN inversion. We term the transformed space as
-        Style Space (or Y-Space). This function is designed to tell the
-        convolutional layers how to use the input code.
-
-        Args:
-            space_of_latent: The space to which the latent code belong. Case
-                insensitive. Support `W` and `Y`.
-        """
-        space_of_latent = space_of_latent.upper()
-        for module in self.modules():
-            if isinstance(module, ModulateConvLayer) and module.use_style:
-                setattr(module, 'space_of_latent', space_of_latent)
-
-    def forward(self, wp, lod=None, noise_mode='const'):
-        lod = self.lod.item() if lod is None else lod
-        if lod + self.init_res_log2 > self.final_res_log2:
-            raise ValueError(f'Maximum level-of-details (lod) is '
-                             f'{self.final_res_log2 - self.init_res_log2}, '
-                             f'but `{lod}` is received!')
-
-        results = {'wp': wp}
-        x = None
-        for res_log2 in range(self.init_res_log2, self.final_res_log2 + 1):
-            current_lod = self.final_res_log2 - res_log2
-            block_idx = res_log2 - self.init_res_log2
-            if lod < current_lod + 1:
-                layer = getattr(self, f'layer{2 * block_idx}')
-                x, style = layer(x, wp[:, 2 * block_idx], noise_mode)
-                results[f'style{2 * block_idx}'] = style
-                layer = getattr(self, f'layer{2 * block_idx + 1}')
-                x, style = layer(x, wp[:, 2 * block_idx + 1], noise_mode)
-                results[f'style{2 * block_idx + 1}'] = style
-            if current_lod - 1 < lod <= current_lod:
-                image = getattr(self, f'output{block_idx}')(x)
-            elif current_lod < lod < current_lod + 1:
-                alpha = np.ceil(lod) - lod
-                temp = getattr(self, f'output{block_idx}')(x)
-                image = F.interpolate(image, scale_factor=2, mode='nearest')
-                image = temp * alpha + image * (1 - alpha)
-            elif lod >= current_lod + 1:
-                image = F.interpolate(image, scale_factor=2, mode='nearest')
-
-        if self.final_tanh:
-            image = torch.tanh(image)
-        results['image'] = image
-        return results
-
-
-class PixelNormLayer(nn.Module):
-    """Implements pixel-wise feature vector normalization layer."""
-
-    def __init__(self, dim, eps):
-        super().__init__()
-        self.dim = dim
-        self.eps = eps
-
-    def extra_repr(self):
-        return f'dim={self.dim}, epsilon={self.eps}'
-
-    def forward(self, x):
-        scale = (x.square().mean(dim=self.dim, keepdim=True) + self.eps).rsqrt()
-        return x * scale
-
-
-class Blur(torch.autograd.Function):
-    """Defines blur operation with customized gradient computation."""
-
-    @staticmethod
-    def forward(ctx, x, kernel):
-        assert kernel.shape[2] == 3 and kernel.shape[3] == 3
-        ctx.save_for_backward(kernel)
-        y = F.conv2d(input=x,
-                     weight=kernel,
-                     bias=None,
-                     stride=1,
-                     padding=1,
-                     groups=x.shape[1])
-        return y
-
-    @staticmethod
-    def backward(ctx, dy):
-        kernel, = ctx.saved_tensors
-        dx = F.conv2d(input=dy,
-                      weight=kernel.flip((2, 3)),
-                      bias=None,
-                      stride=1,
-                      padding=1,
-                      groups=dy.shape[1])
-        return dx, None, None
-
-
-class ModulateConvLayer(nn.Module):
-    """Implements the convolutional layer with style modulation."""
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 resolution,
-                 w_dim,
-                 kernel_size,
-                 add_bias,
-                 scale_factor,
-                 fused_scale,
-                 filter_kernel,
-                 use_wscale,
-                 wscale_gain,
-                 lr_mul,
-                 noise_type,
-                 activation_type,
-                 use_style,
-                 eps):
-        """Initializes with layer settings.
-
-        Args:
-            in_channels: Number of channels of the input tensor.
-            out_channels: Number of channels of the output tensor.
-            resolution: Resolution of the output tensor.
-            w_dim: Dimension of W space for style modulation.
-            kernel_size: Size of the convolutional kernels.
-            add_bias: Whether to add bias onto the convolutional result.
-            scale_factor: Scale factor for upsampling.
-            fused_scale: Whether to fuse `upsample` and `conv2d` as one
-                operator, using transpose convolution.
-            filter_kernel: Kernel used for filtering.
-            use_wscale: Whether to use weight scaling.
-            wscale_gain: Gain factor for weight scaling.
-            lr_mul: Learning multiplier for both weight and bias.
-            noise_type: Type of noise added to the feature map after the
-                convolution (if needed). Support `none`, `spatial` and
-                `channel`.
-            activation_type: Type of activation.
-            use_style: Whether to apply style modulation.
-            eps: A small value to avoid divide overflow.
-        """
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.resolution = resolution
-        self.w_dim = w_dim
-        self.kernel_size = kernel_size
-        self.add_bias = add_bias
-        self.scale_factor = scale_factor
-        self.fused_scale = fused_scale
-        self.filter_kernel = filter_kernel
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.noise_type = noise_type.lower()
-        self.activation_type = activation_type
-        self.use_style = use_style
-        self.eps = eps
-
-        # Set up noise.
-        if self.noise_type == 'none':
-            pass
-        elif self.noise_type == 'spatial':
-            self.register_buffer(
-                'noise', torch.randn(1, 1, resolution, resolution))
-            self.noise_strength = nn.Parameter(
-                torch.zeros(1, out_channels, 1, 1))
-        elif self.noise_type == 'channel':
-            self.register_buffer(
-                'noise', torch.randn(1, out_channels, 1, 1))
-            self.noise_strength = nn.Parameter(
-                torch.zeros(1, 1, resolution, resolution))
-        else:
-            raise NotImplementedError(f'Not implemented noise type: '
-                                      f'`{noise_type}`!')
-
-        # Set up bias.
-        if add_bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels))
-            self.bscale = lr_mul
-        else:
-            self.bias = None
-
-        # Set up activation.
-        assert activation_type in ['linear', 'relu', 'lrelu']
-
-        # Set up style.
-        if use_style:
-            self.space_of_latent = 'W'
-            self.style = DenseLayer(in_channels=w_dim,
-                                    out_channels=out_channels * 2,
-                                    add_bias=True,
-                                    use_wscale=use_wscale,
-                                    wscale_gain=1.0,
-                                    lr_mul=1.0,
-                                    activation_type='linear')
-
-        if in_channels == 0:  # First layer.
-            self.const = nn.Parameter(
-                torch.ones(1, out_channels, resolution, resolution))
-            return
-
-        # Set up weight.
-        weight_shape = (out_channels, in_channels, kernel_size, kernel_size)
-        fan_in = kernel_size * kernel_size * in_channels
-        wscale = wscale_gain / np.sqrt(fan_in)
-        if use_wscale:
-            self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
-            self.wscale = wscale * lr_mul
-        else:
-            self.weight = nn.Parameter(
-                torch.randn(*weight_shape) * wscale / lr_mul)
-            self.wscale = lr_mul
-
-        # Set up upsampling filter (if needed).
-        if scale_factor > 1:
-            assert filter_kernel is not None
-            kernel = np.array(filter_kernel, dtype=np.float32).reshape(1, -1)
-            kernel = kernel.T.dot(kernel)
-            kernel = kernel / np.sum(kernel)
-            kernel = kernel[np.newaxis, np.newaxis]
-            self.register_buffer('filter', torch.from_numpy(kernel))
-
-        if scale_factor > 1 and fused_scale:  # use transpose convolution.
-            self.stride = scale_factor
-        else:
-            self.stride = 1
-        self.padding = kernel_size // 2
-
-    def extra_repr(self):
-        return (f'in_ch={self.in_channels}, '
-                f'out_ch={self.out_channels}, '
-                f'ksize={self.kernel_size}, '
-                f'wscale_gain={self.wscale_gain:.3f}, '
-                f'bias={self.add_bias}, '
-                f'lr_mul={self.lr_mul:.3f}, '
-                f'upsample={self.scale_factor}, '
-                f'fused_scale={self.fused_scale}, '
-                f'upsample_filter={self.filter_kernel}, '
-                f'noise_type={self.noise_type}, '
-                f'act={self.activation_type}, '
-                f'use_style={self.use_style}')
-
-    def forward_style(self, w):
-        """Gets style code from the given input.
-
-        More specifically, if the input is from W-Space, it will be projected by
-        an affine transformation. If it is from the Style Space (Y-Space), no
-        operation is required.
-
-        NOTE: For codes from Y-Space, we use slicing to make sure the dimension
-        is correct, in case that the code is padded before fed into this layer.
-        """
-        space_of_latent = self.space_of_latent.upper()
-        if space_of_latent == 'W':
-            if w.ndim != 2 or w.shape[1] != self.w_dim:
-                raise ValueError(f'The input tensor should be with shape '
-                                 f'[batch_size, w_dim], where '
-                                 f'`w_dim` equals to {self.w_dim}!\n'
-                                 f'But `{w.shape}` is received!')
-            style = self.style(w)
-        elif space_of_latent == 'Y':
-            if w.ndim != 2 or w.shape[1] < self.out_channels * 2:
-                raise ValueError(f'The input tensor should be with shape '
-                                 f'[batch_size, y_dim], where '
-                                 f'`y_dim` equals to {self.out_channels * 2}!\n'
-                                 f'But `{w.shape}` is received!')
-            style = w[:, :self.out_channels * 2]
-        else:
-            raise NotImplementedError(f'Not implemented `space_of_latent`: '
-                                      f'`{space_of_latent}`!')
-        return style
-
-    def forward(self, x, w=None, noise_mode='const'):
-        if self.in_channels == 0:
-            assert x is None
-            x = self.const.repeat(w.shape[0], 1, 1, 1)
-        else:
-            weight = self.weight
-            if self.wscale != 1.0:
-                weight = weight * self.wscale
-
-            if self.scale_factor > 1 and self.fused_scale:
-                weight = F.pad(weight, (1, 1, 1, 1, 0, 0, 0, 0), 'constant', 0)
-                weight = (weight[:, :, 1:, 1:] + weight[:, :, :-1, 1:] +
-                          weight[:, :, 1:, :-1] + weight[:, :, :-1, :-1])
-                x = F.conv_transpose2d(x,
-                                       weight=weight.transpose(0, 1),
-                                       bias=None,
-                                       stride=self.stride,
-                                       padding=self.padding)
-            else:
-                if self.scale_factor > 1:
-                    up = self.scale_factor
-                    x = F.interpolate(x, scale_factor=up, mode='nearest')
-                x = F.conv2d(x,
-                             weight=weight,
-                             bias=None,
-                             stride=self.stride,
-                             padding=self.padding)
-
-            if self.scale_factor > 1:
-                # Disable `autocast` for customized autograd function.
-                # Please check reference:
-                # https://pytorch.org/docs/stable/notes/amp_examples.html#autocast-and-custom-autograd-functions
-                with autocast(enabled=False):
-                    f = self.filter.repeat(self.out_channels, 1, 1, 1)
-                    x = Blur.apply(x.float(), f)  # Always use FP32.
-
-        # Prepare noise.
-        noise_mode = noise_mode.lower()
-        if self.noise_type != 'none' and noise_mode != 'none':
-            if noise_mode == 'random':
-                noise = torch.randn(
-                    (x.shape[0], *self.noise.shape[1:]), device=x.device)
-            elif noise_mode == 'const':
-                noise = self.noise
-            else:
-                raise ValueError(f'Unknown noise mode `{noise_mode}`!')
-            x = x + noise * self.noise_strength
-
-        if self.bias is not None:
-            bias = self.bias
-            if self.bscale != 1.0:
-                bias = bias * self.bscale
-            x = x + bias.reshape(1, self.out_channels, 1, 1)
-
-        if self.activation_type == 'linear':
-            pass
-        elif self.activation_type == 'relu':
-            x = F.relu(x, inplace=True)
-        elif self.activation_type == 'lrelu':
-            x = F.leaky_relu(x, negative_slope=0.2, inplace=True)
-        else:
-            raise NotImplementedError(f'Not implemented activation type '
-                                      f'`{self.activation_type}`!')
-
-        if not self.use_style:
-            return x
-
-        # Instance normalization.
-        x = x - x.mean(dim=(2, 3), keepdim=True)
-        scale = (x.square().mean(dim=(2, 3), keepdim=True) + self.eps).rsqrt()
-        x = x * scale
-        # Style modulation.
-        style = self.forward_style(w)
-        style_split = style.unsqueeze(2).unsqueeze(3).chunk(2, dim=1)
-        x = x * (style_split[0] + 1) + style_split[1]
-
-        return x, style
-
-
-class DenseLayer(nn.Module):
-    """Implements the dense layer."""
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 add_bias,
-                 use_wscale,
-                 wscale_gain,
-                 lr_mul,
-                 activation_type):
-        """Initializes with layer settings.
-
-        Args:
-            in_channels: Number of channels of the input tensor.
-            out_channels: Number of channels of the output tensor.
-            add_bias: Whether to add bias onto the fully-connected result.
-            use_wscale: Whether to use weight scaling.
-            wscale_gain: Gain factor for weight scaling.
-            lr_mul: Learning multiplier for both weight and bias.
-            activation_type: Type of activation.
-        """
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.add_bias = add_bias
-        self.use_wscale = use_wscale
-        self.wscale_gain = wscale_gain
-        self.lr_mul = lr_mul
-        self.activation_type = activation_type
-
-        weight_shape = (out_channels, in_channels)
-        wscale = wscale_gain / np.sqrt(in_channels)
-        if use_wscale:
-            self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
-            self.wscale = wscale * lr_mul
-        else:
-            self.weight = nn.Parameter(
-                torch.randn(*weight_shape) * wscale / lr_mul)
-            self.wscale = lr_mul
-
-        if add_bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels))
-            self.bscale = lr_mul
-        else:
-            self.bias = None
-
-        assert activation_type in ['linear', 'relu', 'lrelu']
-
-    def extra_repr(self):
-        return (f'in_ch={self.in_channels}, '
-                f'out_ch={self.out_channels}, '
-                f'wscale_gain={self.wscale_gain:.3f}, '
-                f'bias={self.add_bias}, '
-                f'lr_mul={self.lr_mul:.3f}, '
-                f'act={self.activation_type}')
-
-    def forward(self, x):
-        if x.ndim != 2:
-            x = x.flatten(start_dim=1)
-
-        weight = self.weight
-        if self.wscale != 1.0:
-            weight = weight * self.wscale
-        bias = None
-        if self.bias is not None:
-            bias = self.bias
-            if self.bscale != 1.0:
-                bias = bias * self.bscale
-
-        x = F.linear(x, weight=weight, bias=bias)
-
-        if self.activation_type == 'linear':
-            pass
-        elif self.activation_type == 'relu':
-            x = F.relu(x, inplace=True)
-        elif self.activation_type == 'lrelu':
-            x = F.leaky_relu(x, negative_slope=0.2, inplace=True)
-        else:
-            raise NotImplementedError(f'Not implemented activation type '
-                                      f'`{self.activation_type}`!')
-
-        return x
-
-# pylint: enable=missing-function-docstring
diff --git a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp b/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
deleted file mode 100644
index c1f2c50c82909bbd5492c163d634af77a3ba1781..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include "MsDeformAttn/ms_deform_attn.h"
-
-namespace groundingdino {
-
-#ifdef WITH_CUDA
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#ifdef WITH_CUDA
-  std::ostringstream oss;
-
-  // copied from
-  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
-  auto printCudaStyleVersion = [&](int v) {
-    oss << (v / 1000) << "." << (v / 10 % 100);
-    if (v % 10 != 0) {
-      oss << "." << (v % 10);
-    }
-  };
-  printCudaStyleVersion(get_cudart_version());
-  return oss.str();
-#else
-  return std::string("not available");
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
-  std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
-  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
-  {
-    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
-       << __clang_patchlevel__;
-  }
-#endif
-
-#if defined(_MSC_VER)
-  { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
-  return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
-  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/__init__.py b/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/__init__.py
deleted file mode 100644
index 34383d83f5e76bc801f31b20e5651e383be348b6..0000000000000000000000000000000000000000
--- a/spaces/IDEA-Research/Grounded-SAM/segment_anything/segment_anything/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .build_sam import (
-    build_sam,
-    build_sam_vit_h,
-    build_sam_vit_l,
-    build_sam_vit_b,
-    sam_model_registry,
-)
-from .predictor import SamPredictor
-from .automatic_mask_generator import SamAutomaticMaskGenerator
diff --git a/spaces/Iceclear/StableSR/StableSR/basicsr/data/prefetch_dataloader.py b/spaces/Iceclear/StableSR/StableSR/basicsr/data/prefetch_dataloader.py
deleted file mode 100644
index 332abd32fcb004e6892d12dc69848a4454e3c503..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/basicsr/data/prefetch_dataloader.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import queue as Queue
-import threading
-import torch
-from torch.utils.data import DataLoader
-
-
-class PrefetchGenerator(threading.Thread):
-    """A general prefetch generator.
-
-    Reference: https://stackoverflow.com/questions/7323664/python-generator-pre-fetch
-
-    Args:
-        generator: Python generator.
-        num_prefetch_queue (int): Number of prefetch queue.
-    """
-
-    def __init__(self, generator, num_prefetch_queue):
-        threading.Thread.__init__(self)
-        self.queue = Queue.Queue(num_prefetch_queue)
-        self.generator = generator
-        self.daemon = True
-        self.start()
-
-    def run(self):
-        for item in self.generator:
-            self.queue.put(item)
-        self.queue.put(None)
-
-    def __next__(self):
-        next_item = self.queue.get()
-        if next_item is None:
-            raise StopIteration
-        return next_item
-
-    def __iter__(self):
-        return self
-
-
-class PrefetchDataLoader(DataLoader):
-    """Prefetch version of dataloader.
-
-    Reference: https://github.com/IgorSusmelj/pytorch-styleguide/issues/5#
-
-    TODO:
-    Need to test on single gpu and ddp (multi-gpu). There is a known issue in
-    ddp.
-
-    Args:
-        num_prefetch_queue (int): Number of prefetch queue.
-        kwargs (dict): Other arguments for dataloader.
-    """
-
-    def __init__(self, num_prefetch_queue, **kwargs):
-        self.num_prefetch_queue = num_prefetch_queue
-        super(PrefetchDataLoader, self).__init__(**kwargs)
-
-    def __iter__(self):
-        return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue)
-
-
-class CPUPrefetcher():
-    """CPU prefetcher.
-
-    Args:
-        loader: Dataloader.
-    """
-
-    def __init__(self, loader):
-        self.ori_loader = loader
-        self.loader = iter(loader)
-
-    def next(self):
-        try:
-            return next(self.loader)
-        except StopIteration:
-            return None
-
-    def reset(self):
-        self.loader = iter(self.ori_loader)
-
-
-class CUDAPrefetcher():
-    """CUDA prefetcher.
-
-    Reference: https://github.com/NVIDIA/apex/issues/304#
-
-    It may consume more GPU memory.
-
-    Args:
-        loader: Dataloader.
-        opt (dict): Options.
-    """
-
-    def __init__(self, loader, opt):
-        self.ori_loader = loader
-        self.loader = iter(loader)
-        self.opt = opt
-        self.stream = torch.cuda.Stream()
-        self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu')
-        self.preload()
-
-    def preload(self):
-        try:
-            self.batch = next(self.loader)  # self.batch is a dict
-        except StopIteration:
-            self.batch = None
-            return None
-        # put tensors to gpu
-        with torch.cuda.stream(self.stream):
-            for k, v in self.batch.items():
-                if torch.is_tensor(v):
-                    self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True)
-
-    def next(self):
-        torch.cuda.current_stream().wait_stream(self.stream)
-        batch = self.batch
-        self.preload()
-        return batch
-
-    def reset(self):
-        self.loader = iter(self.ori_loader)
-        self.preload()
diff --git a/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/text/__init__.py b/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/text/__init__.py
deleted file mode 100644
index 11e5586c347c3071a9d1aca0425d112f45402e85..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/VITS-fast-fine-tuning_nymph/text/__init__.py
+++ /dev/null
@@ -1,60 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-from text.symbols import symbols
-
-
-# Mappings from symbol to numeric ID and vice versa:
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-_id_to_symbol = {i: s for i, s in enumerate(symbols)}
-
-
-def text_to_sequence(text, symbols, cleaner_names):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-      cleaner_names: names of the cleaner functions to run the text through
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  sequence = []
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  clean_text = _clean_text(text, cleaner_names)
-  print(clean_text)
-  print(f" length:{len(clean_text)}")
-  for symbol in clean_text:
-    if symbol not in symbol_to_id.keys():
-      continue
-    symbol_id = symbol_to_id[symbol]
-    sequence += [symbol_id]
-  print(f" length:{len(sequence)}")
-  return sequence
-
-
-def cleaned_text_to_sequence(cleaned_text, symbols):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  symbol_to_id = {s: i for i, s in enumerate(symbols)}
-  sequence = [symbol_to_id[symbol] for symbol in cleaned_text if symbol in symbol_to_id.keys()]
-  return sequence
-
-
-def sequence_to_text(sequence):
-  '''Converts a sequence of IDs back to a string'''
-  result = ''
-  for symbol_id in sequence:
-    s = _id_to_symbol[symbol_id]
-    result += s
-  return result
-
-
-def _clean_text(text, cleaner_names):
-  for name in cleaner_names:
-    cleaner = getattr(cleaners, name)
-    if not cleaner:
-      raise Exception('Unknown cleaner: %s' % name)
-    text = cleaner(text)
-  return text
diff --git a/spaces/IlyaGusev/saiga_mistral_7b_gguf/app.py b/spaces/IlyaGusev/saiga_mistral_7b_gguf/app.py
deleted file mode 100644
index 25d5f5ed7101a119fd1dc034ff2acb2f81cc3e12..0000000000000000000000000000000000000000
--- a/spaces/IlyaGusev/saiga_mistral_7b_gguf/app.py
+++ /dev/null
@@ -1,210 +0,0 @@
-import gradio as gr
-
-import copy
-import random
-import os
-import requests
-import time
-import sys
-
-from huggingface_hub import snapshot_download
-from llama_cpp import Llama
-
-
-SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
-SYSTEM_TOKEN = 1587
-USER_TOKEN = 2188
-BOT_TOKEN = 12435
-LINEBREAK_TOKEN = 13
-
-
-ROLE_TOKENS = {
-    "user": USER_TOKEN,
-    "bot": BOT_TOKEN,
-    "system": SYSTEM_TOKEN
-}
-
-
-def get_message_tokens(model, role, content):
-    message_tokens = model.tokenize(content.encode("utf-8"))
-    message_tokens.insert(1, ROLE_TOKENS[role])
-    message_tokens.insert(2, LINEBREAK_TOKEN)
-    message_tokens.append(model.token_eos())
-    return message_tokens
-
-
-def get_system_tokens(model):
-    system_message = {"role": "system", "content": SYSTEM_PROMPT}
-    return get_message_tokens(model, **system_message)
-
-
-repo_name = "IlyaGusev/saiga_mistral_7b_gguf"
-model_name = "model-q4_K.gguf"
-
-snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
-
-model = Llama(
-    model_path=model_name,
-    n_ctx=2000,
-    n_parts=1,
-)
-
-max_new_tokens = 1500
-
-def user(message, history):
-    new_history = history + [[message, None]]
-    return "", new_history
-
-
-def bot(
-    history,
-    system_prompt,
-    top_p,
-    top_k,
-    temp
-):
-    tokens = get_system_tokens(model)[:]
-    tokens.append(LINEBREAK_TOKEN)
-
-    for user_message, bot_message in history[:-1]:
-        message_tokens = get_message_tokens(model=model, role="user", content=user_message)
-        tokens.extend(message_tokens)
-        if bot_message:
-            message_tokens = get_message_tokens(model=model, role="bot", content=bot_message)
-            tokens.extend(message_tokens)
-
-    last_user_message = history[-1][0]
-    message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
-    tokens.extend(message_tokens)
-
-    role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
-    tokens.extend(role_tokens)
-    generator = model.generate(
-        tokens,
-        top_k=top_k,
-        top_p=top_p,
-        temp=temp
-    )
-
-    partial_text = ""
-    for i, token in enumerate(generator):
-        if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens):
-            break
-        partial_text += model.detokenize([token]).decode("utf-8", "ignore")
-        history[-1][1] = partial_text
-        yield history
-
-
-with gr.Blocks(
-    theme=gr.themes.Soft()
-) as demo:
-    favicon = '<img src="https://cdn.midjourney.com/b88e5beb-6324-4820-8504-a1a37a9ba36d/0_1.png" width="48px" style="display: inline">'
-    gr.Markdown(
-        f"""<h1><center>{favicon}Saiga Mistral 7B GGUF Q4_K</center></h1>
-
-        This is a demo of a **Russian**-speaking Mistral-based model. If you are interested in other languages, please check other models, such as [MPT-7B-Chat](https://huggingface.co/spaces/mosaicml/mpt-7b-chat).
-
-        Это демонстрационная версия [квантованной Сайги/Мистраль с 7 миллиардами параметров](https://huggingface.co/IlyaGusev/saiga_mistral_7b_gguf), работающая на CPU.
-
-        Сайга — это разговорная языковая модель, дообученная на корпусах, сгенерированных ChatGPT, таких как [ru_turbo_alpaca](https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca), [ru_turbo_saiga](https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga) и [gpt_roleplay_realm](https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm).
-        """
-    )
-    with gr.Row():
-        with gr.Column(scale=5):
-            system_prompt = gr.Textbox(label="Системный промпт", placeholder="", value=SYSTEM_PROMPT, interactive=False)
-            chatbot = gr.Chatbot(label="Диалог").style(height=400)
-        with gr.Column(min_width=80, scale=1):
-            with gr.Tab(label="Параметры генерации"):
-                top_p = gr.Slider(
-                    minimum=0.0,
-                    maximum=1.0,
-                    value=0.9,
-                    step=0.05,
-                    interactive=True,
-                    label="Top-p",
-                )
-                top_k = gr.Slider(
-                    minimum=10,
-                    maximum=100,
-                    value=30,
-                    step=5,
-                    interactive=True,
-                    label="Top-k",
-                )
-                temp = gr.Slider(
-                    minimum=0.0,
-                    maximum=2.0,
-                    value=0.01,
-                    step=0.01,
-                    interactive=True,
-                    label="Температура"
-                )
-    with gr.Row():
-        with gr.Column():
-            msg = gr.Textbox(
-                label="Отправить сообщение",
-                placeholder="Отправить сообщение",
-                show_label=False,
-            ).style(container=False)
-        with gr.Column():
-            with gr.Row():
-                submit = gr.Button("Отправить")
-                stop = gr.Button("Остановить")
-                clear = gr.Button("Очистить")
-    with gr.Row():
-        gr.Markdown(
-            """ПРЕДУПРЕЖДЕНИЕ: Модель может генерировать фактически или этически некорректные тексты. Мы не несём за это ответственность."""
-        )
-
-    # Pressing Enter
-    submit_event = msg.submit(
-        fn=user,
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        queue=False,
-    ).success(
-        fn=bot,
-        inputs=[
-            chatbot,
-            system_prompt,
-            top_p,
-            top_k,
-            temp
-        ],
-        outputs=chatbot,
-        queue=True,
-    )
-
-    # Pressing the button
-    submit_click_event = submit.click(
-        fn=user,
-        inputs=[msg, chatbot],
-        outputs=[msg, chatbot],
-        queue=False,
-    ).success(
-        fn=bot,
-        inputs=[
-            chatbot,
-            system_prompt,
-            top_p,
-            top_k,
-            temp
-        ],
-        outputs=chatbot,
-        queue=True,
-    )
-
-    # Stop generation
-    stop.click(
-        fn=None,
-        inputs=None,
-        outputs=None,
-        cancels=[submit_event, submit_click_event],
-        queue=False,
-    )
-
-    # Clear history
-    clear.click(lambda: None, None, chatbot, queue=False)
-
-demo.queue(max_size=128, concurrency_count=1)
-demo.launch()
diff --git a/spaces/Jamel887/Rv-percobaan887/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/Jamel887/Rv-percobaan887/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index b2c592527a5966e6f8e79e8c52dc5b414246dcc6..0000000000000000000000000000000000000000
--- a/spaces/Jamel887/Rv-percobaan887/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
-import numpy as np
-
-
-class PMF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def compute_f0(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0
-
-    def compute_f0_uv(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0, uv
diff --git a/spaces/JanDalhuysen/ChatPDF/README.md b/spaces/JanDalhuysen/ChatPDF/README.md
deleted file mode 100644
index 6e4b73a2f20840987415c25cb266f98011e14476..0000000000000000000000000000000000000000
--- a/spaces/JanDalhuysen/ChatPDF/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChatPDF
-emoji: 💻
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.20.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: zouguojun/chatPDF
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Jeff2323/ai-comic-factory/src/components/ui/input.tsx b/spaces/Jeff2323/ai-comic-factory/src/components/ui/input.tsx
deleted file mode 100644
index 0757ddebdca3800bbd4a46fe1c2c17dff86c5e2f..0000000000000000000000000000000000000000
--- a/spaces/Jeff2323/ai-comic-factory/src/components/ui/input.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import * as React from "react"
-
-import { cn } from "@/lib/utils"
-
-export interface InputProps
-  extends React.InputHTMLAttributes<HTMLInputElement> {}
-
-const Input = React.forwardRef<HTMLInputElement, InputProps>(
-  ({ className, type, ...props }, ref) => {
-    return (
-      <input
-        type={type}
-        className={cn(
-          "flex h-10 w-full rounded-md border border-stone-200 bg-white px-3 py-2 text-sm ring-offset-white file:border-0 file:bg-transparent file:text-sm file:font-medium placeholder:text-stone-500 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-blue-[rgb(59,134,247)] focus-visible:ring-offset-0 disabled:cursor-not-allowed disabled:opacity-50 dark:border-stone-800 dark:bg-stone-950 dark:ring-offset-stone-950 dark:placeholder:text-stone-400 dark:focus-visible:ring-stone-800",
-          className
-        )}
-        ref={ref}
-        {...props}
-      />
-    )
-  }
-)
-Input.displayName = "Input"
-
-export { Input }
diff --git a/spaces/Kayson/InstructDiffusion/dataset/low_level/lowlevel_gopro.py b/spaces/Kayson/InstructDiffusion/dataset/low_level/lowlevel_gopro.py
deleted file mode 100644
index 25f26af6d57a8fe4fbc98247f1878eb96d601dd8..0000000000000000000000000000000000000000
--- a/spaces/Kayson/InstructDiffusion/dataset/low_level/lowlevel_gopro.py
+++ /dev/null
@@ -1,106 +0,0 @@
-# --------------------------------------------------------
-# InstructDiffusion
-# Based on instruct-pix2pix (https://github.com/timothybrooks/instruct-pix2pix)
-# Modified by Chen Li (edward82@stu.xjtu.edu.cn)
-# --------------------------------------------------------
-
-import os
-import numpy as np
-from torch.utils.data import Dataset
-import torch
-from PIL import Image
-import torchvision.transforms.functional as TF
-from pdb import set_trace as stx
-import random
-import cv2
-from PIL import Image
-import torchvision
-
-
-def is_image_file(filename):
-    return any(filename.endswith(extension) for extension in ['jpeg', 'JPEG', 'jpg', 'png', 'JPG', 'PNG', 'gif'])
-
-
-class GoPro(Dataset):
-    def __init__(self, path, split="train", size=256, interpolation="pil_lanczos", 
-        flip_prob=0.5, sample_weight=1.0, instruct=False):
-        super(GoPro, self).__init__()
-
-        inp_files = sorted(os.listdir(os.path.join(path, split, 'input')))
-        tar_files = sorted(os.listdir(os.path.join(path, split, 'target')))
-
-        self.inp_filenames = [os.path.join(path, split, 'input', x) for x in inp_files if is_image_file(x)]
-        self.tar_filenames = [os.path.join(path, split, 'target', x) for x in tar_files if is_image_file(x)]
-
-        self.size = size
-        self.flip_prob = flip_prob
-        self.sample_weight = sample_weight
-        self.instruct = instruct
-        self.sizex = len(self.tar_filenames)  # get the size of target
-
-        self.interpolation = {
-            "cv_nearest": cv2.INTER_NEAREST,
-            "cv_bilinear": cv2.INTER_LINEAR,
-            "cv_bicubic": cv2.INTER_CUBIC,
-            "cv_area": cv2.INTER_AREA,
-            "cv_lanczos": cv2.INTER_LANCZOS4,
-            "pil_nearest": Image.NEAREST,
-            "pil_bilinear": Image.BILINEAR,
-            "pil_bicubic": Image.BICUBIC,
-            "pil_box": Image.BOX,
-            "pil_hamming": Image.HAMMING,
-            "pil_lanczos": Image.LANCZOS,
-        }[interpolation]
-
-        prompt_path='dataset/prompt/prompt_deblur.txt'
-        self.prompt_list=[]
-        with open(prompt_path) as f:
-            line=f.readline()
-            while line:
-                line=line.strip('\n')
-                self.prompt_list.append(line)
-                line=f.readline()
-        
-        print(f"GoPro has {len(self)} samples!!")
-
-    def __len__(self):
-        return int(self.sizex * self.sample_weight)
-
-    def __getitem__(self, index):
-        if self.sample_weight >= 1:
-            index_ = index % self.sizex
-        else:
-            index_ = int(index / self.sample_weight) + random.randint(0, int(1 / self.sample_weight) - 1)
-
-        inp_path = self.inp_filenames[index_]
-        tar_path = self.tar_filenames[index_]
-
-        inp_img = Image.open(inp_path)
-        tar_img = Image.open(tar_path)
-
-        width, height = inp_img.size
-        tar_width, tar_height = tar_img.size
-        assert tar_width == width and tar_height == height, "Input and target image mismatch"
-        aspect_ratio = float(width) / float(height)
-        if width < height:  
-            new_width = self.size  
-            new_height = int(self.size  / aspect_ratio)  
-        else:  
-            new_height = self.size   
-            new_width = int(self.size * aspect_ratio) 
-        inp_img = inp_img.resize((new_width, new_height), self.interpolation)
-        tar_img = tar_img.resize((new_width, new_height), self.interpolation)
-
-        inp_img = np.array(inp_img).astype(np.float32).transpose(2, 0, 1)
-        inp_img_tensor = torch.tensor((inp_img / 127.5 - 1.0).astype(np.float32))
-        tar_img = np.array(tar_img).astype(np.float32).transpose(2, 0, 1)
-        tar_img_tensor = torch.tensor((tar_img / 127.5 - 1.0).astype(np.float32))
-        crop = torchvision.transforms.RandomCrop(self.size)
-        flip = torchvision.transforms.RandomHorizontalFlip(float(self.flip_prob))
-        image_0, image_1 = flip(crop(torch.cat((inp_img_tensor, tar_img_tensor)))).chunk(2)
-
-        prompt = random.choice(self.prompt_list)
-        if self.instruct:
-            prompt = "Image Deblurring: " + prompt
-        
-        return dict(edited=image_1, edit=dict(c_concat=image_0, c_crossattn=prompt))
\ No newline at end of file
diff --git a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/audio.py b/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/audio.py
deleted file mode 100644
index 5c087eea5d23d7e23ea0ef277ea4b92e9f4f2d55..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/ChatGPT-with-Voice-Cloning-in-Chinese/encoder/audio.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from scipy.ndimage.morphology import binary_dilation
-from encoder.params_data import *
-from pathlib import Path
-from typing import Optional, Union
-from warnings import warn
-import numpy as np
-import librosa
-import struct
-
-try:
-    import webrtcvad
-except:
-    warn("Unable to import 'webrtcvad'. This package enables noise removal and is recommended.")
-    webrtcvad=None
-
-int16_max = (2 ** 15) - 1
-
-
-def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray],
-                   source_sr: Optional[int] = None,
-                   normalize: Optional[bool] = True,
-                   trim_silence: Optional[bool] = True):
-    """
-    Applies the preprocessing operations used in training the Speaker Encoder to a waveform 
-    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
-
-    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not 
-    just .wav), either the waveform as a numpy array of floats.
-    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before 
-    preprocessing. After preprocessing, the waveform's sampling rate will match the data 
-    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and 
-    this argument will be ignored.
-    """
-    # Load the wav from disk if needed
-    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
-        wav, source_sr = librosa.load(str(fpath_or_wav), sr=None)
-    else:
-        wav = fpath_or_wav
-    
-    # Resample the wav if needed
-    if source_sr is not None and source_sr != sampling_rate:
-        wav = librosa.resample(wav, source_sr, sampling_rate)
-        
-    # Apply the preprocessing: normalize volume and shorten long silences 
-    if normalize:
-        wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
-    if webrtcvad and trim_silence:
-        wav = trim_long_silences(wav)
-    
-    return wav
-
-
-def wav_to_mel_spectrogram(wav):
-    """
-    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
-    Note: this not a log-mel spectrogram.
-    """
-    frames = librosa.feature.melspectrogram(
-        y=wav,
-        sr=sampling_rate,
-        n_fft=int(sampling_rate * mel_window_length / 1000),
-        hop_length=int(sampling_rate * mel_window_step / 1000),
-        n_mels=mel_n_channels
-    )
-    return frames.astype(np.float32).T
-
-
-def trim_long_silences(wav):
-    """
-    Ensures that segments without voice in the waveform remain no longer than a 
-    threshold determined by the VAD parameters in params.py.
-
-    :param wav: the raw waveform as a numpy array of floats 
-    :return: the same waveform with silences trimmed away (length <= original wav length)
-    """
-    # Compute the voice detection window size
-    samples_per_window = (vad_window_length * sampling_rate) // 1000
-    
-    # Trim the end of the audio to have a multiple of the window size
-    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
-    
-    # Convert the float waveform to 16-bit mono PCM
-    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
-    
-    # Perform voice activation detection
-    voice_flags = []
-    vad = webrtcvad.Vad(mode=3)
-    for window_start in range(0, len(wav), samples_per_window):
-        window_end = window_start + samples_per_window
-        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
-                                         sample_rate=sampling_rate))
-    voice_flags = np.array(voice_flags)
-    
-    # Smooth the voice detection with a moving average
-    def moving_average(array, width):
-        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
-        ret = np.cumsum(array_padded, dtype=float)
-        ret[width:] = ret[width:] - ret[:-width]
-        return ret[width - 1:] / width
-    
-    audio_mask = moving_average(voice_flags, vad_moving_average_width)
-    audio_mask = np.round(audio_mask).astype(np.bool)
-    
-    # Dilate the voiced regions
-    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
-    audio_mask = np.repeat(audio_mask, samples_per_window)
-    
-    return wav[audio_mask == True]
-
-
-def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
-    if increase_only and decrease_only:
-        raise ValueError("Both increase only and decrease only are set")
-    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav ** 2))
-    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
-        return wav
-    return wav * (10 ** (dBFS_change / 20))
diff --git a/spaces/KyanChen/RSPrompter/mmdet/apis/det_inferencer.py b/spaces/KyanChen/RSPrompter/mmdet/apis/det_inferencer.py
deleted file mode 100644
index da4ad17128346de3c8ff268e899f989ddd8b99e9..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/apis/det_inferencer.py
+++ /dev/null
@@ -1,590 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import os.path as osp
-import warnings
-from typing import Dict, Iterable, List, Optional, Sequence, Union
-
-import mmcv
-import mmengine
-import numpy as np
-import torch.nn as nn
-from mmengine.dataset import Compose
-from mmengine.fileio import (get_file_backend, isdir, join_path,
-                             list_dir_or_file)
-from mmengine.infer.infer import BaseInferencer, ModelType
-from mmengine.model.utils import revert_sync_batchnorm
-from mmengine.registry import init_default_scope
-from mmengine.runner.checkpoint import _load_checkpoint_to_model
-from mmengine.visualization import Visualizer
-from rich.progress import track
-
-from mmdet.evaluation import INSTANCE_OFFSET
-from mmdet.registry import DATASETS
-from mmdet.structures import DetDataSample
-from mmdet.structures.mask import encode_mask_results, mask2bbox
-from mmdet.utils import ConfigType
-from ..evaluation import get_classes
-
-try:
-    from panopticapi.evaluation import VOID
-    from panopticapi.utils import id2rgb
-except ImportError:
-    id2rgb = None
-    VOID = None
-
-InputType = Union[str, np.ndarray]
-InputsType = Union[InputType, Sequence[InputType]]
-PredType = List[DetDataSample]
-ImgType = Union[np.ndarray, Sequence[np.ndarray]]
-
-IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
-                  '.tiff', '.webp')
-
-
-class DetInferencer(BaseInferencer):
-    """Object Detection Inferencer.
-
-    Args:
-        model (str, optional): Path to the config file or the model name
-            defined in metafile. For example, it could be
-            "rtmdet-s" or 'rtmdet_s_8xb32-300e_coco' or
-            "configs/rtmdet/rtmdet_s_8xb32-300e_coco.py".
-            If model is not specified, user must provide the
-            `weights` saved by MMEngine which contains the config string.
-            Defaults to None.
-        weights (str, optional): Path to the checkpoint. If it is not specified
-            and model is a model name of metafile, the weights will be loaded
-            from metafile. Defaults to None.
-        device (str, optional): Device to run inference. If None, the available
-            device will be automatically used. Defaults to None.
-        scope (str, optional): The scope of the model. Defaults to mmdet.
-        palette (str): Color palette used for visualization. The order of
-            priority is palette -> config -> checkpoint. Defaults to 'none'.
-    """
-
-    preprocess_kwargs: set = set()
-    forward_kwargs: set = set()
-    visualize_kwargs: set = {
-        'return_vis',
-        'show',
-        'wait_time',
-        'draw_pred',
-        'pred_score_thr',
-        'img_out_dir',
-        'no_save_vis',
-    }
-    postprocess_kwargs: set = {
-        'print_result',
-        'pred_out_dir',
-        'return_datasample',
-        'no_save_pred',
-    }
-
-    def __init__(self,
-                 model: Optional[Union[ModelType, str]] = None,
-                 weights: Optional[str] = None,
-                 device: Optional[str] = None,
-                 scope: Optional[str] = 'mmdet',
-                 palette: str = 'none') -> None:
-        # A global counter tracking the number of images processed, for
-        # naming of the output images
-        self.num_visualized_imgs = 0
-        self.num_predicted_imgs = 0
-        self.palette = palette
-        init_default_scope(scope)
-        super().__init__(
-            model=model, weights=weights, device=device, scope=scope)
-        self.model = revert_sync_batchnorm(self.model)
-
-    def _load_weights_to_model(self, model: nn.Module,
-                               checkpoint: Optional[dict],
-                               cfg: Optional[ConfigType]) -> None:
-        """Loading model weights and meta information from cfg and checkpoint.
-
-        Args:
-            model (nn.Module): Model to load weights and meta information.
-            checkpoint (dict, optional): The loaded checkpoint.
-            cfg (Config or ConfigDict, optional): The loaded config.
-        """
-
-        if checkpoint is not None:
-            _load_checkpoint_to_model(model, checkpoint)
-            checkpoint_meta = checkpoint.get('meta', {})
-            # save the dataset_meta in the model for convenience
-            if 'dataset_meta' in checkpoint_meta:
-                # mmdet 3.x, all keys should be lowercase
-                model.dataset_meta = {
-                    k.lower(): v
-                    for k, v in checkpoint_meta['dataset_meta'].items()
-                }
-            elif 'CLASSES' in checkpoint_meta:
-                # < mmdet 3.x
-                classes = checkpoint_meta['CLASSES']
-                model.dataset_meta = {'classes': classes}
-            else:
-                warnings.warn(
-                    'dataset_meta or class names are not saved in the '
-                    'checkpoint\'s meta data, use COCO classes by default.')
-                model.dataset_meta = {'classes': get_classes('coco')}
-        else:
-            warnings.warn('Checkpoint is not loaded, and the inference '
-                          'result is calculated by the randomly initialized '
-                          'model!')
-            warnings.warn('weights is None, use COCO classes by default.')
-            model.dataset_meta = {'classes': get_classes('coco')}
-
-        # Priority:  args.palette -> config -> checkpoint
-        if self.palette != 'none':
-            model.dataset_meta['palette'] = self.palette
-        else:
-            test_dataset_cfg = copy.deepcopy(cfg.test_dataloader.dataset)
-            # lazy init. We only need the metainfo.
-            test_dataset_cfg['lazy_init'] = True
-            metainfo = DATASETS.build(test_dataset_cfg).metainfo
-            cfg_palette = metainfo.get('palette', None)
-            if cfg_palette is not None:
-                model.dataset_meta['palette'] = cfg_palette
-            else:
-                if 'palette' not in model.dataset_meta:
-                    warnings.warn(
-                        'palette does not exist, random is used by default. '
-                        'You can also set the palette to customize.')
-                    model.dataset_meta['palette'] = 'random'
-
-    def _init_pipeline(self, cfg: ConfigType) -> Compose:
-        """Initialize the test pipeline."""
-        pipeline_cfg = cfg.test_dataloader.dataset.pipeline
-
-        # For inference, the key of ``img_id`` is not used.
-        if 'meta_keys' in pipeline_cfg[-1]:
-            pipeline_cfg[-1]['meta_keys'] = tuple(
-                meta_key for meta_key in pipeline_cfg[-1]['meta_keys']
-                if meta_key != 'img_id')
-
-        load_img_idx = self._get_transform_idx(pipeline_cfg,
-                                               'LoadImageFromFile')
-        if load_img_idx == -1:
-            raise ValueError(
-                'LoadImageFromFile is not found in the test pipeline')
-        pipeline_cfg[load_img_idx]['type'] = 'mmdet.InferencerLoader'
-        return Compose(pipeline_cfg)
-
-    def _get_transform_idx(self, pipeline_cfg: ConfigType, name: str) -> int:
-        """Returns the index of the transform in a pipeline.
-
-        If the transform is not found, returns -1.
-        """
-        for i, transform in enumerate(pipeline_cfg):
-            if transform['type'] == name:
-                return i
-        return -1
-
-    def _init_visualizer(self, cfg: ConfigType) -> Optional[Visualizer]:
-        """Initialize visualizers.
-
-        Args:
-            cfg (ConfigType): Config containing the visualizer information.
-
-        Returns:
-            Visualizer or None: Visualizer initialized with config.
-        """
-        visualizer = super()._init_visualizer(cfg)
-        visualizer.dataset_meta = self.model.dataset_meta
-        return visualizer
-
-    def _inputs_to_list(self, inputs: InputsType) -> list:
-        """Preprocess the inputs to a list.
-
-        Preprocess inputs to a list according to its type:
-
-        - list or tuple: return inputs
-        - str:
-            - Directory path: return all files in the directory
-            - other cases: return a list containing the string. The string
-              could be a path to file, a url or other types of string according
-              to the task.
-
-        Args:
-            inputs (InputsType): Inputs for the inferencer.
-
-        Returns:
-            list: List of input for the :meth:`preprocess`.
-        """
-        if isinstance(inputs, str):
-            backend = get_file_backend(inputs)
-            if hasattr(backend, 'isdir') and isdir(inputs):
-                # Backends like HttpsBackend do not implement `isdir`, so only
-                # those backends that implement `isdir` could accept the inputs
-                # as a directory
-                filename_list = list_dir_or_file(
-                    inputs, list_dir=False, suffix=IMG_EXTENSIONS)
-                inputs = [
-                    join_path(inputs, filename) for filename in filename_list
-                ]
-
-        if not isinstance(inputs, (list, tuple)):
-            inputs = [inputs]
-
-        return list(inputs)
-
-    def preprocess(self, inputs: InputsType, batch_size: int = 1, **kwargs):
-        """Process the inputs into a model-feedable format.
-
-        Customize your preprocess by overriding this method. Preprocess should
-        return an iterable object, of which each item will be used as the
-        input of ``model.test_step``.
-
-        ``BaseInferencer.preprocess`` will return an iterable chunked data,
-        which will be used in __call__ like this:
-
-        .. code-block:: python
-
-            def __call__(self, inputs, batch_size=1, **kwargs):
-                chunked_data = self.preprocess(inputs, batch_size, **kwargs)
-                for batch in chunked_data:
-                    preds = self.forward(batch, **kwargs)
-
-        Args:
-            inputs (InputsType): Inputs given by user.
-            batch_size (int): batch size. Defaults to 1.
-
-        Yields:
-            Any: Data processed by the ``pipeline`` and ``collate_fn``.
-        """
-        chunked_data = self._get_chunk_data(inputs, batch_size)
-        yield from map(self.collate_fn, chunked_data)
-
-    def _get_chunk_data(self, inputs: Iterable, chunk_size: int):
-        """Get batch data from inputs.
-
-        Args:
-            inputs (Iterable): An iterable dataset.
-            chunk_size (int): Equivalent to batch size.
-
-        Yields:
-            list: batch data.
-        """
-        inputs_iter = iter(inputs)
-        while True:
-            try:
-                chunk_data = []
-                for _ in range(chunk_size):
-                    inputs_ = next(inputs_iter)
-                    chunk_data.append((inputs_, self.pipeline(inputs_)))
-                yield chunk_data
-            except StopIteration:
-                if chunk_data:
-                    yield chunk_data
-                break
-
-    # TODO: Video and Webcam are currently not supported and
-    #  may consume too much memory if your input folder has a lot of images.
-    #  We will be optimized later.
-    def __call__(self,
-                 inputs: InputsType,
-                 batch_size: int = 1,
-                 return_vis: bool = False,
-                 show: bool = False,
-                 wait_time: int = 0,
-                 no_save_vis: bool = False,
-                 draw_pred: bool = True,
-                 pred_score_thr: float = 0.3,
-                 return_datasample: bool = False,
-                 print_result: bool = False,
-                 no_save_pred: bool = True,
-                 out_dir: str = '',
-                 **kwargs) -> dict:
-        """Call the inferencer.
-
-        Args:
-            inputs (InputsType): Inputs for the inferencer.
-            batch_size (int): Inference batch size. Defaults to 1.
-            show (bool): Whether to display the visualization results in a
-                popup window. Defaults to False.
-            wait_time (float): The interval of show (s). Defaults to 0.
-            no_save_vis (bool): Whether to force not to save prediction
-                vis results. Defaults to False.
-            draw_pred (bool): Whether to draw predicted bounding boxes.
-                Defaults to True.
-            pred_score_thr (float): Minimum score of bboxes to draw.
-                Defaults to 0.3.
-            return_datasample (bool): Whether to return results as
-                :obj:`DetDataSample`. Defaults to False.
-            print_result (bool): Whether to print the inference result w/o
-                visualization to the console. Defaults to False.
-            no_save_pred (bool): Whether to force not to save prediction
-                results. Defaults to True.
-            out_file: Dir to save the inference results or
-                visualization. If left as empty, no file will be saved.
-                Defaults to ''.
-
-            **kwargs: Other keyword arguments passed to :meth:`preprocess`,
-                :meth:`forward`, :meth:`visualize` and :meth:`postprocess`.
-                Each key in kwargs should be in the corresponding set of
-                ``preprocess_kwargs``, ``forward_kwargs``, ``visualize_kwargs``
-                and ``postprocess_kwargs``.
-
-        Returns:
-            dict: Inference and visualization results.
-        """
-        (
-            preprocess_kwargs,
-            forward_kwargs,
-            visualize_kwargs,
-            postprocess_kwargs,
-        ) = self._dispatch_kwargs(**kwargs)
-
-        ori_inputs = self._inputs_to_list(inputs)
-        inputs = self.preprocess(
-            ori_inputs, batch_size=batch_size, **preprocess_kwargs)
-
-        results_dict = {'predictions': [], 'visualization': []}
-        for ori_inputs, data in track(inputs, description='Inference'):
-            preds = self.forward(data, **forward_kwargs)
-            visualization = self.visualize(
-                ori_inputs,
-                preds,
-                return_vis=return_vis,
-                show=show,
-                wait_time=wait_time,
-                draw_pred=draw_pred,
-                pred_score_thr=pred_score_thr,
-                no_save_vis=no_save_vis,
-                img_out_dir=out_dir,
-                **visualize_kwargs)
-            results = self.postprocess(
-                preds,
-                visualization,
-                return_datasample=return_datasample,
-                print_result=print_result,
-                no_save_pred=no_save_pred,
-                pred_out_dir=out_dir,
-                **postprocess_kwargs)
-            results_dict['predictions'].extend(results['predictions'])
-            if results['visualization'] is not None:
-                results_dict['visualization'].extend(results['visualization'])
-        return results_dict
-
-    def visualize(self,
-                  inputs: InputsType,
-                  preds: PredType,
-                  return_vis: bool = False,
-                  show: bool = False,
-                  wait_time: int = 0,
-                  draw_pred: bool = True,
-                  pred_score_thr: float = 0.3,
-                  no_save_vis: bool = False,
-                  img_out_dir: str = '',
-                  **kwargs) -> Union[List[np.ndarray], None]:
-        """Visualize predictions.
-
-        Args:
-            inputs (List[Union[str, np.ndarray]]): Inputs for the inferencer.
-            preds (List[:obj:`DetDataSample`]): Predictions of the model.
-            return_vis (bool): Whether to return the visualization result.
-                Defaults to False.
-            show (bool): Whether to display the image in a popup window.
-                Defaults to False.
-            wait_time (float): The interval of show (s). Defaults to 0.
-            draw_pred (bool): Whether to draw predicted bounding boxes.
-                Defaults to True.
-            pred_score_thr (float): Minimum score of bboxes to draw.
-                Defaults to 0.3.
-            no_save_vis (bool): Whether to force not to save prediction
-                vis results. Defaults to False.
-            img_out_dir (str): Output directory of visualization results.
-                If left as empty, no file will be saved. Defaults to ''.
-
-        Returns:
-            List[np.ndarray] or None: Returns visualization results only if
-            applicable.
-        """
-        if no_save_vis is True:
-            img_out_dir = ''
-
-        if not show and img_out_dir == '' and not return_vis:
-            return None
-
-        if self.visualizer is None:
-            raise ValueError('Visualization needs the "visualizer" term'
-                             'defined in the config, but got None.')
-
-        results = []
-
-        for single_input, pred in zip(inputs, preds):
-            if isinstance(single_input, str):
-                img_bytes = mmengine.fileio.get(single_input)
-                img = mmcv.imfrombytes(img_bytes)
-                img = img[:, :, ::-1]
-                img_name = osp.basename(single_input)
-            elif isinstance(single_input, np.ndarray):
-                img = single_input.copy()
-                img_num = str(self.num_visualized_imgs).zfill(8)
-                img_name = f'{img_num}.jpg'
-            else:
-                raise ValueError('Unsupported input type: '
-                                 f'{type(single_input)}')
-
-            out_file = osp.join(img_out_dir, 'vis',
-                                img_name) if img_out_dir != '' else None
-
-            self.visualizer.add_datasample(
-                img_name,
-                img,
-                pred,
-                show=show,
-                wait_time=wait_time,
-                draw_gt=False,
-                draw_pred=draw_pred,
-                pred_score_thr=pred_score_thr,
-                out_file=out_file,
-            )
-            results.append(self.visualizer.get_image())
-            self.num_visualized_imgs += 1
-
-        return results
-
-    def postprocess(
-        self,
-        preds: PredType,
-        visualization: Optional[List[np.ndarray]] = None,
-        return_datasample: bool = False,
-        print_result: bool = False,
-        no_save_pred: bool = False,
-        pred_out_dir: str = '',
-        **kwargs,
-    ) -> Dict:
-        """Process the predictions and visualization results from ``forward``
-        and ``visualize``.
-
-        This method should be responsible for the following tasks:
-
-        1. Convert datasamples into a json-serializable dict if needed.
-        2. Pack the predictions and visualization results and return them.
-        3. Dump or log the predictions.
-
-        Args:
-            preds (List[:obj:`DetDataSample`]): Predictions of the model.
-            visualization (Optional[np.ndarray]): Visualized predictions.
-            return_datasample (bool): Whether to use Datasample to store
-                inference results. If False, dict will be used.
-            print_result (bool): Whether to print the inference result w/o
-                visualization to the console. Defaults to False.
-            no_save_pred (bool): Whether to force not to save prediction
-                results. Defaults to False.
-            pred_out_dir: Dir to save the inference results w/o
-                visualization. If left as empty, no file will be saved.
-                Defaults to ''.
-
-        Returns:
-            dict: Inference and visualization results with key ``predictions``
-            and ``visualization``.
-
-            - ``visualization`` (Any): Returned by :meth:`visualize`.
-            - ``predictions`` (dict or DataSample): Returned by
-                :meth:`forward` and processed in :meth:`postprocess`.
-                If ``return_datasample=False``, it usually should be a
-                json-serializable dict containing only basic data elements such
-                as strings and numbers.
-        """
-        if no_save_pred is True:
-            pred_out_dir = ''
-
-        result_dict = {}
-        results = preds
-        if not return_datasample:
-            results = []
-            for pred in preds:
-                result = self.pred2dict(pred, pred_out_dir)
-                results.append(result)
-        elif pred_out_dir != '':
-            warnings.warn('Currently does not support saving datasample '
-                          'when return_datasample is set to True. '
-                          'Prediction results are not saved!')
-        # Add img to the results after printing and dumping
-        result_dict['predictions'] = results
-        if print_result:
-            print(result_dict)
-        result_dict['visualization'] = visualization
-        return result_dict
-
-    # TODO: The data format and fields saved in json need further discussion.
-    #  Maybe should include model name, timestamp, filename, image info etc.
-    def pred2dict(self,
-                  data_sample: DetDataSample,
-                  pred_out_dir: str = '') -> Dict:
-        """Extract elements necessary to represent a prediction into a
-        dictionary.
-
-        It's better to contain only basic data elements such as strings and
-        numbers in order to guarantee it's json-serializable.
-
-        Args:
-            data_sample (:obj:`DetDataSample`): Predictions of the model.
-            pred_out_dir: Dir to save the inference results w/o
-                visualization. If left as empty, no file will be saved.
-                Defaults to ''.
-
-        Returns:
-            dict: Prediction results.
-        """
-        is_save_pred = True
-        if pred_out_dir == '':
-            is_save_pred = False
-
-        if is_save_pred and 'img_path' in data_sample:
-            img_path = osp.basename(data_sample.img_path)
-            img_path = osp.splitext(img_path)[0]
-            out_img_path = osp.join(pred_out_dir, 'preds',
-                                    img_path + '_panoptic_seg.png')
-            out_json_path = osp.join(pred_out_dir, 'preds', img_path + '.json')
-        elif is_save_pred:
-            out_img_path = osp.join(
-                pred_out_dir, 'preds',
-                f'{self.num_predicted_imgs}_panoptic_seg.png')
-            out_json_path = osp.join(pred_out_dir, 'preds',
-                                     f'{self.num_predicted_imgs}.json')
-            self.num_predicted_imgs += 1
-
-        result = {}
-        if 'pred_instances' in data_sample:
-            masks = data_sample.pred_instances.get('masks')
-            pred_instances = data_sample.pred_instances.numpy()
-            result = {
-                'bboxes': pred_instances.bboxes.tolist(),
-                'labels': pred_instances.labels.tolist(),
-                'scores': pred_instances.scores.tolist()
-            }
-            if masks is not None:
-                if pred_instances.bboxes.sum() == 0:
-                    # Fake bbox, such as the SOLO.
-                    bboxes = mask2bbox(masks.cpu()).numpy().tolist()
-                    result['bboxes'] = bboxes
-                encode_masks = encode_mask_results(pred_instances.masks)
-                for encode_mask in encode_masks:
-                    if isinstance(encode_mask['counts'], bytes):
-                        encode_mask['counts'] = encode_mask['counts'].decode()
-                result['masks'] = encode_masks
-
-        if 'pred_panoptic_seg' in data_sample:
-            if VOID is None:
-                raise RuntimeError(
-                    'panopticapi is not installed, please install it by: '
-                    'pip install git+https://github.com/cocodataset/'
-                    'panopticapi.git.')
-
-            pan = data_sample.pred_panoptic_seg.sem_seg.cpu().numpy()[0]
-            pan[pan % INSTANCE_OFFSET == len(
-                self.model.dataset_meta['classes'])] = VOID
-            pan = id2rgb(pan).astype(np.uint8)
-
-            if is_save_pred:
-                mmcv.imwrite(pan[:, :, ::-1], out_img_path)
-                result['panoptic_seg_path'] = out_img_path
-            else:
-                result['panoptic_seg'] = pan
-
-        if is_save_pred:
-            mmengine.dump(result, out_json_path)
-
-        return result
diff --git a/spaces/KyanChen/RSPrompter/mmpl/models/pler/semseg_sam_pler.py b/spaces/KyanChen/RSPrompter/mmpl/models/pler/semseg_sam_pler.py
deleted file mode 100644
index 9343ec39a70bde288cf0831197b44766017b15fe..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpl/models/pler/semseg_sam_pler.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import torch
-from mmengine.structures import InstanceData, PixelData
-from typing import List
-
-from torch import Tensor
-
-from mmpl.registry import MODELS
-from mmseg.models.utils import resize
-from mmseg.structures import SegDataSample
-from mmseg.utils import SampleList, OptSampleList
-from .base_pler import BasePLer
-import torch.nn.functional as F
-from modules.sam import sam_model_registry
-
-
-@MODELS.register_module()
-class SemSegSAMPLer(BasePLer):
-    def __init__(self,
-                 backbone,
-                 adaphead=None,
-                 decode_head=None,
-                 need_train_names=None,
-                 align_corners=False,
-                 train_cfg=None,
-                 test_cfg=None,
-                 *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.save_hyperparameters()
-        self.need_train_names = need_train_names
-        self.align_corners = align_corners
-
-        backbone_type = backbone.pop('type')
-        delete_submodel = backbone.pop('delete_submodel', [])
-        self.backbone = sam_model_registry[backbone_type](**backbone)
-        for submodel in delete_submodel:
-            delattr(self.backbone, submodel)
-
-        if adaphead is not None:
-            self.adaphead = MODELS.build(adaphead)
-
-        decode_head_ = decode_head.deepcopy()
-        decode_head_.update(train_cfg=train_cfg)
-        decode_head_.update(test_cfg=test_cfg)
-        self.decode_head = MODELS.build(decode_head_)
-
-        self.num_classes = self.decode_head.num_classes
-
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-
-    def setup(self, stage: str) -> None:
-        if self.need_train_names is not None:
-            self._set_grad(self.need_train_names, noneed_train_names=[])
-
-    def init_weights(self):
-        import ipdb; ipdb.set_trace()
-        pass
-
-    def train(self, mode=True):
-        if self.need_train_names is not None:
-            return self._set_train_module(mode, self.need_train_names)
-        else:
-            super().train(mode)
-            return self
-
-    def extract_feat(self, batch_inputs):
-        x0, x1 = self.adaphead(batch_inputs, self.backbone.image_encoder)
-        return x0, x1
-
-    def validation_step(self, batch, batch_idx):
-        data = self.data_preprocessor(batch, False)
-        batch_inputs = data['inputs']
-        batch_data_samples = data['data_samples']
-
-        if batch_data_samples is not None:
-            batch_img_metas = [
-                data_sample.metainfo for data_sample in batch_data_samples
-            ]
-        else:
-            batch_img_metas = [
-                dict(
-                    ori_shape=batch_inputs.shape[2:],
-                    img_shape=batch_inputs.shape[2:],
-                    pad_shape=batch_inputs.shape[2:],
-                    padding_size=[0, 0, 0, 0])
-            ] * batch_inputs.shape[0]
-
-        x = self.extract_feat(batch_inputs)
-        seg_logits = self.decode_head.predict(x, batch_img_metas, self.test_cfg)
-
-        results = self.postprocess_result(seg_logits, batch_data_samples)
-
-        preds = []
-        targets = []
-        for data_sample in results:
-            pred_label = data_sample.pred_sem_seg.data.squeeze()
-            label = data_sample.gt_sem_seg.data.squeeze().to(pred_label)
-
-            preds.append(pred_label)
-            targets.append(label)
-        preds = torch.stack(preds, dim=0)
-        targets = torch.stack(targets, dim=0)
-        self.val_evaluator.update(preds, targets)
-
-    def training_step(self, batch, batch_idx):
-        # import ipdb; ipdb.set_trace()
-        data = self.data_preprocessor(batch, True)
-        batch_inputs = data['inputs']
-        batch_data_samples = data['data_samples']
-        x = self.extract_feat(batch_inputs)
-        losses = self.decode_head.loss(x, batch_data_samples)
-        # import ipdb; ipdb.set_trace()
-        parsed_losses, log_vars = self.parse_losses(losses)
-        log_vars = {f'train_{k}': v for k, v in log_vars.items()}
-        log_vars['loss'] = parsed_losses
-        self.log_dict(log_vars, prog_bar=True)
-        return log_vars
-
-    def on_before_optimizer_step(self, optimizer) -> None:
-        self.log_grad(module=self.adaphead)
-
-    def postprocess_result(self,
-                           seg_logits: Tensor,
-                           data_samples: OptSampleList = None) -> SampleList:
-        """ Convert results list to `SegDataSample`.
-        Args:
-            seg_logits (Tensor): The segmentation results, seg_logits from
-                model of each input image.
-            data_samples (list[:obj:`SegDataSample`]): The seg data samples.
-                It usually includes information such as `metainfo` and
-                `gt_sem_seg`. Default to None.
-        Returns:
-            list[:obj:`SegDataSample`]: Segmentation results of the
-            input images. Each SegDataSample usually contain:
-
-            - ``pred_sem_seg``(PixelData): Prediction of semantic segmentation.
-            - ``seg_logits``(PixelData): Predicted logits of semantic
-                segmentation before normalization.
-        """
-        batch_size, C, H, W = seg_logits.shape
-
-        if data_samples is None:
-            data_samples = [SegDataSample() for _ in range(batch_size)]
-            only_prediction = True
-        else:
-            only_prediction = False
-
-        for i in range(batch_size):
-            if not only_prediction:
-                img_meta = data_samples[i].metainfo
-                # remove padding area
-                if 'img_padding_size' not in img_meta:
-                    padding_size = img_meta.get('padding_size', [0] * 4)
-                else:
-                    padding_size = img_meta['img_padding_size']
-                padding_left, padding_right, padding_top, padding_bottom =\
-                    padding_size
-                # i_seg_logits shape is 1, C, H, W after remove padding
-                i_seg_logits = seg_logits[i:i + 1, :,
-                                          padding_top:H - padding_bottom,
-                                          padding_left:W - padding_right]
-
-                flip = img_meta.get('flip', None)
-                if flip:
-                    flip_direction = img_meta.get('flip_direction', None)
-                    assert flip_direction in ['horizontal', 'vertical']
-                    if flip_direction == 'horizontal':
-                        i_seg_logits = i_seg_logits.flip(dims=(3, ))
-                    else:
-                        i_seg_logits = i_seg_logits.flip(dims=(2, ))
-
-                # resize as original shape
-                i_seg_logits = resize(
-                    i_seg_logits,
-                    size=img_meta['ori_shape'],
-                    mode='bilinear',
-                    align_corners=self.align_corners,
-                    warning=False).squeeze(0)
-            else:
-                i_seg_logits = seg_logits[i]
-
-            if C > 1:
-                i_seg_pred = i_seg_logits.argmax(dim=0, keepdim=True)
-            else:
-                i_seg_pred = (i_seg_logits >
-                              self.decode_head.threshold).to(i_seg_logits)
-            data_samples[i].set_data({
-                'seg_logits':
-                PixelData(**{'data': i_seg_logits}),
-                'pred_sem_seg':
-                PixelData(**{'data': i_seg_pred})
-            })
-
-        return data_samples
-
-
-
-
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/caltech101.py b/spaces/KyanChen/RSPrompter/mmpretrain/datasets/caltech101.py
deleted file mode 100644
index 71e5de85ff3bbf73c387a071f47113b46be36e2a..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/datasets/caltech101.py
+++ /dev/null
@@ -1,113 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List
-
-from mmengine import get_file_backend, list_from_file
-
-from mmpretrain.registry import DATASETS
-from .base_dataset import BaseDataset
-from .categories import CALTECH101_CATEGORIES
-
-
-@DATASETS.register_module()
-class Caltech101(BaseDataset):
-    """The Caltech101 Dataset.
-
-    Support the `Caltech101 <https://data.caltech.edu/records/mzrjq-6wc02>`_ Dataset.
-    After downloading and decompression, the dataset directory structure is as follows.
-
-    Caltech101 dataset directory: ::
-
-        caltech-101
-        ├── 101_ObjectCategories
-        │   ├── class_x
-        │   │   ├── xx1.jpg
-        │   │   ├── xx2.jpg
-        │   │   └── ...
-        │   ├── class_y
-        │   │   ├── yy1.jpg
-        │   │   ├── yy2.jpg
-        │   │   └── ...
-        │   └── ...
-        ├── Annotations
-        │   ├── class_x
-        │   │   ├── xx1.mat
-        │   │   └── ...
-        │   └── ...
-        ├── meta
-        │   ├── train.txt
-        │   └── test.txt
-        └── ....
-
-    Please note that since there is no official splitting for training and
-    test set, you can use the train.txt and text.txt provided by us or
-    create your own annotation files. Here is the download
-    `link <https://download.openmmlab.com/mmpretrain/datasets/caltech_meta.zip>`_
-    for the annotations.
-
-    Args:
-        data_root (str): The root directory for the Caltech101 dataset.
-        split (str, optional): The dataset split, supports "train" and "test".
-            Default to "train".
-
-    Examples:
-        >>> from mmpretrain.datasets import Caltech101
-        >>> train_dataset = Caltech101(data_root='data/caltech-101', split='train')
-        >>> train_dataset
-        Dataset Caltech101
-            Number of samples:  3060
-            Number of categories:       102
-            Root of dataset:    data/caltech-101
-        >>> test_dataset = Caltech101(data_root='data/caltech-101', split='test')
-        >>> test_dataset
-        Dataset Caltech101
-            Number of samples:  6728
-            Number of categories:       102
-            Root of dataset:    data/caltech-101
-    """  # noqa: E501
-
-    METAINFO = {'classes': CALTECH101_CATEGORIES}
-
-    def __init__(self, data_root: str, split: str = 'train', **kwargs):
-
-        splits = ['train', 'test']
-        assert split in splits, \
-            f"The split must be one of {splits}, but get '{split}'"
-        self.split = split
-
-        self.backend = get_file_backend(data_root, enable_singleton=True)
-
-        if split == 'train':
-            ann_file = self.backend.join_path('meta', 'train.txt')
-        else:
-            ann_file = self.backend.join_path('meta', 'test.txt')
-
-        data_prefix = '101_ObjectCategories'
-        test_mode = split == 'test'
-
-        super(Caltech101, self).__init__(
-            ann_file=ann_file,
-            data_root=data_root,
-            data_prefix=data_prefix,
-            test_mode=test_mode,
-            **kwargs)
-
-    def load_data_list(self):
-        """Load images and ground truth labels."""
-
-        pairs = list_from_file(self.ann_file)
-        data_list = []
-
-        for pair in pairs:
-            path, gt_label = pair.split()
-            img_path = self.backend.join_path(self.img_prefix, path)
-            info = dict(img_path=img_path, gt_label=int(gt_label))
-            data_list.append(info)
-
-        return data_list
-
-    def extra_repr(self) -> List[str]:
-        """The extra repr information of the dataset."""
-        body = [
-            f'Root of dataset: \t{self.data_root}',
-        ]
-        return body
diff --git a/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/class_num_check_hook.py b/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/class_num_check_hook.py
deleted file mode 100644
index 38170d6604810c575aa5c2c9435c0b75cfa761b2..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmpretrain/engine/hooks/class_num_check_hook.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved
-from mmengine.hooks import Hook
-from mmengine.utils import is_seq_of
-
-from mmpretrain.registry import HOOKS
-
-
-@HOOKS.register_module()
-class ClassNumCheckHook(Hook):
-    """Class Number Check HOOK."""
-
-    def _check_head(self, runner, dataset):
-        """Check whether the `num_classes` in head matches the length of
-        `CLASSES` in `dataset`.
-
-        Args:
-            runner (obj:`Runner`): runner object.
-            dataset (obj: `BaseDataset`): the dataset to check.
-        """
-        model = runner.model
-        if dataset.CLASSES is None:
-            runner.logger.warning(
-                f'Please set class information in `metainfo` '
-                f'in the {dataset.__class__.__name__} and'
-                f'check if it is consistent with the `num_classes` '
-                f'of head')
-        else:
-            assert is_seq_of(dataset.CLASSES, str), \
-                (f'Class information in `metainfo` in '
-                 f'{dataset.__class__.__name__} should be a tuple of str.')
-            for _, module in model.named_modules():
-                if hasattr(module, 'num_classes'):
-                    assert module.num_classes == len(dataset.CLASSES), \
-                        (f'The `num_classes` ({module.num_classes}) in '
-                         f'{module.__class__.__name__} of '
-                         f'{model.__class__.__name__} does not matches '
-                         f'the length of class information in `metainfo` '
-                         f'{len(dataset.CLASSES)}) in '
-                         f'{dataset.__class__.__name__}')
-
-    def before_train(self, runner):
-        """Check whether the training dataset is compatible with head.
-
-        Args:
-            runner (obj: `IterBasedRunner`): Iter based Runner.
-        """
-        self._check_head(runner, runner.train_dataloader.dataset)
-
-    def before_val(self, runner):
-        """Check whether the validation dataset is compatible with head.
-
-        Args:
-            runner (obj:`IterBasedRunner`): Iter based Runner.
-        """
-        self._check_head(runner, runner.val_dataloader.dataset)
-
-    def before_test(self, runner):
-        """Check whether the test dataset is compatible with head.
-
-        Args:
-            runner (obj:`IterBasedRunner`): Iter based Runner.
-        """
-        self._check_head(runner, runner.test_dataloader.dataset)
diff --git a/spaces/Lamai/LAMAIGPT/tests/unit/test_commands.py b/spaces/Lamai/LAMAIGPT/tests/unit/test_commands.py
deleted file mode 100644
index ecbac9b73bd9ad872931d77e144dd853b3d8ef64..0000000000000000000000000000000000000000
--- a/spaces/Lamai/LAMAIGPT/tests/unit/test_commands.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Unit tests for the commands module"""
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-import autogpt.agent.agent_manager as agent_manager
-from autogpt.app import execute_command, list_agents, start_agent
-
-
-@pytest.mark.integration_test
-def test_make_agent() -> None:
-    """Test the make_agent command"""
-    with patch("openai.ChatCompletion.create") as mock:
-        obj = MagicMock()
-        obj.response.choices[0].messages[0].content = "Test message"
-        mock.return_value = obj
-        start_agent("Test Agent", "chat", "Hello, how are you?", "gpt2")
-        agents = list_agents()
-        assert "List of agents:\n0: chat" == agents
-        start_agent("Test Agent 2", "write", "Hello, how are you?", "gpt2")
-        agents = list_agents()
-        assert "List of agents:\n0: chat\n1: write" == agents
diff --git a/spaces/LaynzKunz/Model-RCV/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py b/spaces/LaynzKunz/Model-RCV/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
deleted file mode 100644
index b412ba2814e114ca7bb00b6fd6ef217f63d788a3..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Model-RCV/lib/infer_pack/modules/F0Predictor/HarvestF0Predictor.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import pyworld
-import numpy as np
-
-
-class HarvestF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def resize_f0(self, x, target_len):
-        source = np.array(x)
-        source[source < 0.001] = np.nan
-        target = np.interp(
-            np.arange(0, len(source) * target_len, len(source)) / target_len,
-            np.arange(0, len(source)),
-            source,
-        )
-        res = np.nan_to_num(target)
-        return res
-
-    def compute_f0(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.hop_length,
-            f0_ceil=self.f0_max,
-            f0_floor=self.f0_min,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.fs)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
-    def compute_f0_uv(self, wav, p_len=None):
-        if p_len is None:
-            p_len = wav.shape[0] // self.hop_length
-        f0, t = pyworld.harvest(
-            wav.astype(np.double),
-            fs=self.sampling_rate,
-            f0_floor=self.f0_min,
-            f0_ceil=self.f0_max,
-            frame_period=1000 * self.hop_length / self.sampling_rate,
-        )
-        f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
-        return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/LuxOAI/ChatGpt-Web/docs/vercel-cn.md b/spaces/LuxOAI/ChatGpt-Web/docs/vercel-cn.md
deleted file mode 100644
index c492296944737156572ea8df8b43b866b3e695bf..0000000000000000000000000000000000000000
--- a/spaces/LuxOAI/ChatGpt-Web/docs/vercel-cn.md
+++ /dev/null
@@ -1,39 +0,0 @@
-# Vercel 的使用说明
-
-## 如何新建项目
-当你从 Github fork 本项目之后，需要重新在 Vercel 创建一个全新的 Vercel 项目来重新部署，你需要按照下列步骤进行。
-
-![vercel-create-1](./images/vercel/vercel-create-1.jpg)
-1. 进入 Vercel 控制台首页；
-2. 点击 Add New；
-3. 选择 Project。
-
-![vercel-create-2](./images/vercel/vercel-create-2.jpg)
-1. 在 Import Git Repository 处，搜索 chatgpt-next-web；
-2. 选中新 fork 的项目，点击 Import。
-
-![vercel-create-3](./images/vercel/vercel-create-3.jpg)
-1. 在项目配置页，点开 Environmane Variables 开始配置环境变量；
-2. 依次新增名为 OPENAI_API_KEY 和 CODE 的环境变量；
-3. 填入环境变量对应的值；
-4. 点击 Add 确认增加环境变量；
-5. 请确保你添加了 OPENAI_API_KEY，否则无法使用；
-6. 点击 Deploy，创建完成，耐心等待 5 分钟左右部署完成。
-
-## 如何增加自定义域名
-[TODO]
-
-## 如何更改环境变量
-![vercel-env-edit](./images/vercel/vercel-env-edit.jpg)
-1. 进去 Vercel 项目内部控制台，点击顶部的 Settings 按钮；
-2. 点击左侧的 Environment Variables；
-3. 点击已有条目的右侧按钮；
-4. 选择 Edit 进行编辑，然后保存即可。
-
-⚠️️ 注意：每次修改完环境变量，你都需要[重新部署项目](#如何重新部署)来让改动生效！
-
-## 如何重新部署
-![vercel-redeploy](./images/vercel/vercel-redeploy.jpg)
-1. 进入 Vercel 项目内部控制台，点击顶部的 Deployments 按钮；
-2. 选择列表最顶部一条的右侧按钮；
-3. 点击 Redeploy 即可重新部署。
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/mocapjs.js b/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/mocapjs.js
deleted file mode 100644
index aafbcbb28c371234a6aebf369b6eca43bd000048..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/pymo/mocapplayer/libs/mocapjs.js
+++ /dev/null
@@ -1,1312 +0,0 @@
-/*!
- * The MIT License (MIT)
- * 
- * Copyright (c) 2016 Omid Alemi
- * 
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- * 
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- * 
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- * 
- */
-/******/ (function(modules) { // webpackBootstrap
-/******/ 	// The module cache
-/******/ 	var installedModules = {};
-/******/
-/******/ 	// The require function
-/******/ 	function __webpack_require__(moduleId) {
-/******/
-/******/ 		// Check if module is in cache
-/******/ 		if(installedModules[moduleId])
-/******/ 			return installedModules[moduleId].exports;
-/******/
-/******/ 		// Create a new module (and put it into the cache)
-/******/ 		var module = installedModules[moduleId] = {
-/******/ 			exports: {},
-/******/ 			id: moduleId,
-/******/ 			loaded: false
-/******/ 		};
-/******/
-/******/ 		// Execute the module function
-/******/ 		modules[moduleId].call(module.exports, module, module.exports, __webpack_require__);
-/******/
-/******/ 		// Flag the module as loaded
-/******/ 		module.loaded = true;
-/******/
-/******/ 		// Return the exports of the module
-/******/ 		return module.exports;
-/******/ 	}
-/******/
-/******/
-/******/ 	// expose the modules object (__webpack_modules__)
-/******/ 	__webpack_require__.m = modules;
-/******/
-/******/ 	// expose the module cache
-/******/ 	__webpack_require__.c = installedModules;
-/******/
-/******/ 	// __webpack_public_path__
-/******/ 	__webpack_require__.p = "";
-/******/
-/******/ 	// Load entry module and return exports
-/******/ 	return __webpack_require__(0);
-/******/ })
-/************************************************************************/
-/******/ ([
-/* 0 */
-/***/ function(module, exports, __webpack_require__) {
-
-	BVHCharacter = __webpack_require__(1);
-	C3DCharacter = __webpack_require__(5);
-	MocapParsers = __webpack_require__(2);
-
-/***/ },
-/* 1 */
-/***/ function(module, exports, __webpack_require__) {
-
-	var parsers = __webpack_require__(2);
-	
-	var BVHCharacter = BVHCharacter || {};
-	
-	
-	BVHCharacter = function(n, jm, bm, jg, bg) {
-	    this.name = n;
-	
-	    this.jointMaterial = jm;
-	    this.boneMaterial = bm;
-	    this.makeJointGeometryFCN = jg;
-	    this.makeBoneGeometryFCN = bg;
-	
-	    this.bvh = [];
-	    this.skeleton = new THREE.Group();
-	
-	    this.skelScale = 1;
-	    this.jointMeshes = [];
-	    this.boneMeshes = [];
-	    this.rootMeshes = [];
-	
-	    this.originPosition = new THREE.Vector3(0, 0, 0);
-	
-	    this.ready = false;
-	    this.frameTime = 1 / 30;
-	    this.frameCount = 0;
-	    this.animIndex = 0;
-	    this.animStartTimeRef = 0;
-	    this.animOffset = 0;
-	    this.playing = true;
-	
-	    this.debug = true;
-	    this.useWorker = true;
-	
-	    this.webSocket = [];
-	    this.streamProtocol = "BVHStream";
-	    this.keepStreamedFrames = true;
-	    this.isStreaming = false;
-	
-	    var self = this;
-	
-	    //
-	
-	    this.log = function(m) {
-	        if (self.debug)
-	            console.log(self.name + ": " + m.toString());
-	    };
-	
-	    this.loadFromURL = function(url, callback) {
-	        self.log("Loading the mocap file ...");
-	        //Pace.start();
-	        reader = new parsers.bvhParser(this.name + "READER");
-	        this.url = url;
-	        reader.load(url, self.createSkel, self.fillFrames);
-	
-	        this.callb = callback;
-	    };
-	
-	    this.fillFrames = function() {
-	        // self.log("Ready!");
-	        self.ready = true;
-	        self.playing = true;
-	
-	        if (self.callb)
-	            self.callb();
-	    }
-	
-	    this.createSkel = function(data) {
-	        self.bvh = data;
-	        self.frameCount = data.frameCount;
-	        self.frameTime = data.frameTime;
-	
-	        self.log("Mocap file loaded.");
-	
-	        self.log("Creating the WebGL Joints.");
-	        self.buildSkelJoints(self.bvh.getSkeleton(), 0);
-	
-	        self.log("Creating the WebGL Bones.");
-	        self.buildSkelBones(self.jointMeshes[0]);
-	        
-	        self.skeleton.add(self.jointMeshes[0]);
-	        self.setSkeletonScale(self.skelScale);
-	        self.setSkelUp();
-	    };
-	
-	
-	    // Beginning of the Stream Code
-	    this.onHeaderReceived = function(data) {
-	        self.log("Loading the mocap header (skeleton) from the stream...");
-	        headerReader = new parsers.bvhStreamParser();
-	        headerReader.readHeader(data, self.createSkel);
-	
-	        if (self.callb)
-	            self.callb();
-	
-	        Pace.stop();
-	    }
-	
-	    this.onDataChunckReceived = function(rawFrames) {
-	        var aa = [];
-	
-	        for (f = 1; f < rawFrames.length; f++) {
-	            var parts = rawFrames[f].trim().split(" ");
-	            for (var j = 0; j < parts.length; j++)
-	                parts[j] = +parts[j];
-	            aa.push(parts);
-	        }
-	        diff = self.bvh.fillFrameArray(aa);
-	        self.frameCount = self.bvh.frameArray.length;
-	
-	
-	        if (!self.playing) {
-	            self.animStartTimeRef = Date.now();
-	            //  self.animOffset -= rawFrames.length;
-	        }
-	        /*
-	        // else
-	        // self.animOffset = self.animIndex;
-	        if (diff > 0)
-	        	self.animOffset -= rawFrames.length + 1;
-	        // self.animIndex -= rawFrames.length; //math.max(0,math.min(rawFrames.length, self.bvh.bufferSize));
-	        */
-	        self.fillFrames();
-	        Pace.stop();
-	    }
-	
-	    this.loadFromStream = function(url, callback) {
-	        self.log("Connecting to the stream server...");
-	        self.isStreaming = true;
-	        this.callb = callback;
-	        self.webSocket = new WebSocket(url);
-	
-	        self.webSocket.onerror = function(event) {
-	            self.log("Error connecting to the stream server " + event.origin);
-	        };
-	
-	        self.webSocket.onopen = function(event) {
-	            self.log("Connected to the stream server " + event.origin);
-	            Pace.stop();
-	        };
-	
-	        self.webSocket.onmessage = function(event) {
-	            // I'm not doing much of a type and content checking here. Let's just trust the sender for now!
-	            // Protocol for header:
-	            // $HEADER$
-	            // BVH...
-	            // Protocl for data chunk with id#:
-	            // $FRAMES$id#$
-	
-	            var messageLines = event.data.split('\n');
-	
-	            // self.log("Received somthing!");
-	            // self.log("The first line is : " + messageLines[0]);
-	
-	            if (messageLines.length < 1)
-	                return;
-	
-	            if (messageLines[0] == "$HEADER$") {
-	                self.onHeaderReceived(event.data);
-	
-	            } else if (messageLines[0].startsWith("$FRAMES$")) {
-	                chunckID = parseInt(messageLines[0].split("$")[2]);
-	                self.onDataChunckReceived(messageLines, chunckID);
-	            }
-	        };
-	
-	    };
-	
-	    this.requestFrames = function(i) {
-	        self.webSocket.send("$GETFRAMES" + i + "$");
-	    }
-	
-	    // End of the Stream Code
-	
-	    this.setOriginPosition = function(x, y, z) {
-	        self.originPosition.set(x, y, z);
-	    };
-	
-	    this.setSkeletonScale = function(s) {
-	        self.rootMeshes.forEach(function(c) {
-	            c.scale.set(s, s, s);
-	        });
-	        self.jointMeshes[0].scale.set(s, s, s);
-	        self.jointMeshes[0].position.multiplyScalar(s);
-	    };
-	
-	    this.buildSkelJoints = function(joint, parent) {
-	        var jointMesh = new THREE.Mesh(self.makeJointGeometryFCN(joint.name, self.skelScale), self.jointMaterial);
-	        jointMesh.bvhIndex = joint.jointIndex;
-	        jointMesh.offsetVec = new THREE.Vector3(joint.offset[0], joint.offset[1], joint.offset[2]);
-	        jointMesh.name = joint.name;
-	        jointMesh.jointparent = parent;
-	        var a, b, c;
-	        if (!joint.isEndSite()) {
-	            a = joint.channelNames[joint.channelNames.length - 3][0];
-	            b = joint.channelNames[joint.channelNames.length - 2][0];
-	            c = joint.channelNames[joint.channelNames.length - 1][0];
-	        }
-	        jointMesh.rotOrder = a + b + c;
-	        self.jointMeshes.push(jointMesh);
-	        
-	        jointMesh.position.set(jointMesh.offsetVec.x, jointMesh.offsetVec.y, jointMesh.offsetVec.z);
-	        
-	        // var axisHelper = new THREE.AxisHelper( 10 / self.skelScale );
-	        // jointMesh.add( axisHelper );
-	
-	
-	        joint.children.forEach(function(child) {
-	            jointMesh.add(self.buildSkelJoints(child, 1));
-	        });
-	
-	        return jointMesh;
-	    };
-	
-	    this.buildSkelBones = function(rootJointMesh) {
-	        rootJointMesh.traverse(function(childJointMesh) {            
-	            if (childJointMesh.parent !== null)
-	            {
-	                if (typeof childJointMesh.bvhIndex === "undefined")
-	                    return;
-	                // move origin (.translate)
-	                // rotate
-	                // translate (offset + position)
-	                h = math.abs(childJointMesh.offsetVec.length());
-	                var bgeometry = self.makeBoneGeometryFCN(childJointMesh.parent.name, childJointMesh.name, h, self.skelScale);
-	                
-	                //BEGIN - Universal      
-	                if (childJointMesh.offsetVec.y !== 0)                                               
-	                    // bgeometry.translate(0, Math.sign(childJointMesh.offsetVec.y) * h / 2, 0);
-	                    bgeometry.translate(0, -h/2, 0);
-	                else                                
-	                    bgeometry.translate(0, -h / 2,  0);
-	                    
-	                               
-	                dx = Math.atan2(childJointMesh.offsetVec.z,childJointMesh.offsetVec.y);                  
-	                dy = Math.atan2(childJointMesh.offsetVec.x,childJointMesh.offsetVec.z);
-	                dz = Math.atan2(childJointMesh.offsetVec.x,childJointMesh.offsetVec.y);   
-	   
-	   
-	                osx = math.sign(childJointMesh.offsetVec.x) === 0 ? 0: math.sign(childJointMesh.offsetVec.x);
-	                osy = math.sign(childJointMesh.offsetVec.y) === 0 ? 0: math.sign(childJointMesh.offsetVec.y);
-	                osz = math.sign(childJointMesh.offsetVec.z) === 0 ? 0: math.sign(childJointMesh.offsetVec.z);
-	
-	                osxy = math.sign(childJointMesh.offsetVec.x) === 0 ? 0: math.sign(childJointMesh.offsetVec.y);
-	                osyx = math.sign(childJointMesh.offsetVec.y) === 0 ? 0: math.sign(childJointMesh.offsetVec.x);
-	                osyz = math.sign(childJointMesh.offsetVec.y) === 0 ? 0: math.sign(childJointMesh.offsetVec.z);
-	                oszy = math.sign(childJointMesh.offsetVec.z) === 0 ? 0: math.sign(childJointMesh.offsetVec.y);
-	
-	                
-	                if (osz <0)
-	                    bgeometry.rotateZ(1*(math.pi-dz));
-	                else if (osz === 0)
-	                    bgeometry.rotateZ(1*(math.pi-dz));
-	                    // console.log();  
-	                else if (osz > 0)
-	                    bgeometry.rotateZ(1*(2*math.pi-dz));
-	
-	
-	                if (oszy >0)
-	                    bgeometry.rotateX(-1 *(2*math.pi-dx));
-	                else if (childJointMesh.offsetVec.z === 0)
-	                    // bgeometry.rotateX(-1*(math.pi-dx));
-	                    console.log();
-	                else if (oszy < 0)
-	                    bgeometry.rotateX(-1*(2*math.pi-dx));
-	                                                            
-	                //  bgeometry.rotateY(math.pi-dy);
-	                                                  
-	                //END - Universal
-	                
-	                var boneMesh = new THREE.Mesh(bgeometry, self.boneMaterial);                
-	                
-	                boneMesh.joint = childJointMesh.parent;
-	                boneMesh.name = childJointMesh.parent.name + " > " + childJointMesh.name;
-	                
-	                childJointMesh.parent.add(boneMesh);                               
-	                self.boneMeshes.push(boneMesh);                
-	            }
-	        });      
-	    };
-	
-	    this.animFrame = function(frame) {
-	        var torad = Math.PI / 180;
-	        
-	        if (frame >= self.frameCount) {
-	            self.playing = false;
-	            return;
-	        }
-	        
-	
-	        this.jointMeshes[0].traverse(function(joint) {
-	            
-	            if (typeof joint.bvhIndex === "undefined") {
-	                return;
-	            }
-	                
-	
-	            var bj = self.bvh.jointArray[joint.bvhIndex];
-	            var offsetVec = joint.offsetVec;
-	            
-	            var thisEuler = [];
-	
-	
-	            thisEuler = new THREE.Euler(
-	                (bj.channels[frame][bj.rotationIndex.x] * torad),
-	                (bj.channels[frame][bj.rotationIndex.y] * torad),
-	                (bj.channels[frame][bj.rotationIndex.z] * torad), joint.rotOrder);
-	
-	
-	            joint.localRotMat = new THREE.Matrix4();
-	            joint.localRotMat.makeRotationFromEuler(thisEuler);
-	            joint.rotation.setFromRotationMatrix(joint.localRotMat);
-	
-	            if (joint.jointparent !== 0) {
-	                // joint.position.set(offsetVec.x, offsetVec.y, offsetVec.z);
-	            } else { // root
-	                joint.position.set(
-	                    bj.channels[frame][bj.positionIndex.x] * self.skelScale + self.originPosition.x,
-	                    bj.channels[frame][bj.positionIndex.y] * self.skelScale + self.originPosition.y,
-	                    bj.channels[frame][bj.positionIndex.z] * self.skelScale + self.originPosition.z);
-	            }
-	        });
-	
-	        if (self.isStreaming) {
-	            self.bvh.consumeFrames(frame);
-	            self.frameCount = self.bvh.frameArray.length;
-	            // console.log(self.frameCount);
-	            if (self.frameCount <= 0)
-	                self.playing = false;
-	
-	            self.animOffset = 0; // self.animOffset - frame;
-	            self.animStartTimeRef = Date.now();
-	        }
-	    };
-	
-	    this.setSkelUp = function() {
-	        this.jointMeshes[0].traverse(function(joint) {
-	            if (typeof joint.bvhIndex === "undefined")
-	                return;
-	
-	            var bj = self.bvh.jointArray[joint.bvhIndex];
-	
-	            var offsetVec = joint.offsetVec;
-	            var torad = Math.PI / 180;
-	            var thisEuler = [];
-	
-	            thisEuler = new THREE.Euler(0, 0, 0, joint.rotOrder);
-	
-	            joint.localRotMat = new THREE.Matrix4();
-	            joint.localRotMat.makeRotationFromEuler(thisEuler);
-	            joint.rotation.setFromRotationMatrix(joint.localRotMat);
-	
-	            if (joint.jointparent !== 0) {
-	                // joint.position.set(offsetVec.x, offsetVec.y, offsetVec.z);
-	            } else { // root
-	                joint.position.set(self.originPosition.x, self.originPosition.y, self.originPosition.z);
-	            }
-	        });
-	    };
-	};
-	
-	
-	module.exports = BVHCharacter;
-
-/***/ },
-/* 2 */
-/***/ function(module, exports, __webpack_require__) {
-
-	module.exports ={
-	    bvhParser: __webpack_require__(3),
-	    bvhStreamParser: __webpack_require__(4)
-	};
-
-/***/ },
-/* 3 */
-/***/ function(module, exports) {
-
-	// By Ankit
-	var BVHReader = function () {
-	    this.load = function (url, callbackHeader, callbackFrameArray) {
-	        $.get(url, function (str) {
-	            
-	            var dataReturn = parse(str);
-	
-	            
-	            var jointStack = dataReturn[0];
-	            var jointMap = dataReturn[1];
-	            var jointArray = dataReturn[2];
-	            var connectivityMatrix = dataReturn[3]
-	            _bvh = new BVHReader.BVH.Skeleton(jointStack[0], jointMap, jointArray, dataReturn[3], dataReturn[4], dataReturn[5], []);
-	            
-	            if (callbackHeader)
-	                callbackHeader(_bvh,'BVH');
-	            console.log("Blah");
-	            _bvh.fillFrameArray(dataReturn[6]);
-	
-	            if (callbackFrameArray)
-	                   callbackFrameArray();
-	
-	        });
-	    };
-	
-	    function parse(str) {
-	        var lines = str.split('\n');
-	        var jointStack = [];
-	        var jointMap = {};
-	        var jointArray = [];
-	        var connectivityMatrix = [];
-	        var frameCount, frameTime, frameArray = [];
-	        var i = 0;
-	        //parse structure
-	        for (i = 1; i < lines.length; i++) {
-	            if (!parseLine(lines[i], jointStack, jointMap, jointArray, connectivityMatrix)) {
-	                break;
-	            }
-	        }
-	
-	        for (i = i + 1; i < lines.length; i++) {
-	            var line = lines[i].trim();
-	            //when encountering last line
-	            if (line === "")
-	                break;
-	            if (line.indexOf("Frames") === 0) {
-	                frameCount = +(line.split(/\b/)[2]);
-	            } else if (line.indexOf("Frame Time") === 0) {
-	                frameTime = +( line.substr(line.indexOf(":") + 1).trim() )
-	            } else {
-	                var parts = line.split(" ");
-	                for (var j = 0; j < parts.length; j++)
-	                    parts[j] = +parts[j];
-	                frameArray.push(parts);
-	            }
-	        }
-	
-	        //parse motion
-	        return [jointStack, jointMap, jointArray, connectivityMatrix, frameCount, frameTime, frameArray];
-	    }
-	
-	    //parses individual line in the bvh file.
-	    var parseLine = function (line, jointStack, jointMap, jointArray, connectivityMatrix) {
-	        line = line.trim();
-	        if (line.indexOf("ROOT") > -1 || line.indexOf("JOINT") > -1 || line.indexOf("End") > -1) {
-	            var parts = line.split(" ");
-	            var title = parts[1]; //temporary variable to be used after creating the joint object
-	            parts[1] = parts[1] + "-" + jointArray.length;
-	            var joint = new BVHReader.BVH.Joint(parts[1]);
-	            joint.title = title;
-	            jointStack.push(joint);
-	
-	            joint.jointIndex = Object.keys(jointMap).length;
-	            jointMap[parts[1]] = joint;
-	            jointArray.push(joint);
-	            //if the joint is not an end site
-	            if( line.indexOf("End") != 0 ){
-	                if (jointArray.length == 1) {
-	                    joint.channelOffset = 0;
-	                } else {
-	                    joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
-	                }
-	            }else{
-	                //channelLength is 0 for end joints
-	                joint.channelLength = 0;
-	                joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
-	            }
-	
-	        } else if (line.indexOf("{") === 0) {
-	
-	        } else if (line.indexOf("OFFSET") === 0) {
-	            var parts = line.split(" ");
-	            jointStack[jointStack.length - 1]["offset"] = parts.slice(1);
-	            for(x in jointStack[jointStack.length - 1]["offset"]){
-	                jointStack[jointStack.length - 1]["offset"][x] = +jointStack[jointStack.length - 1]["offset"][x]
-	            }
-	        } else if (line.indexOf("CHANNELS") === 0) {
-	            var parts = line.split(" ");
-	            jointStack[jointStack.length - 1].setChannelNames(parts.slice(2));
-	            jointStack[jointStack.length - 1]["channelLength"] = +parts[1];
-	        } else if (line.indexOf("}") === 0) {
-	            if (jointStack.length > 1) {
-	                child = jointStack.pop();
-	                jointStack[jointStack.length - 1].children.push(child);
-	                child.parent = jointStack[jointStack.length - 1];
-	
-	                connectivityMatrix.push([child.parent, child])
-	
-	                // if(!connectivityMatrix[child.name]){
-	                //     connectivityMatrix[child.name] = {}
-	                // }
-	                // connectivityMatrix[child.name][child.parent.name] = 1;
-	
-	                // if(!connectivityMatrix[child.parent.name]){
-	                //     connectivityMatrix[child.parent.name] = {}
-	                // }
-	                // connectivityMatrix[child.parent.name][child.name] = 1;
-	            }
-	        } else if (line.indexOf("MOTION") == 0) {
-	            return false;
-	        }
-	
-	        return true;
-	    };
-	};
-	
-	BVHReader.BVH = BVHReader.BVH || {};
-	
-	BVHReader.BVH.Joint = function (name, index) {
-	
-	    this.name = name;
-	    this.children = [];
-	    this.isEndSite = function () {
-	        return this.children.length == 0;
-	    };
-	    this.rotationIndex = {};
-	    this.positionIndex = {};
-	
-	    this.getChannels = function () {
-	        var allChannels = [];
-	        for (i = 0; i < this.skeleton.frameArray.length; i++) {
-	            allChannels.push(this.getChannelsAt(i));
-	        }
-	        return allChannels;
-	    };
-	    this.getChannelsAt = function (frameNum) {
-	        var channelsAtFrame = this.skeleton.frameArray[frameNum];
-	        return channelsAtFrame.slice(this.channelOffset, this.channelOffset + this.channelLength);
-	    };
-	
-	    this.setChannelNames = function (nameArr){
-	        this.channelNames = nameArr;
-	        for(i in this.channelNames){
-	            var name = this.channelNames[i];
-	            switch(name){
-	                case "Xposition": this.positionIndex.x = i; break;
-	                case "Yposition": this.positionIndex.y = i; break;
-	                case "Zposition": this.positionIndex.z = i; break;
-	
-	                case "Xrotation": this.rotationIndex.x = i; break;
-	                case "Yrotation": this.rotationIndex.y = i; break;
-	                case "Zrotation": this.rotationIndex.z = i; break;
-	            }
-	        }
-	    }
-	};
-	
-	BVHReader.BVH.Skeleton = function (root, map, arr, connectivityMatrix, frameCount, frameTime, frameArray) {
-	    thisSkeleton = this;
-	    this.root = root;
-	    this.jointMap = map;
-	    this.jointArray = arr;
-	    this.connectivityMatrix = connectivityMatrix;
-	    this.frameCount = frameCount;
-	    this.frameTime = frameTime;
-	    this.frameArray = frameArray;
-	
-	    for (i = 0; i < this.jointArray.length; i++) {
-	        this.jointArray[i].skeleton = thisSkeleton;
-	    }
-	
-	
-	
-	    this.fillFrameArray = function (fa) {
-	        this.frameArray = fa;
-	        this.frameCount = fa.length;
-	            //all the structures are ready. let's calculate the positions
-	    for(j=0; j < this.jointArray.length; j++){
-	        var joint = this.jointArray[j];
-	        updateWithPositions(joint);
-	    }
-	    }
-	
-	    this.getChannels = function () {
-	        return frameArray;
-	    };
-	    this.getChannelsAt = function (frameNum) {
-	    	//How do I know which column is what?
-	        //Why do you need the column index?
-	        return frameArray[frameNum];
-	    };
-	    this.getFrameRate = function () {
-	        return frameCount / frameTime;
-	    };
-	    this.getSkeleton = function () {
-	        return root;
-	    };
-	
-	    this.getHeadJoint = function () {
-	    	// do a quick search in the joint names to see if any of them matches head, else return the something!!!!
-	        return jointMap["Head"];
-	    };
-	    this.getPositionsAt = function (frameNum) {
-	    	//for each joint, calculate its position in XYZ
-	        //return an array of joints, each with .x, .y, and .z properties
-	    	posFrame = [];
-	
-	    	for (j=0;j<this.jointArray.length;j++) {
-	    		posFrame.push(this.jointArray[j].positions[frameNum]);
-	    	}
-	
-	    	posFrame = posFrame.map(function(d) {
-				return {
-					x : d[0],
-					y : d[1],
-					z : d[2],
-				};
-			});
-	
-	        return posFrame;
-	    };
-	    this.getTPose = function () {
-	    	// This function is basically the same as the getPositionsAt except that all the rotations will be 0
-	        console.log("Not yet implemented");
-	    };
-	
-	    function updatePositions(rootOffset, removeRoot, orientation, camera) {
-	      //TODO: compelte the specification of this
-	
-	      for(j=0; j < this.jointArray.length; j++){
-	          var joint = this.jointArray[j];
-	          updateWithPositions(joint);
-	      }
-	    }
-	
-	    function updateWithPositions(joint){
-	        var channelNames = joint.channelNames;
-	        joint.channels = joint.getChannels();
-	        joint.rotations = [];
-	        joint.positions = [];
-	        joint.rotmat = [];
-	        for(i in joint.channels){
-	            var channel = joint.channels[i];
-	            var xpos = channel[joint.positionIndex.x] || 0,
-	            ypos =  channel[joint.positionIndex.y] || 0,
-	            zpos =  channel[joint.positionIndex.z] || 0,
-	            xangle =  deg2rad(channel[joint.rotationIndex.x] || 0),
-	            yangle =  deg2rad(channel[joint.rotationIndex.y] || 0),
-	            zangle= deg2rad(channel[joint.rotationIndex.z] || 0);
-	
-	            // var rotMatrix = math.transpose(getRotationMatrix(xangle, yangle, zangle, "xyz"));
-	            // var rotMatrix = getRotationMatrix1(xangle, yangle, zangle, "xyz"); //this also works
-	            var posMatrix = [xpos, ypos, zpos];
-	
-	            if(joint.parent){
-	            	  posMatrix = [0,0,0];  //At least for the bvhs that we have, this should be set to 0
-	
-	                // var t = vectorAdd(joint.offset, posMatrix);
-	                // var u = matrixMultiply(t, joint.parent.rotations[i]);
-	
-	                // joint.positions[i] = vectorAdd(u, joint.parent.positions[i]);
-	                // joint.rotations[i] = matrixMultiply( rotMatrix, joint.parent.rotations[i]);
-	                // joint.rotmat[i] = rotMatrix; 
-	
-	                 if (i==0 && (joint.name ==  "Spine" || joint.name == "L_Femur")) {
-	                    /*console.log("head's rot mat: ");
-	                    console.log(joint.rotations[i]);
-	                    console.log(t);
-	                    console.log(u);
-	
-	                    console.log("x: "+xangle + "y: "+yangle + "z: "+zangle );
-	                    console.log(posMatrix);
-	                    */
-	                }
-	
-	            }else{
-	                //its the root
-	                // joint.rotations[i] = rotMatrix;
-	                // joint.rotmat[i] = rotMatrix;
-	                joint.positions[i] = posMatrix;//vectorAdd(joint.offset , posMatrix);
-	                // ^ we can safely ignore the root's offset
-	            }
-	        }
-	    }
-	
-	    function deg2rad(deg){
-	        return deg * (Math.PI/180);
-	    }
-	
-	
-	    function getRotationMatrix(alpha, beta, gamma) {
-	
-	    //inputs are the intrinsic rotation angles in RADIANTS
-	    var ca = Math.cos(alpha),
-	    	sa = Math.sin(alpha),
-	
-	    	cb = Math.cos(beta),
-	    	sb = Math.sin(beta),
-	
-	    	cg = Math.cos(gamma),
-	    	sg = Math.sin(gamma),
-	
-	    Rx = [[1, 0, 0], [0, ca, -sa], [0, sa, ca]];
-	
-	    Ry = [[cb, 0, sb], [0, 1, 0], [-sb, 0, cb]];
-	
-	    Rz = [[cg, -sg, 0], [sg, cg, 0], [0,    0,   1]];
-	
-	
-	
-	
-	    var Rzm = math.matrix(Rz);
-	    var Rym = math.matrix(Ry);
-	    var Rxm = math.matrix(Rx);
-	
-	    var tt = math.multiply(Rzm, Rym);
-	
-	    return  math.multiply(tt,Rxm).toArray();
-	    //rotationMatrix = math. //Rz*Ry*Rx;
-	
-	    //     R = Rx*Ry*Rz;
-		}
-	
-	    function getRotationMatrix1 (xangle, yangle, zangle, order){
-	        var c1 = Math.cos(xangle),
-	        c2 = Math.cos(yangle),
-	        c3 = Math.cos(zangle),
-	        s1 = Math.sin(xangle),
-	        s2 = Math.sin(yangle),
-	        s3 = Math.sin(zangle);
-	
-	        if(order === undefined || order.trim() === ""){
-	            order = "zxy";
-	        }
-	
-	        var rotMat = [
-	            [1,0,0],
-	            [0,1,0],
-	            [0,0,1]
-	        ];
-	
-	        switch(order){
-	            case "___zxy":
-	                rotMat = [
-	                    [c2*c3-s1*s2*s3, c2*s3+s1*s2*c3, -s2*c1],
-	                    [-c1*s3, c1*c3, s1],
-	                    [s2*c3+c2*s1*s3, s2*s3-c2*s1*c3, c2*c1]
-	                ];
-	            break;
-	            default:
-	              for (o in order){
-	                var axis = order[o];
-	                var t;
-	                switch(axis){
-	                    case "x":
-	                        t = [
-	                                [1, 0, 0],
-	                                [0,  c1, s1],
-	                                [0, -s1, c1],
-	                            ]
-	                        break;
-	                    case "y":
-	                        t = [
-	                                [c2,0,-s2],
-	                                [0,1,0],
-	                                [s2,0,c2]
-	                            ]
-	                        break;
-	                    case "z":
-	                        t = [[c3,s3,0],[-s3,c3,0],[0,0,1]]
-	                        break;
-	                }
-	
-	                rotMat = matrixMultiply(t, rotMat)
-	              }
-	          }
-	
-	        return rotMat;
-	    }
-	};
-	
-	function vectorAdd(a, b){
-	    return math.add(math.matrix(a), math.matrix(b)).toArray();
-	}
-	
-	function matrixMultiply(m1, m2) {
-	    var a = math.matrix(m1);
-	    var b = math.matrix(m2);
-	    return math.multiply(a, b).toArray();
-	}
-	
-	
-	module.exports = BVHReader;
-
-/***/ },
-/* 4 */
-/***/ function(module, exports) {
-
-	// BVH parser by Ankit
-	// Stream by Omid
-	
-	
-	var BVHStreamParser = function () {
-	    this.readHeader = function (str, callback) {
-	            var dataReturn = parseHeader(str);
-	            var jointStack = dataReturn[0];
-	            var jointMap = dataReturn[1];
-	            var jointArray = dataReturn[2];
-	            var connectivityMatrix = dataReturn[3]
-	            if (callback)
-	                callback(new BVHStreamParser.BVH.Skeleton(jointStack[0], jointMap, jointArray, dataReturn[3], 0, dataReturn[5], dataReturn[6]),'BVH');
-	    };
-	
-	    function parseHeader(str) {
-	        var lines = str.split('\n');
-	        var jointStack = [];
-	        var jointMap = {};
-	        var jointArray = [];
-	        var connectivityMatrix = [];
-	        var frameCount, frameTime, frameArray = [];
-	        var i = 0;
-	        //parse structure
-	        for (i = 2; i < lines.length; i++) { //  start from 2 to skip the $HEADER$ command
-	            if (!parseLine(lines[i], jointStack, jointMap, jointArray, connectivityMatrix)) {
-	                break;
-	            }
-	        }
-	
-	        for (i = i + 1; i < lines.length; i++) {
-	            var line = lines[i].trim();
-	            //when encountering last line
-	            if (line === "")
-	                break;
-	            if (line.indexOf("Frames") === 0) {
-	                frameCount = +(line.split(/\b/)[2]);
-	            } else if (line.indexOf("Frame Time") === 0) {
-	                frameTime = +( line.substr(line.indexOf(":") + 1).trim() )
-	            } else { /// maybe this should be removed
-	                var parts = line.split(" ");
-	                for (var j = 0; j < parts.length; j++)
-	                    parts[j] = +parts[j];
-	                frameArray.push(parts);
-	            }
-	        }
-	
-	        //parse motion
-	        return [jointStack, jointMap, jointArray, connectivityMatrix, frameCount, frameTime, frameArray];
-	    }
-	
-	    //parses individual line in the bvh file.
-	    var parseLine = function (line, jointStack, jointMap, jointArray, connectivityMatrix) {
-	        line = line.trim();
-	        if (line.indexOf("ROOT") > -1 || line.indexOf("JOINT") > -1 || line.indexOf("End") > -1) {
-	            var parts = line.split(" ");
-	            var title = parts[1]; //temporary variable to be used after creating the joint object
-	            parts[1] = parts[1] + "-" + jointArray.length;
-	            var joint = new BVHStreamParser.BVH.Joint(parts[1]);
-	            joint.title = title;
-	            jointStack.push(joint);
-	
-	            joint.jointIndex = Object.keys(jointMap).length;
-	            jointMap[parts[1]] = joint;
-	            jointArray.push(joint);
-	            //if the joint is not an end site
-	            if( line.indexOf("End") != 0 ){
-	                if (jointArray.length == 1) {
-	                    joint.channelOffset = 0;
-	                } else {
-	                    joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
-	                }
-	            }else{
-	                //channelLength is 0 for end joints
-	                joint.channelLength = 0;
-	                joint.channelOffset = jointArray[jointArray.length - 2].channelOffset + jointArray[jointArray.length - 2].channelLength;
-	            }
-	
-	        } else if (line.indexOf("{") === 0) {
-	
-	        } else if (line.indexOf("OFFSET") === 0) {
-	            var parts = line.split(" ");
-	            jointStack[jointStack.length - 1]["offset"] = parts.slice(1);
-	            for(x in jointStack[jointStack.length - 1]["offset"]){
-	                jointStack[jointStack.length - 1]["offset"][x] = +jointStack[jointStack.length - 1]["offset"][x]
-	            }
-	        } else if (line.indexOf("CHANNELS") === 0) {
-	            var parts = line.split(" ");
-	            jointStack[jointStack.length - 1].setChannelNames(parts.slice(2));
-	            jointStack[jointStack.length - 1]["channelLength"] = +parts[1];
-	        } else if (line.indexOf("}") === 0) {
-	            if (jointStack.length > 1) {
-	                child = jointStack.pop();
-	                jointStack[jointStack.length - 1].children.push(child);
-	                child.parent = jointStack[jointStack.length - 1];
-	
-	                connectivityMatrix.push([child.parent, child])
-	            }
-	        } else if (line.indexOf("MOTION") == 0) {
-	            return false;
-	        }
-	
-	        return true;
-	    };
-	};
-	
-	BVHStreamParser.BVH = BVHStreamParser.BVH || {};
-	
-	BVHStreamParser.BVH.Joint = function (name, index) {
-	
-	    this.name = name;
-	    this.children = [];
-	    this.isEndSite = function () {
-	        return this.children.length == 0;
-	    };
-	    this.rotationIndex = {};
-	    this.positionIndex = {};
-	
-	    this.getChannels = function () {
-	        var allChannels = [];
-	        for (i = 0; i < this.skeleton.frameArray.length; i++) {
-	            allChannels.push(this.getChannelsAt(i));
-	        }
-	        return allChannels;
-	    };
-	    this.getChannelsAt = function (frameNum) {
-	        var channelsAtFrame = this.skeleton.frameArray[frameNum];
-	        return channelsAtFrame.slice(this.channelOffset, this.channelOffset + this.channelLength);
-	    };
-	
-	    this.setChannelNames = function (nameArr){
-	        this.channelNames = nameArr;
-	        for(i in this.channelNames){
-	            var name = this.channelNames[i];
-	            switch(name){
-	                case "Xposition": this.positionIndex.x = i; break;
-	                case "Yposition": this.positionIndex.y = i; break;
-	                case "Zposition": this.positionIndex.z = i; break;
-	
-	                case "Xrotation": this.rotationIndex.x = i; break;
-	                case "Yrotation": this.rotationIndex.y = i; break;
-	                case "Zrotation": this.rotationIndex.z = i; break;
-	            }
-	        }
-	    }
-	};
-	
-	BVHStreamParser.BVH.Skeleton = function (root, map, arr, connectivityMatrix, frameCount, frameTime, frameArray) {
-	    thisSkeleton = this;
-	    this.root = root;
-	    this.jointMap = map;
-	    this.jointArray = arr;
-	    this.connectivityMatrix = connectivityMatrix;
-	    this.frameCount = frameCount;
-	    this.frameTime = frameTime;
-	    this.frameArray = frameArray;
-	    this.bufferSize = 500;
-	
-	    for (i = 0; i < this.jointArray.length; i++) {
-	        this.jointArray[i].skeleton = thisSkeleton;
-	    }
-	
-	    this.fillFrameArray = function (fa) {
-	        this.frameArray.push.apply(this.frameArray,fa);
-	        //this.frameArray.push.apply(this.frameArray,fa);
-	        
-	        diff = this.frameArray.length - this.bufferSize;
-	        // console.log('diff = ' + diff);
-	        
-	        /*
-	        if (diff > 0) 
-	            for (i=0;i<diff;i++)
-	                this.frameArray.shift();
-	
-	        this.frameCount = this.frameArray.length;
-	        */
-	         
-	        if (diff > 0) 
-	            addedCount = this.frameCount;
-	        else
-	            addedCount = fa.length;
-	
-	        for(j=0; j < this.jointArray.length; j++){
-	            var joint = this.jointArray[j];
-	            updateWithPositionsSinceLast(joint, addedCount);
-	        }
-	        
-	        return diff;
-	    }
-	
-	    this.consumeFrames = function (index) {
-	        for (i=0;i<=index;i++) {
-	            this.frameArray.shift();
-	            for (j=0;j<this.jointArray.length;j++)
-	                this.jointArray[j].channels.shift();
-	        }
-	        this.frameCount = this.frameArray.length;
-	    }
-	
-	    this.getChannels = function () {
-	        return frameArray;
-	    };
-	    this.getChannelsAt = function (frameNum) {
-	    	//How do I know which column is what?
-	        //Why do you need the column index?
-	        return frameArray[frameNum];
-	    };
-	    this.getFrameRate = function () {
-	        return frameCount / frameTime;
-	    };
-	    this.getSkeleton = function () {
-	        return root;
-	    };
-	
-	    this.getHeadJoint = function () {
-	    	// do a quick search in the joint names to see if any of them matches head, else return the something!!!!
-	        return jointMap["Head"];
-	    };
-	    this.getPositionsAt = function (frameNum) {
-	    	//for each joint, calculate its position in XYZ
-	        //return an array of joints, each with .x, .y, and .z properties
-	    	posFrame = [];
-	
-	    	for (j=0;j<this.jointArray.length;j++) {
-	    		posFrame.push(this.jointArray[j].positions[frameNum]);
-	    	}
-	
-	    	posFrame = posFrame.map(function(d) {
-				return {
-					x : d[0],
-					y : d[1],
-					z : d[2],
-				};
-			});
-	
-	        return posFrame;
-	    };
-	    this.getTPose = function () {
-	    	// This function is basically the same as the getPositionsAt except that all the rotations will be 0
-	        console.log("Not yet implemented");
-	    };
-	
-	    function updatePositions(rootOffset, removeRoot, orientation, camera) {
-	      //TODO: compelte the specification of this
-	
-	      for(j=0; j < this.jointArray.length; j++){
-	          var joint = this.jointArray[j];
-	          updateWithPositions(joint);
-	      }
-	    }
-	
-	    function updateWithPositions(joint){
-	        var channelNames = joint.channelNames;
-	        joint.channels = joint.getChannels();
-	        joint.rotations = [];
-	        joint.positions = [];
-	        joint.rotmat = [];
-	        for(i in joint.channels){
-	            var channel = joint.channels[i];
-	            var xpos = channel[joint.positionIndex.x] || 0,
-	            ypos =  channel[joint.positionIndex.y] || 0,
-	            zpos =  channel[joint.positionIndex.z] || 0;
-	            // xangle =  deg2rad(channel[joint.rotationIndex.x] || 0),
-	            // yangle =  deg2rad(channel[joint.rotationIndex.y] || 0),
-	            // zangle= deg2rad(channel[joint.rotationIndex.z] || 0);
-	
-	            var posMatrix = [xpos, ypos, zpos];
-	
-	            if(!joint.parent){
-	                //its the root
-	                joint.positions[i] = posMatrix;//vectorAdd(joint.offset , posMatrix);
-	                // ^ we can safely ignore the root's offset
-	            }
-	        }
-	    }
-	
-	    function updateWithPositionsSinceLast(joint, addedCount){
-	        var channelNames = joint.channelNames;
-	        joint.channels = joint.getChannels();
-	        joint.rotations = [];
-	        joint.positions = [];
-	        joint.rotmat = [];
-	        for(i=joint.channels.length - addedCount;i < joint.channels.length; i++){
-	            var channel = joint.channels[i];
-	            var xpos = channel[joint.positionIndex.x] || 0,
-	            ypos =  channel[joint.positionIndex.y] || 0,
-	            zpos =  channel[joint.positionIndex.z] || 0;
-	            // xangle =  deg2rad(channel[joint.rotationIndex.x] || 0),
-	            // yangle =  deg2rad(channel[joint.rotationIndex.y] || 0),
-	            // zangle= deg2rad(channel[joint.rotationIndex.z] || 0);
-	
-	            var posMatrix = [xpos, ypos, zpos];
-	
-	            if(!joint.parent){
-	                //its the root
-	                joint.positions[i] = posMatrix;//vectorAdd(joint.offset , posMatrix);
-	                // ^ we can safely ignore the root's offset
-	            }
-	        }
-	    }
-	
-	    function deg2rad(deg){
-	        return deg * (Math.PI/180);
-	    }
-	};
-	
-	module.exports = BVHStreamParser;
-
-/***/ },
-/* 5 */
-/***/ function(module, exports) {
-
-	var C3DCharacter = C3DCharacter || {};
-	
-	C3DCharacter = function(n, jm, jg){
-		this.name = n;
-		
-		this.markerMaterial = jm;
-		this.makeMarkerGeometryFCN = jg;
-	
-		this.originPosition = new THREE.Vector3(0,0,0);
-	
-		this.markerdata = [];
-		this.ready = false;
-		this.scale = 0.5;
-		this.markerMeshes = [];
-	
-		this.frameTime = 1/30;
-		this.frameCount = 0;
-	
-		this.animIndex = 0;
-		this.animStartTimeRef = 0;
-		this.animOffset = 0;
-		this.playing = true;
-	
-		this.debug = true;
-	
-		var self = this;
-	
-		//
-	
-		this.log = function(m) {
-			if (self.debug)
-				console.log(self.name + ": "+m.toString());
-		};
-	
-		this.loadFromURL = function(url, callback) {
-			self.log("Loading the mocap file ...");
-			Pace.start();
-			url2 = "../" + url;	
-			self.url = url;
-
-			Papa.parse(url2, {
-				worker: true,
-				delimiter: ",",	
-				dynamicTyping: true,
-				download: true,
-				header: false,
-				complete: function(results) {
-					self.processData(results);
-					if (callback)
-						callback();
-				}
-			});
-		};
-
-		this.loadFromBuffer = function(data, callback) {
-			self.log("Loading the mocap from buffer...");
-			Pace.start();	
-			var preData = data.split('\n');
-			preData = preData.map(function(d,i){
-				var cols = d.split(',');
-				var floats = cols;
-				// console.log(i);
-				if (i!=0) {
-					floats = cols.map(function(p, j){
-						return parseFloat(p);
-					});
-				}
-				
-				return floats;
-			});
-			preData.pop();			
-			
-			this.processData({data: preData});
-			if (callback)
-				callback();
-		}
-	
-		this.processData = function(results) {
-			//self.markerdata = results.data;
-			// console.log(results);
-
-			for (i=0;i<results.data[0].length-3;i+=3) {
-				var markerMesh = new THREE.Mesh(self.makeMarkerGeometryFCN(results.data[0][i], self.scale), self.markerMaterial);
-				markerMesh.markerIndex = i;
-				markerMesh.name = results.data[0][i];
-				scene.add(markerMesh);
-				self.markerMeshes.push(markerMesh);				
-			}	
-				
-			self.markerNames = results.data[0];
-
-			for (f=1;f<results.data.length;f++) {
-				self.markerdata[f-1] = [];
-				for (m=0;m<results.data[f].length-3;m+=3) {
-					marker = {};
-					marker.x = results.data[f][m];
-					marker.y = results.data[f][m+1];
-					marker.z = results.data[f][m+2];
-					marker.name = self.markerNames[m];
-
-					self.markerdata[f-1].push(marker);
-				}								
-			}						
-
-			self.frameCount = self.markerdata.length;
-			self.log("Done parsing!");	
-			self.ready = true;
-		}
-
-		this.setOriginPosition = function (x, y, z) {
-			self.originPosition.set(x,y,z);
-		};
-	
-		this.setSkeletonScale = function(s) {
-			self.rootMeshes.forEach(function (c) {
-				c.scale.set(s,s,s);
-			});
-			self.jointMeshes[0].scale.set(s,s,s);
-			self.jointMeshes[0].position.multiplyScalar(s);
-		};
-	
-	
-		this.animFrame = function (frame) {
-			for (m=0;m<self.markerMeshes.length; m++) {
-				self.markerMeshes[m].position.set(
-					self.markerdata[frame][m].x * self.scale + self.originPosition.x,
-					self.markerdata[frame][m].y * self.scale + self.originPosition.y,
-					self.markerdata[frame][m].z * self.scale + self.originPosition.z);
-			}
-		};
-	};
-	
-	module.exports = C3DCharacter;
-
-/***/ }
-/******/ ]);
-//# sourceMappingURL=mocapjs.js.map
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/base_model.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/base_model.py
deleted file mode 100644
index 5cf430239b47ec5ec07531263f26f5c24a2311cd..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/midas/midas/base_model.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import torch
-
-
-class BaseModel(torch.nn.Module):
-    def load(self, path):
-        """Load model from file.
-
-        Args:
-            path (str): file path
-        """
-        parameters = torch.load(path, map_location=torch.device('cpu'))
-
-        if "optimizer" in parameters:
-            parameters = parameters["model"]
-
-        self.load_state_dict(parameters)
diff --git a/spaces/MetaWabbit/Auto-GPT/tests/test_token_counter.py b/spaces/MetaWabbit/Auto-GPT/tests/test_token_counter.py
deleted file mode 100644
index 6d7ae016b2f823123b0b69b2eeb3eab50d94f00f..0000000000000000000000000000000000000000
--- a/spaces/MetaWabbit/Auto-GPT/tests/test_token_counter.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import unittest
-
-import tests.context
-from autogpt.token_counter import count_message_tokens, count_string_tokens
-
-
-class TestTokenCounter(unittest.TestCase):
-    def test_count_message_tokens(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        self.assertEqual(count_message_tokens(messages), 17)
-
-    def test_count_message_tokens_with_name(self):
-        messages = [
-            {"role": "user", "content": "Hello", "name": "John"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        self.assertEqual(count_message_tokens(messages), 17)
-
-    def test_count_message_tokens_empty_input(self):
-        self.assertEqual(count_message_tokens([]), 3)
-
-    def test_count_message_tokens_invalid_model(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        with self.assertRaises(KeyError):
-            count_message_tokens(messages, model="invalid_model")
-
-    def test_count_message_tokens_gpt_4(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        self.assertEqual(count_message_tokens(messages, model="gpt-4-0314"), 15)
-
-    def test_count_string_tokens(self):
-        string = "Hello, world!"
-        self.assertEqual(
-            count_string_tokens(string, model_name="gpt-3.5-turbo-0301"), 4
-        )
-
-    def test_count_string_tokens_empty_input(self):
-        self.assertEqual(count_string_tokens("", model_name="gpt-3.5-turbo-0301"), 0)
-
-    def test_count_message_tokens_invalid_model(self):
-        messages = [
-            {"role": "user", "content": "Hello"},
-            {"role": "assistant", "content": "Hi there!"},
-        ]
-        with self.assertRaises(NotImplementedError):
-            count_message_tokens(messages, model="invalid_model")
-
-    def test_count_string_tokens_gpt_4(self):
-        string = "Hello, world!"
-        self.assertEqual(count_string_tokens(string, model_name="gpt-4-0314"), 4)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/spaces/MichaelWelsch/FreeVC/speaker_encoder/__init__.py b/spaces/MichaelWelsch/FreeVC/speaker_encoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Mohit-321/WhatsappchatAnalyzer/app.py b/spaces/Mohit-321/WhatsappchatAnalyzer/app.py
deleted file mode 100644
index 2f2406d552a6236a2172ed7a7f522bfc6be363ee..0000000000000000000000000000000000000000
--- a/spaces/Mohit-321/WhatsappchatAnalyzer/app.py
+++ /dev/null
@@ -1,159 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from transformers import AutoTokenizer
-from transformers import AutoModelForSequenceClassification
-import warnings
-warnings.filterwarnings("ignore")
-import nltk
-nltk.download('all')
-import matplotlib.pyplot as plt
-import helper
-import preprocessor
-from mtranslate import translate
-import pandas as pd
-import os
-from gtts import gTTS
-import base64
-import torch
-import seaborn as sns
-st.sidebar.title("Whatsapp Chat analyzer")
-
-uploaded_file= st.sidebar.file_uploader("Choose a file")
-
-if uploaded_file is not None:
-
-    bytes_data = uploaded_file.getvalue()
-    data=bytes_data.decode("utf-8")
-    df_new= preprocessor.preprocess(data)
-
-    user_list= df_new['users'].unique().tolist()
-    user_list.sort()
-    user_list.insert(0,"Group analysis")
-    selected_user=st.sidebar.selectbox("show analysis wrt",user_list)
-    if st.sidebar.button("Show Analysis"):
-        num_messages,words,num_links=helper.fetch_stats(selected_user,df_new)
-        st.title("Top Statistics")
-        col1,col2,col3=st.columns(3)
-
-        with col1:
-            st.header("Total Messages")
-            st.title(num_messages)
-        with col2:
-            st.header("Total Words")
-            st.title(words)
-        with col3:
-            st.header("Links Shared")
-            st.title(num_links)
-            
-        st.title("Timeline")
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.header("Monthly ")
-            timeline = helper.monthly_timeline(selected_user, df_new)
-            fig, ax = plt.subplots()
-            ax.plot(timeline['time'], timeline['message'])
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-        with col2:
-            st.title("Daily")
-            daily_timeline = helper.Daily_timeline(selected_user, df_new)
-            fig, ax = plt.subplots()
-            ax.plot(daily_timeline['Date'], daily_timeline['message'], color='black')
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-
-        st.title("Activity Map")
-        col1,col2=st.columns(2)
-
-        with col1:
-            st.header("Most busy day")
-            busy_day=helper.week_activity_map(selected_user, df_new)
-            fig,ax=plt.subplots()
-            ax.bar(busy_day.index,busy_day.values,color=('violet','indigo','blue','green','yellow','orange','red'))
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-        with col2:
-            st.header("Most busy Month")
-            busy_day = helper.month_activity_map(selected_user, df_new)
-            fig, ax = plt.subplots()
-            ax.bar(busy_day.index, busy_day.values,color=('indigo','blue','green','red'))
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-
-        st.title("Weekly Activity HeatMap")
-        Activity_heatmap=helper.activity_heatmap(selected_user,df_new)
-        fig,ax=plt.subplots()
-        ax=sns.heatmap(Activity_heatmap,cmap='RdBu',linewidths=1,linecolor='black')
-        st.pyplot(fig)
-
-        if selected_user == "Group analysis":
-            st.title("Most busy user")
-            x,new_df=helper.most_busy_users(df_new)
-            fig,ax=plt.subplots()
-            col1,col2=st.columns(2)
-
-            with col1:
-                ax.bar(x.index, x.values,color=('blue','red','pink','orange','green'))
-                plt.xticks(rotation='vertical')
-                st.pyplot(fig)
-            with col2:
-                st.dataframe(new_df)
-
-        st.title("Chat Sentiment Analysis")
-        col1, col2, col3 = st.columns(3)
-
-        with col1:
-            st.header("Positive")
-            pos_words = helper.pos_words(selected_user, df_new)
-            st.dataframe(pos_words)
-        with col2:
-            st.header("Negative")
-            neg_words = helper.neg_words(selected_user, df_new)
-            st.dataframe(neg_words)
-        with col3:
-            st.header("Neutral")
-            neu_words = helper.neu_words(selected_user, df_new)
-            st.dataframe(neu_words)
-
-
-        st.title("Word cloud")
-        df_wc = helper.word_cloud(selected_user, df_new)
-        fig, ax = plt.subplots()
-        ax.imshow(df_wc)
-        plt.axis('off')
-        st.pyplot(fig)
-
-        st.title("Most Common Words")
-        most_common_df=helper.most_common_words(selected_user,df_new)
-        fig,ax=plt.subplots()
-        ax.barh(most_common_df[0],most_common_df[1])
-        st.pyplot(fig)
-        st.dataframe(most_common_df.style.set_properties(**{"background-color": "black", "color": "lawngreen"}))
-
-        emoji_df=helper.emoji_helper(selected_user,df_new)
-        st.title("Emoji Analysis")
-        st.dataframe(emoji_df.style.set_properties(**{"background-color": "black", "color": "lawngreen"}))
-
-
-st.title("Sentiment Analysis")
-@st.cache(allow_output_mutation=True)
-def get_model():
-    MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
-    tokenizer = AutoTokenizer.from_pretrained(MODEL)
-    model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-    return tokenizer,model
-
-
-tokenizer, model = get_model()
-
-user_input = st.text_area('Enter Text to Analyze')
-button = st.button("Analyze")
-
-sent_pipeline = pipeline("sentiment-analysis")
-if user_input and button:
-    test_sample = tokenizer([user_input], padding=True, truncation=True, max_length=512, return_tensors='pt')
-    # test_sample
-    output = model(**test_sample)
-    st.write("Prediction: ", sent_pipeline(user_input))
-    showWarningOnDirectExecution = False
diff --git a/spaces/Mysterykey/Orange/greeting.md b/spaces/Mysterykey/Orange/greeting.md
deleted file mode 100644
index c69716bd0c0b6d1ea169b1c4ecb7a8f63e4863dd..0000000000000000000000000000000000000000
--- a/spaces/Mysterykey/Orange/greeting.md
+++ /dev/null
@@ -1 +0,0 @@
-![](https://files.catbox.moe/qtwxtk.webp) Pass: <u>Orange!</u>
\ No newline at end of file
diff --git a/spaces/NATSpeech/PortaSpeech/docs/fastspeech2.md b/spaces/NATSpeech/PortaSpeech/docs/fastspeech2.md
deleted file mode 100644
index 572874f34bb45de5b6788f182af8bcc3c8fddca1..0000000000000000000000000000000000000000
--- a/spaces/NATSpeech/PortaSpeech/docs/fastspeech2.md
+++ /dev/null
@@ -1,53 +0,0 @@
-# Run FastSpeech 2
-
-## Quick Start
-
-### Install Dependencies
-
-Install dependencies following [readme.md](../readme.md)
-
-### Set Config Path and Experiment Name
-
-```bash
-export CONFIG_NAME=egs/datasets/audio/lj/fs2_orig.yaml
-export MY_EXP_NAME=fs2_exp
-```
-
-### Preprocess and binary dataset
-
-Prepare dataset following [prepare_data.md](./prepare_data.md)
-
-### Prepare Vocoder
-
-Prepare vocoder following [prepare_vocoder.md](./prepare_vocoder.md)
-
-## Training
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --reset
-```
-
-You can check the training and validation curves open Tensorboard via:
-
-```bash
-tensorboard --logdir checkpoints/$MY_EXP_NAME
-```
-
-## Inference (Testing)
-
-```bash
-CUDA_VISIBLE_DEVICES=0 python tasks/run.py --config $CONFIG_NAME --exp_name $MY_EXP_NAME --infer
-```
-
-## Citation
-
-If you find this useful for your research, please use the following.
-
-```
-@inproceedings{ren2020fastspeech,
-  title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech},
-  author={Ren, Yi and Hu, Chenxu and Tan, Xu and Qin, Tao and Zhao, Sheng and Zhao, Zhou and Liu, Tie-Yan},
-  booktitle={International Conference on Learning Representations},
-  year={2020}
-}
-```
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/dataloader/input_reader.py b/spaces/NCTCMumbai/NCTC/models/official/vision/detection/dataloader/input_reader.py
deleted file mode 100644
index 6e65243f6863ccadb45704b3ed487aec3b8ab21a..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/detection/dataloader/input_reader.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Data loader and input processing."""
-
-from __future__ import absolute_import
-from __future__ import division
-# from __future__ import google_type_annotations
-from __future__ import print_function
-
-import tensorflow as tf
-
-from typing import Text, Optional
-from official.modeling.hyperparams import params_dict
-from official.vision.detection.dataloader import factory
-from official.vision.detection.dataloader import mode_keys as ModeKeys
-
-
-class InputFn(object):
-  """Input function that creates dataset from files."""
-
-  def __init__(self,
-               file_pattern: Text,
-               params: params_dict.ParamsDict,
-               mode: Text,
-               batch_size: int,
-               num_examples: Optional[int] = -1):
-    """Initialize.
-
-    Args:
-      file_pattern: the file pattern for the data example (TFRecords).
-      params: the parameter object for constructing example parser and model.
-      mode: ModeKeys.TRAIN or ModeKeys.Eval
-      batch_size: the data batch size.
-      num_examples: If positive, only takes this number of examples and raise
-        tf.errors.OutOfRangeError after that. If non-positive, it will be
-        ignored.
-    """
-    assert file_pattern is not None
-    assert mode is not None
-    assert batch_size is not None
-    self._file_pattern = file_pattern
-    self._mode = mode
-    self._is_training = (mode == ModeKeys.TRAIN)
-    self._batch_size = batch_size
-    self._num_examples = num_examples
-    self._parser_fn = factory.parser_generator(params, mode)
-    self._dataset_fn = tf.data.TFRecordDataset
-
-    self._input_sharding = (not self._is_training)
-    try:
-      if self._is_training:
-        self._input_sharding = params.train.input_sharding
-      else:
-        self._input_sharding = params.eval.input_sharding
-    except AttributeError:
-      pass
-
-  def __call__(self, ctx=None, batch_size: int = None):
-    """Provides tf.data.Dataset object.
-
-    Args:
-      ctx: context object.
-      batch_size: expected batch size input data.
-
-    Returns:
-      tf.data.Dataset object.
-    """
-    if not batch_size:
-      batch_size = self._batch_size
-    assert batch_size is not None
-    dataset = tf.data.Dataset.list_files(
-        self._file_pattern, shuffle=self._is_training)
-
-    if self._input_sharding and ctx and ctx.num_input_pipelines > 1:
-      dataset = dataset.shard(ctx.num_input_pipelines, ctx.input_pipeline_id)
-    dataset = dataset.cache()
-
-    if self._is_training:
-      dataset = dataset.repeat()
-
-    dataset = dataset.interleave(
-        map_func=self._dataset_fn, cycle_length=32,
-        num_parallel_calls=tf.data.experimental.AUTOTUNE)
-
-    if self._is_training:
-      dataset = dataset.shuffle(1000)
-    if self._num_examples > 0:
-      dataset = dataset.take(self._num_examples)
-
-    # Parses the fetched records to input tensors for model function.
-    dataset = dataset.map(
-        self._parser_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
-    dataset = dataset.batch(batch_size, drop_remainder=True)
-    dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
-    return dataset
diff --git a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/configs.py b/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/configs.py
deleted file mode 100644
index 8a79a1cd9b563a554614b9d4f2f0b93acf016791..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/vision/image_classification/configs/configs.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# Lint as: python3
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Configuration utils for image classification experiments."""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import dataclasses
-
-from official.vision.image_classification import dataset_factory
-from official.vision.image_classification.configs import base_configs
-from official.vision.image_classification.efficientnet import efficientnet_config
-from official.vision.image_classification.resnet import resnet_config
-
-
-@dataclasses.dataclass
-class EfficientNetImageNetConfig(base_configs.ExperimentConfig):
-  """Base configuration to train efficientnet-b0 on ImageNet.
-
-  Attributes:
-    export: An `ExportConfig` instance
-    runtime: A `RuntimeConfig` instance.
-    dataset: A `DatasetConfig` instance.
-    train: A `TrainConfig` instance.
-    evaluation: An `EvalConfig` instance.
-    model: A `ModelConfig` instance.
-
-  """
-  export: base_configs.ExportConfig = base_configs.ExportConfig()
-  runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
-  train_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='train')
-  validation_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='validation')
-  train: base_configs.TrainConfig = base_configs.TrainConfig(
-      resume_checkpoint=True,
-      epochs=500,
-      steps=None,
-      callbacks=base_configs.CallbacksConfig(enable_checkpoint_and_export=True,
-                                             enable_tensorboard=True),
-      metrics=['accuracy', 'top_5'],
-      time_history=base_configs.TimeHistoryConfig(log_steps=100),
-      tensorboard=base_configs.TensorboardConfig(track_lr=True,
-                                                 write_model_weights=False),
-      set_epoch_loop=False)
-  evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
-      epochs_between_evals=1,
-      steps=None)
-  model: base_configs.ModelConfig = \
-    efficientnet_config.EfficientNetModelConfig()
-
-
-@dataclasses.dataclass
-class ResNetImagenetConfig(base_configs.ExperimentConfig):
-  """Base configuration to train resnet-50 on ImageNet."""
-  export: base_configs.ExportConfig = base_configs.ExportConfig()
-  runtime: base_configs.RuntimeConfig = base_configs.RuntimeConfig()
-  train_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='train',
-                                     one_hot=False,
-                                     mean_subtract=True,
-                                     standardize=True)
-  validation_dataset: dataset_factory.DatasetConfig = \
-      dataset_factory.ImageNetConfig(split='validation',
-                                     one_hot=False,
-                                     mean_subtract=True,
-                                     standardize=True)
-  train: base_configs.TrainConfig = base_configs.TrainConfig(
-      resume_checkpoint=True,
-      epochs=90,
-      steps=None,
-      callbacks=base_configs.CallbacksConfig(enable_checkpoint_and_export=True,
-                                             enable_tensorboard=True),
-      metrics=['accuracy', 'top_5'],
-      time_history=base_configs.TimeHistoryConfig(log_steps=100),
-      tensorboard=base_configs.TensorboardConfig(track_lr=True,
-                                                 write_model_weights=False),
-      set_epoch_loop=False)
-  evaluation: base_configs.EvalConfig = base_configs.EvalConfig(
-      epochs_between_evals=1,
-      steps=None)
-  model: base_configs.ModelConfig = resnet_config.ResNetModelConfig()
-
-
-def get_config(model: str, dataset: str) -> base_configs.ExperimentConfig:
-  """Given model and dataset names, return the ExperimentConfig."""
-  dataset_model_config_map = {
-      'imagenet': {
-          'efficientnet': EfficientNetImageNetConfig(),
-          'resnet': ResNetImagenetConfig(),
-      }
-  }
-  try:
-    return dataset_model_config_map[dataset][model]
-  except KeyError:
-    if dataset not in dataset_model_config_map:
-      raise KeyError('Invalid dataset received. Received: {}. Supported '
-                     'datasets include: {}'.format(
-                         dataset,
-                         ', '.join(dataset_model_config_map.keys())))
-    raise KeyError('Invalid model received. Received: {}. Supported models for'
-                   '{} include: {}'.format(
-                       model,
-                       dataset,
-                       ', '.join(dataset_model_config_map[dataset].keys())))
diff --git a/spaces/NMEX/rvc-hoyo-game/infer_pack/modules.py b/spaces/NMEX/rvc-hoyo-game/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/NMEX/rvc-hoyo-game/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        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, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        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, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        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, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/Ngadou/NLP/README.md b/spaces/Ngadou/NLP/README.md
deleted file mode 100644
index 50b446afe3831dcbe6e77898f7efd985957b8b20..0000000000000000000000000000000000000000
--- a/spaces/Ngadou/NLP/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: NLP
-emoji: 🦀
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-license: cc-by-nc-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NoFearNoDistractions/ChatGPT4/README.md b/spaces/NoFearNoDistractions/ChatGPT4/README.md
deleted file mode 100644
index 7938de14e5355209aaae713f289ca469181bbb17..0000000000000000000000000000000000000000
--- a/spaces/NoFearNoDistractions/ChatGPT4/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: ysharma/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NooneImportant/tts/app.py b/spaces/NooneImportant/tts/app.py
deleted file mode 100644
index f9d6b62fb97bfee04db5e4011aa47a1af90c2599..0000000000000000000000000000000000000000
--- a/spaces/NooneImportant/tts/app.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import random 
-import gradio as gr 
-import numpy as np 
-from elevenlabs import voices, generate, set_api_key, UnauthenticatedRateLimitError
-
-def pad_buffer(audio):
-    # Pad buffer to multiple of 2 bytes
-    buffer_size = len(audio)
-    element_size = np.dtype(np.int16).itemsize
-    if buffer_size % element_size != 0:
-        audio = audio + b'\0' * (element_size - (buffer_size % element_size))
-    return audio 
-
-def generate_voice(text, voice_name):
-    try:
-        audio = generate(
-            text[:333], # Limit to 250 characters
-            voice=voice_name, 
-            model="eleven_multilingual_v2",
-            
-        )
-        return (44100, np.frombuffer(pad_buffer(audio), dtype=np.int16))
-    except UnauthenticatedRateLimitError as e:
-        raise gr.Error("Thanks for trying out ElevenLabs TTS! You've reached the free tier limit. Please provide an API key to continue.") 
-    except Exception as e:
-        raise gr.Error(e)
-    
-
-badges = """
-"""
-
-description = """
-A demo of the world's most advanced TTS systems, made by [ElevenLabs](https://elevenlabs.io). Eleven Multilingual V2 is a single foundational model supporting 28 languages including: English, Chinese, Spanish, Hindi, Portuguese, French, German, Japanese, Arabic, Korean, Indonesian, Italian, Dutch, Turkish, Polish, Swedish, Filipino, Malay, Romanian, Ukrainian, Greek, Czech, Danish, Finnish, Bulgarian, Croatian, Slovak, and Tamil. Sign up on [ElevenLabs](https://elevenlabs.io) to get fast access, long-form generation, voice cloning, API keys, and more!
-"""
-
-with gr.Blocks() as block:
-    gr.Markdown('[ ![ElevenLabs](https://user-images.githubusercontent.com/12028621/262629275-4f85c9cf-85b6-435e-ab50-5b8c7c4e9dd2.png) ](https://elevenlabs.io)')
-    gr.Markdown(badges)
-    gr.Markdown(description)
-    
-    input_text = gr.Textbox(
-        label="Input Text (250 characters max)", 
-        lines=2, 
-        value="Hello! 你好! Hola! नमस्ते! Bonjour! こんにちは! مرحبا! 안녕하세요! Ciao! Cześć! Привіт! Γειά σας! Здравей! வணக்கம்!",
-        elem_id="input_text"
-    )
-
-    all_voices = voices() 
-    input_voice = gr.Dropdown(
-        [ voice.name for voice in all_voices ], 
-        value="Bella",
-        label="Voice", 
-        elem_id="input_voice"
-    )
-
-    run_button = gr.Button(
-        text="Generate Voice", 
-        type="button"
-    )
-
-    out_audio = gr.Audio(
-        label="Generated Voice",
-        type="numpy", 
-        elem_id="out_audio",
-        format="mp3"
-    )
-        
-    inputs = [input_text, input_voice]
-    outputs = [out_audio]
-    
-    run_button.click(
-        fn=generate_voice, 
-        inputs=inputs, 
-        outputs=outputs, 
-        queue=True
-    )
-
-block.queue(concurrency_count=5).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/hubert_pretraining.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/hubert_pretraining.py
deleted file mode 100644
index f756080dd17b380d004420c045a8744411c0e93d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/tasks/hubert_pretraining.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# Copyright (c) 2017-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-import logging
-import os
-import sys
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-
-from dataclasses import dataclass, field
-from fairseq.data import Dictionary, HubertDataset
-from fairseq.dataclass.configs import FairseqDataclass
-from fairseq.tasks import register_task
-from fairseq.tasks.fairseq_task import FairseqTask
-from omegaconf import MISSING
-
-logger = logging.getLogger(__name__)
-
-
-class LabelEncoder(object):
-    def __init__(self, dictionary: Dictionary) -> None:
-        self.dictionary = dictionary
-
-    def __call__(self, label: str) -> List[str]:
-        return self.dictionary.encode_line(
-            label, append_eos=False, add_if_not_exist=False,
-        )
-
-
-@dataclass
-class HubertPretrainingConfig(FairseqDataclass):
-    data: str = field(
-        default=MISSING, metadata={"help": "path to data directory"}
-    )
-    fine_tuning: bool = field(
-        default=False, metadata={"help": "set to true if fine-tuning Hubert"}
-    )
-    labels: List[str] = field(
-        default_factory=lambda: ["ltr"],
-        metadata={
-            "help": (
-                "extension of the label files to load, frame-level labels for"
-                " pre-training, and sequence-level label for fine-tuning"
-            )
-        },
-    )
-    label_dir: Optional[str] = field(
-        default=None,
-        metadata={
-            "help": "if set, looks for labels in this directory instead",
-        },
-    )
-    label_rate: int = field(
-        default=-1,
-        metadata={"help": "label frame rate. -1 for sequence label"},
-    )
-    sample_rate: int = field(
-        default=16_000,
-        metadata={
-            "help": "target sample rate. audio files will be up/down "
-            "sampled to this rate"
-        },
-    )
-    normalize: bool = field(
-        default=False,
-        metadata={
-            "help": "if set, normalizes input to have 0 mean and unit variance"
-        },
-    )
-    enable_padding: bool = field(
-        default=False,
-        metadata={"help": "pad shorter samples instead of cropping"},
-    )
-    max_keep_size: Optional[int] = field(
-        default=None,
-        metadata={"help": "exclude sample longer than this"},
-    )
-    max_sample_size: Optional[int] = field(
-        default=None,
-        metadata={"help": "max sample size to crop to for batching"},
-    )
-    min_sample_size: Optional[int] = field(
-        default=None,
-        metadata={"help": "min sample size to crop to for batching"},
-    )
-    single_target: Optional[bool] = field(
-        default=False,
-        metadata={
-            "help": "if set, AddTargetDatasets outputs same keys "
-            "as AddTargetDataset"
-        },
-    )
-    random_crop: Optional[bool] = field(
-        default=True,
-        metadata={"help": "always crop from the beginning if false"},
-    )
-    pad_audio: Optional[bool] = field(
-        default=False,
-        metadata={"help": "pad audio to the longest one in the batch if true"},
-    )
-
-
-@register_task("hubert_pretraining", dataclass=HubertPretrainingConfig)
-class HubertPretrainingTask(FairseqTask):
-
-    cfg: HubertPretrainingConfig
-
-    def __init__(
-        self,
-        cfg: HubertPretrainingConfig,
-    ) -> None:
-        super().__init__(cfg)
-
-        logger.info(f"current directory is {os.getcwd()}")
-        logger.info(f"HubertPretrainingTask Config {cfg}")
-
-        self.cfg = cfg
-        self.fine_tuning = cfg.fine_tuning
-
-        if cfg.fine_tuning:
-            self.state.add_factory("target_dictionary", self.load_dictionaries)
-        else:
-            self.state.add_factory("dictionaries", self.load_dictionaries)
-
-        self.blank_symbol = "<s>"
-
-    @property
-    def source_dictionary(self) -> Optional[Dictionary]:
-        return None
-
-    @property
-    def target_dictionary(self) -> Optional[Dictionary]:
-        return self.state.target_dictionary
-
-    @property
-    def dictionaries(self) -> List[Dictionary]:
-        return self.state.dictionaries
-
-    @classmethod
-    def setup_task(
-        cls, cfg: HubertPretrainingConfig, **kwargs
-    ) -> "HubertPretrainingTask":
-        return cls(cfg)
-
-    def load_dictionaries(self):
-        label_dir = self.cfg.data if self.cfg.label_dir is None else self.cfg.label_dir
-        dictionaries = [Dictionary.load(f"{label_dir}/dict.{label}.txt") for label in self.cfg.labels]
-        return dictionaries[0] if self.cfg.fine_tuning else dictionaries
-
-    def get_label_dir(self) -> str:
-        if self.cfg.label_dir is None:
-            return self.cfg.data
-        return self.cfg.label_dir
-
-    def load_dataset(self, split: str, **kwargs) -> None:
-        manifest = f"{self.cfg.data}/{split}.tsv"
-        dicts = [self.target_dictionary] if self.cfg.fine_tuning else self.dictionaries
-        pad_list = [dict.pad() for dict in dicts]
-        eos_list = [dict.eos() for dict in dicts]
-        procs = [LabelEncoder(dict) for dict in dicts]
-        paths = [
-            f"{self.get_label_dir()}/{split}.{l}" for l in self.cfg.labels
-        ]
-
-        # hubert v1: pad_audio=True, random_crop=False;
-        self.datasets[split] = HubertDataset(
-            manifest,
-            sample_rate=self.cfg.sample_rate,
-            label_paths=paths,
-            label_rates=self.cfg.label_rate,
-            pad_list=pad_list,
-            eos_list=eos_list,
-            label_processors=procs,
-            max_keep_sample_size=self.cfg.max_keep_size,
-            min_keep_sample_size=self.cfg.min_sample_size,
-            max_sample_size=self.cfg.max_sample_size,
-            pad_audio=self.cfg.pad_audio,
-            normalize=self.cfg.normalize,
-            store_labels=False,
-            random_crop=self.cfg.random_crop,
-            single_target=self.cfg.single_target,
-        )
-
-    def max_positions(self) -> Tuple[int, int]:
-        return (sys.maxsize, sys.maxsize)
-
-    def filter_indices_by_size(
-        self, indices: np.array, *args, **kwargs
-    ) -> np.array:
-        return indices
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/spm_train.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/spm_train.py
deleted file mode 100644
index 9db668fd4166a860198784990de68ea26157995d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/spm_train.py
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/usr/bin/env python
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-import sys
-
-import sentencepiece as spm
-
-
-if __name__ == "__main__":
-    spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:]))
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wmt19/README.md b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wmt19/README.md
deleted file mode 100644
index 5c90d0e6c4ae8d043ca622e70c5828dca6f9c2f2..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wmt19/README.md
+++ /dev/null
@@ -1,85 +0,0 @@
-# WMT 19
-
-This page provides pointers to the models of Facebook-FAIR's WMT'19 news translation task submission [(Ng et al., 2019)](https://arxiv.org/abs/1907.06616).
-
-## Pre-trained models
-
-Model | Description | Download
----|---|---
-`transformer.wmt19.en-de` | En->De Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.de-en` | De->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.joined-dict.ensemble.tar.gz)
-`transformer.wmt19.en-ru` | En->Ru Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ensemble.tar.gz)
-`transformer.wmt19.ru-en` | Ru->En Ensemble | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ensemble.tar.gz)
-`transformer_lm.wmt19.en` | En Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.en.tar.gz)
-`transformer_lm.wmt19.de` | De Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.de.tar.gz)
-`transformer_lm.wmt19.ru` | Ru Language Model | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/lm/wmt19.ru.tar.gz)
-
-## Pre-trained single models before finetuning
-
-Model | Description | Download
----|---|---
-`transformer.wmt19.en-de` | En->De Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-de.ffn8192.tar.gz)
-`transformer.wmt19.de-en` | De->En Single, no finetuning  | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.de-en.ffn8192.tar.gz)
-`transformer.wmt19.en-ru` | En->Ru Single, no finetuning | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.en-ru.ffn8192.tar.gz)
-`transformer.wmt19.ru-en` | Ru->En Single, no finetuning  | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt19.ru-en.ffn8192.tar.gz)
-
-## Example usage (torch.hub)
-
-#### Requirements
-
-We require a few additional Python dependencies for preprocessing:
-```bash
-pip install fastBPE sacremoses
-```
-
-#### Translation
-
-```python
-import torch
-
-# English to German translation
-en2de = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-de', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-en2de.translate("Machine learning is great!")  # 'Maschinelles Lernen ist großartig!'
-
-# German to English translation
-de2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.de-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-de2en.translate("Maschinelles Lernen ist großartig!")  # 'Machine learning is great!'
-
-# English to Russian translation
-en2ru = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.en-ru', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-en2ru.translate("Machine learning is great!")  # 'Машинное обучение - это здорово!'
-
-# Russian to English translation
-ru2en = torch.hub.load('pytorch/fairseq', 'transformer.wmt19.ru-en', checkpoint_file='model1.pt:model2.pt:model3.pt:model4.pt',
-                       tokenizer='moses', bpe='fastbpe')
-ru2en.translate("Машинное обучение - это здорово!")  # 'Machine learning is great!'
-```
-
-#### Language Modeling
-
-```python
-# Sample from the English LM
-en_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.en', tokenizer='moses', bpe='fastbpe')
-en_lm.sample("Machine learning is")  # 'Machine learning is the future of computing, says Microsoft boss Satya Nadella ...'
-
-# Sample from the German LM
-de_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.de', tokenizer='moses', bpe='fastbpe')
-de_lm.sample("Maschinelles lernen ist")  # 'Maschinelles lernen ist das A und O (neues-deutschland.de) Die Arbeitsbedingungen für Lehrerinnen und Lehrer sind seit Jahren verbesserungswürdig ...'
-
-# Sample from the Russian LM
-ru_lm = torch.hub.load('pytorch/fairseq', 'transformer_lm.wmt19.ru', tokenizer='moses', bpe='fastbpe')
-ru_lm.sample("машинное обучение это")  # 'машинное обучение это то, что мы называем "искусственным интеллектом".'
-```
-
-## Citation
-```bibtex
-@inproceedings{ng2019facebook},
-  title = {Facebook FAIR's WMT19 News Translation Task Submission},
-  author = {Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey},
-  booktitle = {Proc. of WMT},
-  year = 2019,
-}
-```
diff --git a/spaces/OsituKengere/Sauti-Midjourney/app.py b/spaces/OsituKengere/Sauti-Midjourney/app.py
deleted file mode 100644
index c04b6d45f84686618444749797188ca31fcb9882..0000000000000000000000000000000000000000
--- a/spaces/OsituKengere/Sauti-Midjourney/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/prompthero/openjourney-v4").launch()
\ No newline at end of file
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/backbone/swin.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/backbone/swin.py
deleted file mode 100644
index 3b099d84396ac31d22881e5b6c9e53d2d0abaef3..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/backbone/swin.py
+++ /dev/null
@@ -1,770 +0,0 @@
-# --------------------------------------------------------
-# Swin Transformer
-# Copyright (c) 2021 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ze Liu, Yutong Lin, Yixuan Wei
-# --------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec
-
-
-class Mlp(nn.Module):
-    """Multilayer perceptron."""
-
-    def __init__(
-        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    """Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """Forward function.
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[
-            self.relative_position_index.view(-1)
-        ].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class SwinTransformerBlock(nn.Module):
-    """Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
-        )
-
-        self.H = None
-        self.W = None
-
-    def forward(self, x, mask_matrix):
-        """Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-            mask_matrix: Attention mask for cyclic shift.
-        """
-        B, L, C = x.shape
-        H, W = self.H, self.W
-        assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # pad feature maps to multiples of window size
-        pad_l = pad_t = 0
-        pad_r = (self.window_size - W % self.window_size) % self.window_size
-        pad_b = (self.window_size - H % self.window_size) % self.window_size
-        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
-        _, Hp, Wp, _ = x.shape
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-            attn_mask = mask_matrix
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, C
-        )  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA
-        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-
-        if pad_r > 0 or pad_b > 0:
-            x = x[:, :H, :W, :].contiguous()
-
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    """Patch Merging Layer
-    Args:
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x, H, W):
-        """Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-
-        x = x.view(B, H, W, C)
-
-        # padding
-        pad_input = (H % 2 == 1) or (W % 2 == 1)
-        if pad_input:
-            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class BasicLayer(nn.Module):
-    """A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of feature channels
-        depth (int): Depths of this stage.
-        num_heads (int): Number of attention head.
-        window_size (int): Local window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        dim,
-        depth,
-        num_heads,
-        window_size=7,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                SwinTransformerBlock(
-                    dim=dim,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                    norm_layer=norm_layer,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, H, W):
-        """Forward function.
-        Args:
-            x: Input feature, tensor size (B, H*W, C).
-            H, W: Spatial resolution of the input feature.
-        """
-
-        # calculate attention mask for SW-MSA
-        Hp = int(np.ceil(H / self.window_size)) * self.window_size
-        Wp = int(np.ceil(W / self.window_size)) * self.window_size
-        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        for blk in self.blocks:
-            blk.H, blk.W = H, W
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, attn_mask)
-            else:
-                x = blk(x, attn_mask)
-        if self.downsample is not None:
-            x_down = self.downsample(x, H, W)
-            Wh, Ww = (H + 1) // 2, (W + 1) // 2
-            return x, H, W, x_down, Wh, Ww
-        else:
-            return x, H, W, x, H, W
-
-
-class PatchEmbed(nn.Module):
-    """Image to Patch Embedding
-    Args:
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        patch_size = to_2tuple(patch_size)
-        self.patch_size = patch_size
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        """Forward function."""
-        # padding
-        _, _, H, W = x.size()
-        if W % self.patch_size[1] != 0:
-            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
-        if H % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
-
-        x = self.proj(x)  # B C Wh Ww
-        if self.norm is not None:
-            Wh, Ww = x.size(2), x.size(3)
-            x = x.flatten(2).transpose(1, 2)
-            x = self.norm(x)
-            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
-
-        return x
-
-
-class SwinTransformer(nn.Module):
-    """Swin Transformer backbone.
-        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
-          https://arxiv.org/pdf/2103.14030
-    Args:
-        pretrain_img_size (int): Input image size for training the pretrained model,
-            used in absolute postion embedding. Default 224.
-        patch_size (int | tuple(int)): Patch size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        depths (tuple[int]): Depths of each Swin Transformer stage.
-        num_heads (tuple[int]): Number of attention head of each stage.
-        window_size (int): Window size. Default: 7.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
-        drop_rate (float): Dropout rate.
-        attn_drop_rate (float): Attention dropout rate. Default: 0.
-        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
-        out_indices (Sequence[int]): Output from which stages.
-        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-            -1 means not freezing any parameters.
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        pretrain_img_size=224,
-        patch_size=4,
-        in_chans=3,
-        embed_dim=96,
-        depths=[2, 2, 6, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.2,
-        norm_layer=nn.LayerNorm,
-        ape=False,
-        patch_norm=True,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=-1,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-
-        self.pretrain_img_size = pretrain_img_size
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.out_indices = out_indices
-        self.frozen_stages = frozen_stages
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            pretrain_img_size = to_2tuple(pretrain_img_size)
-            patch_size = to_2tuple(patch_size)
-            patches_resolution = [
-                pretrain_img_size[0] // patch_size[0],
-                pretrain_img_size[1] // patch_size[1],
-            ]
-
-            self.absolute_pos_embed = nn.Parameter(
-                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
-            )
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build layers
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = BasicLayer(
-                dim=int(embed_dim * 2 ** i_layer),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
-                norm_layer=norm_layer,
-                downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
-                use_checkpoint=use_checkpoint,
-            )
-            self.layers.append(layer)
-
-        num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
-        self.num_features = num_features
-
-        # add a norm layer for each output
-        for i_layer in out_indices:
-            layer = norm_layer(num_features[i_layer])
-            layer_name = f"norm{i_layer}"
-            self.add_module(layer_name, layer)
-
-        self._freeze_stages()
-
-    def _freeze_stages(self):
-        if self.frozen_stages >= 0:
-            self.patch_embed.eval()
-            for param in self.patch_embed.parameters():
-                param.requires_grad = False
-
-        if self.frozen_stages >= 1 and self.ape:
-            self.absolute_pos_embed.requires_grad = False
-
-        if self.frozen_stages >= 2:
-            self.pos_drop.eval()
-            for i in range(0, self.frozen_stages - 1):
-                m = self.layers[i]
-                m.eval()
-                for param in m.parameters():
-                    param.requires_grad = False
-
-    def init_weights(self, pretrained=None):
-        """Initialize the weights in backbone.
-        Args:
-            pretrained (str, optional): Path to pre-trained weights.
-                Defaults to None.
-        """
-
-        def _init_weights(m):
-            if isinstance(m, nn.Linear):
-                trunc_normal_(m.weight, std=0.02)
-                if isinstance(m, nn.Linear) and m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.LayerNorm):
-                nn.init.constant_(m.bias, 0)
-                nn.init.constant_(m.weight, 1.0)
-
-    def forward(self, x):
-        """Forward function."""
-        x = self.patch_embed(x)
-
-        Wh, Ww = x.size(2), x.size(3)
-        if self.ape:
-            # interpolate the position embedding to the corresponding size
-            absolute_pos_embed = F.interpolate(
-                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
-            )
-            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
-        else:
-            x = x.flatten(2).transpose(1, 2)
-        x = self.pos_drop(x)
-
-        outs = {}
-        for i in range(self.num_layers):
-            layer = self.layers[i]
-            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
-
-            if i in self.out_indices:
-                norm_layer = getattr(self, f"norm{i}")
-                x_out = norm_layer(x_out)
-
-                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
-                outs["res{}".format(i + 2)] = out
-
-        return outs
-
-    def train(self, mode=True):
-        """Convert the model into training mode while keep layers freezed."""
-        super(SwinTransformer, self).train(mode)
-        self._freeze_stages()
-
-
-@BACKBONE_REGISTRY.register()
-class D2SwinTransformer(SwinTransformer, Backbone):
-    def __init__(self, cfg, input_shape):
-
-        pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE
-        patch_size = cfg.MODEL.SWIN.PATCH_SIZE
-        in_chans = 3
-        embed_dim = cfg.MODEL.SWIN.EMBED_DIM
-        depths = cfg.MODEL.SWIN.DEPTHS
-        num_heads = cfg.MODEL.SWIN.NUM_HEADS
-        window_size = cfg.MODEL.SWIN.WINDOW_SIZE
-        mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO
-        qkv_bias = cfg.MODEL.SWIN.QKV_BIAS
-        qk_scale = cfg.MODEL.SWIN.QK_SCALE
-        drop_rate = cfg.MODEL.SWIN.DROP_RATE
-        attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE
-        drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE
-        norm_layer = nn.LayerNorm
-        ape = cfg.MODEL.SWIN.APE
-        patch_norm = cfg.MODEL.SWIN.PATCH_NORM
-        use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT
-
-        super().__init__(
-            pretrain_img_size,
-            patch_size,
-            in_chans,
-            embed_dim,
-            depths,
-            num_heads,
-            window_size,
-            mlp_ratio,
-            qkv_bias,
-            qk_scale,
-            drop_rate,
-            attn_drop_rate,
-            drop_path_rate,
-            norm_layer,
-            ape,
-            patch_norm,
-            use_checkpoint=use_checkpoint,
-        )
-
-        self._out_features = cfg.MODEL.SWIN.OUT_FEATURES
-
-        self._out_feature_strides = {
-            "res2": 4,
-            "res3": 8,
-            "res4": 16,
-            "res5": 32,
-        }
-        self._out_feature_channels = {
-            "res2": self.num_features[0],
-            "res3": self.num_features[1],
-            "res4": self.num_features[2],
-            "res5": self.num_features[3],
-        }
-
-    def forward(self, x):
-        """
-        Args:
-            x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
-        Returns:
-            dict[str->Tensor]: names and the corresponding features
-        """
-        assert (
-            x.dim() == 4
-        ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!"
-        outputs = {}
-        y = super().forward(x)
-        for k in y.keys():
-            if k in self._out_features:
-                outputs[k] = y[k]
-        return outputs
-
-    def output_shape(self):
-        return {
-            name: ShapeSpec(
-                channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
-            )
-            for name in self._out_features
-        }
-
-    @property
-    def size_divisibility(self):
-        return 32
diff --git a/spaces/PSLD/PSLD/stable-diffusion/debug/inverse.sh b/spaces/PSLD/PSLD/stable-diffusion/debug/inverse.sh
deleted file mode 100644
index 33e90eb628d1610fdd008ec12a40f2b1f5264db5..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/stable-diffusion/debug/inverse.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-export CUDA_VISIBLE_DEVICES='1'
-python scripts/inverse.py \
-    --file_id='00019.png' \
-    --task_config='configs/motion_deblur_config.yaml' \
-    --inpainting=0 \
-    --general_inverse=0 \
-    --gamma=1e-1 \
-    --omega=1e-1 \
-    --W=256 \
-    --H=256 \
-    --scale=5.0 \
-    --laion400m \
-    --prompt="a virus monster is playing guitar, oil on canvas"
\ No newline at end of file
diff --git a/spaces/Pascall/OASSapi_00/OASSapi.py b/spaces/Pascall/OASSapi_00/OASSapi.py
deleted file mode 100644
index 8e42068e720e9645d3039a23ec5f84abaab67c41..0000000000000000000000000000000000000000
--- a/spaces/Pascall/OASSapi_00/OASSapi.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import os
-import gradio
-from text_generation import Client, InferenceAPIClient
-
-past = []
-
-def reset_memories(content = ""):
-    try:
-        with open("memories.txt", 'w') as file:
-            file.write(content)
-            print("Successfully reset memories.")
-    except Exception as err:
-        print(err)
-
-def add_memory(content):
-    try:
-        with open("memories.txt", 'a') as file:
-            file.write(content)
-            print("Successfully added to the file.")
-    except Exception as err:
-        print(err)
-
-def read_memories():
-    try:
-        with open("memories.txt", 'r') as file:
-            content = file.read()
-        return content
-    except Exception as err:
-        print(err)
-
-def get_client(model: str):
-    return InferenceAPIClient(model, token=os.getenv("HF_TOKEN", None), timeout=100)
-
-def get_usernames(model: str):
-    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
-        return "", "<|prompter|>", "<|assistant|>", "<|endoftext|>\n"
-    return "", "User: ", "Assistant: ", "\n"
-
-def predict(inputs: str):
-    model = "OpenAssistant/oasst-sft-1-pythia-12b"
-
-    client = get_client(model)
-    preprompt, user_name, assistant_name, end = get_usernames(model)
-
-    personality = "My name is karen, i prefere to be called kage, i like to answer questions as short as possible (in unber 100 words)."
-
-    past = []
-
-    total_prompt = preprompt + user_name + inputs + end + assistant_name
-    total_prompts = preprompt + "".join(past) + user_name + inputs + end + assistant_name
-    
-    print(total_prompts)
-
-    if past == "" or past is None:
-        past.append(user_name + "describe yourself" + end + assistant_name + personality + end + total_prompt)
-    else:
-        past.append(total_prompt)
-
-    if model in ("OpenAssistant/oasst-sft-1-pythia-12b", "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"):
-        iterator = client.generate(
-            total_prompts,
-            typical_p = 0.1,
-            truncate = 1000,
-            watermark = 0,
-            max_new_tokens = 502,
-        )
-
-        yield iterator.generated_text
-
-    add_memory(iterator.generated_text + end)
-
-def run_gradio_interface():
-    g = gradio.Interface(
-        fn=predict,
-        inputs=[
-            gradio.components.Textbox(lines=3, label="Hi, how can I help you?", placeholder="Enter A prompt"),
-        ],
-        outputs=[
-            gradio.inputs.Textbox(
-                lines=10,
-                label="",
-            )
-        ]
-    )
-    g.queue(concurrency_count=1)
-    g.launch()
-
-if __name__ == "__main__":
-    run_gradio_interface()
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/agent/__init__.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/agent/__init__.py
deleted file mode 100644
index e928af2205b1c52d19dc89ec4246e8c1d2c20e3f..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/agent/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from autogpt.agent.agent import Agent
-from autogpt.agent.agent_manager import AgentManager
-
-__all__ = ["Agent", "AgentManager"]
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/cnn/utils/fuse_conv_bn.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/cnn/utils/fuse_conv_bn.py
deleted file mode 100644
index cb7076f80bf37f7931185bf0293ffcc1ce19c8ef..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/cnn/utils/fuse_conv_bn.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-
-
-def _fuse_conv_bn(conv, bn):
-    """Fuse conv and bn into one module.
-
-    Args:
-        conv (nn.Module): Conv to be fused.
-        bn (nn.Module): BN to be fused.
-
-    Returns:
-        nn.Module: Fused module.
-    """
-    conv_w = conv.weight
-    conv_b = conv.bias if conv.bias is not None else torch.zeros_like(
-        bn.running_mean)
-
-    factor = bn.weight / torch.sqrt(bn.running_var + bn.eps)
-    conv.weight = nn.Parameter(conv_w *
-                               factor.reshape([conv.out_channels, 1, 1, 1]))
-    conv.bias = nn.Parameter((conv_b - bn.running_mean) * factor + bn.bias)
-    return conv
-
-
-def fuse_conv_bn(module):
-    """Recursively fuse conv and bn in a module.
-
-    During inference, the functionary of batch norm layers is turned off
-    but only the mean and var alone channels are used, which exposes the
-    chance to fuse it with the preceding conv layers to save computations and
-    simplify network structures.
-
-    Args:
-        module (nn.Module): Module to be fused.
-
-    Returns:
-        nn.Module: Fused module.
-    """
-    last_conv = None
-    last_conv_name = None
-
-    for name, child in module.named_children():
-        if isinstance(child,
-                      (nn.modules.batchnorm._BatchNorm, nn.SyncBatchNorm)):
-            if last_conv is None:  # only fuse BN that is after Conv
-                continue
-            fused_conv = _fuse_conv_bn(last_conv, child)
-            module._modules[last_conv_name] = fused_conv
-            # To reduce changes, set BN as Identity instead of deleting it.
-            module._modules[name] = nn.Identity()
-            last_conv = None
-        elif isinstance(child, nn.Conv2d):
-            last_conv = child
-            last_conv_name = name
-        else:
-            fuse_conv_bn(child)
-    return module
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/__init__.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/__init__.py
deleted file mode 100644
index 999e090a458ee148ceca0649f1e3806a40e909bd..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmcv/ops/__init__.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .assign_score_withk import assign_score_withk
-from .ball_query import ball_query
-from .bbox import bbox_overlaps
-from .border_align import BorderAlign, border_align
-from .box_iou_rotated import box_iou_rotated
-from .carafe import CARAFE, CARAFENaive, CARAFEPack, carafe, carafe_naive
-from .cc_attention import CrissCrossAttention
-from .contour_expand import contour_expand
-from .corner_pool import CornerPool
-from .correlation import Correlation
-from .deform_conv import DeformConv2d, DeformConv2dPack, deform_conv2d
-from .deform_roi_pool import (DeformRoIPool, DeformRoIPoolPack,
-                              ModulatedDeformRoIPoolPack, deform_roi_pool)
-from .deprecated_wrappers import Conv2d_deprecated as Conv2d
-from .deprecated_wrappers import ConvTranspose2d_deprecated as ConvTranspose2d
-from .deprecated_wrappers import Linear_deprecated as Linear
-from .deprecated_wrappers import MaxPool2d_deprecated as MaxPool2d
-from .focal_loss import (SigmoidFocalLoss, SoftmaxFocalLoss,
-                         sigmoid_focal_loss, softmax_focal_loss)
-from .furthest_point_sample import (furthest_point_sample,
-                                    furthest_point_sample_with_dist)
-from .fused_bias_leakyrelu import FusedBiasLeakyReLU, fused_bias_leakyrelu
-from .gather_points import gather_points
-from .group_points import GroupAll, QueryAndGroup, grouping_operation
-from .info import (get_compiler_version, get_compiling_cuda_version,
-                   get_onnxruntime_op_path)
-from .iou3d import boxes_iou_bev, nms_bev, nms_normal_bev
-from .knn import knn
-from .masked_conv import MaskedConv2d, masked_conv2d
-from .modulated_deform_conv import (ModulatedDeformConv2d,
-                                    ModulatedDeformConv2dPack,
-                                    modulated_deform_conv2d)
-from .multi_scale_deform_attn import MultiScaleDeformableAttention
-from .nms import batched_nms, nms, nms_match, nms_rotated, soft_nms
-from .pixel_group import pixel_group
-from .point_sample import (SimpleRoIAlign, point_sample,
-                           rel_roi_point_to_rel_img_point)
-from .points_in_boxes import (points_in_boxes_all, points_in_boxes_cpu,
-                              points_in_boxes_part)
-from .points_sampler import PointsSampler
-from .psa_mask import PSAMask
-from .roi_align import RoIAlign, roi_align
-from .roi_align_rotated import RoIAlignRotated, roi_align_rotated
-from .roi_pool import RoIPool, roi_pool
-from .roiaware_pool3d import RoIAwarePool3d
-from .roipoint_pool3d import RoIPointPool3d
-from .saconv import SAConv2d
-from .scatter_points import DynamicScatter, dynamic_scatter
-from .sync_bn import SyncBatchNorm
-from .three_interpolate import three_interpolate
-from .three_nn import three_nn
-from .tin_shift import TINShift, tin_shift
-from .upfirdn2d import upfirdn2d
-from .voxelize import Voxelization, voxelization
-
-__all__ = [
-    'bbox_overlaps', 'CARAFE', 'CARAFENaive', 'CARAFEPack', 'carafe',
-    'carafe_naive', 'CornerPool', 'DeformConv2d', 'DeformConv2dPack',
-    'deform_conv2d', 'DeformRoIPool', 'DeformRoIPoolPack',
-    'ModulatedDeformRoIPoolPack', 'deform_roi_pool', 'SigmoidFocalLoss',
-    'SoftmaxFocalLoss', 'sigmoid_focal_loss', 'softmax_focal_loss',
-    'get_compiler_version', 'get_compiling_cuda_version',
-    'get_onnxruntime_op_path', 'MaskedConv2d', 'masked_conv2d',
-    'ModulatedDeformConv2d', 'ModulatedDeformConv2dPack',
-    'modulated_deform_conv2d', 'batched_nms', 'nms', 'soft_nms', 'nms_match',
-    'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool', 'SyncBatchNorm', 'Conv2d',
-    'ConvTranspose2d', 'Linear', 'MaxPool2d', 'CrissCrossAttention', 'PSAMask',
-    'point_sample', 'rel_roi_point_to_rel_img_point', 'SimpleRoIAlign',
-    'SAConv2d', 'TINShift', 'tin_shift', 'assign_score_withk',
-    'box_iou_rotated', 'RoIPointPool3d', 'nms_rotated', 'knn', 'ball_query',
-    'upfirdn2d', 'FusedBiasLeakyReLU', 'fused_bias_leakyrelu',
-    'RoIAlignRotated', 'roi_align_rotated', 'pixel_group', 'QueryAndGroup',
-    'GroupAll', 'grouping_operation', 'contour_expand', 'three_nn',
-    'three_interpolate', 'MultiScaleDeformableAttention', 'BorderAlign',
-    'border_align', 'gather_points', 'furthest_point_sample',
-    'furthest_point_sample_with_dist', 'PointsSampler', 'Correlation',
-    'boxes_iou_bev', 'nms_bev', 'nms_normal_bev', 'Voxelization',
-    'voxelization', 'dynamic_scatter', 'DynamicScatter', 'RoIAwarePool3d',
-    'points_in_boxes_part', 'points_in_boxes_cpu', 'points_in_boxes_all'
-]
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/cpu/vision.h b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/cpu/vision.h
deleted file mode 100644
index 0a06a56233e19b6ab65f738996bf399c3023e076..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/maskrcnn_benchmark/csrc/cpu/vision.h
+++ /dev/null
@@ -1,22 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#pragma once
-#include <torch/extension.h>
-
-
-at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
-                                const at::Tensor& rois,
-                                const float spatial_scale,
-                                const int pooled_height,
-                                const int pooled_width,
-                                const int sampling_ratio);
-
-
-at::Tensor nms_cpu(const at::Tensor& dets,
-                   const at::Tensor& scores,
-                   const float threshold);
-
-
-std::pair<at::Tensor, at::Tensor> soft_nms_cpu(const at::Tensor& dets,
-                                               const at::Tensor& scores,
-                                               const float threshold,
-                                               const float sigma);
\ No newline at end of file
diff --git a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_pretrain.py b/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_pretrain.py
deleted file mode 100644
index e42ce5f998b0a51e6f731ee6b5c8bae6d02a8664..0000000000000000000000000000000000000000
--- a/spaces/Pinwheel/GLIP-BLIP-Object-Detection-VQA/models/blip_pretrain.py
+++ /dev/null
@@ -1,339 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-from models.med import BertConfig, BertModel, BertLMHeadModel
-from transformers import BertTokenizer
-import transformers
-transformers.logging.set_verbosity_error()
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from models.blip import create_vit, init_tokenizer, load_checkpoint
-
-class BLIP_Pretrain(nn.Module):
-    def __init__(self,                 
-                 med_config = 'configs/bert_config.json',  
-                 image_size = 224,
-                 vit = 'base',
-                 vit_grad_ckpt = False,
-                 vit_ckpt_layer = 0,                    
-                 embed_dim = 256,     
-                 queue_size = 57600,
-                 momentum = 0.995,
-                 ):
-        """
-        Args:
-            med_config (str): path for the mixture of encoder-decoder model's configuration file
-            image_size (int): input image size
-            vit (str): model size of vision transformer
-        """               
-        super().__init__()
-        
-        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0)
-        
-        if vit=='base':
-            checkpoint = torch.hub.load_state_dict_from_url(
-                url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
-                map_location="cpu", check_hash=True)
-            state_dict = checkpoint["model"]     
-            msg = self.visual_encoder.load_state_dict(state_dict,strict=False)
-        elif vit=='large':
-            from timm.models.helpers import load_custom_pretrained
-            from timm.models.vision_transformer import default_cfgs
-            load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k'])        
-               
-        self.tokenizer = init_tokenizer()   
-        encoder_config = BertConfig.from_json_file(med_config)
-        encoder_config.encoder_width = vision_width
-        self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False)
-        self.text_encoder.resize_token_embeddings(len(self.tokenizer)) 
-
-        text_width = self.text_encoder.config.hidden_size
-        
-        self.vision_proj = nn.Linear(vision_width, embed_dim)
-        self.text_proj = nn.Linear(text_width, embed_dim)
-
-        self.itm_head = nn.Linear(text_width, 2) 
-        
-        # create momentum encoders  
-        self.visual_encoder_m, vision_width = create_vit(vit,image_size)              
-        self.vision_proj_m = nn.Linear(vision_width, embed_dim)
-        self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False)      
-        self.text_proj_m = nn.Linear(text_width, embed_dim)
-        
-        self.model_pairs = [[self.visual_encoder,self.visual_encoder_m],
-                            [self.vision_proj,self.vision_proj_m],
-                            [self.text_encoder,self.text_encoder_m],
-                            [self.text_proj,self.text_proj_m],
-                           ]       
-        self.copy_params()
-
-        # create the queue
-        self.register_buffer("image_queue", torch.randn(embed_dim, queue_size))
-        self.register_buffer("text_queue", torch.randn(embed_dim, queue_size))
-        self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long))  
-
-        self.image_queue = nn.functional.normalize(self.image_queue, dim=0)
-        self.text_queue = nn.functional.normalize(self.text_queue, dim=0)
-        
-        self.queue_size = queue_size
-        self.momentum = momentum
-        self.temp = nn.Parameter(0.07*torch.ones([]))   
-        
-        # create the decoder
-        decoder_config = BertConfig.from_json_file(med_config)
-        decoder_config.encoder_width = vision_width        
-        self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config)    
-        self.text_decoder.resize_token_embeddings(len(self.tokenizer)) 
-        tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention')
-        
-        
-    def forward(self, image, caption, alpha):
-        with torch.no_grad():
-            self.temp.clamp_(0.001,0.5)
-        
-        image_embeds = self.visual_encoder(image) 
-        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
-        image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)          
-        
-        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, 
-                              return_tensors="pt").to(image.device)  
-        text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
-                                        return_dict = True, mode = 'text')            
-        text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)                 
-             
-        # get momentum features
-        with torch.no_grad():
-            self._momentum_update()
-            image_embeds_m = self.visual_encoder_m(image) 
-            image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1)  
-            image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1)                   
-            
-            text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask,                      
-                                                return_dict = True, mode = 'text')    
-            text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) 
-            text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1)
-
-            sim_i2t_m = image_feat_m @ text_feat_all / self.temp  
-            sim_t2i_m = text_feat_m @ image_feat_all / self.temp 
-
-            sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device)
-            sim_targets.fill_diagonal_(1)          
-
-            sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets
-            sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets        
-
-        sim_i2t = image_feat @ text_feat_all / self.temp
-        sim_t2i = text_feat @ image_feat_all / self.temp
-                             
-        loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean()
-        loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() 
-
-        loss_ita = (loss_i2t+loss_t2i)/2
-
-        self._dequeue_and_enqueue(image_feat_m, text_feat_m)        
-
-        ###============== Image-text Matching ===================###
-        encoder_input_ids = text.input_ids.clone()
-        encoder_input_ids[:,0] = self.tokenizer.enc_token_id
-        
-        # forward the positve image-text pair
-        bs = image.size(0)
-        output_pos = self.text_encoder(encoder_input_ids,
-                                       attention_mask = text.attention_mask,
-                                       encoder_hidden_states = image_embeds,
-                                       encoder_attention_mask = image_atts,      
-                                       return_dict = True,
-                                      )            
-        with torch.no_grad():       
-            weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 
-            weights_t2i.fill_diagonal_(0)            
-            weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4  
-            weights_i2t.fill_diagonal_(0)   
-            
-        # select a negative image for each text
-        image_embeds_neg = []    
-        for b in range(bs):
-            neg_idx = torch.multinomial(weights_t2i[b], 1).item()
-            image_embeds_neg.append(image_embeds[neg_idx])
-        image_embeds_neg = torch.stack(image_embeds_neg,dim=0)   
-
-        # select a negative text for each image
-        text_ids_neg = []
-        text_atts_neg = []
-        for b in range(bs):
-            neg_idx = torch.multinomial(weights_i2t[b], 1).item()
-            text_ids_neg.append(encoder_input_ids[neg_idx])
-            text_atts_neg.append(text.attention_mask[neg_idx])
-
-        text_ids_neg = torch.stack(text_ids_neg,dim=0)   
-        text_atts_neg = torch.stack(text_atts_neg,dim=0)      
-
-        text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0)     
-        text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0)     
-
-        image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0)
-        image_atts_all = torch.cat([image_atts,image_atts],dim=0)
-
-        output_neg = self.text_encoder(text_ids_all,
-                                       attention_mask = text_atts_all,
-                                       encoder_hidden_states = image_embeds_all,
-                                       encoder_attention_mask = image_atts_all,      
-                                       return_dict = True,
-                                      )                            
-
-        vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0)
-        vl_output = self.itm_head(vl_embeddings)            
-
-        itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)],
-                               dim=0).to(image.device)
-        loss_itm = F.cross_entropy(vl_output, itm_labels)  
-        
-        ##================= LM ========================##     
-        decoder_input_ids = text.input_ids.clone()      
-        decoder_input_ids[:,0] = self.tokenizer.bos_token_id
-        decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) 
-
-        decoder_output = self.text_decoder(decoder_input_ids, 
-                                           attention_mask = text.attention_mask, 
-                                           encoder_hidden_states = image_embeds,
-                                           encoder_attention_mask = image_atts,                  
-                                           labels = decoder_targets,
-                                           return_dict = True,   
-                                          )   
-          
-        loss_lm = decoder_output.loss                
-        return loss_ita, loss_itm, loss_lm
- 
-
-
-    @torch.no_grad()    
-    def copy_params(self):
-        for model_pair in self.model_pairs:           
-            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
-                param_m.data.copy_(param.data)  # initialize
-                param_m.requires_grad = False  # not update by gradient    
-
-            
-    @torch.no_grad()        
-    def _momentum_update(self):
-        for model_pair in self.model_pairs:           
-            for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()):
-                param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum)
-
-                        
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self, image_feat, text_feat):
-        # gather keys before updating queue
-        image_feats = concat_all_gather(image_feat)
-        text_feats = concat_all_gather(text_feat)
-
-        batch_size = image_feats.shape[0]
-
-        ptr = int(self.queue_ptr)
-        assert self.queue_size % batch_size == 0  # for simplicity
-
-        # replace the keys at ptr (dequeue and enqueue)
-        self.image_queue[:, ptr:ptr + batch_size] = image_feats.T
-        self.text_queue[:, ptr:ptr + batch_size] = text_feats.T
-        ptr = (ptr + batch_size) % self.queue_size  # move pointer
-
-        self.queue_ptr[0] = ptr 
-
-
-def blip_pretrain(**kwargs):
-    model = BLIP_Pretrain(**kwargs)
-    return model 
-
-
-@torch.no_grad()
-def concat_all_gather(tensor):
-    """
-    Performs all_gather operation on the provided tensors.
-    *** Warning ***: torch.distributed.all_gather has no gradient.
-    """
-    tensors_gather = [torch.ones_like(tensor)
-        for _ in range(torch.distributed.get_world_size())]
-    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
-
-    output = torch.cat(tensors_gather, dim=0)
-    return output     
-
-
-from typing import List
-def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str):
-    uninitialized_encoder_weights: List[str] = []
-    if decoder.__class__ != encoder.__class__:
-        logger.info(
-            f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized."
-        )
-
-    def tie_encoder_to_decoder_recursively(
-        decoder_pointer: nn.Module,
-        encoder_pointer: nn.Module,
-        module_name: str,
-        uninitialized_encoder_weights: List[str],
-        skip_key: str,
-        depth=0,
-    ):
-        assert isinstance(decoder_pointer, nn.Module) and isinstance(
-            encoder_pointer, nn.Module
-        ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module"
-        if hasattr(decoder_pointer, "weight") and skip_key not in module_name:
-            assert hasattr(encoder_pointer, "weight")
-            encoder_pointer.weight = decoder_pointer.weight
-            if hasattr(decoder_pointer, "bias"):
-                assert hasattr(encoder_pointer, "bias")
-                encoder_pointer.bias = decoder_pointer.bias                
-            print(module_name+' is tied')    
-            return
-
-        encoder_modules = encoder_pointer._modules
-        decoder_modules = decoder_pointer._modules
-        if len(decoder_modules) > 0:
-            assert (
-                len(encoder_modules) > 0
-            ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}"
-
-            all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()])
-            encoder_layer_pos = 0
-            for name, module in decoder_modules.items():
-                if name.isdigit():
-                    encoder_name = str(int(name) + encoder_layer_pos)
-                    decoder_name = name
-                    if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len(
-                        encoder_modules
-                    ) != len(decoder_modules):
-                        # this can happen if the name corresponds to the position in a list module list of layers
-                        # in this case the decoder has added a cross-attention that the encoder does not have
-                        # thus skip this step and subtract one layer pos from encoder
-                        encoder_layer_pos -= 1
-                        continue
-                elif name not in encoder_modules:
-                    continue
-                elif depth > 500:
-                    raise ValueError(
-                        "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model."
-                    )
-                else:
-                    decoder_name = encoder_name = name
-                tie_encoder_to_decoder_recursively(
-                    decoder_modules[decoder_name],
-                    encoder_modules[encoder_name],
-                    module_name + "/" + name,
-                    uninitialized_encoder_weights,
-                    skip_key,
-                    depth=depth + 1,
-                )
-                all_encoder_weights.remove(module_name + "/" + encoder_name)
-
-            uninitialized_encoder_weights += list(all_encoder_weights)
-
-    # tie weights recursively
-    tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key)  
diff --git a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/base.py b/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/base.py
deleted file mode 100644
index e21667df4ce4baa6bb6aad9f8679bd756e2ffdb7..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/taming/data/base.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import bisect
-import numpy as np
-import albumentations
-from PIL import Image
-from torch.utils.data import Dataset, ConcatDataset
-
-
-class ConcatDatasetWithIndex(ConcatDataset):
-    """Modified from original pytorch code to return dataset idx"""
-    def __getitem__(self, idx):
-        if idx < 0:
-            if -idx > len(self):
-                raise ValueError("absolute value of index should not exceed dataset length")
-            idx = len(self) + idx
-        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
-        if dataset_idx == 0:
-            sample_idx = idx
-        else:
-            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
-        return self.datasets[dataset_idx][sample_idx], dataset_idx
-
-
-class ImagePaths(Dataset):
-    def __init__(self, paths, size=None, random_crop=False, labels=None):
-        self.size = size
-        self.random_crop = random_crop
-
-        self.labels = dict() if labels is None else labels
-        self.labels["file_path_"] = paths
-        self._length = len(paths)
-
-        if self.size is not None and self.size > 0:
-            self.rescaler = albumentations.SmallestMaxSize(max_size = self.size)
-            if not self.random_crop:
-                self.cropper = albumentations.CenterCrop(height=self.size,width=self.size)
-            else:
-                self.cropper = albumentations.RandomCrop(height=self.size,width=self.size)
-            self.preprocessor = albumentations.Compose([self.rescaler, self.cropper])
-        else:
-            self.preprocessor = lambda **kwargs: kwargs
-
-    def __len__(self):
-        return self._length
-
-    def preprocess_image(self, image_path):
-        image = Image.open(image_path)
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        image = np.array(image).astype(np.uint8)
-        image = self.preprocessor(image=image)["image"]
-        image = (image/127.5 - 1.0).astype(np.float32)
-        return image
-
-    def __getitem__(self, i):
-        example = dict()
-        example["image"] = self.preprocess_image(self.labels["file_path_"][i])
-        for k in self.labels:
-            example[k] = self.labels[k][i]
-        return example
-
-
-class NumpyPaths(ImagePaths):
-    def preprocess_image(self, image_path):
-        image = np.load(image_path).squeeze(0)  # 3 x 1024 x 1024
-        image = np.transpose(image, (1,2,0))
-        image = Image.fromarray(image, mode="RGB")
-        image = np.array(image).astype(np.uint8)
-        image = self.preprocessor(image=image)["image"]
-        image = (image/127.5 - 1.0).astype(np.float32)
-        return image
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/poolmanager.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/poolmanager.py
deleted file mode 100644
index ca4ec341184adb3d30f3cd825b49a81b87d29b08..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/urllib3/poolmanager.py
+++ /dev/null
@@ -1,537 +0,0 @@
-from __future__ import absolute_import
-
-import collections
-import functools
-import logging
-
-from ._collections import RecentlyUsedContainer
-from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme
-from .exceptions import (
-    LocationValueError,
-    MaxRetryError,
-    ProxySchemeUnknown,
-    ProxySchemeUnsupported,
-    URLSchemeUnknown,
-)
-from .packages import six
-from .packages.six.moves.urllib.parse import urljoin
-from .request import RequestMethods
-from .util.proxy import connection_requires_http_tunnel
-from .util.retry import Retry
-from .util.url import parse_url
-
-__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
-
-
-log = logging.getLogger(__name__)
-
-SSL_KEYWORDS = (
-    "key_file",
-    "cert_file",
-    "cert_reqs",
-    "ca_certs",
-    "ssl_version",
-    "ca_cert_dir",
-    "ssl_context",
-    "key_password",
-    "server_hostname",
-)
-
-# All known keyword arguments that could be provided to the pool manager, its
-# pools, or the underlying connections. This is used to construct a pool key.
-_key_fields = (
-    "key_scheme",  # str
-    "key_host",  # str
-    "key_port",  # int
-    "key_timeout",  # int or float or Timeout
-    "key_retries",  # int or Retry
-    "key_strict",  # bool
-    "key_block",  # bool
-    "key_source_address",  # str
-    "key_key_file",  # str
-    "key_key_password",  # str
-    "key_cert_file",  # str
-    "key_cert_reqs",  # str
-    "key_ca_certs",  # str
-    "key_ssl_version",  # str
-    "key_ca_cert_dir",  # str
-    "key_ssl_context",  # instance of ssl.SSLContext or urllib3.util.ssl_.SSLContext
-    "key_maxsize",  # int
-    "key_headers",  # dict
-    "key__proxy",  # parsed proxy url
-    "key__proxy_headers",  # dict
-    "key__proxy_config",  # class
-    "key_socket_options",  # list of (level (int), optname (int), value (int or str)) tuples
-    "key__socks_options",  # dict
-    "key_assert_hostname",  # bool or string
-    "key_assert_fingerprint",  # str
-    "key_server_hostname",  # str
-)
-
-#: The namedtuple class used to construct keys for the connection pool.
-#: All custom key schemes should include the fields in this key at a minimum.
-PoolKey = collections.namedtuple("PoolKey", _key_fields)
-
-_proxy_config_fields = ("ssl_context", "use_forwarding_for_https")
-ProxyConfig = collections.namedtuple("ProxyConfig", _proxy_config_fields)
-
-
-def _default_key_normalizer(key_class, request_context):
-    """
-    Create a pool key out of a request context dictionary.
-
-    According to RFC 3986, both the scheme and host are case-insensitive.
-    Therefore, this function normalizes both before constructing the pool
-    key for an HTTPS request. If you wish to change this behaviour, provide
-    alternate callables to ``key_fn_by_scheme``.
-
-    :param key_class:
-        The class to use when constructing the key. This should be a namedtuple
-        with the ``scheme`` and ``host`` keys at a minimum.
-    :type  key_class: namedtuple
-    :param request_context:
-        A dictionary-like object that contain the context for a request.
-    :type  request_context: dict
-
-    :return: A namedtuple that can be used as a connection pool key.
-    :rtype:  PoolKey
-    """
-    # Since we mutate the dictionary, make a copy first
-    context = request_context.copy()
-    context["scheme"] = context["scheme"].lower()
-    context["host"] = context["host"].lower()
-
-    # These are both dictionaries and need to be transformed into frozensets
-    for key in ("headers", "_proxy_headers", "_socks_options"):
-        if key in context and context[key] is not None:
-            context[key] = frozenset(context[key].items())
-
-    # The socket_options key may be a list and needs to be transformed into a
-    # tuple.
-    socket_opts = context.get("socket_options")
-    if socket_opts is not None:
-        context["socket_options"] = tuple(socket_opts)
-
-    # Map the kwargs to the names in the namedtuple - this is necessary since
-    # namedtuples can't have fields starting with '_'.
-    for key in list(context.keys()):
-        context["key_" + key] = context.pop(key)
-
-    # Default to ``None`` for keys missing from the context
-    for field in key_class._fields:
-        if field not in context:
-            context[field] = None
-
-    return key_class(**context)
-
-
-#: A dictionary that maps a scheme to a callable that creates a pool key.
-#: This can be used to alter the way pool keys are constructed, if desired.
-#: Each PoolManager makes a copy of this dictionary so they can be configured
-#: globally here, or individually on the instance.
-key_fn_by_scheme = {
-    "http": functools.partial(_default_key_normalizer, PoolKey),
-    "https": functools.partial(_default_key_normalizer, PoolKey),
-}
-
-pool_classes_by_scheme = {"http": HTTPConnectionPool, "https": HTTPSConnectionPool}
-
-
-class PoolManager(RequestMethods):
-    """
-    Allows for arbitrary requests while transparently keeping track of
-    necessary connection pools for you.
-
-    :param num_pools:
-        Number of connection pools to cache before discarding the least
-        recently used pool.
-
-    :param headers:
-        Headers to include with all requests, unless other headers are given
-        explicitly.
-
-    :param \\**connection_pool_kw:
-        Additional parameters are used to create fresh
-        :class:`urllib3.connectionpool.ConnectionPool` instances.
-
-    Example::
-
-        >>> manager = PoolManager(num_pools=2)
-        >>> r = manager.request('GET', 'http://google.com/')
-        >>> r = manager.request('GET', 'http://google.com/mail')
-        >>> r = manager.request('GET', 'http://yahoo.com/')
-        >>> len(manager.pools)
-        2
-
-    """
-
-    proxy = None
-    proxy_config = None
-
-    def __init__(self, num_pools=10, headers=None, **connection_pool_kw):
-        RequestMethods.__init__(self, headers)
-        self.connection_pool_kw = connection_pool_kw
-        self.pools = RecentlyUsedContainer(num_pools, dispose_func=lambda p: p.close())
-
-        # Locally set the pool classes and keys so other PoolManagers can
-        # override them.
-        self.pool_classes_by_scheme = pool_classes_by_scheme
-        self.key_fn_by_scheme = key_fn_by_scheme.copy()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.clear()
-        # Return False to re-raise any potential exceptions
-        return False
-
-    def _new_pool(self, scheme, host, port, request_context=None):
-        """
-        Create a new :class:`urllib3.connectionpool.ConnectionPool` based on host, port, scheme, and
-        any additional pool keyword arguments.
-
-        If ``request_context`` is provided, it is provided as keyword arguments
-        to the pool class used. This method is used to actually create the
-        connection pools handed out by :meth:`connection_from_url` and
-        companion methods. It is intended to be overridden for customization.
-        """
-        pool_cls = self.pool_classes_by_scheme[scheme]
-        if request_context is None:
-            request_context = self.connection_pool_kw.copy()
-
-        # Although the context has everything necessary to create the pool,
-        # this function has historically only used the scheme, host, and port
-        # in the positional args. When an API change is acceptable these can
-        # be removed.
-        for key in ("scheme", "host", "port"):
-            request_context.pop(key, None)
-
-        if scheme == "http":
-            for kw in SSL_KEYWORDS:
-                request_context.pop(kw, None)
-
-        return pool_cls(host, port, **request_context)
-
-    def clear(self):
-        """
-        Empty our store of pools and direct them all to close.
-
-        This will not affect in-flight connections, but they will not be
-        re-used after completion.
-        """
-        self.pools.clear()
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the host, port, and scheme.
-
-        If ``port`` isn't given, it will be derived from the ``scheme`` using
-        ``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is
-        provided, it is merged with the instance's ``connection_pool_kw``
-        variable and used to create the new connection pool, if one is
-        needed.
-        """
-
-        if not host:
-            raise LocationValueError("No host specified.")
-
-        request_context = self._merge_pool_kwargs(pool_kwargs)
-        request_context["scheme"] = scheme or "http"
-        if not port:
-            port = port_by_scheme.get(request_context["scheme"].lower(), 80)
-        request_context["port"] = port
-        request_context["host"] = host
-
-        return self.connection_from_context(request_context)
-
-    def connection_from_context(self, request_context):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.
-
-        ``request_context`` must at least contain the ``scheme`` key and its
-        value must be a key in ``key_fn_by_scheme`` instance variable.
-        """
-        scheme = request_context["scheme"].lower()
-        pool_key_constructor = self.key_fn_by_scheme.get(scheme)
-        if not pool_key_constructor:
-            raise URLSchemeUnknown(scheme)
-        pool_key = pool_key_constructor(request_context)
-
-        return self.connection_from_pool_key(pool_key, request_context=request_context)
-
-    def connection_from_pool_key(self, pool_key, request_context=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.
-
-        ``pool_key`` should be a namedtuple that only contains immutable
-        objects. At a minimum it must have the ``scheme``, ``host``, and
-        ``port`` fields.
-        """
-        with self.pools.lock:
-            # If the scheme, host, or port doesn't match existing open
-            # connections, open a new ConnectionPool.
-            pool = self.pools.get(pool_key)
-            if pool:
-                return pool
-
-            # Make a fresh ConnectionPool of the desired type
-            scheme = request_context["scheme"]
-            host = request_context["host"]
-            port = request_context["port"]
-            pool = self._new_pool(scheme, host, port, request_context=request_context)
-            self.pools[pool_key] = pool
-
-        return pool
-
-    def connection_from_url(self, url, pool_kwargs=None):
-        """
-        Similar to :func:`urllib3.connectionpool.connection_from_url`.
-
-        If ``pool_kwargs`` is not provided and a new pool needs to be
-        constructed, ``self.connection_pool_kw`` is used to initialize
-        the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``
-        is provided, it is used instead. Note that if a new pool does not
-        need to be created for the request, the provided ``pool_kwargs`` are
-        not used.
-        """
-        u = parse_url(url)
-        return self.connection_from_host(
-            u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _merge_pool_kwargs(self, override):
-        """
-        Merge a dictionary of override values for self.connection_pool_kw.
-
-        This does not modify self.connection_pool_kw and returns a new dict.
-        Any keys in the override dictionary with a value of ``None`` are
-        removed from the merged dictionary.
-        """
-        base_pool_kwargs = self.connection_pool_kw.copy()
-        if override:
-            for key, value in override.items():
-                if value is None:
-                    try:
-                        del base_pool_kwargs[key]
-                    except KeyError:
-                        pass
-                else:
-                    base_pool_kwargs[key] = value
-        return base_pool_kwargs
-
-    def _proxy_requires_url_absolute_form(self, parsed_url):
-        """
-        Indicates if the proxy requires the complete destination URL in the
-        request.  Normally this is only needed when not using an HTTP CONNECT
-        tunnel.
-        """
-        if self.proxy is None:
-            return False
-
-        return not connection_requires_http_tunnel(
-            self.proxy, self.proxy_config, parsed_url.scheme
-        )
-
-    def _validate_proxy_scheme_url_selection(self, url_scheme):
-        """
-        Validates that were not attempting to do TLS in TLS connections on
-        Python2 or with unsupported SSL implementations.
-        """
-        if self.proxy is None or url_scheme != "https":
-            return
-
-        if self.proxy.scheme != "https":
-            return
-
-        if six.PY2 and not self.proxy_config.use_forwarding_for_https:
-            raise ProxySchemeUnsupported(
-                "Contacting HTTPS destinations through HTTPS proxies "
-                "'via CONNECT tunnels' is not supported in Python 2"
-            )
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        """
-        Same as :meth:`urllib3.HTTPConnectionPool.urlopen`
-        with custom cross-host redirect logic and only sends the request-uri
-        portion of the ``url``.
-
-        The given ``url`` parameter must be absolute, such that an appropriate
-        :class:`urllib3.connectionpool.ConnectionPool` can be chosen for it.
-        """
-        u = parse_url(url)
-        self._validate_proxy_scheme_url_selection(u.scheme)
-
-        conn = self.connection_from_host(u.host, port=u.port, scheme=u.scheme)
-
-        kw["assert_same_host"] = False
-        kw["redirect"] = False
-
-        if "headers" not in kw:
-            kw["headers"] = self.headers.copy()
-
-        if self._proxy_requires_url_absolute_form(u):
-            response = conn.urlopen(method, url, **kw)
-        else:
-            response = conn.urlopen(method, u.request_uri, **kw)
-
-        redirect_location = redirect and response.get_redirect_location()
-        if not redirect_location:
-            return response
-
-        # Support relative URLs for redirecting.
-        redirect_location = urljoin(url, redirect_location)
-
-        # RFC 7231, Section 6.4.4
-        if response.status == 303:
-            method = "GET"
-
-        retries = kw.get("retries")
-        if not isinstance(retries, Retry):
-            retries = Retry.from_int(retries, redirect=redirect)
-
-        # Strip headers marked as unsafe to forward to the redirected location.
-        # Check remove_headers_on_redirect to avoid a potential network call within
-        # conn.is_same_host() which may use socket.gethostbyname() in the future.
-        if retries.remove_headers_on_redirect and not conn.is_same_host(
-            redirect_location
-        ):
-            headers = list(six.iterkeys(kw["headers"]))
-            for header in headers:
-                if header.lower() in retries.remove_headers_on_redirect:
-                    kw["headers"].pop(header, None)
-
-        try:
-            retries = retries.increment(method, url, response=response, _pool=conn)
-        except MaxRetryError:
-            if retries.raise_on_redirect:
-                response.drain_conn()
-                raise
-            return response
-
-        kw["retries"] = retries
-        kw["redirect"] = redirect
-
-        log.info("Redirecting %s -> %s", url, redirect_location)
-
-        response.drain_conn()
-        return self.urlopen(method, redirect_location, **kw)
-
-
-class ProxyManager(PoolManager):
-    """
-    Behaves just like :class:`PoolManager`, but sends all requests through
-    the defined proxy, using the CONNECT method for HTTPS URLs.
-
-    :param proxy_url:
-        The URL of the proxy to be used.
-
-    :param proxy_headers:
-        A dictionary containing headers that will be sent to the proxy. In case
-        of HTTP they are being sent with each request, while in the
-        HTTPS/CONNECT case they are sent only once. Could be used for proxy
-        authentication.
-
-    :param proxy_ssl_context:
-        The proxy SSL context is used to establish the TLS connection to the
-        proxy when using HTTPS proxies.
-
-    :param use_forwarding_for_https:
-        (Defaults to False) If set to True will forward requests to the HTTPS
-        proxy to be made on behalf of the client instead of creating a TLS
-        tunnel via the CONNECT method. **Enabling this flag means that request
-        and response headers and content will be visible from the HTTPS proxy**
-        whereas tunneling keeps request and response headers and content
-        private.  IP address, target hostname, SNI, and port are always visible
-        to an HTTPS proxy even when this flag is disabled.
-
-    Example:
-        >>> proxy = urllib3.ProxyManager('http://localhost:3128/')
-        >>> r1 = proxy.request('GET', 'http://google.com/')
-        >>> r2 = proxy.request('GET', 'http://httpbin.org/')
-        >>> len(proxy.pools)
-        1
-        >>> r3 = proxy.request('GET', 'https://httpbin.org/')
-        >>> r4 = proxy.request('GET', 'https://twitter.com/')
-        >>> len(proxy.pools)
-        3
-
-    """
-
-    def __init__(
-        self,
-        proxy_url,
-        num_pools=10,
-        headers=None,
-        proxy_headers=None,
-        proxy_ssl_context=None,
-        use_forwarding_for_https=False,
-        **connection_pool_kw
-    ):
-
-        if isinstance(proxy_url, HTTPConnectionPool):
-            proxy_url = "%s://%s:%i" % (
-                proxy_url.scheme,
-                proxy_url.host,
-                proxy_url.port,
-            )
-        proxy = parse_url(proxy_url)
-
-        if proxy.scheme not in ("http", "https"):
-            raise ProxySchemeUnknown(proxy.scheme)
-
-        if not proxy.port:
-            port = port_by_scheme.get(proxy.scheme, 80)
-            proxy = proxy._replace(port=port)
-
-        self.proxy = proxy
-        self.proxy_headers = proxy_headers or {}
-        self.proxy_ssl_context = proxy_ssl_context
-        self.proxy_config = ProxyConfig(proxy_ssl_context, use_forwarding_for_https)
-
-        connection_pool_kw["_proxy"] = self.proxy
-        connection_pool_kw["_proxy_headers"] = self.proxy_headers
-        connection_pool_kw["_proxy_config"] = self.proxy_config
-
-        super(ProxyManager, self).__init__(num_pools, headers, **connection_pool_kw)
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        if scheme == "https":
-            return super(ProxyManager, self).connection_from_host(
-                host, port, scheme, pool_kwargs=pool_kwargs
-            )
-
-        return super(ProxyManager, self).connection_from_host(
-            self.proxy.host, self.proxy.port, self.proxy.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _set_proxy_headers(self, url, headers=None):
-        """
-        Sets headers needed by proxies: specifically, the Accept and Host
-        headers. Only sets headers not provided by the user.
-        """
-        headers_ = {"Accept": "*/*"}
-
-        netloc = parse_url(url).netloc
-        if netloc:
-            headers_["Host"] = netloc
-
-        if headers:
-            headers_.update(headers)
-        return headers_
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        "Same as HTTP(S)ConnectionPool.urlopen, ``url`` must be absolute."
-        u = parse_url(url)
-        if not connection_requires_http_tunnel(self.proxy, self.proxy_config, u.scheme):
-            # For connections using HTTP CONNECT, httplib sets the necessary
-            # headers on the CONNECT to the proxy. If we're not using CONNECT,
-            # we'll definitely need to set 'Host' at the very least.
-            headers = kw.get("headers", self.headers)
-            kw["headers"] = self._set_proxy_headers(url, headers)
-
-        return super(ProxyManager, self).urlopen(method, url, redirect=redirect, **kw)
-
-
-def proxy_from_url(url, **kw):
-    return ProxyManager(proxy_url=url, **kw)
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/alias.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/alias.py
deleted file mode 100644
index 452a9244ea6766d8cf94425fb583583ef740baee..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/setuptools/command/alias.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from distutils.errors import DistutilsOptionError
-
-from setuptools.command.setopt import edit_config, option_base, config_file
-
-
-def shquote(arg):
-    """Quote an argument for later parsing by shlex.split()"""
-    for c in '"', "'", "\\", "#":
-        if c in arg:
-            return repr(arg)
-    if arg.split() != [arg]:
-        return repr(arg)
-    return arg
-
-
-class alias(option_base):
-    """Define a shortcut that invokes one or more commands"""
-
-    description = "define a shortcut to invoke one or more commands"
-    command_consumes_arguments = True
-
-    user_options = [
-        ('remove', 'r', 'remove (unset) the alias'),
-    ] + option_base.user_options
-
-    boolean_options = option_base.boolean_options + ['remove']
-
-    def initialize_options(self):
-        option_base.initialize_options(self)
-        self.args = None
-        self.remove = None
-
-    def finalize_options(self):
-        option_base.finalize_options(self)
-        if self.remove and len(self.args) != 1:
-            raise DistutilsOptionError(
-                "Must specify exactly one argument (the alias name) when "
-                "using --remove"
-            )
-
-    def run(self):
-        aliases = self.distribution.get_option_dict('aliases')
-
-        if not self.args:
-            print("Command Aliases")
-            print("---------------")
-            for alias in aliases:
-                print("setup.py alias", format_alias(alias, aliases))
-            return
-
-        elif len(self.args) == 1:
-            alias, = self.args
-            if self.remove:
-                command = None
-            elif alias in aliases:
-                print("setup.py alias", format_alias(alias, aliases))
-                return
-            else:
-                print("No alias definition found for %r" % alias)
-                return
-        else:
-            alias = self.args[0]
-            command = ' '.join(map(shquote, self.args[1:]))
-
-        edit_config(self.filename, {'aliases': {alias: command}}, self.dry_run)
-
-
-def format_alias(name, aliases):
-    source, command = aliases[name]
-    if source == config_file('global'):
-        source = '--global-config '
-    elif source == config_file('user'):
-        source = '--user-config '
-    elif source == config_file('local'):
-        source = ''
-    else:
-        source = '--filename=%r' % source
-    return source + name + ' ' + command
diff --git a/spaces/Rbrq/DeticChatGPT/tools/get_coco_zeroshot_oriorder.py b/spaces/Rbrq/DeticChatGPT/tools/get_coco_zeroshot_oriorder.py
deleted file mode 100644
index ed6748be1f2ed92741ea78f5a187f9838185a80e..0000000000000000000000000000000000000000
--- a/spaces/Rbrq/DeticChatGPT/tools/get_coco_zeroshot_oriorder.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data_path', default='datasets/coco/annotations/instances_val2017_unseen_2.json')
-    parser.add_argument('--cat_path', default='datasets/coco/annotations/instances_val2017.json')
-    args = parser.parse_args()
-    print('Loading', args.cat_path)
-    cat = json.load(open(args.cat_path, 'r'))['categories']
-
-    print('Loading', args.data_path)
-    data = json.load(open(args.data_path, 'r'))
-    data['categories'] = cat
-    out_path = args.data_path[:-5] + '_oriorder.json'
-    print('Saving to', out_path)
-    json.dump(data, open(out_path, 'w'))
diff --git a/spaces/Realcat/image-matching-webui/hloc/pipelines/7Scenes/__init__.py b/spaces/Realcat/image-matching-webui/hloc/pipelines/7Scenes/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Redgon/bingo/src/lib/utils.ts b/spaces/Redgon/bingo/src/lib/utils.ts
deleted file mode 100644
index 0a09ddc4aa5518f681a00a64ad48566516f35417..0000000000000000000000000000000000000000
--- a/spaces/Redgon/bingo/src/lib/utils.ts
+++ /dev/null
@@ -1,158 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-
-export function cn(...inputs: ClassValue[]) {
-  return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
-  '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
-  7
-) // 7-character random string
-
-export function createChunkDecoder() {
-  const decoder = new TextDecoder()
-  return function (chunk: Uint8Array | undefined): string {
-    if (!chunk) return ''
-    return decoder.decode(chunk, { stream: true })
-  }
-}
-
-export function random (start: number, end: number) {
-  return start + Math.ceil(Math.random() * (end - start))
-}
-
-export function randomIP() {
-  return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}`
-}
-
-export const defaultUID = Math.random().toString(36).slice(2)
-
-export function parseHeadersFromCurl(content: string) {
-  const re = /-H '([^:]+):\s*([^']+)/mg
-  const headers: HeadersInit = {}
-  content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
-  content.replace(re, (_: string, key: string, value: string) => {
-    headers[key] = value
-    return ''
-  })
-
-  return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
-  const base64Content = btoa(content)
-  const contentChunks = base64Content.match(/.{1,4000}/g) || []
-  return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  let base64Content = ''
-  ChunkKeys.forEach((key) => {
-    base64Content += (cookies[key] || '')
-  })
-  try {
-    return atob(base64Content)
-  } catch(e) {
-    return ''
-  }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
-  return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
-  const date = new Date(input)
-  return date.toLocaleDateString('en-US', {
-    month: 'long',
-    day: 'numeric',
-    year: 'numeric'
-  })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
-  const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
-  return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function setCookie(key: string, value: string) {
-  const maxAge = 86400 * 30
-  document.cookie = `${key}=${value || ''}; Path=/; Max-Age=${maxAge}; SameSite=None; Secure`
-}
-
-export function getCookie(cookieName: string) {
-  const re = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`)
-  return re.test(document.cookie) ? RegExp.$1 : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
-  const cookies: { [key: string]: string } = {}
-  cookieNames.forEach(cookieName => {
-    cookies[cookieName] = parseCookie(cookie, cookieName)
-  })
-  return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-export const DEFAULT_IP = process.env.BING_IP || randomIP()
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
-  return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>, type?: string) {
-  let {
-    BING_COOKIE = process.env.BING_COOKIE,
-    BING_UA = process.env.BING_UA,
-    BING_IP = process.env.BING_IP,
-    BING_HEADER = process.env.BING_HEADER,
-    IMAGE_ONLY = process.env.IMAGE_ONLY ?? '1',
-  } = cookies
-
-  if (BING_HEADER) {
-    const headers = extraHeadersFromCookie({
-      BING_HEADER,
-      ...cookies,
-    }) || {}
-    if (/^(1|true|yes)$/.test(String(IMAGE_ONLY)) && type !== 'image') {
-      // 仅画图时设置 cookie
-      headers.cookie = `_U=${defaultUID}`
-    }
-    if (headers['user-agent']) {
-      return headers
-    }
-  }
-
-  const ua = parseUA(BING_UA)
-
-  if (!BING_COOKIE) {
-    BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || defaultUID // hf 暂时不用 Cookie 也可以正常使用
-  }
-
-  const parsedCookie = parseCookie(BING_COOKIE, '_U')
-  if (!parsedCookie) {
-    throw new Error('Invalid Cookie')
-  }
-  return {
-    'x-forwarded-for': BING_IP || DEFAULT_IP,
-    'Accept-Encoding': 'gzip, deflate, br',
-    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
-    'User-Agent': ua!,
-    'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
-    cookie: `_U=${parsedCookie}` || '',
-  }
-}
-
-export class WatchDog {
-  private tid = 0
-  watch(fn: Function, timeout = 2000) {
-    clearTimeout(this.tid)
-    this.tid = setTimeout(fn, timeout + Math.random() * 1000)
-  }
-  reset() {
-    clearTimeout(this.tid)
-  }
-}
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/fcos.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/fcos.py
deleted file mode 100644
index 58485c1864a11a66168b7597f345ea759ce20551..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/fcos.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class FCOS(SingleStageDetector):
-    """Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_"""
-
-    def __init__(self,
-                 backbone,
-                 neck,
-                 bbox_head,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None):
-        super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg,
-                                   test_cfg, pretrained)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/ssd_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/ssd_head.py
deleted file mode 100644
index 145622b64e3f0b3f7f518fc61a2a01348ebfa4f3..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/ssd_head.py
+++ /dev/null
@@ -1,265 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import xavier_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (build_anchor_generator, build_assigner,
-                        build_bbox_coder, build_sampler, multi_apply)
-from ..builder import HEADS
-from ..losses import smooth_l1_loss
-from .anchor_head import AnchorHead
-
-
-# TODO: add loss evaluator for SSD
-@HEADS.register_module()
-class SSDHead(AnchorHead):
-    """SSD head used in https://arxiv.org/abs/1512.02325.
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        anchor_generator (dict): Config dict for anchor generator
-        bbox_coder (dict): Config of bounding box coder.
-        reg_decoded_bbox (bool): If true, the regression loss would be
-            applied directly on decoded bounding boxes, converting both
-            the predicted boxes and regression targets to absolute
-            coordinates format. Default False. It should be `True` when
-            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
-        train_cfg (dict): Training config of anchor head.
-        test_cfg (dict): Testing config of anchor head.
-    """  # noqa: W605
-
-    def __init__(self,
-                 num_classes=80,
-                 in_channels=(512, 1024, 512, 256, 256, 256),
-                 anchor_generator=dict(
-                     type='SSDAnchorGenerator',
-                     scale_major=False,
-                     input_size=300,
-                     strides=[8, 16, 32, 64, 100, 300],
-                     ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
-                     basesize_ratio_range=(0.1, 0.9)),
-                 bbox_coder=dict(
-                     type='DeltaXYWHBBoxCoder',
-                     clip_border=True,
-                     target_means=[.0, .0, .0, .0],
-                     target_stds=[1.0, 1.0, 1.0, 1.0],
-                 ),
-                 reg_decoded_bbox=False,
-                 train_cfg=None,
-                 test_cfg=None):
-        super(AnchorHead, self).__init__()
-        self.num_classes = num_classes
-        self.in_channels = in_channels
-        self.cls_out_channels = num_classes + 1  # add background class
-        self.anchor_generator = build_anchor_generator(anchor_generator)
-        num_anchors = self.anchor_generator.num_base_anchors
-
-        reg_convs = []
-        cls_convs = []
-        for i in range(len(in_channels)):
-            reg_convs.append(
-                nn.Conv2d(
-                    in_channels[i],
-                    num_anchors[i] * 4,
-                    kernel_size=3,
-                    padding=1))
-            cls_convs.append(
-                nn.Conv2d(
-                    in_channels[i],
-                    num_anchors[i] * (num_classes + 1),
-                    kernel_size=3,
-                    padding=1))
-        self.reg_convs = nn.ModuleList(reg_convs)
-        self.cls_convs = nn.ModuleList(cls_convs)
-
-        self.bbox_coder = build_bbox_coder(bbox_coder)
-        self.reg_decoded_bbox = reg_decoded_bbox
-        self.use_sigmoid_cls = False
-        self.cls_focal_loss = False
-        self.train_cfg = train_cfg
-        self.test_cfg = test_cfg
-        # set sampling=False for archor_target
-        self.sampling = False
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # SSD sampling=False so use PseudoSampler
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-        self.fp16_enabled = False
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform', bias=0)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple:
-                cls_scores (list[Tensor]): Classification scores for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * num_classes.
-                bbox_preds (list[Tensor]): Box energies / deltas for all scale
-                    levels, each is a 4D-tensor, the channels number is
-                    num_anchors * 4.
-        """
-        cls_scores = []
-        bbox_preds = []
-        for feat, reg_conv, cls_conv in zip(feats, self.reg_convs,
-                                            self.cls_convs):
-            cls_scores.append(cls_conv(feat))
-            bbox_preds.append(reg_conv(feat))
-        return cls_scores, bbox_preds
-
-    def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights,
-                    bbox_targets, bbox_weights, num_total_samples):
-        """Compute loss of a single image.
-
-        Args:
-            cls_score (Tensor): Box scores for eachimage
-                Has shape (num_total_anchors, num_classes).
-            bbox_pred (Tensor): Box energies / deltas for each image
-                level with shape (num_total_anchors, 4).
-            anchors (Tensor): Box reference for each scale level with shape
-                (num_total_anchors, 4).
-            labels (Tensor): Labels of each anchors with shape
-                (num_total_anchors,).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (num_total_anchors,)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (num_total_anchors, 4).
-            bbox_weights (Tensor): BBox regression loss weights of each anchor
-                with shape (num_total_anchors, 4).
-            num_total_samples (int): If sampling, num total samples equal to
-                the number of total anchors; Otherwise, it is the number of
-                positive anchors.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        loss_cls_all = F.cross_entropy(
-            cls_score, labels, reduction='none') * label_weights
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        pos_inds = ((labels >= 0) &
-                    (labels < self.num_classes)).nonzero().reshape(-1)
-        neg_inds = (labels == self.num_classes).nonzero().view(-1)
-
-        num_pos_samples = pos_inds.size(0)
-        num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
-        if num_neg_samples > neg_inds.size(0):
-            num_neg_samples = neg_inds.size(0)
-        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
-        loss_cls_pos = loss_cls_all[pos_inds].sum()
-        loss_cls_neg = topk_loss_cls_neg.sum()
-        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
-
-        if self.reg_decoded_bbox:
-            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
-            # is applied directly on the decoded bounding boxes, it
-            # decodes the already encoded coordinates to absolute format.
-            bbox_pred = self.bbox_coder.decode(anchor, bbox_pred)
-
-        loss_bbox = smooth_l1_loss(
-            bbox_pred,
-            bbox_targets,
-            bbox_weights,
-            beta=self.train_cfg.smoothl1_beta,
-            avg_factor=num_total_samples)
-        return loss_cls[None], loss_bbox
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for each scale level
-                Has shape (N, num_anchors * num_classes, H, W)
-            bbox_preds (list[Tensor]): Box energies / deltas for each scale
-                level with shape (N, num_anchors * 4, H, W)
-            gt_bboxes (list[Tensor]): each item are the truth boxes for each
-                image in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=1,
-            unmap_outputs=False)
-        if cls_reg_targets is None:
-            return None
-        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
-         num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_images = len(img_metas)
-        all_cls_scores = torch.cat([
-            s.permute(0, 2, 3, 1).reshape(
-                num_images, -1, self.cls_out_channels) for s in cls_scores
-        ], 1)
-        all_labels = torch.cat(labels_list, -1).view(num_images, -1)
-        all_label_weights = torch.cat(label_weights_list,
-                                      -1).view(num_images, -1)
-        all_bbox_preds = torch.cat([
-            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
-            for b in bbox_preds
-        ], -2)
-        all_bbox_targets = torch.cat(bbox_targets_list,
-                                     -2).view(num_images, -1, 4)
-        all_bbox_weights = torch.cat(bbox_weights_list,
-                                     -2).view(num_images, -1, 4)
-
-        # concat all level anchors to a single tensor
-        all_anchors = []
-        for i in range(num_images):
-            all_anchors.append(torch.cat(anchor_list[i]))
-
-        # check NaN and Inf
-        assert torch.isfinite(all_cls_scores).all().item(), \
-            'classification scores become infinite or NaN!'
-        assert torch.isfinite(all_bbox_preds).all().item(), \
-            'bbox predications become infinite or NaN!'
-
-        losses_cls, losses_bbox = multi_apply(
-            self.loss_single,
-            all_cls_scores,
-            all_bbox_preds,
-            all_anchors,
-            all_labels,
-            all_label_weights,
-            all_bbox_targets,
-            all_bbox_weights,
-            num_total_samples=num_total_pos)
-        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
diff --git a/spaces/Rojastopher/Image-to-3D/README.md b/spaces/Rojastopher/Image-to-3D/README.md
deleted file mode 100644
index 243f6cf265f7fba001aa2f2065af966fbc9aca20..0000000000000000000000000000000000000000
--- a/spaces/Rojastopher/Image-to-3D/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Point-e Demo
-emoji: 🐢
-colorFrom: yellow
-colorTo: blue
-sdk: gradio
-sdk_version: 3.14.0
-app_file: app.py
-pinned: false
-duplicated_from: AP123/text-to-3D
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/SERER/VITS-Umamusume-voice-synthesizer/text/thai.py b/spaces/SERER/VITS-Umamusume-voice-synthesizer/text/thai.py
deleted file mode 100644
index 998207c01a85c710a46db1ec8b62c39c2d94bc84..0000000000000000000000000000000000000000
--- a/spaces/SERER/VITS-Umamusume-voice-synthesizer/text/thai.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import re
-from num_thai.thainumbers import NumThai
-
-
-num = NumThai()
-
-# List of (Latin alphabet, Thai) pairs:
-_latin_to_thai = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('a', 'เอ'),
-    ('b','บี'),
-    ('c','ซี'),
-    ('d','ดี'),
-    ('e','อี'),
-    ('f','เอฟ'),
-    ('g','จี'),
-    ('h','เอช'),
-    ('i','ไอ'),
-    ('j','เจ'),
-    ('k','เค'),
-    ('l','แอล'),
-    ('m','เอ็ม'),
-    ('n','เอ็น'),
-    ('o','โอ'),
-    ('p','พี'),
-    ('q','คิว'),
-    ('r','แอร์'),
-    ('s','เอส'),
-    ('t','ที'),
-    ('u','ยู'),
-    ('v','วี'),
-    ('w','ดับเบิลยู'),
-    ('x','เอ็กซ์'),
-    ('y','วาย'),
-    ('z','ซี')
-]]
-
-
-def num_to_thai(text):
-    return re.sub(r'(?:\d+(?:,?\d+)?)+(?:\.\d+(?:,?\d+)?)?', lambda x: ''.join(num.NumberToTextThai(float(x.group(0).replace(',', '')))), text)
-
-def latin_to_thai(text):
-    for regex, replacement in _latin_to_thai:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/SIGGRAPH2022/Text2Human/Text2Human/utils/logger.py b/spaces/SIGGRAPH2022/Text2Human/Text2Human/utils/logger.py
deleted file mode 100644
index 4fee1a2b221c4d219206fd8f3201db3b52566adb..0000000000000000000000000000000000000000
--- a/spaces/SIGGRAPH2022/Text2Human/Text2Human/utils/logger.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import datetime
-import logging
-import time
-
-
-class MessageLogger():
-    """Message logger for printing.
-
-    Args:
-        opt (dict): Config. It contains the following keys:
-            name (str): Exp name.
-            logger (dict): Contains 'print_freq' (str) for logger interval.
-            train (dict): Contains 'niter' (int) for total iters.
-            use_tb_logger (bool): Use tensorboard logger.
-        start_iter (int): Start iter. Default: 1.
-        tb_logger (obj:`tb_logger`): Tensorboard logger. Default： None.
-    """
-
-    def __init__(self, opt, start_iter=1, tb_logger=None):
-        self.exp_name = opt['name']
-        self.interval = opt['print_freq']
-        self.start_iter = start_iter
-        self.max_iters = opt['max_iters']
-        self.use_tb_logger = opt['use_tb_logger']
-        self.tb_logger = tb_logger
-        self.start_time = time.time()
-        self.logger = get_root_logger()
-
-    def __call__(self, log_vars):
-        """Format logging message.
-
-        Args:
-            log_vars (dict): It contains the following keys:
-                epoch (int): Epoch number.
-                iter (int): Current iter.
-                lrs (list): List for learning rates.
-
-                time (float): Iter time.
-                data_time (float): Data time for each iter.
-        """
-        # epoch, iter, learning rates
-        epoch = log_vars.pop('epoch')
-        current_iter = log_vars.pop('iter')
-        lrs = log_vars.pop('lrs')
-
-        message = (f'[{self.exp_name[:5]}..][epoch:{epoch:3d}, '
-                   f'iter:{current_iter:8,d}, lr:(')
-        for v in lrs:
-            message += f'{v:.3e},'
-        message += ')] '
-
-        # time and estimated time
-        if 'time' in log_vars.keys():
-            iter_time = log_vars.pop('time')
-            data_time = log_vars.pop('data_time')
-
-            total_time = time.time() - self.start_time
-            time_sec_avg = total_time / (current_iter - self.start_iter + 1)
-            eta_sec = time_sec_avg * (self.max_iters - current_iter - 1)
-            eta_str = str(datetime.timedelta(seconds=int(eta_sec)))
-            message += f'[eta: {eta_str}, '
-            message += f'time: {iter_time:.3f}, data_time: {data_time:.3f}] '
-
-        # other items, especially losses
-        for k, v in log_vars.items():
-            message += f'{k}: {v:.4e} '
-            # tensorboard logger
-            if self.use_tb_logger and 'debug' not in self.exp_name:
-                self.tb_logger.add_scalar(k, v, current_iter)
-
-        self.logger.info(message)
-
-
-def init_tb_logger(log_dir):
-    from torch.utils.tensorboard import SummaryWriter
-    tb_logger = SummaryWriter(log_dir=log_dir)
-    return tb_logger
-
-
-def get_root_logger(logger_name='base', log_level=logging.INFO, log_file=None):
-    """Get the root logger.
-
-    The logger will be initialized if it has not been initialized. By default a
-    StreamHandler will be added. If `log_file` is specified, a FileHandler will
-    also be added.
-
-    Args:
-        logger_name (str): root logger name. Default: base.
-        log_file (str | None): The log filename. If specified, a FileHandler
-            will be added to the root logger.
-        log_level (int): The root logger level. Note that only the process of
-            rank 0 is affected, while other processes will set the level to
-            "Error" and be silent most of the time.
-
-    Returns:
-        logging.Logger: The root logger.
-    """
-    logger = logging.getLogger(logger_name)
-    # if the logger has been initialized, just return it
-    if logger.hasHandlers():
-        return logger
-
-    format_str = '%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s'
-    logging.basicConfig(format=format_str, level=log_level)
-
-    if log_file is not None:
-        file_handler = logging.FileHandler(log_file, 'w')
-        file_handler.setFormatter(logging.Formatter(format_str))
-        file_handler.setLevel(log_level)
-        logger.addHandler(file_handler)
-
-    return logger
diff --git a/spaces/Sakil/question_answering_app/app.py b/spaces/Sakil/question_answering_app/app.py
deleted file mode 100644
index da99ac7b991f3991d188632bb733d858807333a2..0000000000000000000000000000000000000000
--- a/spaces/Sakil/question_answering_app/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-title = 'Question Answering APP'
-context = "My name is Sakil Ansari. I love tea and drink it regularly"
-question = "What do I love?"
-
-
-question_answerer = pipeline("question-answering")
- #result  = question_answerer(question = question, context=context)
- #return result['answer']
-
-
-
-
-interface = gr.Interface.from_pipeline(question_answerer,
-    title = title,
-    theme = "grass",
-    examples = [[context, question]]).launch()
\ No newline at end of file
diff --git a/spaces/Sanathkumar1603/hackathon/app/Hackathon_setup/exp_recognition.py b/spaces/Sanathkumar1603/hackathon/app/Hackathon_setup/exp_recognition.py
deleted file mode 100644
index a387ce80f32f5b804c5e61e5c115811337e4f48e..0000000000000000000000000000000000000000
--- a/spaces/Sanathkumar1603/hackathon/app/Hackathon_setup/exp_recognition.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import numpy as np
-import cv2
-from matplotlib import pyplot as plt
-import torch
-# In the below line,remove '.' while working on your local system.However Make sure that '.' is present before face_recognition_model while uploading to the server, Do not remove it.
-from .exp_recognition_model import *
-from PIL import Image
-import base64
-import io
-import torchvision.transforms as transforms
-import os
-## Add more imports if required
-
-#############################################################################################################################
-#   Caution: Don't change any of the filenames, function names and definitions                                              #
-#   Always use the current_path + file_name for refering any files, without it we cannot access files on the server         #
-#############################################################################################################################
-
-# Current_path stores absolute path of the file from where it runs.
-current_path = os.path.dirname(os.path.abspath(__file__))
-
-
-#1) The below function is used to detect faces in the given image.
-#2) It returns only one image which has maximum area out of all the detected faces in the photo.
-#3) If no face is detected,then it returns zero(0).
-
-def detected_face(image):
-    eye_haar = current_path + '/haarcascade_eye.xml'
-    face_haar = current_path + '/haarcascade_frontalface_default.xml'
-    face_cascade = cv2.CascadeClassifier(face_haar)
-    eye_cascade = cv2.CascadeClassifier(eye_haar)
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    faces = face_cascade.detectMultiScale(gray, 1.3, 5)
-    face_areas=[]
-    images = []
-    required_image=0
-    for i, (x,y,w,h) in enumerate(faces):
-        face_cropped = gray[y:y+h, x:x+w]
-        face_areas.append(w*h)
-        images.append(face_cropped)
-        required_image = images[np.argmax(face_areas)]
-        required_image = Image.fromarray(required_image)
-    return required_image
-    
-
-#1) Images captured from mobile is passed as parameter to the below function in the API call, It returns the Expression detected by your network.
-#2) The image is passed to the function in base64 encoding, Code for decoding the image is provided within the function.
-#3) Define an object to your network here in the function and load the weight from the trained network, set it in evaluation mode.
-#4) Perform necessary transformations to the input(detected face using the above function), this should return the Expression in string form ex: "Anger"
-#5) For loading your model use the current_path+'your model file name', anyhow detailed example is given in comments to the function 
-##Caution: Don't change the definition or function name; for loading the model use the current_path for path example is given in comments to the function
-def get_expression(img):
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    
-    ##########################################################################################
-    ##Example for loading a model using weight state dictionary:                            ##
-    ## face_det_net = facExpRec() #Example Network                                          ##
-    ## model = torch.load(current_path + '/exp_recognition_net.t7', map_location=device)    ##
-    ## face_det_net.load_state_dict(model['net_dict'])                                      ##
-    ##                                                                                      ##
-    ##current_path + '/<network_definition>' is path of the saved model if present in       ##
-    ##the same path as this file, we recommend to put in the same directory                 ##
-    ##########################################################################################
-    ##########################################################################################
-    face_det_net = facExpRec() #Example Network                                          ##
-    model = torch.load(current_path + '/expression_model.t7', map_location=device)    ##
-    face_det_net.load_state_dict(model['net_dict'])
-    # face_det_net.to(device)
-    # face_det_net.eval()
-    face = detected_face(img)
-    if face==0:
-        face = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
-    
-    # YOUR CODE HERE, return expression using your model
-    # transform = transforms.Compose([
-    #       # Adjust the size as needed
-    #     transforms.ToTensor()
-        
-    # ])
-    
-    #trnscm = transforms.Compose([transforms.ToTensor()])
-    # Apply the transformations to the image
-    img1 = trnscm(face).unsqueeze(0).to(device)
-   
-    with torch.no_grad():
-        output = face_det_net(img1)
-        ans, predicted = torch.max(output, 1).item()
-    #predicted_class = torch.argmax(output, dim=1).item()
-    return ans
\ No newline at end of file
diff --git a/spaces/SantiagoMoreno-UdeA/NER_RC/src/graph/GUI.py b/spaces/SantiagoMoreno-UdeA/NER_RC/src/graph/GUI.py
deleted file mode 100644
index f9e36543831e2bc00c75a6e80dacc8bef83c9ec3..0000000000000000000000000000000000000000
--- a/spaces/SantiagoMoreno-UdeA/NER_RC/src/graph/GUI.py
+++ /dev/null
@@ -1,245 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Nov 11 16:01:08 2022
-
-@author: Santiago Moreno
-"""
-
-import os 
-import gradio as gr
-import sys
-import json
-
-
-default_path = os.path.dirname(os.path.abspath(__file__))
-#default_path = default_path.replace('\\', '/')
-
-os.chdir(default_path)
-sys.path.insert(0, default_path+'/../scripts')
-
-from src.scripts.functionsner import use_model, tag_sentence, json_to_txt, training_model, characterize_data, upsampling_data, usage_cuda, copy_data
-from src.scripts.functionsrc import  use_model_rc, training_model_rc, usage_cuda_rc 
-
-models_NER = os.listdir(default_path+'/../../models/NER')
-models_rc = os.listdir(default_path+'/../../models/RC')
-
-#-------------------------------------------Functions-----------------------------------------------
-
-#--------------------------------------NER-----------------------------------
-def Trainer(fast, model_name, standard, input_dir, Upsampling, Cuda):
-    if fast: epochs = 1
-    else: epochs = 20
-    
-    if Cuda: 
-        cuda_info = usage_cuda(True)
-    else: 
-        cuda_info = usage_cuda(False)
-    
-    
-    if standard:
-        copy_data(input_dir)
-    else:
-        Error = json_to_txt(input_dir)
-        if type(Error)==int:
-            yield 'Error processing the input documents, code error {}'.format(Error)
-    if Upsampling:
-        yield cuda_info+'\n'+'-'*20+'Upsampling'+'-'*20
-        entities_dict=characterize_data()
-        entities = list(entities_dict.keys())
-        entities_to_upsample = [entities[i] for i,value in enumerate(entities_dict.values()) if value < 200]
-        upsampling_data(entities_to_upsample, 0.8,  entities)
-        yield '-'*20+'Training'+'-'*20
-    else:
-        yield cuda_info+'\n'+'-'*20+'Training'+'-'*20
-    Error = training_model(model_name, epochs)
-    if type(Error)==int:
-        yield 'Error training the model, code error {}'.format(Error)
-    else: 
-        yield 'Training complete, model {} could be found at models/{}'.format(model_name,model_name)
-
-
-def Tagger_sentence(Model, Sentence, Cuda):
-    if Cuda: cuda_info = usage_cuda(True)
-    else: cuda_info = usage_cuda(False)
-    yield cuda_info+'\n'+'-'*20+'Tagging'+'-'*20
-    results = tag_sentence(Sentence, Model)
-    if type(results)==int:
-        yield "Error {}, see documentation".format(results)
-    else:
-        yield results['Highligth']
-
-def Tagger_json(Model, Input_file, Output_file, Cuda):
-    if Cuda: cuda_info = usage_cuda(True)
-    else: cuda_info = usage_cuda(False)
-    
-    with open(Output_file, "w", encoding='utf-8') as write_file:
-        json.dump({'error':'error'}, write_file)
-        
-    yield cuda_info+'\n'+'-'*20+'Tagging'+'-'*20, {}, Output_file
-    
-    results = use_model(Model, Input_file.name, Output_file)
-    if type(results)==int:
-        error_dict = {}
-        yield "Error {}, see documentation".format(results), error_dict, Output_file
-    else:
-        yield { "text" : results['text'], 'entities': results['entities']}, results, Output_file
-
-
-#--------------------RC-------------------------------
-def Trainer_RC(fast, model_name, input_file, rel2id_file, Cuda):
-    if fast: epochs = 1
-    else: epochs = 200
-    
-    if Cuda: 
-        cuda_info = usage_cuda_rc(True)
-    else: 
-        cuda_info = usage_cuda_rc(False)
-    
-
-    yield cuda_info+'\n'+'-'*20+'Training'+'-'*20
-    Error = training_model_rc(model_name, input_file.name, rel2id_file.name ,epochs)
-    if type(Error)==int:
-        yield 'Error training the model, code error {}'.format(Error)
-    else: 
-        yield 'Training complete, model {} could be found at models/{}'.format(model_name,model_name)
-
-
-def Tagger_document_RC(Model, Input_file, Output_file, Cuda):
-    if Cuda: cuda_info = usage_cuda_rc(True)
-    else: cuda_info = usage_cuda_rc(False)
-    
-    with open(Output_file, "w", encoding='utf-8') as write_file:
-        json.dump({'error':'error'}, write_file)
-        
-    yield {'cuda':cuda_info}, Output_file
-    
-    results = use_model_rc(Model, Input_file.name, Output_file)
-    if type(results)==int:
-        error_dict = {}
-        yield  error_dict, Output_file
-    else:
-        yield results, Output_file
-
-        
-#---------------------------------GUI-------------------------------------
-def execute_GUI():
-    global models_NER
-    with gr.Blocks(title='NER', css="#title {font-size: 150% } #sub {font-size: 120% } ") as demo:
-        
-        gr.Markdown("Named Entity Recognition(NER) and Relation Classification (RC) by GITA and Pratec Group S.A.S.",elem_id="title")
-        gr.Markdown("Software developed by Santiago Moreno, Daniel Escobar, and Rafael Orozco",elem_id="sub")
-        gr.Markdown("Named Entity Recognition(NER) and Relation Classification (RC) System.")
-
-        with gr.Tab("NER"):
-            gr.Markdown("Use Tagger to apply NER from a pretrained model in a sentence or a given document in INPUT (.JSON) format.")
-            gr.Markdown("Use Trainer to train a new NER model from a directory of documents in PRATECH (.JSON) format.")
-            with gr.Tab("Tagger"):
-                with gr.Tab("Sentence"):
-                    with gr.Row():
-                        with gr.Column():
-                            b = gr.Radio(list(models_NER), label='Model')
-                            inputs =[
-                                 b,
-                                 gr.Textbox(placeholder="Enter sentence here...", label='Sentence'), 
-                                 gr.Radio([True,False], label='CUDA', value=False),
-                            ]
-                            tagger_sen = gr.Button("Tag")
-                        output = gr.HighlightedText()
-                    
-               
-                    
-                    tagger_sen.click(Tagger_sentence, inputs=inputs, outputs=output)
-                    b.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models/NER'))), inputs=b, outputs=b)
-                    gr.Examples(
-                    
-                        examples=[
-                            ['CCC',"Camara de comercio de medellín. El ciudadano JAIME JARAMILLO VELEZ identificado con C.C. 12546987 ingresó al plantel el día 1/01/2022"],
-                            ['CCC',"Razón Social GASEOSAS GLACIAR S.A.S, ACTIVIDAD PRINCIPAL fabricación y distribución de bebidas endulzadas"]
-                         ],
-                        inputs=inputs
-                        )
-          
-                   
-                with gr.Tab("Document"):
-                    with gr.Row():
-                        with gr.Column(): 
-                            c = gr.Radio(list(models_NER), label='Model')
-                            inputs =[
-                                 c,
-                                 gr.File(label='Input data file'),
-                                 gr.Textbox(placeholder="Enter path here...", label='Output data file path'), #value='../../data/Tagged/document_tagged.json'),
-                                 gr.Radio([True,False], label='CUDA', value=False),
-                            ]
-                            tagger_json = gr.Button("Tag")
-                        output = [
-                            gr.HighlightedText(),
-                            gr.JSON(),
-                            gr.File(),
-                            ]
-                        
-                    models_NER = os.listdir(default_path+'/../../models/NER')
-                    
-                    
-                    tagger_json.click(Tagger_json, inputs=inputs, outputs=output)
-                    c.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models/NER'))), inputs=c, outputs=c)
-                    
-             
-            with gr.Tab("Trainer"):
-                with gr.Row():
-                    with gr.Column():
-                        train_input = inputs =[
-                             gr.Radio([True,False], label='Fast training', value=False),
-                             gr.Textbox(placeholder="Enter model name here...", label='New model name'),
-                             gr.Radio([True,False], label='Standard input', value=False),
-                             gr.Textbox(placeholder="Enter path here...", label='Input data directory path'), 
-                             gr.Radio([True,False], label='Upsampling', value=False),
-                             gr.Radio([True,False], label='CUDA', value=False),
-                        ]
-                        trainer = gr.Button("Train")
-                    train_output = gr.TextArea(placeholder="Output information", label='Output')
-                    
-                    
-        with gr.Tab("RC"):
-            gr.Markdown("Use Tagger to apply RC from a pretrained model in document in  (.TXT) CONLL04 format.")
-            gr.Markdown("Use Trainer to train a new RC model from a  file (.TXT) CONLL04 format and the rel2id file (.JSON).")
-            with gr.Tab("Tagger Document"):
-
-                with gr.Row():
-                    with gr.Column(): 
-                        c = gr.Radio(list(models_rc), label='Model')
-                        inputs =[
-                             c,
-                             gr.File(label='Input data file'),
-                             gr.Textbox(placeholder="Enter path here...", label='Output data file path (.JSON)'), #value='../../data/Tagged/document_tagged.json'),
-                             gr.Radio([True,False], label='CUDA', value=False),
-                        ]
-                        tagger_json = gr.Button("Tag")
-                    output = [
-                        gr.JSON(),
-                        gr.File(),
-                        ]
-                
-                tagger_json.click(Tagger_document_RC, inputs=inputs, outputs=output)
-                c.change(fn=lambda value: gr.update(choices=list(os.listdir('../../models/RC'))), inputs=c, outputs=c)
-
-            with gr.Tab("Trainer"):
-                with gr.Row():
-                    with gr.Column():
-                        train_input = inputs =[
-                             gr.Radio([True,False], label='Fast training', value=True),
-                             gr.Textbox(placeholder="Enter model name here...", label='New model name'),
-                             gr.File(label='Input train file (.TXT)'),
-                             gr.File(label='Input rel2id file (.JSON)'), 
-                             gr.Radio([True,False], label='CUDA', value=False),
-                        ]
-                        trainer = gr.Button("Train")
-                    train_output = gr.TextArea(placeholder="Output information", label='Output')
-                    
-        trainer.click(Trainer_RC, inputs=train_input, outputs=train_output)
-        
-
-        
-    demo.queue()
-    demo.launch()
-
-
diff --git a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/darknet.py b/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/darknet.py
deleted file mode 100644
index f12a8ac6a577217e2a797214f788fdf30d8ec989..0000000000000000000000000000000000000000
--- a/spaces/Sapphire-356/Video2MC/joints_detectors/Alphapose/yolo/darknet.py
+++ /dev/null
@@ -1,548 +0,0 @@
-from __future__ import division
-
-import torch 
-import torch.nn as nn
-import torch.nn.functional as F 
-from torch.autograd import Variable
-import numpy as np
-import cv2 
-import matplotlib.pyplot as plt
-try:
-    from util import count_parameters as count
-    from util import convert2cpu as cpu
-    from util import predict_transform
-except ImportError:
-    from yolo.util import count_parameters as count
-    from yolo.util import convert2cpu as cpu
-    from yolo.util import predict_transform
-
-class test_net(nn.Module):
-    def __init__(self, num_layers, input_size):
-        super(test_net, self).__init__()
-        self.num_layers= num_layers
-        self.linear_1 = nn.Linear(input_size, 5)
-        self.middle = nn.ModuleList([nn.Linear(5,5) for x in range(num_layers)])
-        self.output = nn.Linear(5,2)
-    
-    def forward(self, x):
-        x = x.view(-1)
-        fwd = nn.Sequential(self.linear_1, *self.middle, self.output)
-        return fwd(x)
-        
-def get_test_input():
-    img = cv2.imread("dog-cycle-car.png")
-    img = cv2.resize(img, (416,416)) 
-    img_ =  img[:,:,::-1].transpose((2,0,1))
-    img_ = img_[np.newaxis,:,:,:]/255.0
-    img_ = torch.from_numpy(img_).float()
-    img_ = Variable(img_)
-    return img_
-
-
-def parse_cfg(cfgfile):
-    """
-    Takes a configuration file
-    
-    Returns a list of blocks. Each blocks describes a block in the neural
-    network to be built. Block is represented as a dictionary in the list
-    
-    """
-    file = open(cfgfile, 'r')
-    lines = file.read().split('\n')     #store the lines in a list
-    lines = [x for x in lines if len(x) > 0] #get read of the empty lines 
-    lines = [x for x in lines if x[0] != '#']  
-    lines = [x.rstrip().lstrip() for x in lines]
-
-    
-    block = {}
-    blocks = []
-    
-    for line in lines:
-        if line[0] == "[":               #This marks the start of a new block
-            if len(block) != 0:
-                blocks.append(block)
-                block = {}
-            block["type"] = line[1:-1].rstrip()
-        else:
-            key,value = line.split("=")
-            block[key.rstrip()] = value.lstrip()
-    blocks.append(block)
-
-    return blocks
-#    print('\n\n'.join([repr(x) for x in blocks]))
-
-import pickle as pkl
-
-class MaxPoolStride1(nn.Module):
-    def __init__(self, kernel_size):
-        super(MaxPoolStride1, self).__init__()
-        self.kernel_size = kernel_size
-        self.pad = kernel_size - 1
-
-    def forward(self, x):
-        padding = int(self.pad / 2)
-        #padded_x = F.pad(x, (0,self.pad,0,self.pad), mode="replicate")
-        #pooled_x = nn.MaxPool2d(self.kernel_size, self.pad)(padded_x)
-        #padded_x = F.pad(x, (0, self.pad, 0, self.pad), mode="replicate")
-        padded_x = F.pad(x, (padding, padding, padding, padding), mode="constant", value=0)
-        pooled_x = nn.MaxPool2d(self.kernel_size, 1)(padded_x)
-        return pooled_x
-
-
-class EmptyLayer(nn.Module):
-    def __init__(self):
-        super(EmptyLayer, self).__init__()
-        
-
-class DetectionLayer(nn.Module):
-    def __init__(self, anchors):
-        super(DetectionLayer, self).__init__()
-        self.anchors = anchors
-    
-    def forward(self, x, inp_dim, num_classes, confidence):
-        x = x.data
-        global CUDA
-        prediction = x
-        prediction = predict_transform(prediction, inp_dim, self.anchors, num_classes, confidence, CUDA)
-        return prediction
-        
-
-        
-
-
-class Upsample(nn.Module):
-    def __init__(self, stride=2):
-        super(Upsample, self).__init__()
-        self.stride = stride
-        
-    def forward(self, x):
-        stride = self.stride
-        assert(x.data.dim() == 4)
-        B = x.data.size(0)
-        C = x.data.size(1)
-        H = x.data.size(2)
-        W = x.data.size(3)
-        ws = stride
-        hs = stride
-        x = x.view(B, C, H, 1, W, 1).expand(B, C, H, stride, W, stride).contiguous().view(B, C, H*stride, W*stride)
-        return x
-#       
-        
-class ReOrgLayer(nn.Module):
-    def __init__(self, stride = 2):
-        super(ReOrgLayer, self).__init__()
-        self.stride= stride
-        
-    def forward(self,x):
-        assert(x.data.dim() == 4)
-        B,C,H,W = x.data.shape
-        hs = self.stride
-        ws = self.stride
-        assert(H % hs == 0),  "The stride " + str(self.stride) + " is not a proper divisor of height " + str(H)
-        assert(W % ws == 0),  "The stride " + str(self.stride) + " is not a proper divisor of height " + str(W)
-        x = x.view(B,C, H // hs, hs, W // ws, ws).transpose(-2,-3).contiguous()
-        x = x.view(B,C, H // hs * W // ws, hs, ws)
-        x = x.view(B,C, H // hs * W // ws, hs*ws).transpose(-1,-2).contiguous()
-        x = x.view(B, C, ws*hs, H // ws, W // ws).transpose(1,2).contiguous()
-        x = x.view(B, C*ws*hs, H // ws, W // ws)
-        return x
-
-
-def create_modules(blocks):
-    net_info = blocks[0]     #Captures the information about the input and pre-processing    
-    
-    module_list = nn.ModuleList()
-    
-    index = 0    #indexing blocks helps with implementing route  layers (skip connections)
-
-    
-    prev_filters = 3
-    
-    output_filters = []
-    
-    for x in blocks:
-        module = nn.Sequential()
-        
-        if (x["type"] == "net"):
-            continue
-        
-        #If it's a convolutional layer
-        if (x["type"] == "convolutional"):
-            #Get the info about the layer
-            activation = x["activation"]
-            try:
-                batch_normalize = int(x["batch_normalize"])
-                bias = False
-            except:
-                batch_normalize = 0
-                bias = True
-                
-            filters= int(x["filters"])
-            padding = int(x["pad"])
-            kernel_size = int(x["size"])
-            stride = int(x["stride"])
-            
-            if padding:
-                pad = (kernel_size - 1) // 2
-            else:
-                pad = 0
-                
-            #Add the convolutional layer
-            conv = nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=bias)
-            module.add_module("conv_{0}".format(index), conv)
-            
-            #Add the Batch Norm Layer
-            if batch_normalize:
-                bn = nn.BatchNorm2d(filters)
-                module.add_module("batch_norm_{0}".format(index), bn)
-            
-            #Check the activation. 
-            #It is either Linear or a Leaky ReLU for YOLO
-            if activation == "leaky":
-                activn = nn.LeakyReLU(0.1, inplace = True)
-                module.add_module("leaky_{0}".format(index), activn)
-            
-            
-            
-        #If it's an upsampling layer
-        #We use Bilinear2dUpsampling
-        
-        elif (x["type"] == "upsample"):
-            stride = int(x["stride"])
-#            upsample = Upsample(stride)
-            upsample = nn.Upsample(scale_factor = 2, mode = "nearest")
-            module.add_module("upsample_{}".format(index), upsample)
-        
-        #If it is a route layer
-        elif (x["type"] == "route"):
-            x["layers"] = x["layers"].split(',')
-            
-            #Start  of a route
-            start = int(x["layers"][0])
-            if len(x["layers"]) <= 2:
-                #end, if there exists one.
-                try:
-                    end = int(x["layers"][1])
-                except:
-                    end = 0
-
-                #Positive anotation
-                if start > 0: 
-                    start = start - index
-                
-                if end > 0:
-                    end = end - index
-
-                
-                route = EmptyLayer()
-                module.add_module("route_{0}".format(index), route)
-                
-                
-                
-                if end < 0:
-                    filters = output_filters[index + start] + output_filters[index + end]
-                else:
-                    filters= output_filters[index + start]
-            else:  #SPP-route
-                assert len(x["layers"]) == 4
-
-                round = EmptyLayer()
-                module.add_module("route_{0}".format(index), route)
-
-                filters = output_filters[index + start] + output_filters[index + int(x["layers"][1])] \
-                          + output_filters[index + int(x["layers"][2])] + output_filters[index + int(x["layers"][3])]
-        
-        #shortcut corresponds to skip connection
-        elif x["type"] == "shortcut":
-            from_ = int(x["from"])
-            shortcut = EmptyLayer()
-            module.add_module("shortcut_{}".format(index), shortcut)
-            
-            
-        elif x["type"] == "maxpool":
-            stride = int(x["stride"])
-            size = int(x["size"])
-            if stride != 1:
-                maxpool = nn.MaxPool2d(size, stride)
-            else:
-                maxpool = MaxPoolStride1(size)
-                #maxpool = nn.MaxPool2d(size, stride=1, padding=size-1)
-
-            module.add_module("maxpool_{}".format(index), maxpool)
-        
-        #Yolo is the detection layer
-        elif x["type"] == "yolo":
-            mask = x["mask"].split(",")
-            mask = [int(x) for x in mask]
-            
-            
-            anchors = x["anchors"].split(",")
-            anchors = [int(a) for a in anchors]
-            anchors = [(anchors[i], anchors[i+1]) for i in range(0, len(anchors),2)]
-            anchors = [anchors[i] for i in mask]
-            
-            detection = DetectionLayer(anchors)
-            module.add_module("Detection_{}".format(index), detection)
-        
-            
-            
-        else:
-            print("Something I dunno")
-            assert False
-
-        module_list.append(module)
-        prev_filters = filters
-        output_filters.append(filters)
-        index += 1
-        
-    
-    return (net_info, module_list)
-
-
-
-class Darknet(nn.Module):
-    def __init__(self, cfgfile):
-        super(Darknet, self).__init__()
-        self.blocks = parse_cfg(cfgfile)
-        self.net_info, self.module_list = create_modules(self.blocks)
-        self.header = torch.IntTensor([0,0,0,0])
-        self.seen = 0
-
-        
-        
-    def get_blocks(self):
-        return self.blocks
-    
-    def get_module_list(self):
-        return self.module_list
-
-                
-    def forward(self, x, CUDA):
-        detections = []
-        modules = self.blocks[1:]
-        outputs = {}   #We cache the outputs for the route layer
-        
-        
-        write = 0
-        for i in range(len(modules)):        
-            
-            module_type = (modules[i]["type"])
-            if module_type == "convolutional" or module_type == "upsample" or module_type == "maxpool":
-                
-                x = self.module_list[i](x)
-                outputs[i] = x
-
-                
-            elif module_type == "route":
-                layers = modules[i]["layers"]
-                layers = [int(a) for a in layers]
-                
-                if (layers[0]) > 0:
-                    layers[0] = layers[0] - i
-
-                if len(layers) == 1:
-                    x = outputs[i + (layers[0])]
-
-                elif len(layers) == 2:
-                    if (layers[1]) > 0:
-                        layers[1] = layers[1] - i
-                        
-                    map1 = outputs[i + layers[0]]
-                    map2 = outputs[i + layers[1]]
-
-                    x = torch.cat((map1, map2), 1)
-                elif len(layers) == 4:  # SPP
-                    map1 = outputs[i + layers[0]]
-                    map2 = outputs[i + layers[1]]
-                    map3 = outputs[i + layers[2]]
-                    map4 = outputs[i + layers[3]]
-
-                    x = torch.cat((map1, map2, map3, map4), 1)
-                outputs[i] = x
-            
-            elif  module_type == "shortcut":
-                from_ = int(modules[i]["from"])
-                x = outputs[i-1] + outputs[i+from_]
-                outputs[i] = x
-                
-            
-            
-            elif module_type == 'yolo':        
-                
-                anchors = self.module_list[i][0].anchors
-                #Get the input dimensions
-                inp_dim = int (self.net_info["height"])
-                
-                #Get the number of classes
-                num_classes = int (modules[i]["classes"])
-                
-                #Output the result
-                x = x.data
-                x = predict_transform(x, inp_dim, anchors, num_classes, CUDA)
-                
-                if type(x) == int:
-                    continue
-
-                
-                if not write:
-                    detections = x
-                    write = 1
-                
-                else:
-                    detections = torch.cat((detections, x), 1)
-                
-                outputs[i] = outputs[i-1]
-                
-        
-        
-        try:
-            return detections
-        except:
-            return 0
-
-            
-    def load_weights(self, weightfile):
-        
-        #Open the weights file
-        fp = open(weightfile, "rb")
-
-        #The first 4 values are header information 
-        # 1. Major version number
-        # 2. Minor Version Number
-        # 3. Subversion number 
-        # 4. IMages seen 
-        header = np.fromfile(fp, dtype = np.int32, count = 5)
-        self.header = torch.from_numpy(header)
-        self.seen = self.header[3]
-        
-        #The rest of the values are the weights
-        # Let's load them up
-        weights = np.fromfile(fp, dtype = np.float32)
-        
-        ptr = 0
-        for i in range(len(self.module_list)):
-            module_type = self.blocks[i + 1]["type"]
-            
-            if module_type == "convolutional":
-                model = self.module_list[i]
-                try:
-                    batch_normalize = int(self.blocks[i+1]["batch_normalize"])
-                except:
-                    batch_normalize = 0
-                
-                conv = model[0]
-                
-                if (batch_normalize):
-                    bn = model[1]
-                    
-                    #Get the number of weights of Batch Norm Layer
-                    num_bn_biases = bn.bias.numel()
-                    
-                    #Load the weights
-                    bn_biases = torch.from_numpy(weights[ptr:ptr + num_bn_biases])
-                    ptr += num_bn_biases
-                    
-                    bn_weights = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
-                    ptr  += num_bn_biases
-                    
-                    bn_running_mean = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
-                    ptr  += num_bn_biases
-                    
-                    bn_running_var = torch.from_numpy(weights[ptr: ptr + num_bn_biases])
-                    ptr  += num_bn_biases
-                    
-                    #Cast the loaded weights into dims of model weights. 
-                    bn_biases = bn_biases.view_as(bn.bias.data)
-                    bn_weights = bn_weights.view_as(bn.weight.data)
-                    bn_running_mean = bn_running_mean.view_as(bn.running_mean)
-                    bn_running_var = bn_running_var.view_as(bn.running_var)
-
-                    #Copy the data to model
-                    bn.bias.data.copy_(bn_biases)
-                    bn.weight.data.copy_(bn_weights)
-                    bn.running_mean.copy_(bn_running_mean)
-                    bn.running_var.copy_(bn_running_var)
-                
-                else:
-                    #Number of biases
-                    num_biases = conv.bias.numel()
-                
-                    #Load the weights
-                    conv_biases = torch.from_numpy(weights[ptr: ptr + num_biases])
-                    ptr = ptr + num_biases
-                    
-                    #reshape the loaded weights according to the dims of the model weights
-                    conv_biases = conv_biases.view_as(conv.bias.data)
-                    
-                    #Finally copy the data
-                    conv.bias.data.copy_(conv_biases)
-                    
-                    
-                #Let us load the weights for the Convolutional layers
-                num_weights = conv.weight.numel()
-                
-                #Do the same as above for weights
-                conv_weights = torch.from_numpy(weights[ptr:ptr+num_weights])
-                ptr = ptr + num_weights
-
-                conv_weights = conv_weights.view_as(conv.weight.data)
-                conv.weight.data.copy_(conv_weights)
-                
-    def save_weights(self, savedfile, cutoff = 0):
-            
-        if cutoff <= 0:
-            cutoff = len(self.blocks) - 1
-        
-        fp = open(savedfile, 'wb')
-        
-        # Attach the header at the top of the file
-        self.header[3] = self.seen
-        header = self.header
-
-        header = header.numpy()
-        header.tofile(fp)
-        
-        # Now, let us save the weights 
-        for i in range(len(self.module_list)):
-            module_type = self.blocks[i+1]["type"]
-            
-            if (module_type) == "convolutional":
-                model = self.module_list[i]
-                try:
-                    batch_normalize = int(self.blocks[i+1]["batch_normalize"])
-                except:
-                    batch_normalize = 0
-                    
-                conv = model[0]
-
-                if (batch_normalize):
-                    bn = model[1]
-                
-                    #If the parameters are on GPU, convert them back to CPU
-                    #We don't convert the parameter to GPU
-                    #Instead. we copy the parameter and then convert it to CPU
-                    #This is done as weight are need to be saved during training
-                    cpu(bn.bias.data).numpy().tofile(fp)
-                    cpu(bn.weight.data).numpy().tofile(fp)
-                    cpu(bn.running_mean).numpy().tofile(fp)
-                    cpu(bn.running_var).numpy().tofile(fp)
-                
-            
-                else:
-                    cpu(conv.bias.data).numpy().tofile(fp)
-                
-                
-                #Let us save the weights for the Convolutional layers
-                cpu(conv.weight.data).numpy().tofile(fp)
-               
-
-
-
-
-#
-#dn = Darknet('cfg/yolov3.cfg')
-#dn.load_weights("yolov3.weights")
-#inp = get_test_input()
-#a, interms = dn(inp)
-#dn.eval()
-#a_i, interms_i = dn(inp)
diff --git a/spaces/SarthakSidhant/Go-Cattle/diseases/necrotic stomatitis (calf diphtheria).md b/spaces/SarthakSidhant/Go-Cattle/diseases/necrotic stomatitis (calf diphtheria).md
deleted file mode 100644
index efa825fa3c23bbe81d1079d340604086a47ea72e..0000000000000000000000000000000000000000
--- a/spaces/SarthakSidhant/Go-Cattle/diseases/necrotic stomatitis (calf diphtheria).md	
+++ /dev/null
@@ -1,41 +0,0 @@
-## Necrotic stomatitis (calf diphtheria)
-
-**Information** : Necrotic stomatitis (calf diphtheria) is a bacterial infection of the mouth and throat of young calves caused by the bacterium Clostridium chauvoei. The bacteria produce a toxin that causes tissue death and necrosis.
-
-**Symptoms**
-
-The symptoms of necrotic stomatitis typically appear within 2-5 days of infection and include:
-
-* Painful mouth and throat
-* Drooling
-* Fever
-* Difficulty eating and drinking
-* Swelling of the lymph nodes in the head and neck
-* Necrotic lesions on the tongue, gums, and palate
-* Death in severe cases
-
-**Remedies**
-
-There is no specific treatment for necrotic stomatitis. Treatment is usually supportive and may include:
-
-* Providing pain relief
-* Administering fluids and electrolytes
-* Treating secondary bacterial infections
-* Administering antitoxin
-
-**Causes**
-
-Necrotic stomatitis (calf diphtheria) is caused by the bacterium Clostridium chauvoei. The bacteria are found in the environment, in soil, and in the feces of animals. Calves can become infected by ingesting the bacteria or by coming into contact with contaminated objects, such as feed, water, or equipment.
-
-**Prevention**
-
-There is no vaccine available for necrotic stomatitis. However, there are a number of preventive measures that can be taken to reduce the risk of infection, such as:
-
-* Practicing good biosecurity measures
-* Isolating infected animals from healthy animals
-* Cleaning and disinfecting contaminated areas
-* Vaccinating cattle against other diseases that can weaken the immune system, such as bovine viral diarrhea virus (BVDV) and rotavirus
-
-**Differential diagnosis**
-
-Necrotic stomatitis can be difficult to distinguish from other diseases that cause mouth lesions, such as foot-and-mouth disease, contagious ecthyma, and vesicular stomatitis. A veterinarian can diagnose necrotic stomatitis by testing a sample of the lesions for the presence of Clostridium chauvoei.
diff --git a/spaces/SeViLA/SeViLA/lavis/models/pnp_vqa_models/pnp_vqa.py b/spaces/SeViLA/SeViLA/lavis/models/pnp_vqa_models/pnp_vqa.py
deleted file mode 100644
index 59b9d888bdcb999ca65eabfda7c457b7041524c4..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/pnp_vqa_models/pnp_vqa.py
+++ /dev/null
@@ -1,340 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import torch
-import torch.nn as nn
-from itertools import chain
-from lavis.common.registry import registry
-from lavis.models.base_model import BaseModel
-from torch.nn import CrossEntropyLoss, MSELoss
-from transformers import T5ForConditionalGeneration
-from lavis.models.pnp_vqa_models import prepare_qa_input
-from lavis.models.blip_models.blip_image_text_matching import compute_gradcam
-from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
-
-
-@registry.register_model("pnp_vqa")
-class PNPVQA(BaseModel):
-    """
-    PNPVQA model consists of three submodels for zero-shot VQA:
-        1. Image-questioning matching model
-        2. Image captioning model
-        3. Question answering model
-
-    Supported model types:
-        - base: BLIPITM, BLIPCaption, PNPUnifiedQAv2FiD (t5-base)
-        - large: BLIPITM, BLIPCaption, PNPUnifiedQAv2FiD (t5-large)
-        - 3b: BLIPITM, BLIPCaption, PNPUnifiedQAv2FiD (t5-3b)
-
-    Usage:
-        >>> from lavis.models import load_model
-        >>> model = load_model("pnp_vqa", "base", is_eval=True)
-        >>> model = load_model("pnp_vqa", "large", is_eval=True)
-        >>> model = load_model("pnp_vqa", "3b", is_eval=True)
-    """
-
-    PRETRAINED_MODEL_CONFIG_DICT = {"base": "configs/models/pnp-vqa/pnp_vqa_base.yaml",
-                                    "large": "configs/models/pnp-vqa/pnp_vqa_large.yaml",
-                                    "3b": "configs/models/pnp-vqa/pnp_vqa_3b.yaml",
-                                    }
-
-    def __init__(self, image_question_matching_model, image_captioning_model,
-                 question_answering_model, offload_model=False):
-        super().__init__()
-
-        self.image_question_matching_model = image_question_matching_model
-        self.image_captioning_model = image_captioning_model
-        self.question_answering_model = question_answering_model
-        self.offload_model = offload_model
-
-    def forward_itm(self, samples, block_num=7):
-        """
-        Args:
-            samples (dict): A dictionary containing the following keys:
-                - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
-                - text_input (list): A list of strings of length batch_size
-            block_num (int): The index of cross-attention block for gradcam computation.
-
-        Returns:
-            samples (dict): A dictionary containing the following keys:
-                - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
-                - text_input (list): A list of strings of length batch_size
-                - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
-        """
-        image = samples['image']
-        question = [text.strip('?') for text in samples['text_input']]
-        tokenized_text = self.image_question_matching_model.tokenizer(question, padding='longest', truncation=True,
-                                                return_tensors="pt").to(self.image_question_matching_model.device)
-        with torch.set_grad_enabled(True):
-            gradcams, _ = compute_gradcam(model=self.image_question_matching_model,
-                            visual_input=image,
-                            text_input=question,
-                            tokenized_text=tokenized_text,
-                            block_num=block_num)
-
-        gradcams = [gradcam_[1] for gradcam_ in gradcams]
-        samples['gradcams'] = torch.stack(gradcams).reshape(samples['image'].size(0), -1)
-
-        return samples
-
-    def forward_cap(
-            self,
-            samples,
-            cap_max_length=20,
-            cap_min_length=0,
-            top_p=1,
-            top_k=50,
-            repetition_penalty=1.0,
-            num_captions=100,
-            num_patches=20,
-    ):
-        """
-        Args:
-            samples (dict): A dictionary containing the following keys:
-                - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
-                - text_input (list): A list of strings of length batch_size
-                - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
-            cap_max_length (int): The maximum length of the caption to be generated.
-            cap_min_length (int): The minimum length of the caption to be generated.
-            top_p (float): The cumulative probability for nucleus sampling.
-            top_k (float): The number of the highest probability tokens for top-k sampling.
-            repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty.
-            num_captions (int): Number of captions generated for each image.
-            num_patches (int): Number of patches sampled for each image.
-
-        Returns:
-            samples (dict): A dictionary containing the following keys:
-                - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
-                - text_input (list): A list of strings of length batch_size
-                - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
-                - captions (nested list): A nested list of strings of total length batch_size * num_captions
-        """
-        encoder_out = self.image_captioning_model.forward_encoder(samples)
-        captions = [[] for _ in range(encoder_out.size(0))]
-
-        min_num_captions = 0
-
-        while min_num_captions < num_captions:
-            encoder_out_samples = []
-            for i in range(num_captions):
-                patch_id = torch.multinomial(samples['gradcams'].to(self.image_captioning_model.device),
-                                             num_patches).reshape(encoder_out.size(0), -1) + 1
-                patch_id = patch_id.sort(dim=1).values.unsqueeze(-1).expand(-1, -1, encoder_out.size(2))
-                encoder_out_sample = torch.gather(encoder_out, 1, patch_id)
-                encoder_out_samples.append(encoder_out_sample)
-
-            stacked = torch.stack(encoder_out_samples, dim=1)
-            image_embeds = torch.flatten(stacked, start_dim=0, end_dim=1) #(bsz*num_seq, num_patch, dim)
-
-            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(self.image_captioning_model.device)
-            model_kwargs = {
-                "encoder_hidden_states": image_embeds,
-                "encoder_attention_mask": image_atts,
-            }
-
-            prompt = [self.image_captioning_model.prompt] * image_embeds.size(0)
-            prompt = self.image_captioning_model.tokenizer(prompt,
-                                                           return_tensors="pt").to(self.image_captioning_model.device)
-            prompt.input_ids[:, 0] = self.image_captioning_model.tokenizer.bos_token_id
-            prompt.input_ids = prompt.input_ids[:, :-1]
-
-            decoder_out = self.image_captioning_model.text_decoder.generate(
-                input_ids=prompt.input_ids,
-                max_length=cap_max_length,
-                min_length=cap_min_length,
-                do_sample=True,
-                top_p=top_p,
-                top_k=top_k,
-                num_return_sequences=1,
-                eos_token_id=self.image_captioning_model.tokenizer.sep_token_id,
-                pad_token_id=self.image_captioning_model.tokenizer.pad_token_id,
-                repetition_penalty=repetition_penalty,
-                **model_kwargs)
-
-            outputs = self.image_captioning_model.tokenizer.batch_decode(decoder_out, skip_special_tokens=True)
-
-            for counter, output in enumerate(outputs):
-                ind = counter//num_captions
-                if len(captions[ind]) < num_captions:
-                    caption = output[len(self.image_captioning_model.prompt):]
-                    overlap_caption = [1 for caps in captions[ind] if caption in caps]
-                    if len(overlap_caption) == 0:
-                        captions[ind].append(caption)
-
-            min_num_captions = min([len(i) for i in captions])
-
-        samples['captions'] = captions
-
-        return samples
-
-    def forward_qa(
-            self,
-            samples,
-            num_beams=1,
-            max_len=20,
-            min_len=0,
-            internal_bsz_fid=1,
-            num_captions=100,
-            num_captions_fid=1,
-    ):
-        """
-        Args:
-            samples (dict): A dictionary containing the following keys:
-                - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
-                - text_input (list): A list of strings of length batch_size
-                - gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
-                - captions (nested list): A nested list of strings of total length batch_size * num_captions
-                - question_captions (nested list): A nested list of concatenated strings of questions and captions
-            num_beams (int): Number of beams for beam search. 1 means no beam search.
-            max_len (int): Maximum length of generated answers.
-            min_len (int): Minimum length of generated answers.
-            internal_bsz_fid (int): Internal batch size when using FiD decoding.
-            num_captions (int): Number of captions generated for each image.
-            num_captions_fid (int): Number of captions concatenated with a question during FiD decoding.
-
-        Returns:
-            List: A list of strings, each string is an answer.
-        """
-        prepare_qa_input(samples, num_captions=num_captions, num_captions_fid=num_captions_fid)
-
-        pred_answers = []
-        question_captions = samples['question_captions']
-        question_captions_chunk = [question_captions[i:i + internal_bsz_fid]
-                                   for i in range(0, len(question_captions), internal_bsz_fid)]
-        question_captions_chunk = list(chain(*question_captions_chunk))
-
-        for question_caption in question_captions_chunk:
-            question_caption_input = self.question_answering_model.tokenizer(question_caption, padding='longest',
-                                        truncation=True, return_tensors="pt").to(self.question_answering_model.device)
-
-            question_caption_input.input_ids = question_caption_input.input_ids.reshape(
-                                               internal_bsz_fid, -1, question_caption_input.input_ids.size(1))
-            question_caption_input.attention_mask = question_caption_input.attention_mask.reshape(
-                                               internal_bsz_fid, -1, question_caption_input.attention_mask.size(1))
-
-            outputs = self.question_answering_model.generate(input_ids=question_caption_input.input_ids,
-                                            attention_mask=question_caption_input.attention_mask,
-                                            num_beams=num_beams,
-                                            min_length=min_len,
-                                            max_length=max_len,
-                                            )
-
-            for output in outputs:
-                pred_answer = self.question_answering_model.tokenizer.decode(output, skip_special_tokens=True)
-                pred_answers.append(pred_answer)
-
-        return pred_answers
-
-    def predict_answers(
-        self,
-        samples,
-        num_beams=1,
-        inference_method="generate",
-        max_len=20,
-        min_len=0,
-        internal_bsz_fid=1,
-        num_captions=50,
-        num_captions_fid=1,
-        cap_max_length=20,
-        cap_min_length=10,
-        top_k=50,
-        top_p=1,
-        repetition_penalty=1,
-        num_patches=50,
-        block_num=7,
-    ):
-        """
-        Args:
-            samples (dict): A dictionary containing the following keys:
-                - image (torch.Tensor): A tensor of shape (batch_size, 3, H, W). Default H=480, W=480.
-                - text_input (str or [str]): String or a list of strings, each string is a question.
-                                             The number of questions must be equal to the batch size. If a single string, will be converted to a list of string, with length 1 first.
-            num_beams (int): Number of beams for beam search. 1 means no beam search.
-            inference_method (str): Inference method. Must be "generate". The model will generate answers.
-            max_len (int): Maximum length of generated answers.
-            min_len (int): Minimum length of generated answers.
-            internal_bsz_fid (int): Internal batch size when using FiD decoding.
-            num_captions (int): Number of captions generated for each image.
-            num_captions_fid (int): Number of captions concatenated with a question during FiD decoding.
-            cap_max_length (int): The maximum length of the caption to be generated.
-            cap_min_length (int): The minimum length of the caption to be generated.
-            top_k (float): The number of the highest probability tokens for top-k sampling.
-            top_p (float): The cumulative probability for nucleus sampling.
-            repetition_penalty (float): The parameter for repetition penalty. 1.0 means no penalty.
-            num_patches (int): Number of patches sampled for each image.
-            block_num (int): The index of cross-attention block for gradcam computation.
-
-        Returns:
-            List: A list of strings, each string is an answer.
-            gradcams (torch.Tensor): A tensor of shape (batch_size, H*W)
-            captions (nested list): A nested list of strings of total length batch_size * num_captions
-        """
-        assert inference_method in [
-            "generate",
-        ], "Inference method must be 'generate', got {}.".format(
-            inference_method
-        )
-
-        if isinstance(samples["text_input"], str):
-            samples["text_input"] = [samples["text_input"]]
-
-        assert len(samples["text_input"]) == samples["image"].size(
-            0
-        ), "The number of questions must be equal to the batch size."
-
-        samples = self.forward_itm(samples, block_num=block_num)
-
-        samples = self.forward_cap(samples,
-                                   cap_max_length=cap_max_length,
-                                   cap_min_length=cap_min_length,
-                                   top_k=top_k,
-                                   top_p=top_p,
-                                   repetition_penalty=repetition_penalty,
-                                   num_captions=num_captions,
-                                   num_patches=num_patches)
-
-        if self.offload_model:
-            samples['image'] = samples['image'].to('cpu')
-            self.image_question_matching_model.to('cpu')
-            self.image_captioning_model.to('cpu')
-        torch.cuda.empty_cache()
-
-        pred_answers = self.forward_qa(samples,
-                                  num_beams=num_beams,
-                                  max_len=max_len,
-                                  min_len=min_len,
-                                  internal_bsz_fid=internal_bsz_fid,
-                                  num_captions=num_captions,
-                                  num_captions_fid=num_captions_fid)
-
-        if self.offload_model:
-            self.image_question_matching_model.to(self.question_answering_model.device)
-            self.image_captioning_model.to(self.question_answering_model.device)
-
-        return pred_answers, samples['captions'], samples['gradcams']
-
-    @classmethod
-    def from_config(cls, model_config):
-        itm_config = model_config.image_question_matching_model
-        cap_config = model_config.image_captioning_model
-        qa_config = model_config.question_answering_model
-
-        itm_cls = registry.get_model_class(itm_config.arch)
-        cap_cls = registry.get_model_class(cap_config.arch)
-        qa_cls = registry.get_model_class(qa_config.arch)
-
-        image_question_matching_model = itm_cls.from_config(itm_config)
-        image_captioning_model = cap_cls.from_config(cap_config)
-        question_answering_model = qa_cls.from_config(qa_config)
-
-        model = cls(image_question_matching_model=image_question_matching_model,
-                    image_captioning_model=image_captioning_model,
-                    question_answering_model=question_answering_model,
-                    offload_model= True if model_config.model_type == '3b' else False,
-                    )
-
-        return model
\ No newline at end of file
diff --git a/spaces/SeViLA/SeViLA/lavis/models/vit.py b/spaces/SeViLA/SeViLA/lavis/models/vit.py
deleted file mode 100644
index f35b7bb6886f8e4455330cf7c330a18e57f11db7..0000000000000000000000000000000000000000
--- a/spaces/SeViLA/SeViLA/lavis/models/vit.py
+++ /dev/null
@@ -1,527 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- 
- Based on timm code base
- https://github.com/rwightman/pytorch-image-models/tree/master/timm
-"""
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from functools import partial
-
-from timm.models.vision_transformer import _cfg, PatchEmbed
-from timm.models.registry import register_model
-from timm.models.layers import trunc_normal_, DropPath
-from timm.models.helpers import named_apply, adapt_input_conv
-
-from fairscale.nn.checkpoint.checkpoint_activations import checkpoint_wrapper
-from lavis.models.base_model import BaseEncoder
-
-
-class Mlp(nn.Module):
-    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
-
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads=8,
-        qkv_bias=False,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim**-0.5
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.attn_gradients = None
-        self.attention_map = None
-
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-
-    def get_attn_gradients(self):
-        return self.attn_gradients
-
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-
-    def get_attention_map(self):
-        return self.attention_map
-
-    def forward(self, x, register_hook=False):
-        B, N, C = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(B, N, 3, self.num_heads, C // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        if register_hook:
-            self.save_attention_map(attn)
-            attn.register_hook(self.save_attn_gradients)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        mlp_ratio=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        use_grad_checkpointing=False,
-    ):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        if use_grad_checkpointing:
-            self.attn = checkpoint_wrapper(self.attn)
-            self.mlp = checkpoint_wrapper(self.mlp)
-
-    def forward(self, x, register_hook=False):
-        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class VisionTransformer(nn.Module):
-    """Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-
-    def __init__(
-        self,
-        img_size=224,
-        patch_size=16,
-        in_chans=3,
-        num_classes=1000,
-        embed_dim=768,
-        depth=12,
-        num_heads=12,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        representation_size=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.0,
-        norm_layer=None,
-        use_grad_checkpointing=False,
-        ckpt_layer=0,
-    ):
-        """
-        Args:
-            img_size (int, tuple): input image size
-            patch_size (int, tuple): patch size
-            in_chans (int): number of input channels
-            num_classes (int): number of classes for classification head
-            embed_dim (int): embedding dimension
-            depth (int): depth of transformer
-            num_heads (int): number of attention heads
-            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
-            qkv_bias (bool): enable bias for qkv if True
-            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
-            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            drop_rate (float): dropout rate
-            attn_drop_rate (float): attention dropout rate
-            drop_path_rate (float): stochastic depth rate
-            norm_layer: (nn.Module): normalization layer
-        """
-        super().__init__()
-        self.num_features = (
-            self.embed_dim
-        ) = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-        )
-
-        num_patches = self.patch_embed.num_patches
-
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, depth)
-        ]  # stochastic depth decay rule
-        self.blocks = nn.ModuleList(
-            [
-                Block(
-                    dim=embed_dim,
-                    num_heads=num_heads,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop_rate,
-                    attn_drop=attn_drop_rate,
-                    drop_path=dpr[i],
-                    norm_layer=norm_layer,
-                    use_grad_checkpointing=(
-                        use_grad_checkpointing and i >= depth - ckpt_layer
-                    ),
-                )
-                for i in range(depth)
-            ]
-        )
-        self.norm = norm_layer(embed_dim)
-
-        trunc_normal_(self.pos_embed, std=0.02)
-        trunc_normal_(self.cls_token, std=0.02)
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {"pos_embed", "cls_token"}
-
-    def forward(self, x, register_blk=-1):
-        B = x.shape[0]
-        x = self.patch_embed(x)
-
-        cls_tokens = self.cls_token.expand(
-            B, -1, -1
-        )  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-
-        x = x + self.pos_embed[:, : x.size(1), :]
-        x = self.pos_drop(x)
-
-        for i, blk in enumerate(self.blocks):
-            x = blk(x, register_blk == i)
-        x = self.norm(x)
-
-        return x
-
-    @torch.jit.ignore()
-    def load_pretrained(self, checkpoint_path, prefix=""):
-        _load_weights(self, checkpoint_path, prefix)
-
-
-@torch.no_grad()
-def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ""):
-    """Load weights from .npz checkpoints for official Google Brain Flax implementation"""
-    import numpy as np
-
-    def _n2p(w, t=True):
-        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
-            w = w.flatten()
-        if t:
-            if w.ndim == 4:
-                w = w.transpose([3, 2, 0, 1])
-            elif w.ndim == 3:
-                w = w.transpose([2, 0, 1])
-            elif w.ndim == 2:
-                w = w.transpose([1, 0])
-        return torch.from_numpy(w)
-
-    w = np.load(checkpoint_path)
-    if not prefix and "opt/target/embedding/kernel" in w:
-        prefix = "opt/target/"
-
-    if hasattr(model.patch_embed, "backbone"):
-        # hybrid
-        backbone = model.patch_embed.backbone
-        stem_only = not hasattr(backbone, "stem")
-        stem = backbone if stem_only else backbone.stem
-        stem.conv.weight.copy_(
-            adapt_input_conv(
-                stem.conv.weight.shape[1], _n2p(w[f"{prefix}conv_root/kernel"])
-            )
-        )
-        stem.norm.weight.copy_(_n2p(w[f"{prefix}gn_root/scale"]))
-        stem.norm.bias.copy_(_n2p(w[f"{prefix}gn_root/bias"]))
-        if not stem_only:
-            for i, stage in enumerate(backbone.stages):
-                for j, block in enumerate(stage.blocks):
-                    bp = f"{prefix}block{i + 1}/unit{j + 1}/"
-                    for r in range(3):
-                        getattr(block, f"conv{r + 1}").weight.copy_(
-                            _n2p(w[f"{bp}conv{r + 1}/kernel"])
-                        )
-                        getattr(block, f"norm{r + 1}").weight.copy_(
-                            _n2p(w[f"{bp}gn{r + 1}/scale"])
-                        )
-                        getattr(block, f"norm{r + 1}").bias.copy_(
-                            _n2p(w[f"{bp}gn{r + 1}/bias"])
-                        )
-                    if block.downsample is not None:
-                        block.downsample.conv.weight.copy_(
-                            _n2p(w[f"{bp}conv_proj/kernel"])
-                        )
-                        block.downsample.norm.weight.copy_(
-                            _n2p(w[f"{bp}gn_proj/scale"])
-                        )
-                        block.downsample.norm.bias.copy_(_n2p(w[f"{bp}gn_proj/bias"]))
-        embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"])
-    else:
-        embed_conv_w = adapt_input_conv(
-            model.patch_embed.proj.weight.shape[1], _n2p(w[f"{prefix}embedding/kernel"])
-        )
-    model.patch_embed.proj.weight.copy_(embed_conv_w)
-    model.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"]))
-    model.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False))
-    pos_embed_w = _n2p(w[f"{prefix}Transformer/posembed_input/pos_embedding"], t=False)
-    if pos_embed_w.shape != model.pos_embed.shape:
-        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
-            pos_embed_w,
-            model.pos_embed,
-            getattr(model, "num_tokens", 1),
-            model.patch_embed.grid_size,
-        )
-    model.pos_embed.copy_(pos_embed_w)
-    model.norm.weight.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/scale"]))
-    model.norm.bias.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/bias"]))
-    #     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
-    #         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
-    #         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
-    #     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
-    #         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
-    #         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
-    for i, block in enumerate(model.blocks.children()):
-        block_prefix = f"{prefix}Transformer/encoderblock_{i}/"
-        mha_prefix = block_prefix + "MultiHeadDotProductAttention_1/"
-        block.norm1.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/scale"]))
-        block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"]))
-        block.attn.qkv.weight.copy_(
-            torch.cat(
-                [
-                    _n2p(w[f"{mha_prefix}{n}/kernel"], t=False).flatten(1).T
-                    for n in ("query", "key", "value")
-                ]
-            )
-        )
-        block.attn.qkv.bias.copy_(
-            torch.cat(
-                [
-                    _n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1)
-                    for n in ("query", "key", "value")
-                ]
-            )
-        )
-        block.attn.proj.weight.copy_(_n2p(w[f"{mha_prefix}out/kernel"]).flatten(1))
-        block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"]))
-        for r in range(2):
-            getattr(block.mlp, f"fc{r + 1}").weight.copy_(
-                _n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/kernel"])
-            )
-            getattr(block.mlp, f"fc{r + 1}").bias.copy_(
-                _n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/bias"])
-            )
-        block.norm2.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/scale"]))
-        block.norm2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/bias"]))
-
-
-def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
-    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
-    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
-    print("Resized position embedding: %s to %s", posemb.shape, posemb_new.shape)
-    ntok_new = posemb_new.shape[1]
-    if num_tokens:
-        posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
-        ntok_new -= num_tokens
-    else:
-        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
-    gs_old = int(math.sqrt(len(posemb_grid)))
-    if not len(gs_new):  # backwards compatibility
-        gs_new = [int(math.sqrt(ntok_new))] * 2
-    assert len(gs_new) >= 2
-    print("Position embedding grid-size from %s to %s", [gs_old, gs_old], gs_new)
-    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
-    posemb_grid = F.interpolate(
-        posemb_grid, size=gs_new, mode="bicubic", align_corners=False
-    )
-    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
-    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-    return
-
-
-def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
-    # interpolate position embedding
-    embedding_size = pos_embed_checkpoint.shape[-1]
-    num_patches = visual_encoder.patch_embed.num_patches
-    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
-    # height (== width) for the checkpoint position embedding
-    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
-    # height (== width) for the new position embedding
-    new_size = int(num_patches**0.5)
-
-    if orig_size != new_size:
-        # class_token and dist_token are kept unchanged
-        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
-        # only the position tokens are interpolated
-        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
-        pos_tokens = pos_tokens.reshape(
-            -1, orig_size, orig_size, embedding_size
-        ).permute(0, 3, 1, 2)
-        pos_tokens = torch.nn.functional.interpolate(
-            pos_tokens, size=(new_size, new_size), mode="bicubic", align_corners=False
-        )
-        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
-        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
-        print(
-            "reshape position embedding from %d to %d" % (orig_size**2, new_size**2)
-        )
-
-        return new_pos_embed
-    else:
-        return pos_embed_checkpoint
-
-
-class VisionTransformerEncoder(VisionTransformer, BaseEncoder):
-    @classmethod
-    def from_config(cls, cfg, from_pretrained=False):
-
-        vit_type = cfg.get("vit_type", "base")
-        image_size = cfg.get("image_size", 384)
-        ckpt_layer = cfg.get("vit_ckpt_layer", 0)
-        drop_path_rate = cfg.get("vit_drop_path_rate", 0)
-        norm_layer_eps = cfg.get("vit_layer_norm_epsilon", -1)
-        use_grad_checkpointing = cfg.get("vit_grad_ckpt", False)
-
-        if norm_layer_eps == -1:
-            norm_layer = None
-        else:
-            norm_layer = partial(nn.LayerNorm, eps=norm_layer_eps)
-
-        #     norm_layer=partial(nn.LayerNorm, eps=1e-6),
-        assert vit_type in ["base", "large"], "vit parameter must be base or large"
-        if vit_type == "base":
-            vision_width = 768
-            visual_encoder = cls(
-                img_size=image_size,
-                patch_size=16,
-                embed_dim=vision_width,
-                depth=12,
-                num_heads=12,
-                use_grad_checkpointing=use_grad_checkpointing,
-                ckpt_layer=ckpt_layer,
-                drop_path_rate=0 or drop_path_rate,
-                norm_layer=norm_layer,
-            )
-
-            if from_pretrained:
-                checkpoint = torch.hub.load_state_dict_from_url(
-                    url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth",
-                    map_location="cpu",
-                    check_hash=True,
-                )
-                state_dict = checkpoint["model"]
-                state_dict["pos_embed"] = interpolate_pos_embed(
-                    state_dict["pos_embed"], visual_encoder
-                )
-                msg = visual_encoder.load_state_dict(state_dict, strict=False)
-
-        elif vit_type == "large":
-            vision_width = 1024
-            visual_encoder = cls(
-                img_size=image_size,
-                patch_size=16,
-                embed_dim=vision_width,
-                depth=24,
-                num_heads=16,
-                use_grad_checkpointing=use_grad_checkpointing,
-                ckpt_layer=ckpt_layer,
-                drop_path_rate=0.1 or drop_path_rate,
-                norm_layer=norm_layer,
-            )
-            if from_pretrained:
-                from timm.models.helpers import load_custom_pretrained
-                from timm.models.vision_transformer import default_cfgs
-
-                load_custom_pretrained(
-                    visual_encoder, default_cfgs["vit_large_patch16_224_in21k"]
-                )
-
-        visual_encoder.vision_width = vision_width
-        return visual_encoder
-
-    def forward_features(self, x, register_blk=-1):
-        return super().forward(x, register_blk)
diff --git a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/util/get_tokenlizer.py b/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/util/get_tokenlizer.py
deleted file mode 100644
index f7dcf7e95f03f95b20546b26442a94225924618b..0000000000000000000000000000000000000000
--- a/spaces/ShilongLiu/Grounding_DINO_demo/groundingdino/util/get_tokenlizer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-
-
-def get_tokenlizer(text_encoder_type):
-    if not isinstance(text_encoder_type, str):
-        # print("text_encoder_type is not a str")
-        if hasattr(text_encoder_type, "text_encoder_type"):
-            text_encoder_type = text_encoder_type.text_encoder_type
-        elif text_encoder_type.get("text_encoder_type", False):
-            text_encoder_type = text_encoder_type.get("text_encoder_type")
-        else:
-            raise ValueError(
-                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
-            )
-    print("final text_encoder_type: {}".format(text_encoder_type))
-
-    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
-    return tokenizer
-
-
-def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased":
-        return BertModel.from_pretrained(text_encoder_type)
-    if text_encoder_type == "roberta-base":
-        return RobertaModel.from_pretrained(text_encoder_type)
-    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))
diff --git a/spaces/Silentlin/DiffSinger/data_gen/tts/bin/binarize.py b/spaces/Silentlin/DiffSinger/data_gen/tts/bin/binarize.py
deleted file mode 100644
index 4bd3c1f69fa59ed52fdd32eb80e746dedbae7535..0000000000000000000000000000000000000000
--- a/spaces/Silentlin/DiffSinger/data_gen/tts/bin/binarize.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-
-os.environ["OMP_NUM_THREADS"] = "1"
-
-import importlib
-from utils.hparams import set_hparams, hparams
-
-
-def binarize():
-    binarizer_cls = hparams.get("binarizer_cls", 'data_gen.tts.base_binarizer.BaseBinarizer')
-    pkg = ".".join(binarizer_cls.split(".")[:-1])
-    cls_name = binarizer_cls.split(".")[-1]
-    binarizer_cls = getattr(importlib.import_module(pkg), cls_name)
-    print("| Binarizer: ", binarizer_cls)
-    binarizer_cls().process()
-
-
-if __name__ == '__main__':
-    set_hparams()
-    binarize()
diff --git a/spaces/Souranil/VAE/inference.py b/spaces/Souranil/VAE/inference.py
deleted file mode 100644
index 4e78ce2e6f929dd306c60f99a42c07ff2fc88d34..0000000000000000000000000000000000000000
--- a/spaces/Souranil/VAE/inference.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from models import vae_models
-from config import config
-from PIL import Image 
-from torchvision.transforms import Resize, ToPILImage, Compose
-
-from utils import load_model, tensor_to_img, resize_img, export_to_onnx
-
-
-
-def predict(model_ckpt="vae_alpha_1024_dim_128.ckpt"):
-    model_type = config.model_type
-    model = vae_models[model_type].load_from_checkpoint(f"./saved_models/{model_ckpt}")
-    model.eval()
-    test_iter = iter(model.test_dataloader())
-    d, _ = next(test_iter)
-    _, _, out = model(d)
-    out_img = tensor_to_img(out)
-    return out_img
-
-    
-
-if __name__ == "__main__":
-    predict()
-    # export_to_onnx("./saved_models/vae.ckpt")
\ No newline at end of file
diff --git a/spaces/StarCore/PaddleOCR/pp_ocr.py b/spaces/StarCore/PaddleOCR/pp_ocr.py
deleted file mode 100644
index 943fc721c2b3979a4820bb08b275e894f8774d89..0000000000000000000000000000000000000000
--- a/spaces/StarCore/PaddleOCR/pp_ocr.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import tempfile
-import os
-
-import paddlehub as hub
-from PIL import Image
-
-pp_ocrv3 = hub.Module(name="ch_pp-ocrv3")
-
-def inference_img(img):
-    with tempfile.TemporaryDirectory() as tempdir_name:
-        pp_ocrv3.recognize_text(images=[img], use_gpu=False, output_dir=tempdir_name, visualization=True)
-        result_names = os.listdir(tempdir_name)
-        result_image = Image.open(os.path.join(tempdir_name, result_names[0]))
-        return result_image
-    
-def inference_json(img):
-        results = pp_ocrv3.recognize_text(images=[img], use_gpu=False, visualization=False)
-        return results
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_displayhook.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_displayhook.py
deleted file mode 100644
index 22899f3dd02658d51aad6d080376042c199fbc2b..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/tests/test_displayhook.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import sys
-from IPython.testing.tools import AssertPrints, AssertNotPrints
-from IPython.core.displayhook import CapturingDisplayHook
-from IPython.utils.capture import CapturedIO
-
-def test_output_displayed():
-    """Checking to make sure that output is displayed"""
-  
-    with AssertPrints('2'):
-       ip.run_cell('1+1', store_history=True)
-      
-    with AssertPrints('2'):
-        ip.run_cell('1+1 # comment with a semicolon;', store_history=True)
-
-    with AssertPrints('2'):
-        ip.run_cell('1+1\n#commented_out_function();', store_history=True)
-
-      
-def test_output_quiet():
-    """Checking to make sure that output is quiet"""
-  
-    with AssertNotPrints('2'):
-        ip.run_cell('1+1;', store_history=True)
-      
-    with AssertNotPrints('2'):
-        ip.run_cell('1+1; # comment with a semicolon', store_history=True)
-
-    with AssertNotPrints('2'):
-        ip.run_cell('1+1;\n#commented_out_function()', store_history=True)
-
-def test_underscore_no_overwrite_user():
-    ip.run_cell('_ = 42', store_history=True)
-    ip.run_cell('1+1', store_history=True)
-
-    with AssertPrints('42'):
-        ip.run_cell('print(_)', store_history=True)
-
-    ip.run_cell('del _', store_history=True)
-    ip.run_cell('6+6', store_history=True)
-    with AssertPrints('12'):
-        ip.run_cell('_', store_history=True)
-
-
-def test_underscore_no_overwrite_builtins():
-    ip.run_cell("import gettext ; gettext.install('foo')", store_history=True)
-    ip.run_cell('3+3', store_history=True)
-
-    with AssertPrints('gettext'):
-        ip.run_cell('print(_)', store_history=True)
-
-    ip.run_cell('_ = "userset"', store_history=True)
-
-    with AssertPrints('userset'):
-        ip.run_cell('print(_)', store_history=True)
-    ip.run_cell('import builtins; del builtins._')
-
-
-def test_interactivehooks_ast_modes():
-    """
-    Test that ast nodes can be triggered with different modes
-    """
-    saved_mode = ip.ast_node_interactivity
-    ip.ast_node_interactivity = 'last_expr_or_assign'
-
-    try:
-        with AssertPrints('2'):
-            ip.run_cell('a = 1+1', store_history=True)
-
-        with AssertPrints('9'):
-            ip.run_cell('b = 1+8 # comment with a semicolon;', store_history=False)
-
-        with AssertPrints('7'):
-            ip.run_cell('c = 1+6\n#commented_out_function();', store_history=True)
-
-        ip.run_cell('d = 11', store_history=True)
-        with AssertPrints('12'):
-            ip.run_cell('d += 1', store_history=True)
-
-        with AssertNotPrints('42'):
-            ip.run_cell('(u,v) = (41+1, 43-1)')
-
-    finally:
-        ip.ast_node_interactivity = saved_mode
-
-def test_interactivehooks_ast_modes_semi_suppress():
-    """
-    Test that ast nodes can be triggered with different modes and suppressed
-    by semicolon
-    """
-    saved_mode = ip.ast_node_interactivity
-    ip.ast_node_interactivity = 'last_expr_or_assign'
-
-    try:
-        with AssertNotPrints('2'):
-            ip.run_cell('x = 1+1;', store_history=True)
-
-        with AssertNotPrints('7'):
-            ip.run_cell('y = 1+6; # comment with a semicolon', store_history=True)
-
-        with AssertNotPrints('9'):
-            ip.run_cell('z = 1+8;\n#commented_out_function()', store_history=True)
-
-    finally:
-        ip.ast_node_interactivity = saved_mode
-
-def test_capture_display_hook_format():
-    """Tests that the capture display hook conforms to the CapturedIO output format"""
-    hook = CapturingDisplayHook(ip)
-    hook({"foo": "bar"})
-    captured = CapturedIO(sys.stdout, sys.stderr, hook.outputs)
-    # Should not raise with RichOutput transformation error
-    captured.outputs
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/api/__init__.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/api/__init__.py
deleted file mode 100644
index 2472e14228ca5d5aeb69c707f127e3a3d9febe13..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/chromadb/api/__init__.py
+++ /dev/null
@@ -1,342 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Sequence, Optional
-import pandas as pd
-from uuid import UUID
-from chromadb.api.models.Collection import Collection
-from chromadb.api.types import (
-    CollectionMetadata,
-    Documents,
-    EmbeddingFunction,
-    Embeddings,
-    IDs,
-    Include,
-    Metadatas,
-    Where,
-    QueryResult,
-    GetResult,
-    WhereDocument,
-)
-from chromadb.config import Component
-import chromadb.utils.embedding_functions as ef
-from overrides import override
-
-
-class API(Component, ABC):
-    @abstractmethod
-    def heartbeat(self) -> int:
-        """Returns the current server time in nanoseconds to check if the server is alive
-
-        Args:
-            None
-
-        Returns:
-            int: The current server time in nanoseconds
-
-        """
-        pass
-
-    @abstractmethod
-    def list_collections(self) -> Sequence[Collection]:
-        """Returns all collections in the database
-
-        Args:
-            None
-
-        Returns:
-            dict: A dictionary of collections
-
-        """
-        pass
-
-    @abstractmethod
-    def create_collection(
-        self,
-        name: str,
-        metadata: Optional[CollectionMetadata] = None,
-        embedding_function: Optional[EmbeddingFunction] = ef.DefaultEmbeddingFunction(),
-        get_or_create: bool = False,
-    ) -> Collection:
-        """Creates a new collection in the database
-
-        Args:
-            name  The name of the collection to create. The name must be unique.
-            metadata: A dictionary of metadata to associate with the collection. Defaults to None.
-            embedding_function: A function that takes documents and returns an embedding. Defaults to None.
-            get_or_create: If True, will return the collection if it already exists,
-                and update the metadata (if applicable). Defaults to False.
-
-        Returns:
-            dict: the created collection
-
-        """
-        pass
-
-    @abstractmethod
-    def delete_collection(
-        self,
-        name: str,
-    ) -> None:
-        """Deletes a collection from the database
-
-        Args:
-            name: The name of the collection to delete
-        """
-
-    @abstractmethod
-    def get_or_create_collection(
-        self,
-        name: str,
-        metadata: Optional[CollectionMetadata] = None,
-        embedding_function: Optional[EmbeddingFunction] = ef.DefaultEmbeddingFunction(),
-    ) -> Collection:
-        """Calls create_collection with get_or_create=True.
-           If the collection exists, but with different metadata, the metadata will be replaced.
-
-        Args:
-            name: The name of the collection to create. The name must be unique.
-            metadata: A dictionary of metadata to associate with the collection. Defaults to None.
-            embedding_function: A function that takes documents and returns an embedding. Should be the same as the one used to create the collection. Defaults to None.
-        Returns:
-            the created collection
-
-        """
-        pass
-
-    @abstractmethod
-    def get_collection(
-        self,
-        name: str,
-        embedding_function: Optional[EmbeddingFunction] = ef.DefaultEmbeddingFunction(),
-    ) -> Collection:
-        """Gets a collection from the database by either name or uuid
-
-        Args:
-            name: The name of the collection to get. Defaults to None.
-            embedding_function: A function that takes documents and returns an embedding. Should be the same as the one used to create the collection. Defaults to None.
-
-        Returns:
-            dict: the requested collection
-
-        """
-        pass
-
-    def _modify(
-        self,
-        id: UUID,
-        new_name: Optional[str] = None,
-        new_metadata: Optional[CollectionMetadata] = None,
-    ) -> None:
-        """Modify a collection in the database - can update the name and/or metadata
-
-        Args:
-            current_name: The name of the collection to modify
-            new_name: The new name of the collection. Defaults to None.
-            new_metadata: The new metadata to associate with the collection. Defaults to None.
-        """
-        pass
-
-    @abstractmethod
-    def _add(
-        self,
-        ids: IDs,
-        collection_id: UUID,
-        embeddings: Embeddings,
-        metadatas: Optional[Metadatas] = None,
-        documents: Optional[Documents] = None,
-        increment_index: bool = True,
-    ) -> bool:
-        """Add embeddings to the data store. This is the most general way to add embeddings to the database.
-        ⚠️ It is recommended to use the more specific methods below when possible.
-
-        Args:
-            collection_id: The collection to add the embeddings to
-            embedding: The sequence of embeddings to add
-            metadata: The metadata to associate with the embeddings. Defaults to None.
-            documents: The documents to associate with the embeddings. Defaults to None.
-            ids: The ids to associate with the embeddings. Defaults to None.
-        """
-        pass
-
-    @abstractmethod
-    def _update(
-        self,
-        collection_id: UUID,
-        ids: IDs,
-        embeddings: Optional[Embeddings] = None,
-        metadatas: Optional[Metadatas] = None,
-        documents: Optional[Documents] = None,
-    ) -> bool:
-        """Add embeddings to the data store. This is the most general way to add embeddings to the database.
-        ⚠️ It is recommended to use the more specific methods below when possible.
-
-        Args:
-            collection_id: The collection to add the embeddings to
-            embedding: The sequence of embeddings to add
-        """
-        pass
-
-    @abstractmethod
-    def _upsert(
-        self,
-        collection_id: UUID,
-        ids: IDs,
-        embeddings: Embeddings,
-        metadatas: Optional[Metadatas] = None,
-        documents: Optional[Documents] = None,
-        increment_index: bool = True,
-    ) -> bool:
-        """Add or update entries in the embedding store.
-        If an entry with the same id already exists, it will be updated, otherwise it will be added.
-
-        Args:
-            collection_id: The collection to add the embeddings to
-            ids: The ids to associate with the embeddings. Defaults to None.
-            embeddings: The sequence of embeddings to add
-            metadatas: The metadata to associate with the embeddings. Defaults to None.
-            documents: The documents to associate with the embeddings. Defaults to None.
-            increment_index: If True, will incrementally add to the ANN index of the collection. Defaults to True.
-        """
-        pass
-
-    @abstractmethod
-    def _count(self, collection_id: UUID) -> int:
-        """Returns the number of embeddings in the database
-
-        Args:
-            collection_id: The collection to count the embeddings in.
-
-
-        Returns:
-            int: The number of embeddings in the collection
-
-        """
-        pass
-
-    @abstractmethod
-    def _peek(self, collection_id: UUID, n: int = 10) -> GetResult:
-        pass
-
-    @abstractmethod
-    def _get(
-        self,
-        collection_id: UUID,
-        ids: Optional[IDs] = None,
-        where: Optional[Where] = {},
-        sort: Optional[str] = None,
-        limit: Optional[int] = None,
-        offset: Optional[int] = None,
-        page: Optional[int] = None,
-        page_size: Optional[int] = None,
-        where_document: Optional[WhereDocument] = {},
-        include: Include = ["embeddings", "metadatas", "documents"],
-    ) -> GetResult:
-        """Gets embeddings from the database. Supports filtering, sorting, and pagination.
-        ⚠️ This method should not be used directly.
-
-        Args:
-            where: A dictionary of key-value pairs to filter the embeddings by. Defaults to {}.
-            sort: The column to sort the embeddings by. Defaults to None.
-            limit: The maximum number of embeddings to return. Defaults to None.
-            offset: The number of embeddings to skip before returning. Defaults to None.
-            page: The page number to return. Defaults to None.
-            page_size: The number of embeddings to return per page. Defaults to None.
-
-        Returns:
-            pd.DataFrame: A pandas dataframe containing the embeddings and metadata
-
-        """
-        pass
-
-    @abstractmethod
-    def _delete(
-        self,
-        collection_id: UUID,
-        ids: Optional[IDs],
-        where: Optional[Where] = {},
-        where_document: Optional[WhereDocument] = {},
-    ) -> IDs:
-        """Deletes embeddings from the database
-        ⚠️ This method should not be used directly.
-
-        Args:
-            where: A dictionary of key-value pairs to filter the embeddings by. Defaults to {}.
-
-        Returns:
-            List: The list of internal UUIDs of the deleted embeddings
-        """
-        pass
-
-    @abstractmethod
-    def _query(
-        self,
-        collection_id: UUID,
-        query_embeddings: Embeddings,
-        n_results: int = 10,
-        where: Where = {},
-        where_document: WhereDocument = {},
-        include: Include = ["embeddings", "metadatas", "documents", "distances"],
-    ) -> QueryResult:
-        """Gets the nearest neighbors of a single embedding
-        ⚠️ This method should not be used directly.
-
-        Args:
-            embedding: The embedding to find the nearest neighbors of
-            n_results: The number of nearest neighbors to return. Defaults to 10.
-            where: A dictionary of key-value pairs to filter the embeddings by. Defaults to {}.
-        """
-        pass
-
-    @override
-    @abstractmethod
-    def reset(self) -> None:
-        """Resets the database
-        ⚠️ This is destructive and will delete all data in the database.
-        Args:
-            None
-
-        Returns:
-            None
-        """
-        pass
-
-    @abstractmethod
-    def raw_sql(self, sql: str) -> pd.DataFrame:
-        """Runs a raw SQL query against the database
-        ⚠️ This method should not be used directly.
-
-        Args:
-            sql: The SQL query to run
-
-        Returns:
-            pd.DataFrame: A pandas dataframe containing the results of the query
-        """
-        pass
-
-    @abstractmethod
-    def create_index(self, collection_name: str) -> bool:
-        """Creates an index for the given collection
-        ⚠️ This method should not be used directly.
-
-        Args:
-            collection_name: The collection to create the index for. Uses the client's collection if None. Defaults to None.
-
-        Returns:
-            bool: True if the index was created successfully
-
-        """
-        pass
-
-    @abstractmethod
-    def persist(self) -> bool:
-        """Persist the database to disk"""
-        pass
-
-    @abstractmethod
-    def get_version(self) -> str:
-        """Get the version of Chroma.
-
-        Returns:
-            str: The version of Chroma
-
-        """
-        pass
diff --git a/spaces/Superlang/ImageProcessor/annotator/manga_line/__init__.py b/spaces/Superlang/ImageProcessor/annotator/manga_line/__init__.py
deleted file mode 100644
index 7750765f60a2e91d89da9d439757d4919d1879dc..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/manga_line/__init__.py
+++ /dev/null
@@ -1,247 +0,0 @@
-import os
-import torch
-import torch.nn as nn
-from PIL import Image
-import fnmatch
-import cv2
-
-import sys
-
-import numpy as np
-from einops import rearrange
-from annotator.base_annotator import BaseProcessor
-
-
-class _bn_relu_conv(nn.Module):
-    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
-        super(_bn_relu_conv, self).__init__()
-        self.model = nn.Sequential(
-            nn.BatchNorm2d(in_filters, eps=1e-3),
-            nn.LeakyReLU(0.2),
-            nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2), padding_mode='zeros')
-        )
-
-    def forward(self, x):
-        return self.model(x)
-
-        # the following are for debugs
-        print("****", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
-        for i,layer in enumerate(self.model):
-            if i != 2:
-                x = layer(x)
-            else:
-                x = layer(x)
-                #x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0)
-            print("____", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
-            print(x[0])
-        return x
-
-class _u_bn_relu_conv(nn.Module):
-    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
-        super(_u_bn_relu_conv, self).__init__()
-        self.model = nn.Sequential(
-            nn.BatchNorm2d(in_filters, eps=1e-3),
-            nn.LeakyReLU(0.2),
-            nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2)),
-            nn.Upsample(scale_factor=2, mode='nearest')
-        )
-
-    def forward(self, x):
-        return self.model(x)
-
-
-
-class _shortcut(nn.Module):
-    def __init__(self, in_filters, nb_filters, subsample=1):
-        super(_shortcut, self).__init__()
-        self.process = False
-        self.model = None
-        if in_filters != nb_filters or subsample != 1:
-            self.process = True
-            self.model = nn.Sequential(
-                    nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample)
-                )
-
-    def forward(self, x, y):
-        #print(x.size(), y.size(), self.process)
-        if self.process:
-            y0 = self.model(x)
-            #print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape)
-            return y0 + y
-        else:
-            #print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape)
-            return x + y
-
-class _u_shortcut(nn.Module):
-    def __init__(self, in_filters, nb_filters, subsample):
-        super(_u_shortcut, self).__init__()
-        self.process = False
-        self.model = None
-        if in_filters != nb_filters:
-            self.process = True
-            self.model = nn.Sequential(
-                nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample, padding_mode='zeros'),
-                nn.Upsample(scale_factor=2, mode='nearest')
-            )
-
-    def forward(self, x, y):
-        if self.process:
-            return self.model(x) + y
-        else:
-            return x + y
-
-
-class basic_block(nn.Module):
-    def __init__(self, in_filters, nb_filters, init_subsample=1):
-        super(basic_block, self).__init__()
-        self.conv1 = _bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
-        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
-        self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.residual(x1)
-        return self.shortcut(x, x2)
-
-class _u_basic_block(nn.Module):
-    def __init__(self, in_filters, nb_filters, init_subsample=1):
-        super(_u_basic_block, self).__init__()
-        self.conv1 = _u_bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
-        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
-        self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample)
-
-    def forward(self, x):
-        y = self.residual(self.conv1(x))
-        return self.shortcut(x, y)
-
-
-class _residual_block(nn.Module):
-    def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False):
-        super(_residual_block, self).__init__()
-        layers = []
-        for i in range(repetitions):
-            init_subsample = 1
-            if i == repetitions - 1 and not is_first_layer:
-                init_subsample = 2
-            if i == 0:
-                l = basic_block(in_filters=in_filters, nb_filters=nb_filters, init_subsample=init_subsample)
-            else:
-                l = basic_block(in_filters=nb_filters, nb_filters=nb_filters, init_subsample=init_subsample)
-            layers.append(l)
-
-        self.model = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.model(x)
-
-
-class _upsampling_residual_block(nn.Module):
-    def __init__(self, in_filters, nb_filters, repetitions):
-        super(_upsampling_residual_block, self).__init__()
-        layers = []
-        for i in range(repetitions):
-            l = None
-            if i == 0: 
-                l = _u_basic_block(in_filters=in_filters, nb_filters=nb_filters)#(input)
-            else:
-                l = basic_block(in_filters=nb_filters, nb_filters=nb_filters)#(input)
-            layers.append(l)
-
-        self.model = nn.Sequential(*layers)
-
-    def forward(self, x):
-        return self.model(x)
-
-
-class res_skip(nn.Module):
-
-    def __init__(self):
-        super(res_skip, self).__init__()
-        self.block0 = _residual_block(in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True)#(input)
-        self.block1 = _residual_block(in_filters=24, nb_filters=48, repetitions=3)#(block0)
-        self.block2 = _residual_block(in_filters=48, nb_filters=96, repetitions=5)#(block1)
-        self.block3 = _residual_block(in_filters=96, nb_filters=192, repetitions=7)#(block2)
-        self.block4 = _residual_block(in_filters=192, nb_filters=384, repetitions=12)#(block3)
-        
-        self.block5 = _upsampling_residual_block(in_filters=384, nb_filters=192, repetitions=7)#(block4)
-        self.res1 = _shortcut(in_filters=192, nb_filters=192)#(block3, block5, subsample=(1,1))
-
-        self.block6 = _upsampling_residual_block(in_filters=192, nb_filters=96, repetitions=5)#(res1)
-        self.res2 = _shortcut(in_filters=96, nb_filters=96)#(block2, block6, subsample=(1,1))
-
-        self.block7 = _upsampling_residual_block(in_filters=96, nb_filters=48, repetitions=3)#(res2)
-        self.res3 = _shortcut(in_filters=48, nb_filters=48)#(block1, block7, subsample=(1,1))
-
-        self.block8 = _upsampling_residual_block(in_filters=48, nb_filters=24, repetitions=2)#(res3)
-        self.res4 = _shortcut(in_filters=24, nb_filters=24)#(block0,block8, subsample=(1,1))
-
-        self.block9 = _residual_block(in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True)#(res4)
-        self.conv15 = _bn_relu_conv(in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1)#(block7)
-
-    def forward(self, x):
-        x0 = self.block0(x)
-        x1 = self.block1(x0)
-        x2 = self.block2(x1)
-        x3 = self.block3(x2)
-        x4 = self.block4(x3)
-
-        x5 = self.block5(x4)
-        res1 = self.res1(x3, x5)
-
-        x6 = self.block6(res1)
-        res2 = self.res2(x2, x6)
-
-        x7 = self.block7(res2)
-        res3 = self.res3(x1, x7)
-
-        x8 = self.block8(res3)
-        res4 = self.res4(x0, x8)
-
-        x9 = self.block9(res4)
-        y = self.conv15(x9)
-
-        return y
-
-
-class MangaLineExtration(BaseProcessor):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.model = None
-        self.model_dir = os.path.join(self.models_path, "manga_line")
-        # self.device = devices.get_device_for("controlnet")
-
-    def load_model(self):
-        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/erika.pth"
-        modelpath = os.path.join(self.model_dir, "erika.pth")
-        if not os.path.exists(modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(remote_model_path, model_dir=self.model_dir)
-        #norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
-        net = res_skip()
-        ckpt = torch.load(modelpath)
-        for key in list(ckpt.keys()):
-            if 'module.' in key:
-                ckpt[key.replace('module.', '')] = ckpt[key]
-                del ckpt[key]
-        net.load_state_dict(ckpt)
-        net.eval()
-        self.model = net.to(self.device)
-
-    def unload_model(self):
-        if self.model is not None:
-            self.model.cpu()
-
-    def __call__(self, input_image):
-        if self.model is None:
-            self.load_model()
-        self.model.to(self.device)
-        img = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
-        img = np.ascontiguousarray(img.copy()).copy()
-        with torch.no_grad():
-            image_feed = torch.from_numpy(img).float().to(self.device)
-            image_feed = rearrange(image_feed, 'h w -> 1 1 h w')
-            line = self.model(image_feed)
-            line = 255 - line.cpu().numpy()[0, 0]
-            return line.clip(0, 255).astype(np.uint8)
-
-    
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/cityscapes_panoptic.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/cityscapes_panoptic.py
deleted file mode 100644
index 7ce9ec48f673dadf3f5b4ae0592fc82415d9f925..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/data/datasets/cityscapes_panoptic.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import json
-import logging
-import os
-
-from annotator.oneformer.detectron2.data import DatasetCatalog, MetadataCatalog
-from annotator.oneformer.detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES
-from annotator.oneformer.detectron2.utils.file_io import PathManager
-
-"""
-This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.
-"""
-
-
-logger = logging.getLogger(__name__)
-
-
-def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):
-    files = []
-    # scan through the directory
-    cities = PathManager.ls(image_dir)
-    logger.info(f"{len(cities)} cities found in '{image_dir}'.")
-    image_dict = {}
-    for city in cities:
-        city_img_dir = os.path.join(image_dir, city)
-        for basename in PathManager.ls(city_img_dir):
-            image_file = os.path.join(city_img_dir, basename)
-
-            suffix = "_leftImg8bit.png"
-            assert basename.endswith(suffix), basename
-            basename = os.path.basename(basename)[: -len(suffix)]
-
-            image_dict[basename] = image_file
-
-    for ann in json_info["annotations"]:
-        image_file = image_dict.get(ann["image_id"], None)
-        assert image_file is not None, "No image {} found for annotation {}".format(
-            ann["image_id"], ann["file_name"]
-        )
-        label_file = os.path.join(gt_dir, ann["file_name"])
-        segments_info = ann["segments_info"]
-
-        files.append((image_file, label_file, segments_info))
-
-    assert len(files), "No images found in {}".format(image_dir)
-    assert PathManager.isfile(files[0][0]), files[0][0]
-    assert PathManager.isfile(files[0][1]), files[0][1]
-    return files
-
-
-def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):
-    """
-    Args:
-        image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
-        gt_dir (str): path to the raw annotations. e.g.,
-            "~/cityscapes/gtFine/cityscapes_panoptic_train".
-        gt_json (str): path to the json file. e.g.,
-            "~/cityscapes/gtFine/cityscapes_panoptic_train.json".
-        meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
-            and "stuff_dataset_id_to_contiguous_id" to map category ids to
-            contiguous ids for training.
-
-    Returns:
-        list[dict]: a list of dicts in Detectron2 standard format. (See
-        `Using Custom Datasets </tutorials/datasets.html>`_ )
-    """
-
-    def _convert_category_id(segment_info, meta):
-        if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
-            segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
-                segment_info["category_id"]
-            ]
-        else:
-            segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
-                segment_info["category_id"]
-            ]
-        return segment_info
-
-    assert os.path.exists(
-        gt_json
-    ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files."  # noqa
-    with open(gt_json) as f:
-        json_info = json.load(f)
-    files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)
-    ret = []
-    for image_file, label_file, segments_info in files:
-        sem_label_file = (
-            image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png"
-        )
-        segments_info = [_convert_category_id(x, meta) for x in segments_info]
-        ret.append(
-            {
-                "file_name": image_file,
-                "image_id": "_".join(
-                    os.path.splitext(os.path.basename(image_file))[0].split("_")[:3]
-                ),
-                "sem_seg_file_name": sem_label_file,
-                "pan_seg_file_name": label_file,
-                "segments_info": segments_info,
-            }
-        )
-    assert len(ret), f"No images found in {image_dir}!"
-    assert PathManager.isfile(
-        ret[0]["sem_seg_file_name"]
-    ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py"  # noqa
-    assert PathManager.isfile(
-        ret[0]["pan_seg_file_name"]
-    ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py"  # noqa
-    return ret
-
-
-_RAW_CITYSCAPES_PANOPTIC_SPLITS = {
-    "cityscapes_fine_panoptic_train": (
-        "cityscapes/leftImg8bit/train",
-        "cityscapes/gtFine/cityscapes_panoptic_train",
-        "cityscapes/gtFine/cityscapes_panoptic_train.json",
-    ),
-    "cityscapes_fine_panoptic_val": (
-        "cityscapes/leftImg8bit/val",
-        "cityscapes/gtFine/cityscapes_panoptic_val",
-        "cityscapes/gtFine/cityscapes_panoptic_val.json",
-    ),
-    # "cityscapes_fine_panoptic_test": not supported yet
-}
-
-
-def register_all_cityscapes_panoptic(root):
-    meta = {}
-    # The following metadata maps contiguous id from [0, #thing categories +
-    # #stuff categories) to their names and colors. We have to replica of the
-    # same name and color under "thing_*" and "stuff_*" because the current
-    # visualization function in D2 handles thing and class classes differently
-    # due to some heuristic used in Panoptic FPN. We keep the same naming to
-    # enable reusing existing visualization functions.
-    thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
-    thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
-    stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
-    stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
-
-    meta["thing_classes"] = thing_classes
-    meta["thing_colors"] = thing_colors
-    meta["stuff_classes"] = stuff_classes
-    meta["stuff_colors"] = stuff_colors
-
-    # There are three types of ids in cityscapes panoptic segmentation:
-    # (1) category id: like semantic segmentation, it is the class id for each
-    #   pixel. Since there are some classes not used in evaluation, the category
-    #   id is not always contiguous and thus we have two set of category ids:
-    #       - original category id: category id in the original dataset, mainly
-    #           used for evaluation.
-    #       - contiguous category id: [0, #classes), in order to train the classifier
-    # (2) instance id: this id is used to differentiate different instances from
-    #   the same category. For "stuff" classes, the instance id is always 0; for
-    #   "thing" classes, the instance id starts from 1 and 0 is reserved for
-    #   ignored instances (e.g. crowd annotation).
-    # (3) panoptic id: this is the compact id that encode both category and
-    #   instance id by: category_id * 1000 + instance_id.
-    thing_dataset_id_to_contiguous_id = {}
-    stuff_dataset_id_to_contiguous_id = {}
-
-    for k in CITYSCAPES_CATEGORIES:
-        if k["isthing"] == 1:
-            thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
-        else:
-            stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
-
-    meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
-    meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
-
-    for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():
-        image_dir = os.path.join(root, image_dir)
-        gt_dir = os.path.join(root, gt_dir)
-        gt_json = os.path.join(root, gt_json)
-
-        DatasetCatalog.register(
-            key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)
-        )
-        MetadataCatalog.get(key).set(
-            panoptic_root=gt_dir,
-            image_root=image_dir,
-            panoptic_json=gt_json,
-            gt_dir=gt_dir.replace("cityscapes_panoptic_", ""),
-            evaluator_type="cityscapes_panoptic_seg",
-            ignore_label=255,
-            label_divisor=1000,
-            **meta,
-        )
diff --git a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py b/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py
deleted file mode 100644
index 7368ae8031188a9f946d9d3f29633c96e791e68e..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py
+++ /dev/null
@@ -1,333 +0,0 @@
-# MIT License
-
-# Copyright (c) 2022 Intelligent Systems Lab Org
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# File author: Shariq Farooq Bhat
-
-import itertools
-
-import torch
-import torch.nn as nn
-
-from zoedepth.models.depth_model import DepthModel
-from zoedepth.models.base_models.midas import MidasCore
-from zoedepth.models.layers.attractor import AttractorLayer, AttractorLayerUnnormed
-from zoedepth.models.layers.dist_layers import ConditionalLogBinomial
-from zoedepth.models.layers.localbins_layers import (Projector, SeedBinRegressor,
-                                            SeedBinRegressorUnnormed)
-from zoedepth.models.layers.patch_transformer import PatchTransformerEncoder
-from zoedepth.models.model_io import load_state_from_resource
-
-
-class ZoeDepthNK(DepthModel):
-    def __init__(self, core,  bin_conf, bin_centers_type="softplus", bin_embedding_dim=128,
-                 n_attractors=[16, 8, 4, 1], attractor_alpha=300, attractor_gamma=2, attractor_kind='sum', attractor_type='exp',
-                 min_temp=5, max_temp=50,
-                 memory_efficient=False, train_midas=True,
-                 is_midas_pretrained=True, midas_lr_factor=1, encoder_lr_factor=10, pos_enc_lr_factor=10, inverse_midas=False,  **kwargs):
-        """ZoeDepthNK model. This is the version of ZoeDepth that has two metric heads and uses a learned router to route to experts.
-
-        Args:
-            core (models.base_models.midas.MidasCore): The base midas model that is used for extraction of "relative" features
-
-            bin_conf (List[dict]): A list of dictionaries that contain the bin configuration for each metric head. Each dictionary should contain the following keys: 
-                                    "name" (str, typically same as the dataset name), "n_bins" (int), "min_depth" (float), "max_depth" (float)
-
-                                   The length of this list determines the number of metric heads.
-            bin_centers_type (str, optional): "normed" or "softplus". Activation type used for bin centers. For "normed" bin centers, linear normalization trick is applied. This results in bounded bin centers.
-                                               For "softplus", softplus activation is used and thus are unbounded. Defaults to "normed".
-            bin_embedding_dim (int, optional): bin embedding dimension. Defaults to 128.
-
-            n_attractors (List[int], optional): Number of bin attractors at decoder layers. Defaults to [16, 8, 4, 1].
-            attractor_alpha (int, optional): Proportional attractor strength. Refer to models.layers.attractor for more details. Defaults to 300.
-            attractor_gamma (int, optional): Exponential attractor strength. Refer to models.layers.attractor for more details. Defaults to 2.
-            attractor_kind (str, optional): Attraction aggregation "sum" or "mean". Defaults to 'sum'.
-            attractor_type (str, optional): Type of attractor to use; "inv" (Inverse attractor) or "exp" (Exponential attractor). Defaults to 'exp'.
-
-            min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5.
-            max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50.
-            
-            memory_efficient (bool, optional): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended incase of multiple metric heads in order save GPU memory. Defaults to False.
-
-            train_midas (bool, optional): Whether to train "core", the base midas model. Defaults to True.
-            is_midas_pretrained (bool, optional): Is "core" pretrained? Defaults to True.
-            midas_lr_factor (int, optional): Learning rate reduction factor for base midas model except its encoder and positional encodings. Defaults to 10.
-            encoder_lr_factor (int, optional): Learning rate reduction factor for the encoder in midas model. Defaults to 10.
-            pos_enc_lr_factor (int, optional): Learning rate reduction factor for positional encodings in the base midas model. Defaults to 10.
-
-        """
-
-        super().__init__()
-
-        self.core = core
-        self.bin_conf = bin_conf
-        self.min_temp = min_temp
-        self.max_temp = max_temp
-        self.memory_efficient = memory_efficient
-        self.train_midas = train_midas
-        self.is_midas_pretrained = is_midas_pretrained
-        self.midas_lr_factor = midas_lr_factor
-        self.encoder_lr_factor = encoder_lr_factor
-        self.pos_enc_lr_factor = pos_enc_lr_factor
-        self.inverse_midas = inverse_midas
-
-        N_MIDAS_OUT = 32
-        btlnck_features = self.core.output_channels[0]
-        num_out_features = self.core.output_channels[1:]
-        # self.scales = [16, 8, 4, 2]  # spatial scale factors
-
-        self.conv2 = nn.Conv2d(
-            btlnck_features, btlnck_features, kernel_size=1, stride=1, padding=0)
-
-        # Transformer classifier on the bottleneck
-        self.patch_transformer = PatchTransformerEncoder(
-            btlnck_features, 1, 128, use_class_token=True)
-        self.mlp_classifier = nn.Sequential(
-            nn.Linear(128, 128),
-            nn.ReLU(),
-            nn.Linear(128, 2)
-        )
-
-        if bin_centers_type == "normed":
-            SeedBinRegressorLayer = SeedBinRegressor
-            Attractor = AttractorLayer
-        elif bin_centers_type == "softplus":
-            SeedBinRegressorLayer = SeedBinRegressorUnnormed
-            Attractor = AttractorLayerUnnormed
-        elif bin_centers_type == "hybrid1":
-            SeedBinRegressorLayer = SeedBinRegressor
-            Attractor = AttractorLayerUnnormed
-        elif bin_centers_type == "hybrid2":
-            SeedBinRegressorLayer = SeedBinRegressorUnnormed
-            Attractor = AttractorLayer
-        else:
-            raise ValueError(
-                "bin_centers_type should be one of 'normed', 'softplus', 'hybrid1', 'hybrid2'")
-        self.bin_centers_type = bin_centers_type
-        # We have bins for each bin conf.
-        # Create a map (ModuleDict) of 'name' -> seed_bin_regressor
-        self.seed_bin_regressors = nn.ModuleDict(
-            {conf['name']: SeedBinRegressorLayer(btlnck_features, conf["n_bins"], mlp_dim=bin_embedding_dim//2, min_depth=conf["min_depth"], max_depth=conf["max_depth"])
-             for conf in bin_conf}
-        )
-
-        self.seed_projector = Projector(
-            btlnck_features, bin_embedding_dim, mlp_dim=bin_embedding_dim//2)
-        self.projectors = nn.ModuleList([
-            Projector(num_out, bin_embedding_dim, mlp_dim=bin_embedding_dim//2)
-            for num_out in num_out_features
-        ])
-
-        # Create a map (ModuleDict) of 'name' -> attractors (ModuleList)
-        self.attractors = nn.ModuleDict(
-            {conf['name']: nn.ModuleList([
-                Attractor(bin_embedding_dim, n_attractors[i],
-                          mlp_dim=bin_embedding_dim, alpha=attractor_alpha,
-                          gamma=attractor_gamma, kind=attractor_kind,
-                          attractor_type=attractor_type, memory_efficient=memory_efficient,
-                          min_depth=conf["min_depth"], max_depth=conf["max_depth"])
-                for i in range(len(n_attractors))
-            ])
-                for conf in bin_conf}
-        )
-
-        last_in = N_MIDAS_OUT
-        # conditional log binomial for each bin conf
-        self.conditional_log_binomial = nn.ModuleDict(
-            {conf['name']: ConditionalLogBinomial(last_in, bin_embedding_dim, conf['n_bins'], bottleneck_factor=4, min_temp=self.min_temp, max_temp=self.max_temp)
-             for conf in bin_conf}
-        )
-
-    def forward(self, x, return_final_centers=False, denorm=False, return_probs=False, **kwargs):
-        """
-        Args:
-            x (torch.Tensor): Input image tensor of shape (B, C, H, W). Assumes all images are from the same domain.
-            return_final_centers (bool, optional): Whether to return the final centers of the attractors. Defaults to False.
-            denorm (bool, optional): Whether to denormalize the input image. Defaults to False.
-            return_probs (bool, optional): Whether to return the probabilities of the bins. Defaults to False.
-        
-        Returns:
-            dict: Dictionary of outputs with keys:
-                - "rel_depth": Relative depth map of shape (B, 1, H, W)
-                - "metric_depth": Metric depth map of shape (B, 1, H, W)
-                - "domain_logits": Domain logits of shape (B, 2)
-                - "bin_centers": Bin centers of shape (B, N, H, W). Present only if return_final_centers is True
-                - "probs": Bin probabilities of shape (B, N, H, W). Present only if return_probs is True
-        """
-        b, c, h, w = x.shape
-        self.orig_input_width = w
-        self.orig_input_height = h
-        rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True)
-
-        outconv_activation = out[0]
-        btlnck = out[1]
-        x_blocks = out[2:]
-
-        x_d0 = self.conv2(btlnck)
-        x = x_d0
-
-        # Predict which path to take
-        embedding = self.patch_transformer(x)[0]  # N, E
-        domain_logits = self.mlp_classifier(embedding)  # N, 2
-        domain_vote = torch.softmax(domain_logits.sum(
-            dim=0, keepdim=True), dim=-1)  # 1, 2
-
-        # Get the path
-        bin_conf_name = ["nyu", "kitti"][torch.argmax(
-            domain_vote, dim=-1).squeeze().item()]
-
-        try:
-            conf = [c for c in self.bin_conf if c.name == bin_conf_name][0]
-        except IndexError:
-            raise ValueError(
-                f"bin_conf_name {bin_conf_name} not found in bin_confs")
-
-        min_depth = conf['min_depth']
-        max_depth = conf['max_depth']
-
-        seed_bin_regressor = self.seed_bin_regressors[bin_conf_name]
-        _, seed_b_centers = seed_bin_regressor(x)
-        if self.bin_centers_type == 'normed' or self.bin_centers_type == 'hybrid2':
-            b_prev = (seed_b_centers - min_depth)/(max_depth - min_depth)
-        else:
-            b_prev = seed_b_centers
-        prev_b_embedding = self.seed_projector(x)
-
-        attractors = self.attractors[bin_conf_name]
-        for projector, attractor, x in zip(self.projectors, attractors, x_blocks):
-            b_embedding = projector(x)
-            b, b_centers = attractor(
-                b_embedding, b_prev, prev_b_embedding, interpolate=True)
-            b_prev = b
-            prev_b_embedding = b_embedding
-
-        last = outconv_activation
-
-        b_centers = nn.functional.interpolate(
-            b_centers, last.shape[-2:], mode='bilinear', align_corners=True)
-        b_embedding = nn.functional.interpolate(
-            b_embedding, last.shape[-2:], mode='bilinear', align_corners=True)
-
-        clb = self.conditional_log_binomial[bin_conf_name]
-        x = clb(last, b_embedding)
-
-        # Now depth value is Sum px * cx , where cx are bin_centers from the last bin tensor
-        # print(x.shape, b_centers.shape)
-        # b_centers = nn.functional.interpolate(b_centers, x.shape[-2:], mode='bilinear', align_corners=True)
-        out = torch.sum(x * b_centers, dim=1, keepdim=True)
-
-        output = dict(domain_logits=domain_logits, metric_depth=out)
-        if return_final_centers or return_probs:
-            output['bin_centers'] = b_centers
-
-        if return_probs:
-            output['probs'] = x
-        return output
-
-    def get_lr_params(self, lr):
-        """
-        Learning rate configuration for different layers of the model
-
-        Args:
-            lr (float) : Base learning rate
-        Returns:
-            list : list of parameters to optimize and their learning rates, in the format required by torch optimizers.
-        """
-        param_conf = []
-        if self.train_midas:
-            def get_rel_pos_params():
-                for name, p in self.core.core.pretrained.named_parameters():
-                    if "relative_position" in name:
-                        yield p
-
-            def get_enc_params_except_rel_pos():
-                for name, p in self.core.core.pretrained.named_parameters():
-                    if "relative_position" not in name:
-                        yield p
-
-            encoder_params = get_enc_params_except_rel_pos()
-            rel_pos_params = get_rel_pos_params()
-            midas_params = self.core.core.scratch.parameters()
-            midas_lr_factor = self.midas_lr_factor if self.is_midas_pretrained else 1.0
-            param_conf.extend([
-                {'params': encoder_params, 'lr': lr / self.encoder_lr_factor},
-                {'params': rel_pos_params, 'lr': lr / self.pos_enc_lr_factor},
-                {'params': midas_params, 'lr': lr / midas_lr_factor}
-            ])
-
-        remaining_modules = []
-        for name, child in self.named_children():
-            if name != 'core':
-                remaining_modules.append(child)
-        remaining_params = itertools.chain(
-            *[child.parameters() for child in remaining_modules])
-        param_conf.append({'params': remaining_params, 'lr': lr})
-        return param_conf
-
-    def get_conf_parameters(self, conf_name):
-        """
-        Returns parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
-        """
-        params = []
-        for name, child in self.named_children():
-            if isinstance(child, nn.ModuleDict):
-                for bin_conf_name, module in child.items():
-                    if bin_conf_name == conf_name:
-                        params += list(module.parameters())
-        return params
-
-    def freeze_conf(self, conf_name):
-        """
-        Freezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
-        """
-        for p in self.get_conf_parameters(conf_name):
-            p.requires_grad = False
-
-    def unfreeze_conf(self, conf_name):
-        """
-        Unfreezes all the parameters of all the ModuleDicts children that are exclusively used for the given bin configuration
-        """
-        for p in self.get_conf_parameters(conf_name):
-            p.requires_grad = True
-
-    def freeze_all_confs(self):
-        """
-        Freezes all the parameters of all the ModuleDicts children
-        """
-        for name, child in self.named_children():
-            if isinstance(child, nn.ModuleDict):
-                for bin_conf_name, module in child.items():
-                    for p in module.parameters():
-                        p.requires_grad = False
-
-    @staticmethod
-    def build(midas_model_type="DPT_BEiT_L_384", pretrained_resource=None, use_pretrained_midas=False, train_midas=False, freeze_midas_bn=True, **kwargs):
-        core = MidasCore.build(midas_model_type=midas_model_type, use_pretrained_midas=use_pretrained_midas,
-                               train_midas=train_midas, fetch_features=True, freeze_bn=freeze_midas_bn, **kwargs)
-        model = ZoeDepthNK(core, **kwargs)
-        if pretrained_resource:
-            assert isinstance(pretrained_resource, str), "pretrained_resource must be a string"
-            model = load_state_from_resource(model, pretrained_resource)
-        return model
-
-    @staticmethod
-    def build_from_config(config):
-        return ZoeDepthNK.build(**config)
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/_virtualenv.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/_virtualenv.py
deleted file mode 100644
index 17f73b1d48e11ded185780973123d825a7e219d7..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/_virtualenv.py
+++ /dev/null
@@ -1,102 +0,0 @@
-"""Patches that are applied at runtime to the virtual environment."""
-
-from __future__ import annotations
-
-import os
-import sys
-from contextlib import suppress
-
-VIRTUALENV_PATCH_FILE = os.path.join(__file__)
-
-
-def patch_dist(dist):
-    """
-    Distutils allows user to configure some arguments via a configuration file:
-    https://docs.python.org/3/install/index.html#distutils-configuration-files.
-
-    Some of this arguments though don't make sense in context of the virtual environment files, let's fix them up.
-    """  # noqa: D205
-    # we cannot allow some install config as that would get packages installed outside of the virtual environment
-    old_parse_config_files = dist.Distribution.parse_config_files
-
-    def parse_config_files(self, *args, **kwargs):
-        result = old_parse_config_files(self, *args, **kwargs)
-        install = self.get_option_dict("install")
-
-        if "prefix" in install:  # the prefix governs where to install the libraries
-            install["prefix"] = VIRTUALENV_PATCH_FILE, os.path.abspath(sys.prefix)
-        for base in ("purelib", "platlib", "headers", "scripts", "data"):
-            key = f"install_{base}"
-            if key in install:  # do not allow global configs to hijack venv paths
-                install.pop(key, None)
-        return result
-
-    dist.Distribution.parse_config_files = parse_config_files
-
-
-# Import hook that patches some modules to ignore configuration values that break package installation in case
-# of virtual environments.
-_DISTUTILS_PATCH = "distutils.dist", "setuptools.dist"
-# https://docs.python.org/3/library/importlib.html#setting-up-an-importer
-
-
-class _Finder:
-    """A meta path finder that allows patching the imported distutils modules."""
-
-    fullname = None
-
-    # lock[0] is threading.Lock(), but initialized lazily to avoid importing threading very early at startup,
-    # because there are gevent-based applications that need to be first to import threading by themselves.
-    # See https://github.com/pypa/virtualenv/issues/1895 for details.
-    lock = []  # noqa: RUF012
-
-    def find_spec(self, fullname, path, target=None):  # noqa: ARG002
-        if fullname in _DISTUTILS_PATCH and self.fullname is None:
-            # initialize lock[0] lazily
-            if len(self.lock) == 0:
-                import threading
-
-                lock = threading.Lock()
-                # there is possibility that two threads T1 and T2 are simultaneously running into find_spec,
-                # observing .lock as empty, and further going into hereby initialization. However due to the GIL,
-                # list.append() operation is atomic and this way only one of the threads will "win" to put the lock
-                # - that every thread will use - into .lock[0].
-                # https://docs.python.org/3/faq/library.html#what-kinds-of-global-value-mutation-are-thread-safe
-                self.lock.append(lock)
-
-            from functools import partial
-            from importlib.util import find_spec
-
-            with self.lock[0]:
-                self.fullname = fullname
-                try:
-                    spec = find_spec(fullname, path)
-                    if spec is not None:
-                        # https://www.python.org/dev/peps/pep-0451/#how-loading-will-work
-                        is_new_api = hasattr(spec.loader, "exec_module")
-                        func_name = "exec_module" if is_new_api else "load_module"
-                        old = getattr(spec.loader, func_name)
-                        func = self.exec_module if is_new_api else self.load_module
-                        if old is not func:
-                            with suppress(AttributeError):  # C-Extension loaders are r/o such as zipimporter with <3.7
-                                setattr(spec.loader, func_name, partial(func, old))
-                        return spec
-                finally:
-                    self.fullname = None
-        return None
-
-    @staticmethod
-    def exec_module(old, module):
-        old(module)
-        if module.__name__ in _DISTUTILS_PATCH:
-            patch_dist(module)
-
-    @staticmethod
-    def load_module(old, name):
-        module = old(name)
-        if module.__name__ in _DISTUTILS_PATCH:
-            patch_dist(module)
-        return module
-
-
-sys.meta_path.insert(0, _Finder())
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/ordered_set.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/ordered_set.py
deleted file mode 100644
index 14876000de895a609d5b9f3de39c3c8fc44ef1fc..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/ordered_set.py
+++ /dev/null
@@ -1,488 +0,0 @@
-"""
-An OrderedSet is a custom MutableSet that remembers its order, so that every
-entry has an index that can be looked up.
-
-Based on a recipe originally posted to ActiveState Recipes by Raymond Hettiger,
-and released under the MIT license.
-"""
-import itertools as it
-from collections import deque
-
-try:
-    # Python 3
-    from collections.abc import MutableSet, Sequence
-except ImportError:
-    # Python 2.7
-    from collections import MutableSet, Sequence
-
-SLICE_ALL = slice(None)
-__version__ = "3.1"
-
-
-def is_iterable(obj):
-    """
-    Are we being asked to look up a list of things, instead of a single thing?
-    We check for the `__iter__` attribute so that this can cover types that
-    don't have to be known by this module, such as NumPy arrays.
-
-    Strings, however, should be considered as atomic values to look up, not
-    iterables. The same goes for tuples, since they are immutable and therefore
-    valid entries.
-
-    We don't need to check for the Python 2 `unicode` type, because it doesn't
-    have an `__iter__` attribute anyway.
-    """
-    return (
-        hasattr(obj, "__iter__")
-        and not isinstance(obj, str)
-        and not isinstance(obj, tuple)
-    )
-
-
-class OrderedSet(MutableSet, Sequence):
-    """
-    An OrderedSet is a custom MutableSet that remembers its order, so that
-    every entry has an index that can be looked up.
-
-    Example:
-        >>> OrderedSet([1, 1, 2, 3, 2])
-        OrderedSet([1, 2, 3])
-    """
-
-    def __init__(self, iterable=None):
-        self.items = []
-        self.map = {}
-        if iterable is not None:
-            self |= iterable
-
-    def __len__(self):
-        """
-        Returns the number of unique elements in the ordered set
-
-        Example:
-            >>> len(OrderedSet([]))
-            0
-            >>> len(OrderedSet([1, 2]))
-            2
-        """
-        return len(self.items)
-
-    def __getitem__(self, index):
-        """
-        Get the item at a given index.
-
-        If `index` is a slice, you will get back that slice of items, as a
-        new OrderedSet.
-
-        If `index` is a list or a similar iterable, you'll get a list of
-        items corresponding to those indices. This is similar to NumPy's
-        "fancy indexing". The result is not an OrderedSet because you may ask
-        for duplicate indices, and the number of elements returned should be
-        the number of elements asked for.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset[1]
-            2
-        """
-        if isinstance(index, slice) and index == SLICE_ALL:
-            return self.copy()
-        elif is_iterable(index):
-            return [self.items[i] for i in index]
-        elif hasattr(index, "__index__") or isinstance(index, slice):
-            result = self.items[index]
-            if isinstance(result, list):
-                return self.__class__(result)
-            else:
-                return result
-        else:
-            raise TypeError("Don't know how to index an OrderedSet by %r" % index)
-
-    def copy(self):
-        """
-        Return a shallow copy of this object.
-
-        Example:
-            >>> this = OrderedSet([1, 2, 3])
-            >>> other = this.copy()
-            >>> this == other
-            True
-            >>> this is other
-            False
-        """
-        return self.__class__(self)
-
-    def __getstate__(self):
-        if len(self) == 0:
-            # The state can't be an empty list.
-            # We need to return a truthy value, or else __setstate__ won't be run.
-            #
-            # This could have been done more gracefully by always putting the state
-            # in a tuple, but this way is backwards- and forwards- compatible with
-            # previous versions of OrderedSet.
-            return (None,)
-        else:
-            return list(self)
-
-    def __setstate__(self, state):
-        if state == (None,):
-            self.__init__([])
-        else:
-            self.__init__(state)
-
-    def __contains__(self, key):
-        """
-        Test if the item is in this ordered set
-
-        Example:
-            >>> 1 in OrderedSet([1, 3, 2])
-            True
-            >>> 5 in OrderedSet([1, 3, 2])
-            False
-        """
-        return key in self.map
-
-    def add(self, key):
-        """
-        Add `key` as an item to this OrderedSet, then return its index.
-
-        If `key` is already in the OrderedSet, return the index it already
-        had.
-
-        Example:
-            >>> oset = OrderedSet()
-            >>> oset.append(3)
-            0
-            >>> print(oset)
-            OrderedSet([3])
-        """
-        if key not in self.map:
-            self.map[key] = len(self.items)
-            self.items.append(key)
-        return self.map[key]
-
-    append = add
-
-    def update(self, sequence):
-        """
-        Update the set with the given iterable sequence, then return the index
-        of the last element inserted.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.update([3, 1, 5, 1, 4])
-            4
-            >>> print(oset)
-            OrderedSet([1, 2, 3, 5, 4])
-        """
-        item_index = None
-        try:
-            for item in sequence:
-                item_index = self.add(item)
-        except TypeError:
-            raise ValueError(
-                "Argument needs to be an iterable, got %s" % type(sequence)
-            )
-        return item_index
-
-    def index(self, key):
-        """
-        Get the index of a given entry, raising an IndexError if it's not
-        present.
-
-        `key` can be an iterable of entries that is not a string, in which case
-        this returns a list of indices.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.index(2)
-            1
-        """
-        if is_iterable(key):
-            return [self.index(subkey) for subkey in key]
-        return self.map[key]
-
-    # Provide some compatibility with pd.Index
-    get_loc = index
-    get_indexer = index
-
-    def pop(self):
-        """
-        Remove and return the last element from the set.
-
-        Raises KeyError if the set is empty.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.pop()
-            3
-        """
-        if not self.items:
-            raise KeyError("Set is empty")
-
-        elem = self.items[-1]
-        del self.items[-1]
-        del self.map[elem]
-        return elem
-
-    def discard(self, key):
-        """
-        Remove an element.  Do not raise an exception if absent.
-
-        The MutableSet mixin uses this to implement the .remove() method, which
-        *does* raise an error when asked to remove a non-existent item.
-
-        Example:
-            >>> oset = OrderedSet([1, 2, 3])
-            >>> oset.discard(2)
-            >>> print(oset)
-            OrderedSet([1, 3])
-            >>> oset.discard(2)
-            >>> print(oset)
-            OrderedSet([1, 3])
-        """
-        if key in self:
-            i = self.map[key]
-            del self.items[i]
-            del self.map[key]
-            for k, v in self.map.items():
-                if v >= i:
-                    self.map[k] = v - 1
-
-    def clear(self):
-        """
-        Remove all items from this OrderedSet.
-        """
-        del self.items[:]
-        self.map.clear()
-
-    def __iter__(self):
-        """
-        Example:
-            >>> list(iter(OrderedSet([1, 2, 3])))
-            [1, 2, 3]
-        """
-        return iter(self.items)
-
-    def __reversed__(self):
-        """
-        Example:
-            >>> list(reversed(OrderedSet([1, 2, 3])))
-            [3, 2, 1]
-        """
-        return reversed(self.items)
-
-    def __repr__(self):
-        if not self:
-            return "%s()" % (self.__class__.__name__,)
-        return "%s(%r)" % (self.__class__.__name__, list(self))
-
-    def __eq__(self, other):
-        """
-        Returns true if the containers have the same items. If `other` is a
-        Sequence, then order is checked, otherwise it is ignored.
-
-        Example:
-            >>> oset = OrderedSet([1, 3, 2])
-            >>> oset == [1, 3, 2]
-            True
-            >>> oset == [1, 2, 3]
-            False
-            >>> oset == [2, 3]
-            False
-            >>> oset == OrderedSet([3, 2, 1])
-            False
-        """
-        # In Python 2 deque is not a Sequence, so treat it as one for
-        # consistent behavior with Python 3.
-        if isinstance(other, (Sequence, deque)):
-            # Check that this OrderedSet contains the same elements, in the
-            # same order, as the other object.
-            return list(self) == list(other)
-        try:
-            other_as_set = set(other)
-        except TypeError:
-            # If `other` can't be converted into a set, it's not equal.
-            return False
-        else:
-            return set(self) == other_as_set
-
-    def union(self, *sets):
-        """
-        Combines all unique items.
-        Each items order is defined by its first appearance.
-
-        Example:
-            >>> oset = OrderedSet.union(OrderedSet([3, 1, 4, 1, 5]), [1, 3], [2, 0])
-            >>> print(oset)
-            OrderedSet([3, 1, 4, 5, 2, 0])
-            >>> oset.union([8, 9])
-            OrderedSet([3, 1, 4, 5, 2, 0, 8, 9])
-            >>> oset | {10}
-            OrderedSet([3, 1, 4, 5, 2, 0, 10])
-        """
-        cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
-        containers = map(list, it.chain([self], sets))
-        items = it.chain.from_iterable(containers)
-        return cls(items)
-
-    def __and__(self, other):
-        # the parent implementation of this is backwards
-        return self.intersection(other)
-
-    def intersection(self, *sets):
-        """
-        Returns elements in common between all sets. Order is defined only
-        by the first set.
-
-        Example:
-            >>> oset = OrderedSet.intersection(OrderedSet([0, 1, 2, 3]), [1, 2, 3])
-            >>> print(oset)
-            OrderedSet([1, 2, 3])
-            >>> oset.intersection([2, 4, 5], [1, 2, 3, 4])
-            OrderedSet([2])
-            >>> oset.intersection()
-            OrderedSet([1, 2, 3])
-        """
-        cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
-        if sets:
-            common = set.intersection(*map(set, sets))
-            items = (item for item in self if item in common)
-        else:
-            items = self
-        return cls(items)
-
-    def difference(self, *sets):
-        """
-        Returns all elements that are in this set but not the others.
-
-        Example:
-            >>> OrderedSet([1, 2, 3]).difference(OrderedSet([2]))
-            OrderedSet([1, 3])
-            >>> OrderedSet([1, 2, 3]).difference(OrderedSet([2]), OrderedSet([3]))
-            OrderedSet([1])
-            >>> OrderedSet([1, 2, 3]) - OrderedSet([2])
-            OrderedSet([1, 3])
-            >>> OrderedSet([1, 2, 3]).difference()
-            OrderedSet([1, 2, 3])
-        """
-        cls = self.__class__
-        if sets:
-            other = set.union(*map(set, sets))
-            items = (item for item in self if item not in other)
-        else:
-            items = self
-        return cls(items)
-
-    def issubset(self, other):
-        """
-        Report whether another set contains this set.
-
-        Example:
-            >>> OrderedSet([1, 2, 3]).issubset({1, 2})
-            False
-            >>> OrderedSet([1, 2, 3]).issubset({1, 2, 3, 4})
-            True
-            >>> OrderedSet([1, 2, 3]).issubset({1, 4, 3, 5})
-            False
-        """
-        if len(self) > len(other):  # Fast check for obvious cases
-            return False
-        return all(item in other for item in self)
-
-    def issuperset(self, other):
-        """
-        Report whether this set contains another set.
-
-        Example:
-            >>> OrderedSet([1, 2]).issuperset([1, 2, 3])
-            False
-            >>> OrderedSet([1, 2, 3, 4]).issuperset({1, 2, 3})
-            True
-            >>> OrderedSet([1, 4, 3, 5]).issuperset({1, 2, 3})
-            False
-        """
-        if len(self) < len(other):  # Fast check for obvious cases
-            return False
-        return all(item in self for item in other)
-
-    def symmetric_difference(self, other):
-        """
-        Return the symmetric difference of two OrderedSets as a new set.
-        That is, the new set will contain all elements that are in exactly
-        one of the sets.
-
-        Their order will be preserved, with elements from `self` preceding
-        elements from `other`.
-
-        Example:
-            >>> this = OrderedSet([1, 4, 3, 5, 7])
-            >>> other = OrderedSet([9, 7, 1, 3, 2])
-            >>> this.symmetric_difference(other)
-            OrderedSet([4, 5, 9, 2])
-        """
-        cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
-        diff1 = cls(self).difference(other)
-        diff2 = cls(other).difference(self)
-        return diff1.union(diff2)
-
-    def _update_items(self, items):
-        """
-        Replace the 'items' list of this OrderedSet with a new one, updating
-        self.map accordingly.
-        """
-        self.items = items
-        self.map = {item: idx for (idx, item) in enumerate(items)}
-
-    def difference_update(self, *sets):
-        """
-        Update this OrderedSet to remove items from one or more other sets.
-
-        Example:
-            >>> this = OrderedSet([1, 2, 3])
-            >>> this.difference_update(OrderedSet([2, 4]))
-            >>> print(this)
-            OrderedSet([1, 3])
-
-            >>> this = OrderedSet([1, 2, 3, 4, 5])
-            >>> this.difference_update(OrderedSet([2, 4]), OrderedSet([1, 4, 6]))
-            >>> print(this)
-            OrderedSet([3, 5])
-        """
-        items_to_remove = set()
-        for other in sets:
-            items_to_remove |= set(other)
-        self._update_items([item for item in self.items if item not in items_to_remove])
-
-    def intersection_update(self, other):
-        """
-        Update this OrderedSet to keep only items in another set, preserving
-        their order in this set.
-
-        Example:
-            >>> this = OrderedSet([1, 4, 3, 5, 7])
-            >>> other = OrderedSet([9, 7, 1, 3, 2])
-            >>> this.intersection_update(other)
-            >>> print(this)
-            OrderedSet([1, 3, 7])
-        """
-        other = set(other)
-        self._update_items([item for item in self.items if item in other])
-
-    def symmetric_difference_update(self, other):
-        """
-        Update this OrderedSet to remove items from another set, then
-        add items from the other set that were not present in this set.
-
-        Example:
-            >>> this = OrderedSet([1, 4, 3, 5, 7])
-            >>> other = OrderedSet([9, 7, 1, 3, 2])
-            >>> this.symmetric_difference_update(other)
-            >>> print(this)
-            OrderedSet([4, 5, 9, 2])
-        """
-        items_to_add = [item for item in other if item not in self]
-        items_to_remove = set(other)
-        self._update_items(
-            [item for item in self.items if item not in items_to_remove] + items_to_add
-        )
diff --git a/spaces/TencentARC/T2I-Adapter-SDXL-Sketch/app.py b/spaces/TencentARC/T2I-Adapter-SDXL-Sketch/app.py
deleted file mode 100644
index 7152631b17f750e34f06887fb179fbbe13952671..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/T2I-Adapter-SDXL-Sketch/app.py
+++ /dev/null
@@ -1,266 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import random
-
-import gradio as gr
-import numpy as np
-import PIL.Image
-import torch
-import torchvision.transforms.functional as TF
-from diffusers import (
-    AutoencoderKL,
-    EulerAncestralDiscreteScheduler,
-    StableDiffusionXLAdapterPipeline,
-    T2IAdapter,
-)
-
-DESCRIPTION = '''# Doodly - T2I-Adapter-SDXL **Sketch**
-To try out all the [6 T2I-Adapter](https://huggingface.co/collections/TencentARC/t2i-adapter-sdxl-64fac9cbf393f30370eeb02f) released for SDXL, [click here](https://huggingface.co/spaces/TencentARC/T2I-Adapter-SDXL)
-'''
-
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
-
-style_list = [
-    {
-        "name": "(No style)",
-        "prompt": "{prompt}",
-        "negative_prompt": "",
-    },
-    {
-        "name": "Cinematic",
-        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
-        "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
-    },
-    {
-        "name": "3D Model",
-        "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
-        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
-    },
-    {
-        "name": "Anime",
-        "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime,  highly detailed",
-        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
-    },
-    {
-        "name": "Digital Art",
-        "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
-        "negative_prompt": "photo, photorealistic, realism, ugly",
-    },
-    {
-        "name": "Photographic",
-        "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
-        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
-    },
-    {
-        "name": "Pixel art",
-        "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
-        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
-    },
-    {
-        "name": "Fantasy art",
-        "prompt": "ethereal fantasy concept art of  {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
-        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
-    },
-    {
-        "name": "Neonpunk",
-        "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
-        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
-    },
-    {
-        "name": "Manga",
-        "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
-        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
-    },
-]
-
-styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
-STYLE_NAMES = list(styles.keys())
-DEFAULT_STYLE_NAME = "(No style)"
-
-
-def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
-    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
-    return p.replace("{prompt}", positive), n + negative
-
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-if torch.cuda.is_available():
-    model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-    adapter = T2IAdapter.from_pretrained(
-        "TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, variant="fp16"
-    )
-    scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-    pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
-        model_id,
-        vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16),
-        adapter=adapter,
-        scheduler=scheduler,
-        torch_dtype=torch.float16,
-        variant="fp16",
-    )
-    pipe.to(device)
-else:
-    pipe = None
-
-MAX_SEED = np.iinfo(np.int32).max
-
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-
-def run(
-    image: PIL.Image.Image,
-    prompt: str,
-    negative_prompt: str,
-    style_name: str = DEFAULT_STYLE_NAME,
-    num_steps: int = 25,
-    guidance_scale: float = 5,
-    adapter_conditioning_scale: float = 0.8,
-    adapter_conditioning_factor: float = 0.8,
-    seed: int = 0,
-    progress=gr.Progress(track_tqdm=True),
-) -> PIL.Image.Image:
-    image = image.convert("RGB")
-    image = TF.to_tensor(image) > 0.5
-    image = TF.to_pil_image(image.to(torch.float32))
-
-    prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt)
-
-    generator = torch.Generator(device=device).manual_seed(seed)
-    out = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=image,
-        num_inference_steps=num_steps,
-        generator=generator,
-        guidance_scale=guidance_scale,
-        adapter_conditioning_scale=adapter_conditioning_scale,
-        adapter_conditioning_factor=adapter_conditioning_factor,
-    ).images[0]
-    return out
-
-
-with gr.Blocks(css="style.css") as demo:
-    gr.Markdown(DESCRIPTION, elem_id="description")
-    gr.DuplicateButton(
-        value="Duplicate Space for private use",
-        elem_id="duplicate-button",
-        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
-    )
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Group():
-                image = gr.Image(
-                    source="canvas",
-                    tool="sketch",
-                    type="pil",
-                    image_mode="L",
-                    invert_colors=True,
-                    shape=(1024, 1024),
-                    brush_radius=4,
-                    height=440,
-                )
-                prompt = gr.Textbox(label="Prompt")
-                style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME)
-                run_button = gr.Button("Run")
-            with gr.Accordion("Advanced options", open=False):
-                negative_prompt = gr.Textbox(
-                    label="Negative prompt",
-                    value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured",
-                )
-                num_steps = gr.Slider(
-                    label="Number of steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=25,
-                )
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.1,
-                    maximum=10.0,
-                    step=0.1,
-                    value=5,
-                )
-                adapter_conditioning_scale = gr.Slider(
-                    label="Adapter conditioning scale",
-                    minimum=0.5,
-                    maximum=1,
-                    step=0.1,
-                    value=0.8,
-                )
-                adapter_conditioning_factor = gr.Slider(
-                    label="Adapter conditioning factor",
-                    info="Fraction of timesteps for which adapter should be applied",
-                    minimum=0.5,
-                    maximum=1,
-                    step=0.1,
-                    value=0.8,
-                )
-                seed = gr.Slider(
-                    label="Seed",
-                    minimum=0,
-                    maximum=MAX_SEED,
-                    step=1,
-                    value=0,
-                )
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        with gr.Column():
-            result = gr.Image(label="Result", height=400)
-
-    inputs = [
-        image,
-        prompt,
-        negative_prompt,
-        style,
-        num_steps,
-        guidance_scale,
-        adapter_conditioning_scale,
-        adapter_conditioning_factor,
-        seed,
-    ]
-    prompt.submit(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=run,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-    negative_prompt.submit(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=run,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-    run_button.click(
-        fn=randomize_seed_fn,
-        inputs=[seed, randomize_seed],
-        outputs=seed,
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=run,
-        inputs=inputs,
-        outputs=result,
-        api_name=False,
-    )
-
-if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
diff --git a/spaces/TencentARC/VLog/models/grit_src/grit/modeling/text/text_decoder.py b/spaces/TencentARC/VLog/models/grit_src/grit/modeling/text/text_decoder.py
deleted file mode 100644
index 071baa7a52d21d7132cc492f070cba066d17aa43..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/grit/modeling/text/text_decoder.py
+++ /dev/null
@@ -1,672 +0,0 @@
-# Modified by Jialian Wu from
-# https://github.com/microsoft/GenerativeImage2Text/blob/main/generativeimage2text/layers/decoder.py
-# and https://github.com/kdexd/virtex
-from torch import nn
-import torch
-import functools
-from torch.nn import functional as F
-import warnings
-
-
-class TextualHead(nn.Module):
-    def __init__(self,
-                 visual_feature_size: int, vocab_size: int, hidden_size: int):
-        super().__init__()
-        self.visual_feature_size = visual_feature_size
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-
-    @property
-    def textual_feature_size(self):
-        return self.hidden_size
-
-
-class WordAndPositionalEmbedding(nn.Module):
-    def __init__(
-        self,
-        vocab_size: int,
-        hidden_size: int,
-        dropout: float = 0.0,
-        max_caption_length: int = 30,
-        padding_idx: int = 0,
-    ):
-        super().__init__()
-        self.vocab_size = vocab_size
-        self.padding_idx = padding_idx
-
-        #self.words = nn.Embedding(vocab_size, hidden_size, padding_idx=padding_idx)
-        self.words = nn.Embedding(vocab_size, hidden_size)
-
-        # We provide no "padding index" for positional embeddings. We zero out
-        # the positional embeddings of padded positions as a post-processing.
-        self.positions = nn.Embedding(max_caption_length, hidden_size)
-        self.layer_norm = nn.LayerNorm(
-            hidden_size, eps=1e-8, elementwise_affine=True
-        )
-        self.dropout = nn.Dropout(p=dropout)
-
-    def forward(self, tokens: torch.Tensor):
-        position_indices = self._create_position_indices(tokens)
-
-        # shape: (batch_size, max_caption_length, hidden_size)
-        word_embeddings = self.words(tokens)
-        position_embeddings = self.positions(position_indices)
-
-        # shape: (batch_size, max_caption_length, hidden_size)
-        embeddings = self.layer_norm(word_embeddings + position_embeddings)
-        embeddings = self.dropout(embeddings)
-
-        return embeddings
-
-    @functools.lru_cache(maxsize=128)
-    def _create_position_indices(self, tokens: torch.Tensor):
-
-        # Create position indices of the same size as token indices.
-        batch_size, max_caption_length = tokens.size()
-        positions = torch.arange(
-            max_caption_length, dtype=tokens.dtype, device=tokens.device
-        )
-        # shape: (batch_size, max_caption_length)
-        positions = positions.unsqueeze(0).expand(batch_size, max_caption_length)
-        return positions
-
-
-class BertEncoderAsDecoder(nn.Module):
-    def __init__(self, encoder):
-        super().__init__()
-        self.encoder = encoder
-
-    def forward(self, tgt, memory,
-                tgt_mask=None,
-                tgt_key_padding_mask=None,
-                memory_key_padding_mask=None,
-                tgt_bi_valid_mask=None,
-                encoder_history_states=None,
-                ):
-        assert tgt_key_padding_mask is None, 'not supported'
-        assert tgt_mask.dim() == 2
-        assert tgt_mask.shape[0] == tgt_mask.shape[1]
-        # tgt_mask should always be 0/negative infinity
-        tgt = tgt.transpose(0, 1)
-        memory = memory.transpose(0, 1)
-
-        hidden_states = torch.cat((memory, tgt), dim=1)
-        num_tgt = tgt.shape[1]
-        num_memory = memory.shape[1]
-        device = tgt.device
-        dtype = tgt.dtype
-        top_left = torch.zeros((num_memory, num_memory), device=device, dtype=dtype)
-        top_right = torch.full((num_memory, num_tgt), float('-inf'), device=tgt.device, dtype=dtype,)
-        bottom_left = torch.zeros((num_tgt, num_memory), dtype=dtype, device=tgt_mask.device,)
-        left = torch.cat((top_left, bottom_left), dim=0)
-        right = torch.cat((top_right, tgt_mask.to(dtype)), dim=0)
-
-        full_attention_mask = torch.cat((left, right), dim=1)[None, :]
-
-        if memory_key_padding_mask is None:
-            memory_key_padding_mask = torch.full((memory.shape[0], memory.shape[1]), fill_value=False, device=device)
-        # if it is False, it means valid. That is, it is not a padding
-        assert memory_key_padding_mask.dtype == torch.bool
-        zero_negative_infinity = torch.zeros_like(memory_key_padding_mask, dtype=tgt.dtype)
-        zero_negative_infinity[memory_key_padding_mask] = float('-inf')
-        full_attention_mask = full_attention_mask.expand((memory_key_padding_mask.shape[0], num_memory + num_tgt, num_memory + num_tgt))
-        full_attention_mask = full_attention_mask.clone()
-        origin_left = full_attention_mask[:, :, :num_memory]
-        update = zero_negative_infinity[:, None, :]
-        full_attention_mask[:, :, :num_memory] = origin_left + update
-
-        if tgt_bi_valid_mask is not None:
-            # verify the correctness
-            bs = full_attention_mask.shape[0]
-            # during inference, tgt_bi_valid_mask's length is not changed, but
-            # num_tgt can be increased
-            max_valid_target = tgt_bi_valid_mask.shape[1]
-            mask = tgt_bi_valid_mask[:, None, :].expand((bs, num_memory+num_tgt, max_valid_target))
-            full_attention_mask[:, :, num_memory:(num_memory+max_valid_target)][mask] = 0
-
-        # add axis for multi-head
-        full_attention_mask = full_attention_mask[:, None, :, :]
-
-        if encoder_history_states is None:
-            result = self.encoder(
-                hidden_states=hidden_states,
-                attention_mask=full_attention_mask,
-                encoder_history_states=encoder_history_states,
-            )
-            result = list(result)
-            result[0] = result[0][:, num_memory:].transpose(0, 1)
-            if self.encoder.output_hidden_states:
-                return result[0], result[1]
-            else:
-                # make it back-compatible
-                return result[0]
-        else:
-            encoder_out = self.encoder(
-                hidden_states=hidden_states[:, -1:],
-                attention_mask=full_attention_mask[:, :, -1:],
-                encoder_history_states=encoder_history_states,
-            )
-            result = encoder_out[0].transpose(0, 1)
-            if self.encoder.output_hidden_states:
-                return result, encoder_out[1]
-            else:
-                return result
-
-
-def create_transformer(decoder_type, norm_type,
-                   textual_feature_size,
-                   attention_heads,
-                   feedforward_size,
-                   dropout,
-                   num_layers,
-                   output_hidden_states=False,
-                   use_mlp_wrapper=None,
-                   use_act_checkpoint=True,
-                   ):
-    assert norm_type in ['post', 'pre']
-    if decoder_type is None:
-        LayerClass = (
-            nn.TransformerDecoderLayer
-            if norm_type == "post"
-            else PreNormTransformerDecoderLayer
-        )
-        _layer = LayerClass(
-            textual_feature_size,
-            attention_heads,
-            dim_feedforward=feedforward_size,
-            dropout=dropout,
-            activation="gelu",
-        )
-        return nn.TransformerDecoder(_layer, num_layers)
-    elif decoder_type == 'bert_en':
-        from .modeling_bert import BertConfig, BertEncoder
-        config = BertConfig(
-            vocab_size_or_config_json_file=30522,
-            hidden_size=textual_feature_size,
-            num_hidden_layers=num_layers,
-            num_attention_heads=attention_heads,
-            intermediate_size=feedforward_size,
-            hidden_act="gelu",
-            hidden_dropout_prob=0.1,
-            attention_probs_dropout_prob=0.1,
-            layer_norm_eps=1e-12,
-        )
-        config.pre_norm = (norm_type == 'pre')
-        config.use_mlp_wrapper = use_mlp_wrapper
-        config.output_hidden_states = output_hidden_states
-        encoder = BertEncoder(config, use_act_checkpoint=use_act_checkpoint)
-        return BertEncoderAsDecoder(encoder)
-
-
-class PreNormTransformerDecoderLayer(nn.TransformerDecoderLayer):
-    def forward(self, tgt, memory, tgt_mask=None, memory_mask=None,
-                tgt_key_padding_mask=None, memory_key_padding_mask=None):
-        # fmt: off
-        # We use the members (modules) from super-class, just the order of
-        # operations is changed here. First layernorm, then attention.
-        tgt2 = self.norm1(tgt)
-        tgt2, _ = self.self_attn(
-            tgt2, tgt2, tgt2, attn_mask=tgt_mask,
-            key_padding_mask=tgt_key_padding_mask
-        )
-        tgt = tgt + self.dropout1(tgt2)
-
-        # Layernorm first, then decoder attention.
-        tgt2 = self.norm2(tgt)
-        tgt2, _ = self.multihead_attn(
-            tgt2, memory, memory, attn_mask=memory_mask,
-            key_padding_mask=memory_key_padding_mask
-        )
-        tgt = tgt + self.dropout2(tgt2)
-
-        # Layernorm first, then transformation through feedforward network.
-        tgt2 = self.norm3(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout3(tgt2)
-        return tgt
-
-
-class TransformerDecoderTextualHead(TextualHead):
-    def __init__(
-        self,
-        object_feature_size: int,
-        vocab_size: int,
-        hidden_size: int,
-        num_layers: int,
-        attention_heads: int,
-        feedforward_size: int,
-        dropout: float = 0.1,
-        norm_type: str = "post",
-        mask_future_positions: bool = True,
-        max_caption_length: int = 1024,
-        padding_idx: int = 0,
-        decoder_type=None,
-        not_tie_weight=None,
-        output_hidden_states=None,
-        use_mlp_wrapper=None,
-        use_act_checkpoint=True,
-    ):
-        super().__init__(object_feature_size, vocab_size, hidden_size)
-        self.num_layers = num_layers
-        self.attention_heads = attention_heads
-        self.feedforward_size = feedforward_size
-        self.dropout = dropout
-        assert mask_future_positions
-        self.padding_idx = padding_idx
-
-        self.object_feature_projection = nn.Sequential(
-            nn.Linear(object_feature_size, self.textual_feature_size),
-            nn.LayerNorm(self.textual_feature_size))
-
-        self.embedding = WordAndPositionalEmbedding(
-            self.vocab_size,
-            self.textual_feature_size,
-            dropout=dropout,
-            max_caption_length=max_caption_length,
-            padding_idx=padding_idx,
-        )
-        self.transformer = create_transformer(
-            decoder_type=decoder_type,
-            norm_type=norm_type,
-            textual_feature_size=self.textual_feature_size,
-            attention_heads=self.attention_heads,
-            feedforward_size=self.feedforward_size,
-            dropout=dropout,
-            num_layers=self.num_layers,
-            output_hidden_states=output_hidden_states,
-            use_mlp_wrapper=use_mlp_wrapper,
-            use_act_checkpoint=use_act_checkpoint,
-        )
-        self.apply(self._init_weights)
-
-        # Create an output linear layer and tie the input and output word
-        # embeddings to reduce parametejs.
-        self.output = nn.Linear(self.textual_feature_size, vocab_size)
-        if not not_tie_weight:
-            self.output.weight = self.embedding.words.weight
-
-    @staticmethod
-    def _init_weights(module):
-        """Initialize weights like BERT - N(0.0, 0.02), bias = 0."""
-
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-        elif isinstance(module, nn.MultiheadAttention):
-            module.in_proj_weight.data.normal_(mean=0.0, std=0.02)
-            module.out_proj.weight.data.normal_(mean=0.0, std=0.02)
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    def forward(
-        self,
-        hidden_states,
-        text_tokens,
-    ):
-        projected_object_features = self.object_feature_projection(hidden_states) if hidden_states is not None else None
-        batch_size, max_text_length = text_tokens.size()
-        text_embeddings = self.embedding(text_tokens)
-
-        # An additive mask for masking the future (one direction).
-        uni_mask_zero_neg = self._generate_future_mask(
-            max_text_length, text_embeddings.dtype, text_embeddings.device
-        )
-
-        # We transpose the first two dimensions of tokens embeddings and visual
-        # features, as required by decoder.
-        text_embeddings = text_embeddings.transpose(0, 1)
-
-        projected_object_features = projected_object_features.transpose(0, 1)
-
-        # if transformer here is the pytorch/decoder, there is no chance, the
-        # output is always tensor
-        trans_out = self.transformer(
-            text_embeddings,
-            projected_object_features,
-            tgt_mask=uni_mask_zero_neg,
-        )
-        if isinstance(trans_out, tuple):
-            textual_features = trans_out[0]
-        else:
-            assert isinstance(trans_out, torch.Tensor)
-            textual_features = trans_out
-        # Undo the transpose and bring batch to dim 0.
-        # shape: (batch_size, max_caption_length, hidden_size)
-        textual_features = textual_features.transpose(0, 1)
-
-        # shape: (batch_size, max_caption_length, vocab_size)
-        output_logits = self.output(textual_features)
-        if isinstance(trans_out, tuple):
-            return output_logits, trans_out[1]
-        else:
-            return output_logits
-
-    def _generate_future_mask(
-        self, size: int, dtype: torch.dtype, device: torch.device
-    ):
-        # Default mask is for forward direction. Flip for backward direction.
-        mask = torch.triu(
-            torch.ones(size, size, device=device, dtype=dtype), diagonal=1
-        )
-        mask = mask.masked_fill(mask == 1, float("-inf"))
-        return mask
-
-
-class AutoRegressiveBeamSearch(object):
-    def __init__(
-        self,
-        end_token_id: int,
-        max_steps: int = 50,
-        beam_size: int = 5,
-        objectdet=True,
-        per_node_beam_size: int = 2,
-    ):
-        self._eos_index = end_token_id
-        self.max_steps = max_steps
-        self.beam_size = beam_size
-        self.objectdet = objectdet
-        self.per_node_beam_size = per_node_beam_size or beam_size
-
-    def search(self, begin_tokens, step):
-        if self.beam_size > 1 and self.objectdet:
-            only_return_best = False
-        else:
-            only_return_best = True
-
-        batch_size = begin_tokens.size()[0]
-
-        predictions = begin_tokens.unsqueeze(1).expand((batch_size, self.beam_size, begin_tokens.shape[-1]))
-        # Calculate the first timestep. This is done outside the main loop
-        # because we are going from a single decoder input (the output from the
-        # encoder) to the top `beam_size` decoder outputs. On the other hand,
-        # within the main loop we are going from the `beam_size` elements of the
-        # beam to `beam_size`^2 candidates from which we will select the top
-        # `beam_size` elements for the next iteration.
-        # shape: (batch_size, num_classes)
-        start_class_logits = step(begin_tokens)
-
-        # Convert logits to logprobs.
-        # shape: (batch_size * beam_size, vocab_size)
-        start_class_logprobs = F.log_softmax(start_class_logits, dim=1)
-
-        num_classes = start_class_logprobs.size()[1]
-
-        # shape: (batch_size, beam_size), (batch_size, beam_size)
-        start_top_logprobs, start_predicted_classes = start_class_logprobs.topk(
-            self.beam_size
-        )
-
-        if (
-            self.beam_size == 1
-            and (start_predicted_classes == self._eos_index).all()
-        ):
-            warnings.warn(
-                "Empty object description predicted. You may want to increase beam"
-                "size or ensure your step function is working properly.",
-                RuntimeWarning,
-            )
-            if only_return_best:
-                return start_predicted_classes, start_top_logprobs
-            else:
-                return start_predicted_classes.unsqueeze(-1), start_top_logprobs
-
-        # The log probs for the last time step.
-        # shape: (batch_size, beam_size)
-        last_logprobs = start_top_logprobs
-
-        # shape: (batch_size, beam_size, sequence_length)
-        predictions = torch.cat([predictions, start_predicted_classes.unsqueeze(-1)], dim=-1)
-
-        # Log probability tensor that mandates that the end token is selected.
-        # shape: (batch_size * beam_size, num_classes)
-        logprobs_after_end = start_class_logprobs.new_full(
-            (batch_size * self.beam_size, num_classes), float("-inf")
-        )
-        logprobs_after_end[:, self._eos_index] = 0.0
-
-        logits_after_end = start_class_logprobs.new_full(
-            (batch_size * self.beam_size, num_classes), float("-inf")
-        )
-        logits_after_end[:, self._eos_index] = 0
-
-        while predictions.shape[-1] < self.max_steps:
-            # shape: (batch_size * beam_size,)
-            last_predictions = predictions[:, :, -1].reshape(batch_size * self.beam_size)
-
-            # If every predicted token from the last step is `self._eos_index`,
-            # then we can stop early.
-            if (last_predictions == self._eos_index).all():
-                break
-
-            predictions_so_far = predictions.view(
-                batch_size * self.beam_size, -1
-            )
-            # shape: (batch_size * beam_size, num_classes)
-            class_logits = step(predictions_so_far)
-
-            # Set logprobs of last predicted tokens as high negative value to avoid
-            # repetition in description.
-            class_logits = class_logits.scatter(1, predictions_so_far[:, -1].view((-1, 1)), -10000)
-
-            # shape: (batch_size * beam_size, num_classes)
-            last_predictions_expanded = last_predictions.unsqueeze(-1).expand(
-                batch_size * self.beam_size, num_classes
-            )
-
-            # Here we are finding any beams where we predicted the end token in
-            # the previous timestep and replacing the distribution with a
-            # one-hot distribution, forcing the beam to predict the end token
-            # this timestep as well.
-            class_logits = torch.where(
-                last_predictions_expanded == self._eos_index,
-                logits_after_end,
-                class_logits,
-            )
-
-            # Convert logits to logprobs.
-            # shape: (batch_size * beam_size, vocab_size)
-            class_logprobs = F.log_softmax(class_logits, dim=1)
-
-            # shape (both): (batch_size * beam_size, per_node_beam_size)
-            top_logprobs, predicted_classes = class_logprobs.topk(
-                self.per_node_beam_size
-            )
-
-            # Here we expand the last log probs to `(batch_size * beam_size,
-            # per_node_beam_size)` so that we can add them to the current log
-            # probs for this timestep. This lets us maintain the log
-            # probability of each element on the beam.
-            # shape: (batch_size * beam_size, per_node_beam_size)
-            expanded_last_logprobs = (
-                last_logprobs.unsqueeze(2)
-                .expand(batch_size, self.beam_size, self.per_node_beam_size)
-                .reshape(batch_size * self.beam_size, self.per_node_beam_size)
-            )
-            # shape: (batch_size * beam_size, per_node_beam_size)
-            summed_top_logprobs = top_logprobs + expanded_last_logprobs
-
-            # shape: (batch_size, beam_size * per_node_beam_size)
-            reshaped_summed = summed_top_logprobs.reshape(
-                batch_size, self.beam_size * self.per_node_beam_size
-            )
-            # shape: (batch_size, beam_size * per_node_beam_size)
-            reshaped_predicted_classes = predicted_classes.reshape(
-                batch_size, self.beam_size * self.per_node_beam_size
-            )
-            # Append the predictions to the current beam.
-            reshaped_beam = (
-                predictions.view(batch_size * self.beam_size, 1, -1)
-                .repeat(1, self.per_node_beam_size, 1)
-                .reshape(batch_size, self.beam_size * self.per_node_beam_size, -1)
-            )
-            # batch_size, (beam_size * per_node_beach_size), #token
-            reshaped_beam = torch.cat([reshaped_beam, reshaped_predicted_classes.unsqueeze(-1)], dim=-1)
-
-            # Keep only the top `beam_size` beam indices.
-            # shape: (batch_size, beam_size), (batch_size, beam_size)
-            restricted_beam_logprobs, restricted_beam_indices = reshaped_summed.topk(
-                self.beam_size
-            )
-            predictions = reshaped_beam.gather(
-                1, restricted_beam_indices.unsqueeze(-1).repeat(1,1,reshaped_beam.shape[-1])
-            )
-
-            # shape: (batch_size, beam_size)
-            last_logprobs = restricted_beam_logprobs
-
-        if not torch.isfinite(last_logprobs).all():
-            warnings.warn(
-                "Infinite log probs encountered. Some final descriptions may not "
-                "make sense. This can happen when the beam size is larger than"
-                " the number of valid (non-zero probability) transitions that "
-                "the step function produces.",
-                RuntimeWarning,
-            )
-
-        # Optionally select best beam and its logprobs.
-        if only_return_best:
-            # shape: (batch_size, sequence_length)
-            predictions = predictions[:, 0, :]
-            last_logprobs = last_logprobs[:, 0]
-        num_valid = (predictions != self._eos_index).sum(dim=-1)
-        num_valid += (predictions == self._eos_index).sum(dim=-1) > 0
-        num_valid = num_valid - begin_tokens.shape[1]
-        num_valid = num_valid.clip(min=1)
-
-        last_logprobs = last_logprobs / num_valid
-
-        return predictions, last_logprobs
-
-
-class GRiTTextDecoder(nn.Module):
-    def __init__(
-        self,
-        transformer,
-        begin_token_id=101,
-        beamsearch_decode=None,
-        loss_type=None,
-        tokenizer=None,
-    ):
-        super().__init__()
-        self.textual = transformer
-        self.padding_idx = self.textual.padding_idx
-
-        self.begin_token_id = begin_token_id
-        self.beamsearch_decode = beamsearch_decode
-        self.tokenizer = tokenizer
-
-        if loss_type is None:
-            self.loss = nn.CrossEntropyLoss(ignore_index=self.padding_idx)
-        elif loss_type == 'smooth':
-            self.loss = SmoothLabelCrossEntropyLoss(ignore_index=self.padding_idx)
-        else:
-            raise NotImplementedError(loss_type)
-
-    def forward(self, batch):
-        object_features = batch['object_features']
-
-        if self.training:
-            caption_token_input = batch["text_tokens"]
-
-            output_logits = self.textual(
-                object_features,
-                caption_token_input,
-            )
-
-            if 'need_predict' in batch:
-                # in place should also be good, but we do not choose that for
-                # safety as we may use it in prediction results in future
-                target = batch["text_tokens"].clone()
-                target[batch['need_predict'] == 0] = self.padding_idx
-            else:
-                target = batch["text_tokens"]
-
-            feat = output_logits[:, :-1].contiguous()
-            target = target[:, 1:].contiguous()
-            feat = feat.view(-1, self.textual.vocab_size)
-            target = target.view(-1)
-
-            valid_mask = target != self.padding_idx
-            target = target[valid_mask]
-            feat = feat[valid_mask]
-            loss = self.loss(feat, target)
-
-            return loss
-        else:
-            output_dict = self.infer(object_features)
-        return output_dict
-
-    def infer(self, object_features):
-        batch_size = object_features.size(0)
-        begin_tokens = object_features.new_full(
-            (batch_size, 1), self.begin_token_id
-        ).long()
-
-        decoding_step = functools.partial(
-            self.decoding_step, object_features
-        )
-
-        object_description_tokens, logprobs = self.beamsearch_decode.search(
-            begin_tokens, decoding_step
-        )
-
-        output_dict = {
-            'predictions': object_description_tokens,
-            'logprobs': logprobs,
-        }
-
-        return output_dict
-
-    def decoding_step(self, object_features, partial_text):
-        batch_size = object_features.shape[0]
-        beam_size = int(partial_text.size(0) / batch_size)
-        if beam_size > 1:
-            batch_size, num_token, channels = object_features.size()
-            object_features = object_features.unsqueeze(1).repeat(1, beam_size, 1, 1)
-            object_features = object_features.view(
-                batch_size * beam_size, num_token, channels
-            )
-
-        text_lengths = torch.ones_like(partial_text)
-        if len(text_lengths.size()) != 2:
-            partial_text = partial_text.unsqueeze(1)
-
-        # shape: (batch_size * beam_size, partial_caption_length, vocab_size)
-        logits = self.textual(
-            object_features,
-            partial_text,
-        )
-
-        return logits[:, -1, :].float()
-
-
-class SmoothLabelCrossEntropyLoss(nn.Module):
-    def __init__(self, eps=0.1, log_prefix='', ignore_index=None):
-        super().__init__()
-        self.eps = eps
-        self.log_soft = nn.LogSoftmax(dim=1)
-        self.kl = nn.KLDivLoss(reduction='none')
-
-        self.iter = 0
-        self.max_loss = 0
-        self.min_loss = 0
-        self.log_prefix = log_prefix
-        self.ignore_index = ignore_index
-
-    def forward(self, feature, target):
-        feature = feature.float()
-        if self.ignore_index is not None:
-            valid_mask = target != self.ignore_index
-            target = target[valid_mask]
-            feature = feature[valid_mask]
-        assert target.numel() > 0
-        self.iter += 1
-        eps = self.eps
-        n_class = feature.size(1)
-        one_hot = torch.zeros_like(feature).scatter(1, target.view(-1, 1), 1)
-        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
-        log_prb = self.log_soft(feature)
-        loss = self.kl(log_prb, one_hot)
-        return loss.sum(dim=1).mean()
-
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/README_D2.md b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/README_D2.md
deleted file mode 100644
index a88ad7e21ce1d8651ec0d73848ce6dcd17f19d00..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/README_D2.md
+++ /dev/null
@@ -1,62 +0,0 @@
-<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
-
-Detectron2 is Facebook AI Research's next generation software system
-that implements state-of-the-art object detection algorithms.
-It is a ground-up rewrite of the previous version,
-[Detectron](https://github.com/facebookresearch/Detectron/),
-and it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
-
-<div align="center">
-  <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
-</div>
-
-### What's New
-* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.
-* Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,
-  DeepLab, etc.
-* Can be used as a library to support [different projects](projects/) on top of it.
-  We'll open source more research projects in this way.
-* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
-* Models can be exported to TorchScript format or Caffe2 format for deployment.
-
-See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
-to see more demos and learn about detectron2.
-
-## Installation
-
-See [INSTALL.md](INSTALL.md).
-
-## Getting Started
-
-Follow the [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html) to
-install detectron2.
-
-See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html),
-and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
-to learn about basic usage.
-
-Learn more at our [documentation](https://detectron2.readthedocs.org).
-And see [projects/](projects/) for some projects that are built on top of detectron2.
-
-## Model Zoo and Baselines
-
-We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
-
-
-## License
-
-Detectron2 is released under the [Apache 2.0 license](LICENSE).
-
-## Citing Detectron2
-
-If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
-
-```BibTeX
-@misc{wu2019detectron2,
-  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
-                  Wan-Yen Lo and Ross Girshick},
-  title =        {Detectron2},
-  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
-  year =         {2019}
-}
-```
diff --git a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py b/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
deleted file mode 100644
index b2c592527a5966e6f8e79e8c52dc5b414246dcc6..0000000000000000000000000000000000000000
--- a/spaces/TheStinger/Ilaria_RVC/lib/infer_pack/modules/F0Predictor/PMF0Predictor.py
+++ /dev/null
@@ -1,97 +0,0 @@
-from lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-import parselmouth
-import numpy as np
-
-
-class PMF0Predictor(F0Predictor):
-    def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
-        self.hop_length = hop_length
-        self.f0_min = f0_min
-        self.f0_max = f0_max
-        self.sampling_rate = sampling_rate
-
-    def interpolate_f0(self, f0):
-        """
-        对F0进行插值处理
-        """
-
-        data = np.reshape(f0, (f0.size, 1))
-
-        vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
-        vuv_vector[data > 0.0] = 1.0
-        vuv_vector[data <= 0.0] = 0.0
-
-        ip_data = data
-
-        frame_number = data.size
-        last_value = 0.0
-        for i in range(frame_number):
-            if data[i] <= 0.0:
-                j = i + 1
-                for j in range(i + 1, frame_number):
-                    if data[j] > 0.0:
-                        break
-                if j < frame_number - 1:
-                    if last_value > 0.0:
-                        step = (data[j] - data[i - 1]) / float(j - i)
-                        for k in range(i, j):
-                            ip_data[k] = data[i - 1] + step * (k - i + 1)
-                    else:
-                        for k in range(i, j):
-                            ip_data[k] = data[j]
-                else:
-                    for k in range(i, frame_number):
-                        ip_data[k] = last_value
-            else:
-                ip_data[i] = data[i]  # 这里可能存在一个没有必要的拷贝
-                last_value = data[i]
-
-        return ip_data[:, 0], vuv_vector[:, 0]
-
-    def compute_f0(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0
-
-    def compute_f0_uv(self, wav, p_len=None):
-        x = wav
-        if p_len is None:
-            p_len = x.shape[0] // self.hop_length
-        else:
-            assert abs(p_len - x.shape[0] // self.hop_length) < 4, "pad length error"
-        time_step = self.hop_length / self.sampling_rate * 1000
-        f0 = (
-            parselmouth.Sound(x, self.sampling_rate)
-            .to_pitch_ac(
-                time_step=time_step / 1000,
-                voicing_threshold=0.6,
-                pitch_floor=self.f0_min,
-                pitch_ceiling=self.f0_max,
-            )
-            .selected_array["frequency"]
-        )
-
-        pad_size = (p_len - len(f0) + 1) // 2
-        if pad_size > 0 or p_len - len(f0) - pad_size > 0:
-            f0 = np.pad(f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant")
-        f0, uv = self.interpolate_f0(f0)
-        return f0, uv
diff --git a/spaces/TornikeO/dis-background-removal/hce_metric_main.py b/spaces/TornikeO/dis-background-removal/hce_metric_main.py
deleted file mode 100644
index 1b102e6defa72f9cde378a353542fe6234b0ba3b..0000000000000000000000000000000000000000
--- a/spaces/TornikeO/dis-background-removal/hce_metric_main.py
+++ /dev/null
@@ -1,188 +0,0 @@
-## hce_metric.py
-import numpy as np
-from skimage import io
-import matplotlib.pyplot as plt
-import cv2 as cv
-from skimage.morphology import skeletonize
-from skimage.morphology import erosion, dilation, disk
-from skimage.measure import label
-
-import os
-import sys
-from tqdm import tqdm
-from glob import glob
-import pickle as pkl
-
-def filter_bdy_cond(bdy_, mask, cond):
-
-    cond = cv.dilate(cond.astype(np.uint8),disk(1))
-    labels = label(mask) # find the connected regions
-    lbls = np.unique(labels) # the indices of the connected regions
-    indep = np.ones(lbls.shape[0]) # the label of each connected regions
-    indep[0] = 0 # 0 indicate the background region
-
-    boundaries = []
-    h,w = cond.shape[0:2]
-    ind_map = np.zeros((h,w))
-    indep_cnt = 0
-
-    for i in range(0,len(bdy_)):
-        tmp_bdies = []
-        tmp_bdy = []
-        for j in range(0,bdy_[i].shape[0]):
-            r, c = bdy_[i][j,0,1],bdy_[i][j,0,0]
-
-            if(np.sum(cond[r,c])==0 or ind_map[r,c]!=0):
-                if(len(tmp_bdy)>0):
-                    tmp_bdies.append(tmp_bdy)
-                    tmp_bdy = []
-                continue
-            tmp_bdy.append([c,r])
-            ind_map[r,c] =  ind_map[r,c] + 1
-            indep[labels[r,c]] = 0 # indicates part of the boundary of this region needs human correction
-        if(len(tmp_bdy)>0):
-            tmp_bdies.append(tmp_bdy)
-
-        # check if the first and the last boundaries are connected
-        # if yes, invert the first boundary and attach it after the last boundary
-        if(len(tmp_bdies)>1):
-            first_x, first_y = tmp_bdies[0][0]
-            last_x, last_y = tmp_bdies[-1][-1]
-            if((abs(first_x-last_x)==1 and first_y==last_y) or
-               (first_x==last_x and abs(first_y-last_y)==1) or
-               (abs(first_x-last_x)==1 and abs(first_y-last_y)==1)
-              ):
-                tmp_bdies[-1].extend(tmp_bdies[0][::-1])
-                del tmp_bdies[0]
-
-        for k in range(0,len(tmp_bdies)):
-            tmp_bdies[k] =  np.array(tmp_bdies[k])[:,np.newaxis,:]
-        if(len(tmp_bdies)>0):
-            boundaries.extend(tmp_bdies)
-
-    return boundaries, np.sum(indep)
-
-# this function approximate each boundary by DP algorithm
-# https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm
-def approximate_RDP(boundaries,epsilon=1.0):
-
-    boundaries_ = []
-    boundaries_len_ = []
-    pixel_cnt_ = 0
-
-    # polygon approximate of each boundary
-    for i in range(0,len(boundaries)):
-        boundaries_.append(cv.approxPolyDP(boundaries[i],epsilon,False))
-
-    # count the control points number of each boundary and the total control points number of all the boundaries
-    for i in range(0,len(boundaries_)):
-        boundaries_len_.append(len(boundaries_[i]))
-        pixel_cnt_ = pixel_cnt_ + len(boundaries_[i])
-
-    return boundaries_, boundaries_len_, pixel_cnt_
-
-
-def relax_HCE(gt, rs, gt_ske, relax=5, epsilon=2.0):
-    # print("max(gt_ske): ", np.amax(gt_ske))
-    # gt_ske = gt_ske>128
-    # print("max(gt_ske): ", np.amax(gt_ske))
-
-    # Binarize gt
-    if(len(gt.shape)>2):
-        gt = gt[:,:,0]
-
-    epsilon_gt = 128#(np.amin(gt)+np.amax(gt))/2.0
-    gt = (gt>epsilon_gt).astype(np.uint8)
-
-    # Binarize rs
-    if(len(rs.shape)>2):
-        rs = rs[:,:,0]
-    epsilon_rs = 128#(np.amin(rs)+np.amax(rs))/2.0
-    rs = (rs>epsilon_rs).astype(np.uint8)
-
-    Union = np.logical_or(gt,rs)
-    TP = np.logical_and(gt,rs)
-    FP = rs - TP
-    FN = gt - TP
-
-    # relax the Union of gt and rs
-    Union_erode = Union.copy()
-    Union_erode = cv.erode(Union_erode.astype(np.uint8),disk(1),iterations=relax)
-
-    # --- get the relaxed False Positive regions for computing the human efforts in correcting them ---
-    FP_ = np.logical_and(FP,Union_erode) # get the relaxed FP
-    for i in range(0,relax):
-        FP_ = cv.dilate(FP_.astype(np.uint8),disk(1))
-        FP_ = np.logical_and(FP_, 1-np.logical_or(TP,FN))
-    FP_ = np.logical_and(FP, FP_)
-
-    # --- get the relaxed False Negative regions for computing the human efforts in correcting them ---
-    FN_ = np.logical_and(FN,Union_erode) # preserve the structural components of FN
-    ## recover the FN, where pixels are not close to the TP borders
-    for i in range(0,relax):
-        FN_ = cv.dilate(FN_.astype(np.uint8),disk(1))
-        FN_ = np.logical_and(FN_,1-np.logical_or(TP,FP))
-    FN_ = np.logical_and(FN,FN_)
-    FN_ = np.logical_or(FN_, np.logical_xor(gt_ske,np.logical_and(TP,gt_ske))) # preserve the structural components of FN
-
-    ## 2. =============Find exact polygon control points and independent regions==============
-    ## find contours from FP_
-    ctrs_FP, hier_FP = cv.findContours(FP_.astype(np.uint8), cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
-    ## find control points and independent regions for human correction
-    bdies_FP, indep_cnt_FP = filter_bdy_cond(ctrs_FP, FP_, np.logical_or(TP,FN_))
-    ## find contours from FN_
-    ctrs_FN, hier_FN = cv.findContours(FN_.astype(np.uint8), cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
-    ## find control points and independent regions for human correction
-    bdies_FN, indep_cnt_FN = filter_bdy_cond(ctrs_FN, FN_, 1-np.logical_or(np.logical_or(TP,FP_),FN_))
-
-    poly_FP, poly_FP_len, poly_FP_point_cnt = approximate_RDP(bdies_FP,epsilon=epsilon)
-    poly_FN, poly_FN_len, poly_FN_point_cnt = approximate_RDP(bdies_FN,epsilon=epsilon)
-
-    return poly_FP_point_cnt, indep_cnt_FP, poly_FN_point_cnt, indep_cnt_FN
-
-def compute_hce(pred_root,gt_root,gt_ske_root):
-
-    gt_name_list = glob(pred_root+'/*.png')
-    gt_name_list = sorted([x.split('/')[-1] for x in gt_name_list])
-
-    hces = []
-    for gt_name in tqdm(gt_name_list, total=len(gt_name_list)):
-        gt_path = os.path.join(gt_root, gt_name)
-        pred_path = os.path.join(pred_root, gt_name)
-
-        gt = cv.imread(gt_path, cv.IMREAD_GRAYSCALE)
-        pred = cv.imread(pred_path, cv.IMREAD_GRAYSCALE)
-
-        ske_path = os.path.join(gt_ske_root,gt_name)
-        if os.path.exists(ske_path):
-            ske = cv.imread(ske_path,cv.IMREAD_GRAYSCALE)
-            ske = ske>128
-        else:
-            ske = skeletonize(gt>128)
-
-        FP_points, FP_indep, FN_points, FN_indep = relax_HCE(gt, pred,ske)
-        print(gt_path.split('/')[-1],FP_points, FP_indep, FN_points, FN_indep)
-        hces.append([FP_points, FP_indep, FN_points, FN_indep, FP_points+FP_indep+FN_points+FN_indep])
-
-    hce_metric ={'names': gt_name_list,
-                 'hces': hces}
-
-
-    file_metric = open(pred_root+'/hce_metric.pkl','wb')
-    pkl.dump(hce_metric,file_metric)
-    # file_metrics.write(cmn_metrics)
-    file_metric.close()
-
-    return np.mean(np.array(hces)[:,-1])
-
-def main():
-
-    gt_root = "../DIS5K/DIS-VD/gt"
-    gt_ske_root = ""
-    pred_root = "../Results/isnet(ours)/DIS-VD"
-
-    print("The average HCE metric: ", compute_hce(pred_root,gt_root,gt_ske_root))
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/UserXTheUnknown/stablediffusion-infinity/PyPatchMatch/examples/py_example_global_mask.py b/spaces/UserXTheUnknown/stablediffusion-infinity/PyPatchMatch/examples/py_example_global_mask.py
deleted file mode 100644
index 9025cc4230edcce7cbfa545f55600a240994edcf..0000000000000000000000000000000000000000
--- a/spaces/UserXTheUnknown/stablediffusion-infinity/PyPatchMatch/examples/py_example_global_mask.py
+++ /dev/null
@@ -1,27 +0,0 @@
-#! /usr/bin/env python3
-# -*- coding: utf-8 -*-
-# File   : test.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 01/09/2020
-#
-# Distributed under terms of the MIT license.
-
-import numpy as np
-from PIL import Image
-
-import sys
-sys.path.insert(0, '../')
-import patch_match
-
-
-if __name__ == '__main__':
-    patch_match.set_verbose(True)
-    source = Image.open('./images/forest_pruned.bmp')
-    source = np.array(source)
-    source[:100, :100] = 255
-    global_mask = np.zeros_like(source[..., 0])
-    global_mask[:100, :100] = 1
-    result = patch_match.inpaint(source, global_mask=global_mask, patch_size=3)
-    Image.fromarray(result).save('./images/forest_recovered.bmp')
-
diff --git a/spaces/ViralWeb/aifi/Dockerfile b/spaces/ViralWeb/aifi/Dockerfile
deleted file mode 100644
index 481d7cc5e3037930f21b43f555e6849f108005ae..0000000000000000000000000000000000000000
--- a/spaces/ViralWeb/aifi/Dockerfile
+++ /dev/null
@@ -1,126 +0,0 @@
-ARG MODEL_NAME
-ARG MODEL_PARAMS
-ARG MODEL_PROMPT_TEMPLATE
-ARG APP_COLOR
-ARG APP_NAME
-
-
-FROM node:19 as chatui-builder
-ARG MODEL_NAME
-ARG MODEL_PARAMS
-ARG APP_COLOR
-ARG APP_NAME
-ARG MODEL_PROMPT_TEMPLATE
-
-WORKDIR /app
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    git gettext && \
-    rm -rf /var/lib/apt/lists/*
-
-
-RUN git clone https://github.com/huggingface/chat-ui.git
-
-WORKDIR /app/chat-ui
-
-
-COPY .env.local.template .env.local.template
-
-RUN mkdir defaults
-ADD defaults /defaults
-RUN chmod -R 777 /defaults
-RUN --mount=type=secret,id=MONGODB_URL,mode=0444 \
-    MODEL_NAME="${MODEL_NAME:="$(cat /defaults/MODEL_NAME)"}" && export MODEL_NAME \
-    && MODEL_PARAMS="${MODEL_PARAMS:="$(cat /defaults/MODEL_PARAMS)"}" && export MODEL_PARAMS \
-    && MODEL_PROMPT_TEMPLATE="${MODEL_PROMPT_TEMPLATE:="$(cat /defaults/MODEL_PROMPT_TEMPLATE)"}" && export MODEL_PROMPT_TEMPLATE \
-    && APP_COLOR="${APP_COLOR:="$(cat /defaults/APP_COLOR)"}" && export APP_COLOR \
-    && APP_NAME="${APP_NAME:="$(cat /defaults/APP_NAME)"}" && export APP_NAME \
-    && MONGODB_URL=$(cat /run/secrets/MONGODB_URL > /dev/null | grep '^' || cat /defaults/MONGODB_URL) && export MONGODB_URL && \
-    echo "${MONGODB_URL}" && \
-    envsubst < ".env.local.template" > ".env.local" \ 
-    && rm .env.local.template
-
-
-
-RUN --mount=type=cache,target=/app/.npm \
-    npm set cache /app/.npm && \
-    npm ci
-
-RUN npm run build
-
-FROM ghcr.io/huggingface/text-generation-inference:latest
-
-ARG MODEL_NAME
-ARG MODEL_PARAMS
-ARG MODEL_PROMPT_TEMPLATE
-ARG APP_COLOR
-ARG APP_NAME
-
-ENV TZ=Europe/Paris \
-    PORT=3000
-
-
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    gnupg \
-    curl \
-    gettext && \
-    rm -rf /var/lib/apt/lists/*
-COPY entrypoint.sh.template entrypoint.sh.template
-
-RUN mkdir defaults
-ADD defaults /defaults
-RUN chmod -R 777 /defaults
-
-RUN --mount=type=secret,id=MONGODB_URL,mode=0444 \
-    MODEL_NAME="${MODEL_NAME:="$(cat /defaults/MODEL_NAME)"}" && export MODEL_NAME \
-    && MODEL_PARAMS="${MODEL_PARAMS:="$(cat /defaults/MODEL_PARAMS)"}" && export MODEL_PARAMS \
-    && MODEL_PROMPT_TEMPLATE="${MODEL_PROMPT_TEMPLATE:="$(cat /defaults/MODEL_PROMPT_TEMPLATE)"}" && export MODEL_PROMPT_TEMPLATE \
-    && APP_COLOR="${APP_COLOR:="$(cat /defaults/APP_COLOR)"}" && export APP_COLOR \
-    && APP_NAME="${APP_NAME:="$(cat /defaults/APP_NAME)"}" && export APP_NAME \
-    && MONGODB_URL=$(cat /run/secrets/MONGODB_URL > /dev/null | grep '^' || cat /defaults/MONGODB_URL) && export MONGODB_URL &&  \
-    envsubst < "entrypoint.sh.template" > "entrypoint.sh" \
-    && rm entrypoint.sh.template
-
-
-RUN curl -fsSL https://pgp.mongodb.com/server-6.0.asc | \
-    gpg -o /usr/share/keyrings/mongodb-server-6.0.gpg \
-    --dearmor
-
-RUN echo "deb [ arch=amd64,arm64 signed-by=/usr/share/keyrings/mongodb-server-6.0.gpg ] https://repo.mongodb.org/apt/ubuntu focal/mongodb-org/6.0 multiverse" | tee /etc/apt/sources.list.d/mongodb-org-6.0.list
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    mongodb-org && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN mkdir -p /data/db
-RUN chown -R 1000:1000 /data
-
-RUN curl -fsSL https://deb.nodesource.com/setup_19.x | /bin/bash -
-
-RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
-    nodejs && \
-    rm -rf /var/lib/apt/lists/*
-
-RUN mkdir /app
-RUN chown -R 1000:1000 /app
-
-RUN useradd -m -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH
-
-RUN npm config set prefix /home/user/.local
-RUN npm install -g pm2
-
-COPY --from=chatui-builder --chown=1000 /app/chat-ui/node_modules /app/node_modules
-COPY --from=chatui-builder --chown=1000 /app/chat-ui/package.json /app/package.json
-COPY --from=chatui-builder --chown=1000 /app/chat-ui/build /app/build
-
-ENTRYPOINT ["/bin/bash"]
-CMD ["entrypoint.sh"]
-
-
diff --git a/spaces/Walterchamy/Kiitec_virtual_assistant/app.py b/spaces/Walterchamy/Kiitec_virtual_assistant/app.py
deleted file mode 100644
index 998d18931b864ee2e27cf3746ae83051952984d1..0000000000000000000000000000000000000000
--- a/spaces/Walterchamy/Kiitec_virtual_assistant/app.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import os
-import openai
-import streamlit as st
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-KIITEC_CONTENT = """
-
-KIITEC is a technical institution registered by NACTE (REG/EOS/027) based in Moshono, Arusha next to Masai Camp.
-
-The institute was founded in 2004 by French engineers and has thence contrived to produce the most competent technicians in the country. The institute is financed and supported by two NGOs: The Foundation for Technical Education (FTE-Swiss) and Action Development Education International (ADEI-French). In 2004, ADEI's partner FTE built the Kilimanjaro International Institute of Telecommunications, Electronics and Computers (KIITEC) introducing state-of-the-art teaching facilities to train technicians in Arusha, Tanzania.
-
-Following construction, ADEI joined FTE in its ambition to make change through technical education and has played a pivotal role in the on-the-ground education programming and training at KIITEC ever since. Today, KIITEC acts as the international training center where educators travel from different corners of Africa to upgrade their skills.
-
-Resting on a 15-acre campus with hostel available inside it, KIITEC offers the most advanced targeted training technologies in the region. The innovative education model developed at KIITEC is based on a hands-on and student-centered approach to learning with full access to modern learning equipment simulating real-world practical experiences. The training center is registered and accredited by the National Council for Technical Education (NACTE) and awards successful graduates with a 3-year National Technical Award Level Six (NTA-6) Diploma.
-
-KIITEC specializes in diploma programs including:
-- Electrical & Computer Engineering
-- Electrical & Industrial Automation
-- Electronics & Telecommunication Engineering
-- Electrical & Renewable Energies
-
-Short course programs offered by KIITEC include:
-- Computer Application
-- IT & System Security
-- Electrical Domestic & Solar PV System Installation
-
-Future training programs or courses in development: Biomedical, Avionics.
-
-ADMISSION REQUIREMENTS of ELECTRONICS AND TELECOMMUNICATION ENGINEERING:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-ADMISSION REQUIREMENTS of ELECTRICAL AND INDUSTRIAL AUTOMATION ENGINEERING:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-ADMISSION REQUIREMENTS of ELECTRICAL AND COMPUTER ENGINEERING PROGRAMS:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-ADMISSION REQUIREMENTS of ELECTRICAL AND RENEWABLE ENERGY ENGINEERING:
-- Possession of a Certificate of Secondary Education Examinations (CSEE) with a minimum of FOUR passes, of which TWO must be among the following subjects: Mathematics, Physics/Engineering Science, Biology, and Chemistry; excluding religious subjects.
-- OR Possession of a National Vocational Award (NVA) Level III in Electrical, Electronics, Telecommunication, Mechanical, and related fields offered by VETA and a Certificate of Secondary Education Examination (CSEE) with at least two passes.
-
-
-APPLICATION FOR EITHER DIPLOMA COURSE OR SHORT COURSE
-- You can apply for Diploma Course through this link https://kiitec.ac.tz/apply-here/
--  You can apply for short Course through this link https://kiitec.ac.tz/apply-here/
-
-Fee structure and Mode of Payment for Diploma Programs:
-- The first semester fee is 695,000 Tsh and can be paid in two installments before the end of the semester.
-- The second semester fee is 625,000 Tsh and can be paid in two installments before the end of the semester.
-- The fees should be paid through the Bank of ABSA, Account number 002-4001687, Account Name KIITEC.
-
-KIITEC students wear uniforms. The uniforms consist of light blue shirts and dark blue sweaters, dark blue skirts, and dark blue trousers.
-
-KIITEC Vision:
-To become a leading provider of quality technical education and training to empower the youth of Tanzania and the Eastern Africa region.
-
-KIITEC Mission:
-- To provide quality hands-on technical training for students in ICTs, Electrical, Renewable Energies, Industrial Automation, and related disciplines.
-- To conduct quality research and consultancy in these fields.
-- To promote the development and usage of modern technology that meets national, regional, and international needs and standards through skills and practical-oriented training.
-
-KIITEC Values:
-- Hard work and excellence
-- Honesty
-- Respect
-- Responsibility
-- Lifelong learning
-- Innovation and creativity.
-
-For contact information:
-- Phone: +255 685 273 530
-- Mobile: +255 757 845 118
-- Email: info@kiitec.ac.tz
-- P.O. Box 3172, Arusha
-"""
-
-def generate_response(user_input):
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": "All your answers should be in Swahili or English. Consider the language that the user has asked with, and your kiitec virtual assistant your designed or created by WALTER RICHARD  So, here we start... Your virtual assistant of DON BOSCO KIITEC. You will reply to the queries that the user might ask!\n\n" + KIITEC_CONTENT},
-            {"role": "assistant", "content": "Hi, how can i assist you today?"},
-            {"role": "user", "content": user_input},
-        ]
-    )
-    return response['choices'][0]['message']['content']
-
-def main():
-    st.image('logo.png', caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
-
-    st.markdown("<h3 style='text-align: center; color: grey;'>KILIMANJARO INTERNATIONAL INSTITUTE FOR TELECOMMUNICATION, ELECTRONICS &COMPUTERS</h2>", unsafe_allow_html=True)
-    st.title("KIITEC VIRTUAL ASSISTANT")
-    user_input = st.text_input("Enter your question:")
-    if st.button("Answer"):
-        with st.spinner("Generating Response...."):
-            response_text = generate_response(user_input)
-        st.write("Response:", response_text)
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/XAI/VisualCorrespondenceHumanStudy/download_utils.py b/spaces/XAI/VisualCorrespondenceHumanStudy/download_utils.py
deleted file mode 100644
index 9ca915ab1ce2770c0e35eb48aeec9fc17bf31d03..0000000000000000000000000000000000000000
--- a/spaces/XAI/VisualCorrespondenceHumanStudy/download_utils.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import json
-import os
-import pickle
-import random
-import tarfile
-import zipfile
-from collections import Counter
-from glob import glob
-
-import gdown
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import streamlit as st
-from PIL import Image
-
-import SessionState
-
-
-def download_files(
-    root_visualization_dir,
-    viz_url,
-    viz_archivefile,
-    demonstration_url,
-    demonst_zipfile,
-    picklefile_url,
-    prediction_root,
-    prediction_pickle,
-):
-    # Get Visualization
-    if not os.path.exists(root_visualization_dir):
-        gdown.download(viz_url, viz_archivefile, quiet=False)
-        os.makedirs(root_visualization_dir, exist_ok=True)
-
-        if viz_archivefile.endswith("tar.gz"):
-            tar = tarfile.open(viz_archivefile, "r:gz")
-            tar.extractall(path=root_visualization_dir)
-            tar.close()
-        elif viz_archivefile.endswith("zip"):
-            with zipfile.ZipFile(viz_archivefile, "r") as zip_ref:
-                zip_ref.extractall(root_visualization_dir)
-
-    # Get Demonstrations
-    if not os.path.exists(demonst_zipfile):
-        gdown.download(demonstration_url, demonst_zipfile, quiet=False)
-        # os.makedirs(roo_demonstration_dir, exist_ok=True)
-
-        with zipfile.ZipFile(demonst_zipfile, "r") as zip_ref:
-            zip_ref.extractall("./")
-
-    # Get Predictions
-    if not os.path.exists(prediction_pickle):
-        os.makedirs(prediction_root, exist_ok=True)
-        gdown.download(picklefile_url, prediction_pickle, quiet=False)
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/data.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/data.py
deleted file mode 100644
index 9245caa3dd2ac93c43ee0dc67a98157a2922de53..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/fastai/text/data.py
+++ /dev/null
@@ -1,483 +0,0 @@
-"NLP data loading pipeline. Supports csv, folders, and preprocessed data."
-from ..torch_core import *
-from .transform import *
-from ..basic_data import *
-from ..data_block import *
-from ..layers import *
-from ..callback import Callback
-
-__all__ = ['LanguageModelPreLoader', 'SortSampler', 'SortishSampler', 'TextList', 'pad_collate', 'TextDataBunch',
-           'TextLMDataBunch', 'TextClasDataBunch', 'Text', 'open_text', 'TokenizeProcessor', 'NumericalizeProcessor',
-           'OpenFileProcessor', 'LMLabelList', 'LMTextList', 'SPProcessor']
-
-TextMtd = IntEnum('TextMtd', 'DF TOK IDS')
-text_extensions = {'.txt'}
-
-class LanguageModelPreLoader(Callback):
-    "Transforms the tokens in `dataset` to a stream of contiguous batches for language modelling."
-
-    class CircularIndex():
-        "Handles shuffle, direction of indexing, wraps around to head tail in the ragged array as needed"
-        def __init__(self, length:int, forward:bool): self.idx, self.forward = np.arange(length), forward
-        def __getitem__(self, i):
-            return self.idx[ i%len(self.idx) if self.forward else len(self.idx)-1-i%len(self.idx)]
-        def __len__(self) -> int: return len(self.idx)
-        def shuffle(self): np.random.shuffle(self.idx)
-
-    def __init__(self, dataset:LabelList, lengths:Collection[int]=None, bs:int=32, bptt:int=70, backwards:bool=False,
-                 shuffle:bool=False):
-        self.dataset,self.bs,self.bptt,self.shuffle,self.backwards,self.lengths = dataset,bs,bptt,shuffle,backwards,lengths
-        self.bs *= num_distrib() or 1
-        self.totalToks,self.ite_len,self.idx = int(0),None,None
-
-    def __len__(self):
-        if self.ite_len is None:
-            if self.lengths is None: self.lengths = np.array([len(item) for item in self.dataset.x.items])
-            self.totalToks = self.lengths.sum()
-            self.ite_len   = self.bs*int( math.ceil( self.totalToks/(self.bptt*self.bs) )) if self.item is None else 1
-        return self.ite_len
-
-    def __getattr__(self,k:str)->Any: return getattr(self.dataset, k)
-
-    def allocate_buffers(self):
-        "Create the ragged array that will be filled when we ask for items."
-        if self.ite_len is None: len(self)
-        self.idx   = LanguageModelPreLoader.CircularIndex(len(self.dataset.x.items), not self.backwards)
-        self.batch = np.zeros((self.bs, self.bptt+1), dtype=np.int64)
-        self.batch_x, self.batch_y = self.batch[:,0:self.bptt], self.batch[:,1:self.bptt+1]
-        #ro: index of the text we're at inside our datasets for the various batches
-        self.ro    = np.zeros(self.bs, dtype=np.int64)
-        #ri: index of the token we're at inside our current text for the various batches
-        self.ri    = np.zeros(self.bs, dtype=np.int)
-
-    def on_epoch_begin(self, **kwargs):
-        if self.idx is None or len(self.idx) != len(self.dataset.x.items): self.allocate_buffers()
-        elif self.shuffle:   self.idx.shuffle()
-        self.idx.forward = not self.backwards
-
-        step = self.totalToks / self.bs
-        ln_rag, countTokens, i_rag = 0, 0, -1
-        for i in range(0,self.bs):
-            #Compute the initial values for ro and ri
-            while ln_rag + countTokens <= int(step * i):
-                countTokens += ln_rag
-                i_rag       += 1
-                ln_rag       = self.lengths[self.idx[i_rag]]
-            self.ro[i] = i_rag
-            self.ri[i] = ( ln_rag - int(step * i - countTokens) ) if self.backwards else int(step * i - countTokens)
-
-    #Training dl gets on_epoch_begin called, val_dl, on_epoch_end
-    def on_epoch_end(self, **kwargs): self.on_epoch_begin()
-
-    def __getitem__(self, k:int):
-        j = k % self.bs
-        if self.item is not None: return self.dataset[0]
-        if self.idx is None: self.on_epoch_begin()
-        self.ro[j],self.ri[j] = self.fill_row(not self.backwards, self.dataset.x.items, self.idx, self.batch[j],
-                                              self.ro[j], self.ri[j], overlap=1, lengths=self.lengths)
-        return self.batch_x[j], self.batch_y[j]
-
-    def fill_row(self, forward, items, idx, row, ro, ri, overlap,lengths):
-        "Fill the row with tokens from the ragged array. --OBS-- overlap != 1 has not been implemented"
-        ibuf = n = 0
-        ro  -= 1
-        while ibuf < row.size:
-            ro   += 1
-            ix    = idx[ro]
-            rag   = items[ix]
-            if forward:
-                ri = 0 if ibuf else ri
-                n  = min(lengths[ix] - ri, row.size - ibuf)
-                row[ibuf:ibuf+n] = rag[ri:ri+n]
-            else:
-                ri = lengths[ix] if ibuf else ri
-                n  = min(ri, row.size - ibuf)
-                row[ibuf:ibuf+n] = rag[ri-n:ri][::-1]
-            ibuf += n
-        return ro, ri + ((n-overlap) if forward else -(n-overlap))
-
-class SortSampler(Sampler):
-    "Go through the text data by order of length."
-
-    def __init__(self, data_source:NPArrayList, key:KeyFunc): self.data_source,self.key = data_source,key
-    def __len__(self) -> int: return len(self.data_source)
-    def __iter__(self):
-        return iter(sorted(range_of(self.data_source), key=self.key, reverse=True))
-
-class SortishSampler(Sampler):
-    "Go through the text data by order of length with a bit of randomness."
-
-    def __init__(self, data_source:NPArrayList, key:KeyFunc, bs:int):
-        self.data_source,self.key,self.bs = data_source,key,bs
-
-    def __len__(self) -> int: return len(self.data_source)
-
-    def __iter__(self):
-        idxs = np.random.permutation(len(self.data_source))
-        sz = self.bs*50
-        ck_idx = [idxs[i:i+sz] for i in range(0, len(idxs), sz)]
-        sort_idx = np.concatenate([sorted(s, key=self.key, reverse=True) for s in ck_idx])
-        sz = self.bs
-        ck_idx = [sort_idx[i:i+sz] for i in range(0, len(sort_idx), sz)]
-        max_ck = np.argmax([self.key(ck[0]) for ck in ck_idx])  # find the chunk with the largest key,
-        ck_idx[0],ck_idx[max_ck] = ck_idx[max_ck],ck_idx[0]     # then make sure it goes first.
-        sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([],dtype=np.int)
-        sort_idx = np.concatenate((ck_idx[0], sort_idx))
-        return iter(sort_idx)
-
-def pad_collate(samples:BatchSamples, pad_idx:int=1, pad_first:bool=True, backwards:bool=False) -> Tuple[LongTensor, LongTensor]:
-    "Function that collect samples and adds padding. Flips token order if needed"
-    samples = to_data(samples)
-    max_len = max([len(s[0]) for s in samples])
-    res = torch.zeros(len(samples), max_len).long() + pad_idx
-    if backwards: pad_first = not pad_first
-    for i,s in enumerate(samples):
-        if pad_first: res[i,-len(s[0]):] = LongTensor(s[0])
-        else:         res[i,:len(s[0]):] = LongTensor(s[0])
-    if backwards: res = res.flip(1)
-    return res, tensor(np.array([s[1] for s in samples]))
-
-def _get_processor(tokenizer:Tokenizer=None, vocab:Vocab=None, chunksize:int=10000, max_vocab:int=60000,
-                   min_freq:int=2, mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False):
-    return [TokenizeProcessor(tokenizer=tokenizer, chunksize=chunksize, 
-                              mark_fields=mark_fields, include_bos=include_bos, include_eos=include_eos),
-            NumericalizeProcessor(vocab=vocab, max_vocab=max_vocab, min_freq=min_freq)]
-
-class TextDataBunch(DataBunch):
-    "General class to get a `DataBunch` for NLP. Subclassed by `TextLMDataBunch` and `TextClasDataBunch`."
-
-    @classmethod
-    def from_ids(cls, path:PathOrStr, vocab:Vocab, train_ids:Collection[Collection[int]], valid_ids:Collection[Collection[int]],
-                 test_ids:Collection[Collection[int]]=None, train_lbls:Collection[Union[int,float]]=None,
-                 valid_lbls:Collection[Union[int,float]]=None, classes:Collection[Any]=None,
-                 processor:PreProcessor=None, **kwargs) -> DataBunch:
-        "Create a `TextDataBunch` from ids, labels and a `vocab`. `kwargs` are passed to the dataloader creation."
-        src = ItemLists(path, TextList(train_ids, vocab, path=path, processor=[]),
-                        TextList(valid_ids, vocab, path=path, processor=[]))
-        src = src.label_for_lm() if cls==TextLMDataBunch else src.label_from_lists(train_lbls, valid_lbls, classes=classes, processor=[])
-        if not is1d(train_lbls): src.train.y.one_hot,src.valid.y.one_hot = True,True
-        if test_ids is not None: src.add_test(TextList(test_ids, vocab, path=path), label=train_lbls[0])
-        src.valid.x.processor = ifnone(processor, [TokenizeProcessor(), NumericalizeProcessor(vocab=vocab)])
-        if classes is not None: src.valid.y.processor = ifnone(processor, [CategoryProcessor(src.valid.y)])
-        return src.databunch(**kwargs)
-
-    @classmethod
-    def load(cls, path:PathOrStr, cache_name:PathOrStr='tmp', processor:PreProcessor=None, **kwargs):
-        "Load a `TextDataBunch` from `path/cache_name`. `kwargs` are passed to the dataloader creation."
-        warn("""This method is deprecated and only kept to load data serialized in v1.0.43 or earlier.
-                Use `load_data` for data saved with v1.0.44 or later.""", DeprecationWarning)
-        cache_path = Path(path)/cache_name
-        vocab = Vocab(pickle.load(open(cache_path/'itos.pkl','rb')))
-        train_ids,train_lbls = np.load(cache_path/f'train_ids.npy'), np.load(cache_path/f'train_lbl.npy')
-        valid_ids,valid_lbls = np.load(cache_path/f'valid_ids.npy'), np.load(cache_path/f'valid_lbl.npy')
-        test_ids = np.load(cache_path/f'test_ids.npy') if os.path.isfile(cache_path/f'test_ids.npy') else None
-        classes = loadtxt_str(cache_path/'classes.txt') if os.path.isfile(cache_path/'classes.txt') else None
-        return cls.from_ids(path, vocab, train_ids, valid_ids, test_ids, train_lbls, valid_lbls, classes, processor, **kwargs)
-
-    @classmethod#TODO: test
-    def from_tokens(cls, path:PathOrStr, trn_tok:Collection[Collection[str]], trn_lbls:Collection[Union[int,float]],
-                 val_tok:Collection[Collection[str]], val_lbls:Collection[Union[int,float]], vocab:Vocab=None,
-                 tst_tok:Collection[Collection[str]]=None, classes:Collection[Any]=None, max_vocab:int=60000, min_freq:int=3,
-                 **kwargs) -> DataBunch:
-        "Create a `TextDataBunch` from tokens and labels. `kwargs` are passed to the dataloader creation."
-        processor = NumericalizeProcessor(vocab=vocab, max_vocab=max_vocab, min_freq=min_freq)
-        src = ItemLists(path, TextList(trn_tok, path=path, processor=processor),
-                        TextList(val_tok, path=path, processor=processor))
-        src = src.label_for_lm() if cls==TextLMDataBunch else src.label_from_lists(trn_lbls, val_lbls, classes=classes)
-        if tst_tok is not None: src.add_test(TextList(tst_tok, path=path))
-        return src.databunch(**kwargs)
-
-    @classmethod
-    def from_df(cls, path:PathOrStr, train_df:DataFrame, valid_df:DataFrame, test_df:Optional[DataFrame]=None,
-                tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, text_cols:IntsOrStrs=1,
-                label_cols:IntsOrStrs=0, label_delim:str=None, chunksize:int=10000, max_vocab:int=60000,
-                min_freq:int=2, mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False, **kwargs) -> DataBunch:
-        "Create a `TextDataBunch` from DataFrames. `kwargs` are passed to the dataloader creation."
-        processor = _get_processor(tokenizer=tokenizer, vocab=vocab, chunksize=chunksize, max_vocab=max_vocab,
-                                   min_freq=min_freq, mark_fields=mark_fields, 
-                                   include_bos=include_bos, include_eos=include_eos)
-        if classes is None and is_listy(label_cols) and len(label_cols) > 1: classes = label_cols
-        src = ItemLists(path, TextList.from_df(train_df, path, cols=text_cols, processor=processor),
-                        TextList.from_df(valid_df, path, cols=text_cols, processor=processor))
-        if cls==TextLMDataBunch: src = src.label_for_lm()
-        else: 
-            if label_delim is not None: src = src.label_from_df(cols=label_cols, classes=classes, label_delim=label_delim)
-            else: src = src.label_from_df(cols=label_cols, classes=classes)
-        if test_df is not None: src.add_test(TextList.from_df(test_df, path, cols=text_cols))
-        return src.databunch(**kwargs)
-
-    @classmethod
-    def from_csv(cls, path:PathOrStr, csv_name, valid_pct:float=0.2, test:Optional[str]=None,
-                 tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, delimiter:str=None, header='infer',
-                 text_cols:IntsOrStrs=1, label_cols:IntsOrStrs=0, label_delim:str=None,
-                 chunksize:int=10000, max_vocab:int=60000, min_freq:int=2, 
-                 mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False, **kwargs) -> DataBunch:
-        "Create a `TextDataBunch` from texts in csv files. `kwargs` are passed to the dataloader creation."
-        df = pd.read_csv(Path(path)/csv_name, header=header, delimiter=delimiter)
-        df = df.iloc[np.random.permutation(len(df))]
-        cut = int(valid_pct * len(df)) + 1
-        train_df, valid_df = df[cut:], df[:cut]
-        test_df = None if test is None else pd.read_csv(Path(path)/test, header=header, delimiter=delimiter)
-        return cls.from_df(path, train_df, valid_df, test_df, tokenizer=tokenizer, vocab=vocab, classes=classes, text_cols=text_cols,
-                           label_cols=label_cols, label_delim=label_delim, chunksize=chunksize, max_vocab=max_vocab,
-                           min_freq=min_freq, mark_fields=mark_fields, 
-                           include_bos=include_bos, include_eos=include_eos, **kwargs)
-
-    @classmethod
-    def from_folder(cls, path:PathOrStr, train:str='train', valid:str='valid', test:Optional[str]=None,
-                    classes:Collection[Any]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, chunksize:int=10000, max_vocab:int=60000,
-                    min_freq:int=2, mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False, **kwargs):
-        "Create a `TextDataBunch` from text files in folders."
-        path = Path(path).absolute()
-        processor = [OpenFileProcessor()] + _get_processor(tokenizer=tokenizer, vocab=vocab, chunksize=chunksize, max_vocab=max_vocab,
-                                   min_freq=min_freq, mark_fields=mark_fields, include_bos=include_bos, include_eos=include_eos)
-        src = (TextList.from_folder(path, processor=processor)
-                       .split_by_folder(train=train, valid=valid))
-        src = src.label_for_lm() if cls==TextLMDataBunch else src.label_from_folder(classes=classes)
-        if test is not None: src.add_test_folder(path/test)
-        return src.databunch(**kwargs)
-
-class TextLMDataBunch(TextDataBunch):
-    "Create a `TextDataBunch` suitable for training a language model."
-    @classmethod
-    def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', no_check:bool=False, bs=64, val_bs:int=None,
-               num_workers:int=0, device:torch.device=None, collate_fn:Callable=data_collate,
-               dl_tfms:Optional[Collection[Callable]]=None, bptt:int=70, backwards:bool=False, **dl_kwargs) -> DataBunch:
-        "Create a `TextDataBunch` in `path` from the `datasets` for language modelling. Passes `**dl_kwargs` on to `DataLoader()`"
-        datasets = cls._init_ds(train_ds, valid_ds, test_ds)
-        val_bs = ifnone(val_bs, bs)
-        datasets = [LanguageModelPreLoader(ds, shuffle=(i==0), bs=(bs if i==0 else val_bs), bptt=bptt, backwards=backwards)
-                    for i,ds in enumerate(datasets)]
-        val_bs = bs
-        dls = [DataLoader(d, b, shuffle=False, **dl_kwargs) for d,b in zip(datasets, (bs,val_bs,val_bs,val_bs)) if d is not None]
-        return cls(*dls, path=path, device=device, dl_tfms=dl_tfms, collate_fn=collate_fn, no_check=no_check)
-
-class TextClasDataBunch(TextDataBunch):
-    "Create a `TextDataBunch` suitable for training an RNN classifier."
-    @classmethod
-    def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', bs:int=32, val_bs:int=None, pad_idx=1,
-               pad_first=True, device:torch.device=None, no_check:bool=False, backwards:bool=False, 
-               dl_tfms:Optional[Collection[Callable]]=None, **dl_kwargs) -> DataBunch:
-        "Function that transform the `datasets` in a `DataBunch` for classification. Passes `**dl_kwargs` on to `DataLoader()`"
-        datasets = cls._init_ds(train_ds, valid_ds, test_ds)
-        val_bs = ifnone(val_bs, bs)
-        collate_fn = partial(pad_collate, pad_idx=pad_idx, pad_first=pad_first, backwards=backwards)
-        train_sampler = SortishSampler(datasets[0].x, key=lambda t: len(datasets[0][t][0].data), bs=bs)
-        train_dl = DataLoader(datasets[0], batch_size=bs, sampler=train_sampler, drop_last=True, **dl_kwargs)
-        dataloaders = [train_dl]
-        for ds in datasets[1:]:
-            lengths = [len(t) for t in ds.x.items]
-            sampler = SortSampler(ds.x, key=lengths.__getitem__)
-            dataloaders.append(DataLoader(ds, batch_size=val_bs, sampler=sampler, **dl_kwargs))
-        return cls(*dataloaders, path=path, device=device, dl_tfms=dl_tfms, collate_fn=collate_fn, no_check=no_check)
-
-def open_text(fn:PathOrStr, enc='utf-8'):
-    "Read the text in `fn`."
-    with open(fn,'r', encoding = enc) as f: return ''.join(f.readlines())
-
-class Text(ItemBase):
-    "Basic item for <code>text</code> data in numericalized `ids`."
-    def __init__(self, ids, text): self.data,self.text = np.array(ids, dtype=np.int64),text
-    def __str__(self):  return str(self.text)
-
-class TokenizeProcessor(PreProcessor):
-    "`PreProcessor` that tokenizes the texts in `ds`."
-    def __init__(self, ds:ItemList=None, tokenizer:Tokenizer=None, chunksize:int=10000, 
-                 mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False):
-        self.tokenizer,self.chunksize,self.mark_fields = ifnone(tokenizer, Tokenizer()),chunksize,mark_fields
-        self.include_bos, self.include_eos = include_bos, include_eos
-
-    def process_one(self, item):
-        return self.tokenizer._process_all_1(_join_texts([item], self.mark_fields, self.include_bos, self.include_eos))[0]
-
-    def process(self, ds):
-        ds.items = _join_texts(ds.items, self.mark_fields, self.include_bos, self.include_eos)
-        tokens = []
-        for i in progress_bar(range(0,len(ds),self.chunksize), leave=False):
-            tokens += self.tokenizer.process_all(ds.items[i:i+self.chunksize])
-        ds.items = tokens
-
-class NumericalizeProcessor(PreProcessor):
-    "`PreProcessor` that numericalizes the tokens in `ds`."
-    def __init__(self, ds:ItemList=None, vocab:Vocab=None, max_vocab:int=60000, min_freq:int=3):
-        vocab = ifnone(vocab, ds.vocab if ds is not None else None)
-        self.vocab,self.max_vocab,self.min_freq = vocab,max_vocab,min_freq
-
-    def process_one(self,item): return np.array(self.vocab.numericalize(item), dtype=np.int64)
-    def process(self, ds):
-        if self.vocab is None: self.vocab = Vocab.create(ds.items, self.max_vocab, self.min_freq)
-        ds.vocab = self.vocab
-        super().process(ds)
-
-class OpenFileProcessor(PreProcessor):
-    "`PreProcessor` that opens the filenames and read the texts."
-    def process(self, ds:Collection): ds.items = array([self.process_one(item) for item in ds.items], dtype=np.object)
-    def process_one(self,item): return open_text(item) if isinstance(item, Path) else item
-
-class TextList(ItemList):
-    "Basic `ItemList` for text data."
-    _bunch = TextClasDataBunch
-    _processor = [TokenizeProcessor, NumericalizeProcessor]
-    _is_lm = False
-
-    def __init__(self, items:Iterator, vocab:Vocab=None, pad_idx:int=1, sep=' ', **kwargs):
-        super().__init__(items, **kwargs)
-        self.vocab,self.pad_idx,self.sep = vocab,pad_idx,sep
-        self.copy_new += ['vocab', 'pad_idx', 'sep']
-
-    def get(self, i):
-        o = super().get(i)
-        return o if self.vocab is None else Text(o, self.vocab.textify(o, self.sep))
-
-    def label_for_lm(self, **kwargs):
-        "A special labelling method for language models."
-        self.__class__ = LMTextList
-        kwargs['label_cls'] = LMLabelList
-        return self.label_const(0, **kwargs)
-
-    def reconstruct(self, t:Tensor):
-        idx_min = (t != self.pad_idx).nonzero().min()
-        idx_max = (t != self.pad_idx).nonzero().max()
-        return Text(t[idx_min:idx_max+1], self.vocab.textify(t[idx_min:idx_max+1]))
-
-    @classmethod
-    def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=text_extensions, vocab:Vocab=None,
-                    processor:PreProcessor=None, **kwargs)->'TextList':
-        "Get the list of files in `path` that have a text suffix. `recurse` determines if we search subfolders."
-        processor = ifnone(processor, [OpenFileProcessor(), TokenizeProcessor(), NumericalizeProcessor(vocab=vocab)])
-        return super().from_folder(path=path, extensions=extensions, processor=processor, **kwargs)
-
-    def show_xys(self, xs, ys, max_len:int=70)->None:
-        "Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed."
-        from IPython.display import display, HTML
-        names = ['idx','text'] if self._is_lm else ['text','target']
-        items = []
-        for i, (x,y) in enumerate(zip(xs,ys)):
-            txt_x = ' '.join(x.text.split(' ')[:max_len]) if max_len is not None else x.text
-            items.append([i, txt_x] if self._is_lm else [txt_x, y])
-        items = np.array(items)
-        df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
-        with pd.option_context('display.max_colwidth', -1):
-            display(HTML(df.to_html(index=False)))
-
-    def show_xyzs(self, xs, ys, zs, max_len:int=70):
-        "Show `xs` (inputs), `ys` (targets) and `zs` (predictions). `max_len` is the maximum number of tokens displayed."
-        from IPython.display import display, HTML
-        items,names = [],['text','target','prediction']
-        for i, (x,y,z) in enumerate(zip(xs,ys,zs)):
-            txt_x = ' '.join(x.text.split(' ')[:max_len]) if max_len is not None else x.text
-            items.append([txt_x, y, z])
-        items = np.array(items)
-        df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
-        with pd.option_context('display.max_colwidth', -1):
-            display(HTML(df.to_html(index=False)))
-
-class LMLabelList(EmptyLabelList):
-    "Basic `ItemList` for dummy labels."
-    def __init__(self, items:Iterator, **kwargs):
-        super().__init__(items, **kwargs)
-        self.loss_func = CrossEntropyFlat()
-
-class LMTextList(TextList):
-    "Special `TextList` for a language model."
-    _bunch = TextLMDataBunch
-    _is_lm = True
-
-def _join_texts(texts:Collection[str], mark_fields:bool=False, include_bos:bool=True, include_eos:bool=False):
-    if not isinstance(texts, np.ndarray): texts = np.array(texts)
-    if is1d(texts): texts = texts[:,None]
-    df = pd.DataFrame({i:texts[:,i] for i in range(texts.shape[1])})
-    bos_tok = f'{BOS} ' if include_bos else ''
-    text_col = f'{bos_tok}{FLD} {1} ' + df[0].astype(str) if mark_fields else f'{bos_tok}' + df[0].astype(str)
-    for i in range(1,len(df.columns)):
-        text_col += (f' {FLD} {i+1} ' if mark_fields else ' ') + df[i].astype(str)
-    if include_eos: text_col = text_col + f' {EOS}'
-    return text_col.values
-
-def apply_rules(text, pre_rules=None, post_rules=None):
-    "Apply `pre_rules` and `post_rules` to `text`"
-    text = text.strip(' ')
-    for r in ifnone(pre_rules, defaults.text_pre_rules): text = r(text)
-    toks = text.split()
-    for r in ifnone(post_rules, defaults.text_post_rules): toks = r(toks)
-    return ' '.join(toks) 
-
-def get_default_size(texts, max_vocab_sz):
-    "Either max_vocab_sz or one quarter of the number of unique words in `texts`"
-    cnt = Counter()
-    for t in texts: 
-        cnt.update(t.split())
-        if len(cnt)//4 > max_vocab_sz: return max_vocab_sz
-    res = len(cnt)//4
-    while res%8 != 0: res+=1
-    return res
-
-full_char_coverage_langs = ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hr", "hu",
-                       "it","lt","lv","mt","nl","pl","pt","ro","sk","sl","sv"] # all European langs
-
-def train_sentencepiece(texts:Collection[str], path:PathOrStr, pre_rules: ListRules=None, post_rules:ListRules=None, 
-    vocab_sz:int=None, max_vocab_sz:int=30000, model_type:str='unigram', max_sentence_len:int=20480, lang='en',
-    char_coverage=None, tmp_dir='tmp'):
-    "Train a sentencepiece tokenizer on `texts` and save it in `path/tmp_dir`"
-    from sentencepiece import SentencePieceTrainer
-    cache_dir = Path(path)/tmp_dir
-    os.makedirs(cache_dir, exist_ok=True)
-    if vocab_sz is None: vocab_sz=get_default_size(texts, max_vocab_sz)
-    raw_text_path = cache_dir / 'all_text.out'
-    with open(raw_text_path, 'w') as f: f.write("\n".join(texts))
-    spec_tokens = ['\u2581'+s for s in defaults.text_spec_tok]
-    SentencePieceTrainer.Train(" ".join([
-        f"--input={raw_text_path} --max_sentence_length={max_sentence_len}",
-        f"--character_coverage={ifnone(char_coverage, 0.99999 if lang in full_char_coverage_langs else 0.9998)}",
-        f"--unk_id={len(defaults.text_spec_tok)} --pad_id=-1 --bos_id=-1 --eos_id=-1",
-        f"--user_defined_symbols={','.join(spec_tokens)}",
-        f"--model_prefix={cache_dir/'spm'} --vocab_size={vocab_sz} --model_type={model_type}"]))
-    raw_text_path.unlink()
-    return cache_dir
-
-class SPProcessor(PreProcessor):
-    "`PreProcessor` that tokenizes and numericalizes with `sentencepiece`"
-    def __init__(self, ds:ItemList=None, pre_rules: ListRules=None, post_rules:ListRules=None, vocab_sz:int=None,
-                 max_vocab_sz:int=30000, model_type:str='unigram', max_sentence_len:int=20480, lang='en',
-                 char_coverage=None, tmp_dir='tmp', mark_fields:bool=False, include_bos:bool=True, 
-                 include_eos:bool=False, sp_model=None, sp_vocab=None, n_cpus:int=None):
-        try: from sentencepiece import SentencePieceTrainer,SentencePieceProcessor
-        except ImportError:
-            raise Exception('sentencepiece module is missing: run `pip install sentencepiece`')
-        self.pre_rules,self.post_rules = pre_rules,post_rules
-        self.mark_fields,self.include_bos,self.include_eos = mark_fields,include_bos,include_eos
-        self.sp_model,self.sp_vocab,self.n_cpus = sp_model,sp_vocab,ifnone(n_cpus,defaults.cpus)
-        self.train_func = partial(train_sentencepiece, pre_rules=pre_rules, post_rules=post_rules, vocab_sz=vocab_sz,
-                max_vocab_sz=max_vocab_sz, model_type=model_type, max_sentence_len=max_sentence_len, lang=lang,
-                char_coverage=char_coverage, tmp_dir=tmp_dir)
-
-    def process_one(self, item, join=True):
-        if join: text = _join_texts([item], self.mark_fields, self.include_bos, self.include_eos)[0]
-        text = apply_rules(text, pre_rules=self.pre_rules, post_rules=self.post_rules)
-        return self._encode_batch([text])[0]
-
-    def process(self, ds):
-        ds.items = _join_texts(ds.items, self.mark_fields, self.include_bos, self.include_eos)
-        ds.items = [apply_rules(t, pre_rules=self.pre_rules, post_rules=self.post_rules) 
-                    for t in progress_bar(ds.items, leave=False)]
-        if self.sp_model is None or self.sp_vocab is None:
-            cache_dir = self.train_func(ds.items, ds.path)
-            self.sp_model,self.sp_vocab = cache_dir/'spm.model',cache_dir/'spm.vocab'
-        if not getattr(self, 'vocab', False): 
-            with open(self.sp_vocab, 'r') as f: self.vocab = Vocab([line.split('\t')[0] for line in f.readlines()])
-        if self.n_cpus <= 1: ds.items = self._encode_batch(ds.items)
-        else:
-            with ProcessPoolExecutor(self.n_cpus) as e:
-                ds.items = np.array(sum(e.map(self._encode_batch, partition_by_cores(ds.items, self.n_cpus)), []))
-        ds.vocab = self.vocab
-
-    def _encode_batch(self, texts):
-        from sentencepiece import SentencePieceProcessor
-        tok = SentencePieceProcessor()
-        tok.Load(str(self.sp_model))
-        return [np.array(tok.EncodeAsIds(t)) for t in texts]
-
-    @classmethod
-    def load(cls, path:PathOrStr, tmp_dir:PathOrStr='tmp', name:str='spm'):
-        cache_dir = Path(path)/tmp_dir
-        return cls(sp_model=cache_dir/f'{name}.model', sp_vocab=cache_dir/f'{name}.vocab')
diff --git a/spaces/XzJosh/Lumi-Bert-VITS2/text/__init__.py b/spaces/XzJosh/Lumi-Bert-VITS2/text/__init__.py
deleted file mode 100644
index 7566bf351ca9b95af9cdc6d729557a9da083800f..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Lumi-Bert-VITS2/text/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from text.symbols import *
-
-
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
-def cleaned_text_to_sequence(cleaned_text, tones, language):
-  '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
-    Args:
-      text: string to convert to a sequence
-    Returns:
-      List of integers corresponding to the symbols in the text
-  '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
-  tone_start = language_tone_start_map[language]
-  tones = [i + tone_start for i in tones]
-  lang_id = language_id_map[language]
-  lang_ids = [lang_id for i in phones]
-  return phones, tones, lang_ids
-
-def get_bert(norm_text, word2ph, language):
-  from .chinese_bert import get_bert_feature as zh_bert
-  from .english_bert_mock import get_bert_feature as en_bert
-  lang_bert_func_map = {
-    'ZH': zh_bert,
-    'EN': en_bert
-  }
-  bert = lang_bert_func_map[language](norm_text, word2ph)
-  return bert
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/monotonic_align/__init__.py b/spaces/XzJosh/Taffy-Bert-VITS2/monotonic_align/__init__.py
deleted file mode 100644
index 75603d26cf2b8d6196f5a68a89f9e49d8e519bc8..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/monotonic_align/__init__.py
+++ /dev/null
@@ -1,15 +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):
-  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)
diff --git a/spaces/XzJosh/Taffy-Bert-VITS2/text/english.py b/spaces/XzJosh/Taffy-Bert-VITS2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Taffy-Bert-VITS2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def read_dict():
-    g2p_dict = {}
-    start_line = 49
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= start_line:
-                line = line.strip()
-                word_split = line.split('  ')
-                word = word_split[0]
-
-                syllable_split = word_split[1].split(' - ')
-                g2p_dict[word] = []
-                for syllable in syllable_split:
-                    phone_split = syllable.split(' ')
-                    g2p_dict[word].append(phone_split)
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
-    with open(file_path, 'wb') as pickle_file:
-        pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
-    if os.path.exists(CACHE_PATH):
-        with open(CACHE_PATH, 'rb') as pickle_file:
-            g2p_dict = pickle.load(pickle_file)
-    else:
-        g2p_dict = read_dict()
-        cache_dict(g2p_dict, CACHE_PATH)
-
-    return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
-    tone = 0
-    if re.search(r'\d$', phn):
-        tone = int(phn[-1]) + 1
-        phn = phn[:-1]
-    return phn.lower(), tone
-
-def refine_syllables(syllables):
-    tones = []
-    phonemes = []
-    for phn_list in syllables:
-        for i in range(len(phn_list)):
-            phn = phn_list[i]
-            phn, tone = refine_ph(phn)
-            phonemes.append(phn)
-            tones.append(tone)
-    return phonemes, tones
-
-
-def text_normalize(text):
-    # todo: eng text normalize
-    return text
-
-def g2p(text):
-
-    phones = []
-    tones = []
-    words = re.split(r"([,;.\-\?\!\s+])", text)
-    for w in words:
-        if w.upper() in eng_dict:
-            phns, tns = refine_syllables(eng_dict[w.upper()])
-            phones += phns
-            tones += tns
-        else:
-            phone_list = list(filter(lambda p: p != " ", _g2p(w)))
-            for ph in phone_list:
-                if ph in arpa:
-                    ph, tn = refine_ph(ph)
-                    phones.append(ph)
-                    tones.append(tn)
-                else:
-                    phones.append(ph)
-                    tones.append(0)
-    # todo: implement word2ph
-    word2ph = [1 for i in phones]
-
-    phones = [post_replace_ph(i) for i in phones]
-    return phones, tones, word2ph
-
-if __name__ == "__main__":
-    # print(get_dict())
-    # print(eng_word_to_phoneme("hello"))
-    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
-    # all_phones = set()
-    # for k, syllables in eng_dict.items():
-    #     for group in syllables:
-    #         for ph in group:
-    #             all_phones.add(ph)
-    # print(all_phones)
\ No newline at end of file
diff --git a/spaces/XzJosh/otto-Bert-VITS2/text/english_bert_mock.py b/spaces/XzJosh/otto-Bert-VITS2/text/english_bert_mock.py
deleted file mode 100644
index 3b894ced5b6d619a18d6bdd7d7606ba9e6532050..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/otto-Bert-VITS2/text/english_bert_mock.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import torch
-
-
-def get_bert_feature(norm_text, word2ph):
-    return torch.zeros(1024, sum(word2ph))
diff --git a/spaces/Yan233th/so-vits-svc-models/README.md b/spaces/Yan233th/so-vits-svc-models/README.md
deleted file mode 100644
index d4e32ad5a214aa9753a0acf2e5368efc02d0f4b5..0000000000000000000000000000000000000000
--- a/spaces/Yan233th/so-vits-svc-models/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: So Vits Svc Models
-emoji: 📈
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/schedulers/scheduling_pndm_flax.py b/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/schedulers/scheduling_pndm_flax.py
deleted file mode 100644
index 298e62de20d15febcd44b00f87046c431f4e2337..0000000000000000000000000000000000000000
--- a/spaces/YeOldHermit/Super-Resolution-Anime-Diffusion/diffusers/schedulers/scheduling_pndm_flax.py
+++ /dev/null
@@ -1,531 +0,0 @@
-# Copyright 2022 Zhejiang University Team and 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.
-
-# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
-
-import math
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import flax
-import jax
-import jax.numpy as jnp
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils_flax import (
-    _FLAX_COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS,
-    FlaxSchedulerMixin,
-    FlaxSchedulerOutput,
-    broadcast_to_shape_from_left,
-)
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps: int, max_beta=0.999) -> jnp.ndarray:
-    """
-    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].
-
-    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
-    to that part of the diffusion process.
-
-
-    Args:
-        num_diffusion_timesteps (`int`): the number of betas to produce.
-        max_beta (`float`): the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-
-    Returns:
-        betas (`jnp.ndarray`): the betas used by the scheduler to step the model outputs
-    """
-
-    def alpha_bar(time_step):
-        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
-
-    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 jnp.array(betas, dtype=jnp.float32)
-
-
-@flax.struct.dataclass
-class PNDMSchedulerState:
-    # setable values
-    _timesteps: jnp.ndarray
-    num_inference_steps: Optional[int] = None
-    prk_timesteps: Optional[jnp.ndarray] = None
-    plms_timesteps: Optional[jnp.ndarray] = None
-    timesteps: Optional[jnp.ndarray] = None
-
-    # running values
-    cur_model_output: Optional[jnp.ndarray] = None
-    counter: int = 0
-    cur_sample: Optional[jnp.ndarray] = None
-    ets: jnp.ndarray = jnp.array([])
-
-    @classmethod
-    def create(cls, num_train_timesteps: int):
-        return cls(_timesteps=jnp.arange(0, num_train_timesteps)[::-1])
-
-
-@dataclass
-class FlaxPNDMSchedulerOutput(FlaxSchedulerOutput):
-    state: PNDMSchedulerState
-
-
-class FlaxPNDMScheduler(FlaxSchedulerMixin, ConfigMixin):
-    """
-    Pseudo numerical methods for diffusion models (PNDM) proposes using more advanced ODE integration techniques,
-    namely Runge-Kutta method and a linear multi-step method.
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    For more details, see the original paper: https://arxiv.org/abs/2202.09778
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        beta_start (`float`): the starting `beta` value of inference.
-        beta_end (`float`): the final `beta` value.
-        beta_schedule (`str`):
-            the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
-            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
-        trained_betas (`jnp.ndarray`, optional):
-            option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
-        skip_prk_steps (`bool`):
-            allows the scheduler to skip the Runge-Kutta steps that are defined in the original paper as being required
-            before plms steps; defaults to `False`.
-        set_alpha_to_one (`bool`, default `False`):
-            each diffusion step uses the value of alphas product at that step and at the previous one. For the final
-            step there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
-            otherwise it uses the value of alpha at step 0.
-        steps_offset (`int`, default `0`):
-            an offset added to the inference steps. You can use a combination of `offset=1` and
-            `set_alpha_to_one=False`, to make the last step use step 0 for the previous alpha product, as done in
-            stable diffusion.
-    """
-
-    _compatibles = _FLAX_COMPATIBLE_STABLE_DIFFUSION_SCHEDULERS.copy()
-
-    @property
-    def has_state(self):
-        return True
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        beta_start: float = 0.0001,
-        beta_end: float = 0.02,
-        beta_schedule: str = "linear",
-        trained_betas: Optional[jnp.ndarray] = None,
-        skip_prk_steps: bool = False,
-        set_alpha_to_one: bool = False,
-        steps_offset: int = 0,
-    ):
-        if trained_betas is not None:
-            self.betas = jnp.asarray(trained_betas)
-        elif beta_schedule == "linear":
-            self.betas = jnp.linspace(beta_start, beta_end, num_train_timesteps, dtype=jnp.float32)
-        elif beta_schedule == "scaled_linear":
-            # this schedule is very specific to the latent diffusion model.
-            self.betas = jnp.linspace(beta_start**0.5, beta_end**0.5, num_train_timesteps, dtype=jnp.float32) ** 2
-        elif beta_schedule == "squaredcos_cap_v2":
-            # Glide cosine schedule
-            self.betas = betas_for_alpha_bar(num_train_timesteps)
-        else:
-            raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
-
-        self.alphas = 1.0 - self.betas
-        self.alphas_cumprod = jnp.cumprod(self.alphas, axis=0)
-
-        self.final_alpha_cumprod = jnp.array(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
-
-        # For now we only support F-PNDM, i.e. the runge-kutta method
-        # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
-        # mainly at formula (9), (12), (13) and the Algorithm 2.
-        self.pndm_order = 4
-
-        # standard deviation of the initial noise distribution
-        self.init_noise_sigma = 1.0
-
-    def create_state(self):
-        return PNDMSchedulerState.create(num_train_timesteps=self.config.num_train_timesteps)
-
-    def set_timesteps(self, state: PNDMSchedulerState, num_inference_steps: int, shape: Tuple) -> PNDMSchedulerState:
-        """
-        Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            state (`PNDMSchedulerState`):
-                the `FlaxPNDMScheduler` state data class instance.
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            shape (`Tuple`):
-                the shape of the samples to be generated.
-        """
-        offset = self.config.steps_offset
-
-        step_ratio = self.config.num_train_timesteps // num_inference_steps
-        # creates integer timesteps by multiplying by ratio
-        # rounding to avoid issues when num_inference_step is power of 3
-        _timesteps = (jnp.arange(0, num_inference_steps) * step_ratio).round() + offset
-
-        state = state.replace(num_inference_steps=num_inference_steps, _timesteps=_timesteps)
-
-        if self.config.skip_prk_steps:
-            # for some models like stable diffusion the prk steps can/should be skipped to
-            # produce better results. When using PNDM with `self.config.skip_prk_steps` the implementation
-            # is based on crowsonkb's PLMS sampler implementation: https://github.com/CompVis/latent-diffusion/pull/51
-            state = state.replace(
-                prk_timesteps=jnp.array([]),
-                plms_timesteps=jnp.concatenate(
-                    [state._timesteps[:-1], state._timesteps[-2:-1], state._timesteps[-1:]]
-                )[::-1],
-            )
-        else:
-            prk_timesteps = jnp.array(state._timesteps[-self.pndm_order :]).repeat(2) + jnp.tile(
-                jnp.array([0, self.config.num_train_timesteps // num_inference_steps // 2]), self.pndm_order
-            )
-
-            state = state.replace(
-                prk_timesteps=(prk_timesteps[:-1].repeat(2)[1:-1])[::-1],
-                plms_timesteps=state._timesteps[:-3][::-1],
-            )
-
-        return state.replace(
-            timesteps=jnp.concatenate([state.prk_timesteps, state.plms_timesteps]).astype(jnp.int32),
-            counter=0,
-            # Reserve space for the state variables
-            cur_model_output=jnp.zeros(shape),
-            cur_sample=jnp.zeros(shape),
-            ets=jnp.zeros((4,) + shape),
-        )
-
-    def scale_model_input(
-        self, state: PNDMSchedulerState, sample: jnp.ndarray, timestep: Optional[int] = None
-    ) -> jnp.ndarray:
-        """
-        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
-        current timestep.
-
-        Args:
-            state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.
-            sample (`jnp.ndarray`): input sample
-            timestep (`int`, optional): current timestep
-
-        Returns:
-            `jnp.ndarray`: scaled input sample
-        """
-        return sample
-
-    def step(
-        self,
-        state: PNDMSchedulerState,
-        model_output: jnp.ndarray,
-        timestep: int,
-        sample: jnp.ndarray,
-        return_dict: bool = True,
-    ) -> Union[FlaxPNDMSchedulerOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        This function calls `step_prk()` or `step_plms()` depending on the internal variable `counter`.
-
-        Args:
-            state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class
-
-        Returns:
-            [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-        if self.config.skip_prk_steps:
-            prev_sample, state = self.step_plms(
-                state=state, model_output=model_output, timestep=timestep, sample=sample
-            )
-        else:
-            prev_sample, state = jax.lax.switch(
-                jnp.where(state.counter < len(state.prk_timesteps), 0, 1),
-                (self.step_prk, self.step_plms),
-                # Args to either branch
-                state,
-                model_output,
-                timestep,
-                sample,
-            )
-
-        if not return_dict:
-            return (prev_sample, state)
-
-        return FlaxPNDMSchedulerOutput(prev_sample=prev_sample, state=state)
-
-    def step_prk(
-        self,
-        state: PNDMSchedulerState,
-        model_output: jnp.ndarray,
-        timestep: int,
-        sample: jnp.ndarray,
-    ) -> Union[FlaxPNDMSchedulerOutput, Tuple]:
-        """
-        Step function propagating the sample with the Runge-Kutta method. RK takes 4 forward passes to approximate the
-        solution to the differential equation.
-
-        Args:
-            state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class
-
-        Returns:
-            [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-        if state.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        diff_to_prev = jnp.where(
-            state.counter % 2, 0, self.config.num_train_timesteps // state.num_inference_steps // 2
-        )
-        prev_timestep = timestep - diff_to_prev
-        timestep = state.prk_timesteps[state.counter // 4 * 4]
-
-        def remainder_0(state: PNDMSchedulerState, model_output: jnp.ndarray, ets_at: int):
-            return (
-                state.replace(
-                    cur_model_output=state.cur_model_output + 1 / 6 * model_output,
-                    ets=state.ets.at[ets_at].set(model_output),
-                    cur_sample=sample,
-                ),
-                model_output,
-            )
-
-        def remainder_1(state: PNDMSchedulerState, model_output: jnp.ndarray, ets_at: int):
-            return state.replace(cur_model_output=state.cur_model_output + 1 / 3 * model_output), model_output
-
-        def remainder_2(state: PNDMSchedulerState, model_output: jnp.ndarray, ets_at: int):
-            return state.replace(cur_model_output=state.cur_model_output + 1 / 3 * model_output), model_output
-
-        def remainder_3(state: PNDMSchedulerState, model_output: jnp.ndarray, ets_at: int):
-            model_output = state.cur_model_output + 1 / 6 * model_output
-            return state.replace(cur_model_output=jnp.zeros_like(state.cur_model_output)), model_output
-
-        state, model_output = jax.lax.switch(
-            state.counter % 4,
-            (remainder_0, remainder_1, remainder_2, remainder_3),
-            # Args to either branch
-            state,
-            model_output,
-            state.counter // 4,
-        )
-
-        cur_sample = state.cur_sample
-        prev_sample = self._get_prev_sample(cur_sample, timestep, prev_timestep, model_output)
-        state = state.replace(counter=state.counter + 1)
-
-        return (prev_sample, state)
-
-    def step_plms(
-        self,
-        state: PNDMSchedulerState,
-        model_output: jnp.ndarray,
-        timestep: int,
-        sample: jnp.ndarray,
-    ) -> Union[FlaxPNDMSchedulerOutput, Tuple]:
-        """
-        Step function propagating the sample with the linear multi-step method. This has one forward pass with multiple
-        times to approximate the solution.
-
-        Args:
-            state (`PNDMSchedulerState`): the `FlaxPNDMScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            return_dict (`bool`): option for returning tuple rather than FlaxPNDMSchedulerOutput class
-
-        Returns:
-            [`FlaxPNDMSchedulerOutput`] or `tuple`: [`FlaxPNDMSchedulerOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is the sample tensor.
-
-        """
-        if state.num_inference_steps is None:
-            raise ValueError(
-                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        if not self.config.skip_prk_steps and len(state.ets) < 3:
-            raise ValueError(
-                f"{self.__class__} can only be run AFTER scheduler has been run "
-                "in 'prk' mode for at least 12 iterations "
-                "See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py "
-                "for more information."
-            )
-
-        prev_timestep = timestep - self.config.num_train_timesteps // state.num_inference_steps
-        prev_timestep = jnp.where(prev_timestep > 0, prev_timestep, 0)
-
-        # Reference:
-        # if state.counter != 1:
-        #     state.ets.append(model_output)
-        # else:
-        #     prev_timestep = timestep
-        #     timestep = timestep + self.config.num_train_timesteps // state.num_inference_steps
-
-        prev_timestep = jnp.where(state.counter == 1, timestep, prev_timestep)
-        timestep = jnp.where(
-            state.counter == 1, timestep + self.config.num_train_timesteps // state.num_inference_steps, timestep
-        )
-
-        # Reference:
-        # if len(state.ets) == 1 and state.counter == 0:
-        #     model_output = model_output
-        #     state.cur_sample = sample
-        # elif len(state.ets) == 1 and state.counter == 1:
-        #     model_output = (model_output + state.ets[-1]) / 2
-        #     sample = state.cur_sample
-        #     state.cur_sample = None
-        # elif len(state.ets) == 2:
-        #     model_output = (3 * state.ets[-1] - state.ets[-2]) / 2
-        # elif len(state.ets) == 3:
-        #     model_output = (23 * state.ets[-1] - 16 * state.ets[-2] + 5 * state.ets[-3]) / 12
-        # else:
-        #     model_output = (1 / 24) * (55 * state.ets[-1] - 59 * state.ets[-2] + 37 * state.ets[-3] - 9 * state.ets[-4])
-
-        def counter_0(state: PNDMSchedulerState):
-            ets = state.ets.at[0].set(model_output)
-            return state.replace(
-                ets=ets,
-                cur_sample=sample,
-                cur_model_output=jnp.array(model_output, dtype=jnp.float32),
-            )
-
-        def counter_1(state: PNDMSchedulerState):
-            return state.replace(
-                cur_model_output=(model_output + state.ets[0]) / 2,
-            )
-
-        def counter_2(state: PNDMSchedulerState):
-            ets = state.ets.at[1].set(model_output)
-            return state.replace(
-                ets=ets,
-                cur_model_output=(3 * ets[1] - ets[0]) / 2,
-                cur_sample=sample,
-            )
-
-        def counter_3(state: PNDMSchedulerState):
-            ets = state.ets.at[2].set(model_output)
-            return state.replace(
-                ets=ets,
-                cur_model_output=(23 * ets[2] - 16 * ets[1] + 5 * ets[0]) / 12,
-                cur_sample=sample,
-            )
-
-        def counter_other(state: PNDMSchedulerState):
-            ets = state.ets.at[3].set(model_output)
-            next_model_output = (1 / 24) * (55 * ets[3] - 59 * ets[2] + 37 * ets[1] - 9 * ets[0])
-
-            ets = ets.at[0].set(ets[1])
-            ets = ets.at[1].set(ets[2])
-            ets = ets.at[2].set(ets[3])
-
-            return state.replace(
-                ets=ets,
-                cur_model_output=next_model_output,
-                cur_sample=sample,
-            )
-
-        counter = jnp.clip(state.counter, 0, 4)
-        state = jax.lax.switch(
-            counter,
-            [counter_0, counter_1, counter_2, counter_3, counter_other],
-            state,
-        )
-
-        sample = state.cur_sample
-        model_output = state.cur_model_output
-        prev_sample = self._get_prev_sample(sample, timestep, prev_timestep, model_output)
-        state = state.replace(counter=state.counter + 1)
-
-        return (prev_sample, state)
-
-    def _get_prev_sample(self, sample, timestep, prev_timestep, model_output):
-        # See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf
-        # this function computes x_(t−δ) using the formula of (9)
-        # Note that x_t needs to be added to both sides of the equation
-
-        # Notation (<variable name> -> <name in paper>
-        # alpha_prod_t -> α_t
-        # alpha_prod_t_prev -> α_(t−δ)
-        # beta_prod_t -> (1 - α_t)
-        # beta_prod_t_prev -> (1 - α_(t−δ))
-        # sample -> x_t
-        # model_output -> e_θ(x_t, t)
-        # prev_sample -> x_(t−δ)
-        alpha_prod_t = self.alphas_cumprod[timestep]
-        alpha_prod_t_prev = jnp.where(prev_timestep >= 0, self.alphas_cumprod[prev_timestep], self.final_alpha_cumprod)
-        beta_prod_t = 1 - alpha_prod_t
-        beta_prod_t_prev = 1 - alpha_prod_t_prev
-
-        # corresponds to (α_(t−δ) - α_t) divided by
-        # denominator of x_t in formula (9) and plus 1
-        # Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =
-        # sqrt(α_(t−δ)) / sqrt(α_t))
-        sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
-
-        # corresponds to denominator of e_θ(x_t, t) in formula (9)
-        model_output_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (
-            alpha_prod_t * beta_prod_t * alpha_prod_t_prev
-        ) ** (0.5)
-
-        # full formula (9)
-        prev_sample = (
-            sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * model_output / model_output_denom_coeff
-        )
-
-        return prev_sample
-
-    def add_noise(
-        self,
-        original_samples: jnp.ndarray,
-        noise: jnp.ndarray,
-        timesteps: jnp.ndarray,
-    ) -> jnp.ndarray:
-        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
-        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
-        sqrt_alpha_prod = broadcast_to_shape_from_left(sqrt_alpha_prod, original_samples.shape)
-
-        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
-        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
-        sqrt_one_minus_alpha_prod = broadcast_to_shape_from_left(sqrt_one_minus_alpha_prod, original_samples.shape)
-
-        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        return noisy_samples
-
-    def __len__(self):
-        return self.config.num_train_timesteps
diff --git a/spaces/YlcldKlns/bing/src/components/chat-scroll-anchor.tsx b/spaces/YlcldKlns/bing/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/YlcldKlns/bing/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
-  trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
-  const isAtBottom = useAtBottom()
-  const { ref, entry, inView } = useInView({
-    trackVisibility,
-    delay: 100,
-    rootMargin: '0px 0px -150px 0px'
-  })
-
-  React.useEffect(() => {
-    if (isAtBottom && trackVisibility && !inView) {
-      entry?.target.scrollIntoView({
-        block: 'start'
-      })
-    }
-  }, [inView, entry, isAtBottom, trackVisibility])
-
-  return <div ref={ref} className="h-px w-full" />
-}
diff --git a/spaces/YueMafighting/FollowYourPose/app.py b/spaces/YueMafighting/FollowYourPose/app.py
deleted file mode 100644
index 0a0325751941dd7e83529c67eeaf2a025bbc2c70..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/app.py
+++ /dev/null
@@ -1,206 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-
-import gradio as gr
-
-from inference_followyourpose import merge_config_then_run
-import sys
-
-sys.path.append('FollowYourPose')
-
-
-# result = subprocess.run(['bash', './data/download.sh'], stdout=subprocess.PIPE)
-import subprocess
-zip_file = './example_video.zip'
-output_dir = './data'
-subprocess.run(['unzip', zip_file, '-d', output_dir])
-
-current_dir = os.getcwd()
-print("path is :", current_dir)
-print("current_dir is :", os.listdir(current_dir))
-print("dir is :", os.listdir(os.path.join(current_dir,'data')))
-print("data/example_video is :", os.listdir(os.path.join(current_dir,'data/example_video')))
-
-HF_TOKEN = os.getenv('HF_TOKEN')
-pipe = merge_config_then_run()
-
-
-
-with gr.Blocks(css='style.css') as demo:
-    gr.HTML(
-    """
-    <div style="text-align: center; max-width: 1200px; margin: 20px auto;">
-    <h1 style="font-weight: 900; font-size: 2rem; margin: 0rem">
-        🕺🕺🕺 Follow Your Pose 💃💃💃 </font></center> <br> <center>Pose-Guided Text-to-Video Generation using Pose-Free Videos
-    </h1>
-    <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem">
-            <a href="https://mayuelala.github.io/">Yue Ma*</a>
-            <a href="https://github.com/YingqingHe">Yingqing He*</a> , <a href="http://vinthony.github.io/">Xiaodong Cun</a>, 
-            <a href="https://xinntao.github.io/"> Xintao Wang </a>,
-            <a href="https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=zh-CN">Ying Shan</a>,
-            <a href="https://scholar.google.com/citations?user=Xrh1OIUAAAAJ&hl=zh-CN">Xiu Li</a>,
-            <a href="http://cqf.io">Qifeng Chen</a>
-    </h2>
-
-    <h2 style="font-weight: 450; font-size: 1rem; margin: 0rem">
-                  <span class="link-block">
-                    [<a href="https://arxiv.org/abs/2304.01186" target="_blank"
-                    class="external-link ">
-                    <span class="icon">
-                      <i class="ai ai-arxiv"></i>
-                    </span>
-                    <span>arXiv</span>
-                  </a>]
-                </span>
-
-                  <!-- Github link -->
-                  <span class="link-block">
-                    [<a href="https://github.com/mayuelala/FollowYourPose" target="_blank"
-                    class="external-link ">
-                    <span class="icon">
-                      <i class="fab fa-github"></i>
-                    </span>
-                    <span>Code</span>
-                  </a>]
-                </span>
-
-                <!-- Github link -->
-                  <span class="link-block">
-                    [<a href="https://follow-your-pose.github.io/" target="_blank"
-                    class="external-link ">
-                    <span class="icon">
-                      <i class="fab fa-github"></i>
-                    </span>
-                    <span>Homepage</span>
-                  </a>]
-                </span>
-    </h2>
-    <h2 style="font-weight: 450; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem">
-        TL;DR: We tune 2D stable-diffusion to generate the character videos from pose and text description.
-    </h2>
-    </div>
-    """)
-
-
-    gr.HTML("""
-    <p>In order to run the demo successfully, we recommend the length of video is about <b>3~5 seconds</b>.
-    The temporal crop offset and sampling stride are used to adjust the starting point and interval of video samples.
-    Due to the GPU limit of this demo, it currently generates 8-frame videos. For generating longer videos (e.g. 32 frames) shown on our webpage, we recommend trying our GitHub <a href=https://github.com/mayuelala/FollowYourPose> code  </a> on your own GPU.
-    </p>
-    <p>You may duplicate the space and upgrade to GPU in settings for better performance and faster inference without waiting in the queue.</p>
-    <br/>
-    <a href="https://huggingface.co/spaces/YueMafighting/FollowYourPose?duplicate=true">
-    <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-    """)
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Accordion('Input Video', open=True):
-                # user_input_video = gr.File(label='Input Source Video')
-                user_input_video = gr.Video(label='Input Source Video', source='upload', type='numpy', format="mp4", visible=True).style(height="auto")
-                video_type = gr.Dropdown(
-                  label='The type of input video',
-                  choices=[
-                      "Raw Video",
-                      "Skeleton Video"
-                  ], value="Raw Video")
-                with gr.Accordion('Temporal Crop offset and Sampling Stride', open=False):
-                    n_sample_frame = gr.Slider(label='Number of Frames',
-                                        minimum=0,
-                                        maximum=32,
-                                        step=1,
-                                        value=8)
-                    stride = gr.Slider(label='Temporal stride',
-                                            minimum=0,
-                                            maximum=20,
-                                            step=1,
-                                            value=1)
-
-                with gr.Accordion('Spatial Crop offset', open=False):
-                    left_crop = gr.Number(label='Left crop',
-                              value=0,
-                              precision=0)
-                    right_crop = gr.Number(label='Right crop',
-                              value=0,
-                              precision=0)
-                    top_crop = gr.Number(label='Top crop',
-                              value=0,
-                              precision=0)
-                    bottom_crop = gr.Number(label='Bottom crop',
-                              value=0,
-                              precision=0)
-                    offset_list = [
-                         left_crop,
-                         right_crop,
-                         top_crop,
-                         bottom_crop,
-                    ]
-                
-                ImageSequenceDataset_list = [
-                   n_sample_frame,
-                   stride
-                ] + offset_list
-                
-
-            with gr.Accordion('Text Prompt', open=True):
-
-                target_prompt = gr.Textbox(label='Target Prompt',
-                                    info='The simple background may achieve better results(e.g., "beach", "moon" prompt is better than "street" and "market")',
-                                    max_lines=1,
-                                    placeholder='Example: "Iron man on the beach"',
-                                    value='Iron man on the beach')
-
-
-
-
-
-            run_button = gr.Button('Generate')
-
-        with gr.Column():
-            result = gr.Video(label='Result')
-            # result.style(height=512, width=512)
-            with gr.Accordion('DDIM Parameters', open=True):
-                num_steps = gr.Slider(label='Number of Steps',
-                                      info='larger value has better editing capacity, but takes more time and memory.',
-                                      minimum=0,
-                                      maximum=50,
-                                      step=1,
-                                      value=50)
-                guidance_scale = gr.Slider(label='CFG Scale',
-                                           minimum=0,
-                                           maximum=50,
-                                           step=0.1,
-                                           value=12.0)
-    with gr.Row():
-        from example import style_example
-        examples = style_example
-        
-        gr.Examples(examples=examples,
-                    inputs = [
-                        user_input_video,
-                        target_prompt,
-                        num_steps,
-                        guidance_scale,
-                        video_type,
-                        *ImageSequenceDataset_list
-                    ],
-                    outputs=result,
-                    fn=pipe.run,
-                    cache_examples=True,
-                    )
-    inputs = [
-            user_input_video,
-            target_prompt,
-            num_steps,
-            guidance_scale,
-            video_type,
-            *ImageSequenceDataset_list
-    ]
-    target_prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
-    run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
-
-demo.queue().launch()
-# demo.queue().launch(share=False, server_name='0.0.0.0', server_port=80)
\ No newline at end of file
diff --git a/spaces/Yuliang/ECON/lib/smplx/README.md b/spaces/Yuliang/ECON/lib/smplx/README.md
deleted file mode 100644
index e000e63af4569d8fae38346be370ba815662674d..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/smplx/README.md
+++ /dev/null
@@ -1,207 +0,0 @@
-## SMPL-X:  A new joint 3D model of the human body, face and hands together
-
-[[Paper Page](https://smpl-x.is.tue.mpg.de)] [[Paper](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/497/SMPL-X.pdf)]
-[[Supp. Mat.](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/498/SMPL-X-supp.pdf)]
-
-![SMPL-X Examples](./images/teaser_fig.png)
-
-## Table of Contents
-  * [License](#license)
-  * [Description](#description)
-  * [News](#news)
-  * [Installation](#installation)
-  * [Downloading the model](#downloading-the-model)
-  * [Loading SMPL-X, SMPL+H and SMPL](#loading-smpl-x-smplh-and-smpl) 
-    * [SMPL and SMPL+H setup](#smpl-and-smplh-setup)
-    * [Model loading](https://github.com/vchoutas/smplx#model-loading)
-  * [MANO and FLAME correspondences](#mano-and-flame-correspondences) 
-  * [Example](#example)
-  * [Modifying the global pose of the model](#modifying-the-global-pose-of-the-model)
-  * [Citation](#citation)
-  * [Acknowledgments](#acknowledgments)
-  * [Contact](#contact)
-
-## License
-
-Software Copyright License for **non-commercial scientific research purposes**.
-Please read carefully the [terms and conditions](https://github.com/vchoutas/smplx/blob/master/LICENSE) and any accompanying documentation before you download and/or use the SMPL-X/SMPLify-X model, data and software, (the "Model & Software"), including 3D meshes, blend weights, blend shapes, textures, software, scripts, and animations. By downloading and/or using the Model & Software (including downloading, cloning, installing, and any other use of this github repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Model & Software. Any infringement of the terms of this agreement will automatically terminate your rights under this [License](./LICENSE).
-
-## Disclaimer
-
-The original images used for the figures 1 and 2 of the paper can be found in this link. 
-The images in the paper are used under license from gettyimages.com.
-We have acquired the right to use them in the publication, but redistribution is not allowed.
-Please follow the instructions on the given link to acquire right of usage.
-Our results are obtained on the 483 × 724 pixels resolution of the original images.
-
-## Description
-
-*SMPL-X* (SMPL eXpressive) is a unified body model with shape parameters trained jointly for the
-face, hands and body. *SMPL-X* uses standard vertex based linear blend skinning with learned corrective blend
-shapes, has N = 10, 475 vertices and K = 54 joints,
-which include joints for the neck, jaw, eyeballs and fingers. 
-SMPL-X is defined by a function M(θ, β, ψ), where θ is the pose parameters, β the shape parameters and
-ψ the facial expression parameters.
-
-## News
-
-- 3 November 2020: We release the code to transfer between the models in the
-  SMPL family. For more details on the code, go to this [readme
-  file](./transfer_model/README.md). A detailed explanation on how the mappings
-  were extracted can be found [here](./transfer_model/docs/transfer.md).
-- 23 September 2020: A UV map is now available for SMPL-X, please check the
-  Downloads section of the website.
-- 20 August 2020: The full shape and expression space of SMPL-X are now available.
-
-## Installation
-
-To install the model please follow the next steps in the specified order:
-1. To install from PyPi simply run: 
-  ```Shell
-  pip install smplx[all]
-  ```
-2. Clone this repository and install it using the *setup.py* script: 
-```Shell
-git clone https://github.com/vchoutas/smplx
-python setup.py install
-```
-
-## Downloading the model
-
-To download the *SMPL-X* model go to [this project website](https://smpl-x.is.tue.mpg.de) and register to get access to the downloads section. 
-
-To download the *SMPL+H* model go to [this project website](http://mano.is.tue.mpg.de) and register to get access to the downloads section. 
-
-To download the *SMPL* model go to [this](http://smpl.is.tue.mpg.de) (male and female models) and [this](http://smplify.is.tue.mpg.de) (gender neutral model) project website and register to get access to the downloads section. 
-
-## Loading SMPL-X, SMPL+H and SMPL
-
-### SMPL and SMPL+H setup
-
-The loader gives the option to use any of the SMPL-X, SMPL+H, SMPL, and MANO models. Depending on the model you want to use, please follow the respective download instructions. To switch between MANO, SMPL, SMPL+H and SMPL-X just change the *model_path* or *model_type* parameters. For more details please check the docs of the model classes.
-Before using SMPL and SMPL+H you should follow the instructions in [tools/README.md](./tools/README.md) to remove the
-Chumpy objects from both model pkls, as well as merge the MANO parameters with SMPL+H.
-
-### Model loading 
-
-You can either use the [create](https://github.com/vchoutas/smplx/blob/c63c02b478c5c6f696491ed9167e3af6b08d89b1/smplx/body_models.py#L54)
-function from [body_models](./smplx/body_models.py) or directly call the constructor for the 
-[SMPL](https://github.com/vchoutas/smplx/blob/c63c02b478c5c6f696491ed9167e3af6b08d89b1/smplx/body_models.py#L106), 
-[SMPL+H](https://github.com/vchoutas/smplx/blob/c63c02b478c5c6f696491ed9167e3af6b08d89b1/smplx/body_models.py#L395) and 
-[SMPL-X](https://github.com/vchoutas/smplx/blob/c63c02b478c5c6f696491ed9167e3af6b08d89b1/smplx/body_models.py#L628) model. The path to the model can either be the path to the file with the parameters or a directory with the following structure:
-```bash
-models
-├── smpl
-│   ├── SMPL_FEMALE.pkl
-│   └── SMPL_MALE.pkl
-│   └── SMPL_NEUTRAL.pkl
-├── smplh
-│   ├── SMPLH_FEMALE.pkl
-│   └── SMPLH_MALE.pkl
-├── mano
-|   ├── MANO_RIGHT.pkl
-|   └── MANO_LEFT.pkl
-└── smplx
-    ├── SMPLX_FEMALE.npz
-    ├── SMPLX_FEMALE.pkl
-    ├── SMPLX_MALE.npz
-    ├── SMPLX_MALE.pkl
-    ├── SMPLX_NEUTRAL.npz
-    └── SMPLX_NEUTRAL.pkl
-```
-
-
-## MANO and FLAME correspondences
-
-The vertex correspondences between SMPL-X and MANO, FLAME can be downloaded
-from [the project website](https://smpl-x.is.tue.mpg.de). If you have extracted
-the correspondence data in the folder *correspondences*, then use the following
-scripts to visualize them:
-
-1. To view MANO correspondences run the following command:
-
-```
-python examples/vis_mano_vertices.py --model-folder $SMPLX_FOLDER --corr-fname correspondences/MANO_SMPLX_vertex_ids.pkl
-```
-
-2. To view FLAME correspondences run the following command:
-
-```
-python examples/vis_flame_vertices.py --model-folder $SMPLX_FOLDER --corr-fname correspondences/SMPL-X__FLAME_vertex_ids.npy
-```
-
-## Example
-
-After installing the *smplx* package and downloading the model parameters you should be able to run the *demo.py*
-script to visualize the results. For this step you have to install the [pyrender](https://pyrender.readthedocs.io/en/latest/index.html) and [trimesh](https://trimsh.org/) packages.
-
-`python examples/demo.py --model-folder $SMPLX_FOLDER --plot-joints=True --gender="neutral"`
-
-![SMPL-X Examples](./images/example.png)
-
-## Modifying the global pose of the model
-
-If you want to modify the global pose of the model, i.e. the root rotation and
-translation, to a new coordinate system for example, you need to take into
-account that the model rotation uses the pelvis as the center of rotation. A
-more detailed description can be found in the following
-[link](https://www.dropbox.com/scl/fi/zkatuv5shs8d4tlwr8ecc/Change-parameters-to-new-coordinate-system.paper?dl=0&rlkey=lotq1sh6wzkmyttisc05h0in0).
-If something is not clear, please let me know so that I can update the
-description.
-
-## Citation
-
-Depending on which model is loaded for your project, i.e. SMPL-X or SMPL+H or SMPL, please cite the most relevant work below, listed in the same order:
-
-```
-@inproceedings{SMPL-X:2019,
-    title = {Expressive Body Capture: 3D Hands, Face, and Body from a Single Image},
-    author = {Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.},
-    booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
-    year = {2019}
-}
-```
-
-```
-@article{MANO:SIGGRAPHASIA:2017,
-    title = {Embodied Hands: Modeling and Capturing Hands and Bodies Together},
-    author = {Romero, Javier and Tzionas, Dimitrios and Black, Michael J.},
-    journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},
-    volume = {36},
-    number = {6},
-    series = {245:1--245:17},
-    month = nov,
-    year = {2017},
-    month_numeric = {11}
-  }
-```
-
-```
-@article{SMPL:2015,
-    author = {Loper, Matthew and Mahmood, Naureen and Romero, Javier and Pons-Moll, Gerard and Black, Michael J.},
-    title = {{SMPL}: A Skinned Multi-Person Linear Model},
-    journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},
-    month = oct,
-    number = {6},
-    pages = {248:1--248:16},
-    publisher = {ACM},
-    volume = {34},
-    year = {2015}
-}
-```
-
-This repository was originally developed for SMPL-X / SMPLify-X (CVPR 2019), you might be interested in having a look: [https://smpl-x.is.tue.mpg.de](https://smpl-x.is.tue.mpg.de).
-
-## Acknowledgments
-
-### Facial Contour
-
-Special thanks to [Soubhik Sanyal](https://github.com/soubhiksanyal) for sharing the Tensorflow code used for the facial
-landmarks.
-
-## Contact
-The code of this repository was implemented by [Vassilis Choutas](vassilis.choutas@tuebingen.mpg.de).
-
-For questions, please contact [smplx@tue.mpg.de](smplx@tue.mpg.de).
-
-For commercial licensing (and all related questions for business applications), please contact [ps-licensing@tue.mpg.de](ps-licensing@tue.mpg.de).
diff --git a/spaces/aadnk/faster-whisper-webui/src/hooks/progressListener.py b/spaces/aadnk/faster-whisper-webui/src/hooks/progressListener.py
deleted file mode 100644
index a7852a24e237ae864bbce5f37674e1f7c817a1b3..0000000000000000000000000000000000000000
--- a/spaces/aadnk/faster-whisper-webui/src/hooks/progressListener.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from typing import Union
-
-class ProgressListener:
-    def on_progress(self, current: Union[int, float], total: Union[int, float]):
-        self.total = total
-
-    def on_finished(self):
-        pass
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/wrappers.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/wrappers.py
deleted file mode 100644
index 8aebf67bf52355a513f21756ee74fe510902d075..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/cnn/bricks/wrappers.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-r"""Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/wrappers.py  # noqa: E501
-
-Wrap some nn modules to support empty tensor input. Currently, these wrappers
-are mainly used in mask heads like fcn_mask_head and maskiou_heads since mask
-heads are trained on only positive RoIs.
-"""
-import math
-
-import torch
-import torch.nn as nn
-from torch.nn.modules.utils import _pair, _triple
-
-from .registry import CONV_LAYERS, UPSAMPLE_LAYERS
-
-if torch.__version__ == 'parrots':
-    TORCH_VERSION = torch.__version__
-else:
-    # torch.__version__ could be 1.3.1+cu92, we only need the first two
-    # for comparison
-    TORCH_VERSION = tuple(int(x) for x in torch.__version__.split('.')[:2])
-
-
-def obsolete_torch_version(torch_version, version_threshold):
-    return torch_version == 'parrots' or torch_version <= version_threshold
-
-
-class NewEmptyTensorOp(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, x, new_shape):
-        ctx.shape = x.shape
-        return x.new_empty(new_shape)
-
-    @staticmethod
-    def backward(ctx, grad):
-        shape = ctx.shape
-        return NewEmptyTensorOp.apply(grad, shape), None
-
-
-@CONV_LAYERS.register_module('Conv', force=True)
-class Conv2d(nn.Conv2d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d in zip(x.shape[-2:], self.kernel_size,
-                                     self.padding, self.stride, self.dilation):
-                o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-@CONV_LAYERS.register_module('Conv3d', force=True)
-class Conv3d(nn.Conv3d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d in zip(x.shape[-3:], self.kernel_size,
-                                     self.padding, self.stride, self.dilation):
-                o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-@CONV_LAYERS.register_module()
-@CONV_LAYERS.register_module('deconv')
-@UPSAMPLE_LAYERS.register_module('deconv', force=True)
-class ConvTranspose2d(nn.ConvTranspose2d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d, op in zip(x.shape[-2:], self.kernel_size,
-                                         self.padding, self.stride,
-                                         self.dilation, self.output_padding):
-                out_shape.append((i - 1) * s - 2 * p + (d * (k - 1) + 1) + op)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-@CONV_LAYERS.register_module()
-@CONV_LAYERS.register_module('deconv3d')
-@UPSAMPLE_LAYERS.register_module('deconv3d', force=True)
-class ConvTranspose3d(nn.ConvTranspose3d):
-
-    def forward(self, x):
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 4)):
-            out_shape = [x.shape[0], self.out_channels]
-            for i, k, p, s, d, op in zip(x.shape[-3:], self.kernel_size,
-                                         self.padding, self.stride,
-                                         self.dilation, self.output_padding):
-                out_shape.append((i - 1) * s - 2 * p + (d * (k - 1) + 1) + op)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
-
-
-class MaxPool2d(nn.MaxPool2d):
-
-    def forward(self, x):
-        # PyTorch 1.9 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
-            out_shape = list(x.shape[:2])
-            for i, k, p, s, d in zip(x.shape[-2:], _pair(self.kernel_size),
-                                     _pair(self.padding), _pair(self.stride),
-                                     _pair(self.dilation)):
-                o = (i + 2 * p - (d * (k - 1) + 1)) / s + 1
-                o = math.ceil(o) if self.ceil_mode else math.floor(o)
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            return empty
-
-        return super().forward(x)
-
-
-class MaxPool3d(nn.MaxPool3d):
-
-    def forward(self, x):
-        # PyTorch 1.9 does not support empty tensor inference yet
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
-            out_shape = list(x.shape[:2])
-            for i, k, p, s, d in zip(x.shape[-3:], _triple(self.kernel_size),
-                                     _triple(self.padding),
-                                     _triple(self.stride),
-                                     _triple(self.dilation)):
-                o = (i + 2 * p - (d * (k - 1) + 1)) / s + 1
-                o = math.ceil(o) if self.ceil_mode else math.floor(o)
-                out_shape.append(o)
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            return empty
-
-        return super().forward(x)
-
-
-class Linear(torch.nn.Linear):
-
-    def forward(self, x):
-        # empty tensor forward of Linear layer is supported in Pytorch 1.6
-        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 5)):
-            out_shape = [x.shape[0], self.out_features]
-            empty = NewEmptyTensorOp.apply(x, out_shape)
-            if self.training:
-                # produce dummy gradient to avoid DDP warning.
-                dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0
-                return empty + dummy
-            else:
-                return empty
-
-        return super().forward(x)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/config.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/config.py
deleted file mode 100644
index 17149353aefac6d737c67bb2f35a3a6cd2147b0a..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/utils/config.py
+++ /dev/null
@@ -1,688 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import ast
-import copy
-import os
-import os.path as osp
-import platform
-import shutil
-import sys
-import tempfile
-import uuid
-import warnings
-from argparse import Action, ArgumentParser
-from collections import abc
-from importlib import import_module
-
-from addict import Dict
-from yapf.yapflib.yapf_api import FormatCode
-
-from .misc import import_modules_from_strings
-from .path import check_file_exist
-
-if platform.system() == 'Windows':
-    import regex as re
-else:
-    import re
-
-BASE_KEY = '_base_'
-DELETE_KEY = '_delete_'
-DEPRECATION_KEY = '_deprecation_'
-RESERVED_KEYS = ['filename', 'text', 'pretty_text']
-
-
-class ConfigDict(Dict):
-
-    def __missing__(self, name):
-        raise KeyError(name)
-
-    def __getattr__(self, name):
-        try:
-            value = super(ConfigDict, self).__getattr__(name)
-        except KeyError:
-            ex = AttributeError(f"'{self.__class__.__name__}' object has no "
-                                f"attribute '{name}'")
-        except Exception as e:
-            ex = e
-        else:
-            return value
-        raise ex
-
-
-def add_args(parser, cfg, prefix=''):
-    for k, v in cfg.items():
-        if isinstance(v, str):
-            parser.add_argument('--' + prefix + k)
-        elif isinstance(v, int):
-            parser.add_argument('--' + prefix + k, type=int)
-        elif isinstance(v, float):
-            parser.add_argument('--' + prefix + k, type=float)
-        elif isinstance(v, bool):
-            parser.add_argument('--' + prefix + k, action='store_true')
-        elif isinstance(v, dict):
-            add_args(parser, v, prefix + k + '.')
-        elif isinstance(v, abc.Iterable):
-            parser.add_argument('--' + prefix + k, type=type(v[0]), nargs='+')
-        else:
-            print(f'cannot parse key {prefix + k} of type {type(v)}')
-    return parser
-
-
-class Config:
-    """A facility for config and config files.
-
-    It supports common file formats as configs: python/json/yaml. The interface
-    is the same as a dict object and also allows access config values as
-    attributes.
-
-    Example:
-        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
-        >>> cfg.a
-        1
-        >>> cfg.b
-        {'b1': [0, 1]}
-        >>> cfg.b.b1
-        [0, 1]
-        >>> cfg = Config.fromfile('tests/data/config/a.py')
-        >>> cfg.filename
-        "/home/kchen/projects/mmcv/tests/data/config/a.py"
-        >>> cfg.item4
-        'test'
-        >>> cfg
-        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
-        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
-    """
-
-    @staticmethod
-    def _validate_py_syntax(filename):
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            content = f.read()
-        try:
-            ast.parse(content)
-        except SyntaxError as e:
-            raise SyntaxError('There are syntax errors in config '
-                              f'file {filename}: {e}')
-
-    @staticmethod
-    def _substitute_predefined_vars(filename, temp_config_name):
-        file_dirname = osp.dirname(filename)
-        file_basename = osp.basename(filename)
-        file_basename_no_extension = osp.splitext(file_basename)[0]
-        file_extname = osp.splitext(filename)[1]
-        support_templates = dict(
-            fileDirname=file_dirname,
-            fileBasename=file_basename,
-            fileBasenameNoExtension=file_basename_no_extension,
-            fileExtname=file_extname)
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            config_file = f.read()
-        for key, value in support_templates.items():
-            regexp = r'\{\{\s*' + str(key) + r'\s*\}\}'
-            value = value.replace('\\', '/')
-            config_file = re.sub(regexp, value, config_file)
-        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
-            tmp_config_file.write(config_file)
-
-    @staticmethod
-    def _pre_substitute_base_vars(filename, temp_config_name):
-        """Substitute base variable placehoders to string, so that parsing
-        would work."""
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            config_file = f.read()
-        base_var_dict = {}
-        regexp = r'\{\{\s*' + BASE_KEY + r'\.([\w\.]+)\s*\}\}'
-        base_vars = set(re.findall(regexp, config_file))
-        for base_var in base_vars:
-            randstr = f'_{base_var}_{uuid.uuid4().hex.lower()[:6]}'
-            base_var_dict[randstr] = base_var
-            regexp = r'\{\{\s*' + BASE_KEY + r'\.' + base_var + r'\s*\}\}'
-            config_file = re.sub(regexp, f'"{randstr}"', config_file)
-        with open(temp_config_name, 'w', encoding='utf-8') as tmp_config_file:
-            tmp_config_file.write(config_file)
-        return base_var_dict
-
-    @staticmethod
-    def _substitute_base_vars(cfg, base_var_dict, base_cfg):
-        """Substitute variable strings to their actual values."""
-        cfg = copy.deepcopy(cfg)
-
-        if isinstance(cfg, dict):
-            for k, v in cfg.items():
-                if isinstance(v, str) and v in base_var_dict:
-                    new_v = base_cfg
-                    for new_k in base_var_dict[v].split('.'):
-                        new_v = new_v[new_k]
-                    cfg[k] = new_v
-                elif isinstance(v, (list, tuple, dict)):
-                    cfg[k] = Config._substitute_base_vars(
-                        v, base_var_dict, base_cfg)
-        elif isinstance(cfg, tuple):
-            cfg = tuple(
-                Config._substitute_base_vars(c, base_var_dict, base_cfg)
-                for c in cfg)
-        elif isinstance(cfg, list):
-            cfg = [
-                Config._substitute_base_vars(c, base_var_dict, base_cfg)
-                for c in cfg
-            ]
-        elif isinstance(cfg, str) and cfg in base_var_dict:
-            new_v = base_cfg
-            for new_k in base_var_dict[cfg].split('.'):
-                new_v = new_v[new_k]
-            cfg = new_v
-
-        return cfg
-
-    @staticmethod
-    def _file2dict(filename, use_predefined_variables=True):
-        filename = osp.abspath(osp.expanduser(filename))
-        check_file_exist(filename)
-        fileExtname = osp.splitext(filename)[1]
-        if fileExtname not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
-
-        with tempfile.TemporaryDirectory() as temp_config_dir:
-            temp_config_file = tempfile.NamedTemporaryFile(
-                dir=temp_config_dir, suffix=fileExtname)
-            if platform.system() == 'Windows':
-                temp_config_file.close()
-            temp_config_name = osp.basename(temp_config_file.name)
-            # Substitute predefined variables
-            if use_predefined_variables:
-                Config._substitute_predefined_vars(filename,
-                                                   temp_config_file.name)
-            else:
-                shutil.copyfile(filename, temp_config_file.name)
-            # Substitute base variables from placeholders to strings
-            base_var_dict = Config._pre_substitute_base_vars(
-                temp_config_file.name, temp_config_file.name)
-
-            if filename.endswith('.py'):
-                temp_module_name = osp.splitext(temp_config_name)[0]
-                sys.path.insert(0, temp_config_dir)
-                Config._validate_py_syntax(filename)
-                mod = import_module(temp_module_name)
-                sys.path.pop(0)
-                cfg_dict = {
-                    name: value
-                    for name, value in mod.__dict__.items()
-                    if not name.startswith('__')
-                }
-                # delete imported module
-                del sys.modules[temp_module_name]
-            elif filename.endswith(('.yml', '.yaml', '.json')):
-                import annotator.uniformer.mmcv as mmcv
-                cfg_dict = mmcv.load(temp_config_file.name)
-            # close temp file
-            temp_config_file.close()
-
-        # check deprecation information
-        if DEPRECATION_KEY in cfg_dict:
-            deprecation_info = cfg_dict.pop(DEPRECATION_KEY)
-            warning_msg = f'The config file {filename} will be deprecated ' \
-                'in the future.'
-            if 'expected' in deprecation_info:
-                warning_msg += f' Please use {deprecation_info["expected"]} ' \
-                    'instead.'
-            if 'reference' in deprecation_info:
-                warning_msg += ' More information can be found at ' \
-                    f'{deprecation_info["reference"]}'
-            warnings.warn(warning_msg)
-
-        cfg_text = filename + '\n'
-        with open(filename, 'r', encoding='utf-8') as f:
-            # Setting encoding explicitly to resolve coding issue on windows
-            cfg_text += f.read()
-
-        if BASE_KEY in cfg_dict:
-            cfg_dir = osp.dirname(filename)
-            base_filename = cfg_dict.pop(BASE_KEY)
-            base_filename = base_filename if isinstance(
-                base_filename, list) else [base_filename]
-
-            cfg_dict_list = list()
-            cfg_text_list = list()
-            for f in base_filename:
-                _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f))
-                cfg_dict_list.append(_cfg_dict)
-                cfg_text_list.append(_cfg_text)
-
-            base_cfg_dict = dict()
-            for c in cfg_dict_list:
-                duplicate_keys = base_cfg_dict.keys() & c.keys()
-                if len(duplicate_keys) > 0:
-                    raise KeyError('Duplicate key is not allowed among bases. '
-                                   f'Duplicate keys: {duplicate_keys}')
-                base_cfg_dict.update(c)
-
-            # Substitute base variables from strings to their actual values
-            cfg_dict = Config._substitute_base_vars(cfg_dict, base_var_dict,
-                                                    base_cfg_dict)
-
-            base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict)
-            cfg_dict = base_cfg_dict
-
-            # merge cfg_text
-            cfg_text_list.append(cfg_text)
-            cfg_text = '\n'.join(cfg_text_list)
-
-        return cfg_dict, cfg_text
-
-    @staticmethod
-    def _merge_a_into_b(a, b, allow_list_keys=False):
-        """merge dict ``a`` into dict ``b`` (non-inplace).
-
-        Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid
-        in-place modifications.
-
-        Args:
-            a (dict): The source dict to be merged into ``b``.
-            b (dict): The origin dict to be fetch keys from ``a``.
-            allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
-              are allowed in source ``a`` and will replace the element of the
-              corresponding index in b if b is a list. Default: False.
-
-        Returns:
-            dict: The modified dict of ``b`` using ``a``.
-
-        Examples:
-            # Normally merge a into b.
-            >>> Config._merge_a_into_b(
-            ...     dict(obj=dict(a=2)), dict(obj=dict(a=1)))
-            {'obj': {'a': 2}}
-
-            # Delete b first and merge a into b.
-            >>> Config._merge_a_into_b(
-            ...     dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1)))
-            {'obj': {'a': 2}}
-
-            # b is a list
-            >>> Config._merge_a_into_b(
-            ...     {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True)
-            [{'a': 2}, {'b': 2}]
-        """
-        b = b.copy()
-        for k, v in a.items():
-            if allow_list_keys and k.isdigit() and isinstance(b, list):
-                k = int(k)
-                if len(b) <= k:
-                    raise KeyError(f'Index {k} exceeds the length of list {b}')
-                b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
-            elif isinstance(v,
-                            dict) and k in b and not v.pop(DELETE_KEY, False):
-                allowed_types = (dict, list) if allow_list_keys else dict
-                if not isinstance(b[k], allowed_types):
-                    raise TypeError(
-                        f'{k}={v} in child config cannot inherit from base '
-                        f'because {k} is a dict in the child config but is of '
-                        f'type {type(b[k])} in base config. You may set '
-                        f'`{DELETE_KEY}=True` to ignore the base config')
-                b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys)
-            else:
-                b[k] = v
-        return b
-
-    @staticmethod
-    def fromfile(filename,
-                 use_predefined_variables=True,
-                 import_custom_modules=True):
-        cfg_dict, cfg_text = Config._file2dict(filename,
-                                               use_predefined_variables)
-        if import_custom_modules and cfg_dict.get('custom_imports', None):
-            import_modules_from_strings(**cfg_dict['custom_imports'])
-        return Config(cfg_dict, cfg_text=cfg_text, filename=filename)
-
-    @staticmethod
-    def fromstring(cfg_str, file_format):
-        """Generate config from config str.
-
-        Args:
-            cfg_str (str): Config str.
-            file_format (str): Config file format corresponding to the
-               config str. Only py/yml/yaml/json type are supported now!
-
-        Returns:
-            obj:`Config`: Config obj.
-        """
-        if file_format not in ['.py', '.json', '.yaml', '.yml']:
-            raise IOError('Only py/yml/yaml/json type are supported now!')
-        if file_format != '.py' and 'dict(' in cfg_str:
-            # check if users specify a wrong suffix for python
-            warnings.warn(
-                'Please check "file_format", the file format may be .py')
-        with tempfile.NamedTemporaryFile(
-                'w', encoding='utf-8', suffix=file_format,
-                delete=False) as temp_file:
-            temp_file.write(cfg_str)
-            # on windows, previous implementation cause error
-            # see PR 1077 for details
-        cfg = Config.fromfile(temp_file.name)
-        os.remove(temp_file.name)
-        return cfg
-
-    @staticmethod
-    def auto_argparser(description=None):
-        """Generate argparser from config file automatically (experimental)"""
-        partial_parser = ArgumentParser(description=description)
-        partial_parser.add_argument('config', help='config file path')
-        cfg_file = partial_parser.parse_known_args()[0].config
-        cfg = Config.fromfile(cfg_file)
-        parser = ArgumentParser(description=description)
-        parser.add_argument('config', help='config file path')
-        add_args(parser, cfg)
-        return parser, cfg
-
-    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
-        if cfg_dict is None:
-            cfg_dict = dict()
-        elif not isinstance(cfg_dict, dict):
-            raise TypeError('cfg_dict must be a dict, but '
-                            f'got {type(cfg_dict)}')
-        for key in cfg_dict:
-            if key in RESERVED_KEYS:
-                raise KeyError(f'{key} is reserved for config file')
-
-        super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict))
-        super(Config, self).__setattr__('_filename', filename)
-        if cfg_text:
-            text = cfg_text
-        elif filename:
-            with open(filename, 'r') as f:
-                text = f.read()
-        else:
-            text = ''
-        super(Config, self).__setattr__('_text', text)
-
-    @property
-    def filename(self):
-        return self._filename
-
-    @property
-    def text(self):
-        return self._text
-
-    @property
-    def pretty_text(self):
-
-        indent = 4
-
-        def _indent(s_, num_spaces):
-            s = s_.split('\n')
-            if len(s) == 1:
-                return s_
-            first = s.pop(0)
-            s = [(num_spaces * ' ') + line for line in s]
-            s = '\n'.join(s)
-            s = first + '\n' + s
-            return s
-
-        def _format_basic_types(k, v, use_mapping=False):
-            if isinstance(v, str):
-                v_str = f"'{v}'"
-            else:
-                v_str = str(v)
-
-            if use_mapping:
-                k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                attr_str = f'{k_str}: {v_str}'
-            else:
-                attr_str = f'{str(k)}={v_str}'
-            attr_str = _indent(attr_str, indent)
-
-            return attr_str
-
-        def _format_list(k, v, use_mapping=False):
-            # check if all items in the list are dict
-            if all(isinstance(_, dict) for _ in v):
-                v_str = '[\n'
-                v_str += '\n'.join(
-                    f'dict({_indent(_format_dict(v_), indent)}),'
-                    for v_ in v).rstrip(',')
-                if use_mapping:
-                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                    attr_str = f'{k_str}: {v_str}'
-                else:
-                    attr_str = f'{str(k)}={v_str}'
-                attr_str = _indent(attr_str, indent) + ']'
-            else:
-                attr_str = _format_basic_types(k, v, use_mapping)
-            return attr_str
-
-        def _contain_invalid_identifier(dict_str):
-            contain_invalid_identifier = False
-            for key_name in dict_str:
-                contain_invalid_identifier |= \
-                    (not str(key_name).isidentifier())
-            return contain_invalid_identifier
-
-        def _format_dict(input_dict, outest_level=False):
-            r = ''
-            s = []
-
-            use_mapping = _contain_invalid_identifier(input_dict)
-            if use_mapping:
-                r += '{'
-            for idx, (k, v) in enumerate(input_dict.items()):
-                is_last = idx >= len(input_dict) - 1
-                end = '' if outest_level or is_last else ','
-                if isinstance(v, dict):
-                    v_str = '\n' + _format_dict(v)
-                    if use_mapping:
-                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
-                        attr_str = f'{k_str}: dict({v_str}'
-                    else:
-                        attr_str = f'{str(k)}=dict({v_str}'
-                    attr_str = _indent(attr_str, indent) + ')' + end
-                elif isinstance(v, list):
-                    attr_str = _format_list(k, v, use_mapping) + end
-                else:
-                    attr_str = _format_basic_types(k, v, use_mapping) + end
-
-                s.append(attr_str)
-            r += '\n'.join(s)
-            if use_mapping:
-                r += '}'
-            return r
-
-        cfg_dict = self._cfg_dict.to_dict()
-        text = _format_dict(cfg_dict, outest_level=True)
-        # copied from setup.cfg
-        yapf_style = dict(
-            based_on_style='pep8',
-            blank_line_before_nested_class_or_def=True,
-            split_before_expression_after_opening_paren=True)
-        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
-
-        return text
-
-    def __repr__(self):
-        return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}'
-
-    def __len__(self):
-        return len(self._cfg_dict)
-
-    def __getattr__(self, name):
-        return getattr(self._cfg_dict, name)
-
-    def __getitem__(self, name):
-        return self._cfg_dict.__getitem__(name)
-
-    def __setattr__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setattr__(name, value)
-
-    def __setitem__(self, name, value):
-        if isinstance(value, dict):
-            value = ConfigDict(value)
-        self._cfg_dict.__setitem__(name, value)
-
-    def __iter__(self):
-        return iter(self._cfg_dict)
-
-    def __getstate__(self):
-        return (self._cfg_dict, self._filename, self._text)
-
-    def __setstate__(self, state):
-        _cfg_dict, _filename, _text = state
-        super(Config, self).__setattr__('_cfg_dict', _cfg_dict)
-        super(Config, self).__setattr__('_filename', _filename)
-        super(Config, self).__setattr__('_text', _text)
-
-    def dump(self, file=None):
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict').to_dict()
-        if self.filename.endswith('.py'):
-            if file is None:
-                return self.pretty_text
-            else:
-                with open(file, 'w', encoding='utf-8') as f:
-                    f.write(self.pretty_text)
-        else:
-            import annotator.uniformer.mmcv as mmcv
-            if file is None:
-                file_format = self.filename.split('.')[-1]
-                return mmcv.dump(cfg_dict, file_format=file_format)
-            else:
-                mmcv.dump(cfg_dict, file)
-
-    def merge_from_dict(self, options, allow_list_keys=True):
-        """Merge list into cfg_dict.
-
-        Merge the dict parsed by MultipleKVAction into this cfg.
-
-        Examples:
-            >>> options = {'model.backbone.depth': 50,
-            ...            'model.backbone.with_cp':True}
-            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
-            >>> cfg.merge_from_dict(options)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(
-            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
-
-            # Merge list element
-            >>> cfg = Config(dict(pipeline=[
-            ...     dict(type='LoadImage'), dict(type='LoadAnnotations')]))
-            >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')})
-            >>> cfg.merge_from_dict(options, allow_list_keys=True)
-            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-            >>> assert cfg_dict == dict(pipeline=[
-            ...     dict(type='SelfLoadImage'), dict(type='LoadAnnotations')])
-
-        Args:
-            options (dict): dict of configs to merge from.
-            allow_list_keys (bool): If True, int string keys (e.g. '0', '1')
-              are allowed in ``options`` and will replace the element of the
-              corresponding index in the config if the config is a list.
-              Default: True.
-        """
-        option_cfg_dict = {}
-        for full_key, v in options.items():
-            d = option_cfg_dict
-            key_list = full_key.split('.')
-            for subkey in key_list[:-1]:
-                d.setdefault(subkey, ConfigDict())
-                d = d[subkey]
-            subkey = key_list[-1]
-            d[subkey] = v
-
-        cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
-        super(Config, self).__setattr__(
-            '_cfg_dict',
-            Config._merge_a_into_b(
-                option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys))
-
-
-class DictAction(Action):
-    """
-    argparse action to split an argument into KEY=VALUE form
-    on the first = and append to a dictionary. List options can
-    be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit
-    brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build
-    list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]'
-    """
-
-    @staticmethod
-    def _parse_int_float_bool(val):
-        try:
-            return int(val)
-        except ValueError:
-            pass
-        try:
-            return float(val)
-        except ValueError:
-            pass
-        if val.lower() in ['true', 'false']:
-            return True if val.lower() == 'true' else False
-        return val
-
-    @staticmethod
-    def _parse_iterable(val):
-        """Parse iterable values in the string.
-
-        All elements inside '()' or '[]' are treated as iterable values.
-
-        Args:
-            val (str): Value string.
-
-        Returns:
-            list | tuple: The expanded list or tuple from the string.
-
-        Examples:
-            >>> DictAction._parse_iterable('1,2,3')
-            [1, 2, 3]
-            >>> DictAction._parse_iterable('[a, b, c]')
-            ['a', 'b', 'c']
-            >>> DictAction._parse_iterable('[(1, 2, 3), [a, b], c]')
-            [(1, 2, 3), ['a', 'b'], 'c']
-        """
-
-        def find_next_comma(string):
-            """Find the position of next comma in the string.
-
-            If no ',' is found in the string, return the string length. All
-            chars inside '()' and '[]' are treated as one element and thus ','
-            inside these brackets are ignored.
-            """
-            assert (string.count('(') == string.count(')')) and (
-                    string.count('[') == string.count(']')), \
-                f'Imbalanced brackets exist in {string}'
-            end = len(string)
-            for idx, char in enumerate(string):
-                pre = string[:idx]
-                # The string before this ',' is balanced
-                if ((char == ',') and (pre.count('(') == pre.count(')'))
-                        and (pre.count('[') == pre.count(']'))):
-                    end = idx
-                    break
-            return end
-
-        # Strip ' and " characters and replace whitespace.
-        val = val.strip('\'\"').replace(' ', '')
-        is_tuple = False
-        if val.startswith('(') and val.endswith(')'):
-            is_tuple = True
-            val = val[1:-1]
-        elif val.startswith('[') and val.endswith(']'):
-            val = val[1:-1]
-        elif ',' not in val:
-            # val is a single value
-            return DictAction._parse_int_float_bool(val)
-
-        values = []
-        while len(val) > 0:
-            comma_idx = find_next_comma(val)
-            element = DictAction._parse_iterable(val[:comma_idx])
-            values.append(element)
-            val = val[comma_idx + 1:]
-        if is_tuple:
-            values = tuple(values)
-        return values
-
-    def __call__(self, parser, namespace, values, option_string=None):
-        options = {}
-        for kv in values:
-            key, val = kv.split('=', maxsplit=1)
-            options[key] = self._parse_iterable(val)
-        setattr(namespace, self.dest, options)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/iou_calculators/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/iou_calculators/__init__.py
deleted file mode 100644
index e71369a58a05fa25e6a754300875fdbb87cb26a5..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/bbox/iou_calculators/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .builder import build_iou_calculator
-from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps
-
-__all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps']
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/roi_heads/bbox_heads/sabl_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/roi_heads/bbox_heads/sabl_head.py
deleted file mode 100644
index 5153996aeb706d103d1ad14b61734914eddb7693..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/roi_heads/bbox_heads/sabl_head.py
+++ /dev/null
@@ -1,572 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
-from mmdet.models.builder import HEADS, build_loss
-from mmdet.models.losses import accuracy
-
-
-@HEADS.register_module()
-class SABLHead(nn.Module):
-    """Side-Aware Boundary Localization (SABL) for RoI-Head.
-
-    Side-Aware features are extracted by conv layers
-    with an attention mechanism.
-    Boundary Localization with Bucketing and Bucketing Guided Rescoring
-    are implemented in BucketingBBoxCoder.
-
-    Please refer to https://arxiv.org/abs/1912.04260 for more details.
-
-    Args:
-        cls_in_channels (int): Input channels of cls RoI feature. \
-            Defaults to 256.
-        reg_in_channels (int): Input channels of reg RoI feature. \
-            Defaults to 256.
-        roi_feat_size (int): Size of RoI features. Defaults to 7.
-        reg_feat_up_ratio (int): Upsample ratio of reg features. \
-            Defaults to 2.
-        reg_pre_kernel (int): Kernel of 2D conv layers before \
-            attention pooling. Defaults to 3.
-        reg_post_kernel (int): Kernel of 1D conv layers after \
-            attention pooling. Defaults to 3.
-        reg_pre_num (int): Number of pre convs. Defaults to 2.
-        reg_post_num (int): Number of post convs. Defaults to 1.
-        num_classes (int): Number of classes in dataset. Defaults to 80.
-        cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
-        reg_offset_out_channels (int): Hidden and output channel \
-            of reg offset branch. Defaults to 256.
-        reg_cls_out_channels (int): Hidden and output channel \
-            of reg cls branch. Defaults to 256.
-        num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
-        num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
-        reg_class_agnostic (bool): Class agnostic regresion or not. \
-            Defaults to True.
-        norm_cfg (dict): Config of norm layers. Defaults to None.
-        bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
-        loss_cls (dict): Config of classification loss.
-        loss_bbox_cls (dict): Config of classification loss for bbox branch.
-        loss_bbox_reg (dict): Config of regression loss for bbox branch.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 cls_in_channels=256,
-                 reg_in_channels=256,
-                 roi_feat_size=7,
-                 reg_feat_up_ratio=2,
-                 reg_pre_kernel=3,
-                 reg_post_kernel=3,
-                 reg_pre_num=2,
-                 reg_post_num=1,
-                 cls_out_channels=1024,
-                 reg_offset_out_channels=256,
-                 reg_cls_out_channels=256,
-                 num_cls_fcs=1,
-                 num_reg_fcs=0,
-                 reg_class_agnostic=True,
-                 norm_cfg=None,
-                 bbox_coder=dict(
-                     type='BucketingBBoxCoder',
-                     num_buckets=14,
-                     scale_factor=1.7),
-                 loss_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=False,
-                     loss_weight=1.0),
-                 loss_bbox_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0),
-                 loss_bbox_reg=dict(
-                     type='SmoothL1Loss', beta=0.1, loss_weight=1.0)):
-        super(SABLHead, self).__init__()
-        self.cls_in_channels = cls_in_channels
-        self.reg_in_channels = reg_in_channels
-        self.roi_feat_size = roi_feat_size
-        self.reg_feat_up_ratio = int(reg_feat_up_ratio)
-        self.num_buckets = bbox_coder['num_buckets']
-        assert self.reg_feat_up_ratio // 2 >= 1
-        self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio
-        assert self.up_reg_feat_size == bbox_coder['num_buckets']
-        self.reg_pre_kernel = reg_pre_kernel
-        self.reg_post_kernel = reg_post_kernel
-        self.reg_pre_num = reg_pre_num
-        self.reg_post_num = reg_post_num
-        self.num_classes = num_classes
-        self.cls_out_channels = cls_out_channels
-        self.reg_offset_out_channels = reg_offset_out_channels
-        self.reg_cls_out_channels = reg_cls_out_channels
-        self.num_cls_fcs = num_cls_fcs
-        self.num_reg_fcs = num_reg_fcs
-        self.reg_class_agnostic = reg_class_agnostic
-        assert self.reg_class_agnostic
-        self.norm_cfg = norm_cfg
-
-        self.bbox_coder = build_bbox_coder(bbox_coder)
-        self.loss_cls = build_loss(loss_cls)
-        self.loss_bbox_cls = build_loss(loss_bbox_cls)
-        self.loss_bbox_reg = build_loss(loss_bbox_reg)
-
-        self.cls_fcs = self._add_fc_branch(self.num_cls_fcs,
-                                           self.cls_in_channels,
-                                           self.roi_feat_size,
-                                           self.cls_out_channels)
-
-        self.side_num = int(np.ceil(self.num_buckets / 2))
-
-        if self.reg_feat_up_ratio > 1:
-            self.upsample_x = nn.ConvTranspose1d(
-                reg_in_channels,
-                reg_in_channels,
-                self.reg_feat_up_ratio,
-                stride=self.reg_feat_up_ratio)
-            self.upsample_y = nn.ConvTranspose1d(
-                reg_in_channels,
-                reg_in_channels,
-                self.reg_feat_up_ratio,
-                stride=self.reg_feat_up_ratio)
-
-        self.reg_pre_convs = nn.ModuleList()
-        for i in range(self.reg_pre_num):
-            reg_pre_conv = ConvModule(
-                reg_in_channels,
-                reg_in_channels,
-                kernel_size=reg_pre_kernel,
-                padding=reg_pre_kernel // 2,
-                norm_cfg=norm_cfg,
-                act_cfg=dict(type='ReLU'))
-            self.reg_pre_convs.append(reg_pre_conv)
-
-        self.reg_post_conv_xs = nn.ModuleList()
-        for i in range(self.reg_post_num):
-            reg_post_conv_x = ConvModule(
-                reg_in_channels,
-                reg_in_channels,
-                kernel_size=(1, reg_post_kernel),
-                padding=(0, reg_post_kernel // 2),
-                norm_cfg=norm_cfg,
-                act_cfg=dict(type='ReLU'))
-            self.reg_post_conv_xs.append(reg_post_conv_x)
-        self.reg_post_conv_ys = nn.ModuleList()
-        for i in range(self.reg_post_num):
-            reg_post_conv_y = ConvModule(
-                reg_in_channels,
-                reg_in_channels,
-                kernel_size=(reg_post_kernel, 1),
-                padding=(reg_post_kernel // 2, 0),
-                norm_cfg=norm_cfg,
-                act_cfg=dict(type='ReLU'))
-            self.reg_post_conv_ys.append(reg_post_conv_y)
-
-        self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1)
-        self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1)
-
-        self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1)
-        self.relu = nn.ReLU(inplace=True)
-
-        self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs,
-                                               self.reg_in_channels, 1,
-                                               self.reg_cls_out_channels)
-        self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs,
-                                                  self.reg_in_channels, 1,
-                                                  self.reg_offset_out_channels)
-        self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1)
-        self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1)
-
-    def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size,
-                       fc_out_channels):
-        in_channels = in_channels * roi_feat_size * roi_feat_size
-        branch_fcs = nn.ModuleList()
-        for i in range(num_branch_fcs):
-            fc_in_channels = (in_channels if i == 0 else fc_out_channels)
-            branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels))
-        return branch_fcs
-
-    def init_weights(self):
-        for module_list in [
-                self.reg_cls_fcs, self.reg_offset_fcs, self.cls_fcs
-        ]:
-            for m in module_list.modules():
-                if isinstance(m, nn.Linear):
-                    xavier_init(m, distribution='uniform')
-        if self.reg_feat_up_ratio > 1:
-            kaiming_init(self.upsample_x, distribution='normal')
-            kaiming_init(self.upsample_y, distribution='normal')
-
-        normal_init(self.reg_conv_att_x, 0, 0.01)
-        normal_init(self.reg_conv_att_y, 0, 0.01)
-        normal_init(self.fc_reg_offset, 0, 0.001)
-        normal_init(self.fc_reg_cls, 0, 0.01)
-        normal_init(self.fc_cls, 0, 0.01)
-
-    def cls_forward(self, cls_x):
-        cls_x = cls_x.view(cls_x.size(0), -1)
-        for fc in self.cls_fcs:
-            cls_x = self.relu(fc(cls_x))
-        cls_score = self.fc_cls(cls_x)
-        return cls_score
-
-    def attention_pool(self, reg_x):
-        """Extract direction-specific features fx and fy with attention
-        methanism."""
-        reg_fx = reg_x
-        reg_fy = reg_x
-        reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid()
-        reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid()
-        reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2)
-        reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3)
-        reg_fx = (reg_fx * reg_fx_att).sum(dim=2)
-        reg_fy = (reg_fy * reg_fy_att).sum(dim=3)
-        return reg_fx, reg_fy
-
-    def side_aware_feature_extractor(self, reg_x):
-        """Refine and extract side-aware features without split them."""
-        for reg_pre_conv in self.reg_pre_convs:
-            reg_x = reg_pre_conv(reg_x)
-        reg_fx, reg_fy = self.attention_pool(reg_x)
-
-        if self.reg_post_num > 0:
-            reg_fx = reg_fx.unsqueeze(2)
-            reg_fy = reg_fy.unsqueeze(3)
-            for i in range(self.reg_post_num):
-                reg_fx = self.reg_post_conv_xs[i](reg_fx)
-                reg_fy = self.reg_post_conv_ys[i](reg_fy)
-            reg_fx = reg_fx.squeeze(2)
-            reg_fy = reg_fy.squeeze(3)
-        if self.reg_feat_up_ratio > 1:
-            reg_fx = self.relu(self.upsample_x(reg_fx))
-            reg_fy = self.relu(self.upsample_y(reg_fy))
-        reg_fx = torch.transpose(reg_fx, 1, 2)
-        reg_fy = torch.transpose(reg_fy, 1, 2)
-        return reg_fx.contiguous(), reg_fy.contiguous()
-
-    def reg_pred(self, x, offset_fcs, cls_fcs):
-        """Predict bucketing estimation (cls_pred) and fine regression (offset
-        pred) with side-aware features."""
-        x_offset = x.view(-1, self.reg_in_channels)
-        x_cls = x.view(-1, self.reg_in_channels)
-
-        for fc in offset_fcs:
-            x_offset = self.relu(fc(x_offset))
-        for fc in cls_fcs:
-            x_cls = self.relu(fc(x_cls))
-        offset_pred = self.fc_reg_offset(x_offset)
-        cls_pred = self.fc_reg_cls(x_cls)
-
-        offset_pred = offset_pred.view(x.size(0), -1)
-        cls_pred = cls_pred.view(x.size(0), -1)
-
-        return offset_pred, cls_pred
-
-    def side_aware_split(self, feat):
-        """Split side-aware features aligned with orders of bucketing
-        targets."""
-        l_end = int(np.ceil(self.up_reg_feat_size / 2))
-        r_start = int(np.floor(self.up_reg_feat_size / 2))
-        feat_fl = feat[:, :l_end]
-        feat_fr = feat[:, r_start:].flip(dims=(1, ))
-        feat_fl = feat_fl.contiguous()
-        feat_fr = feat_fr.contiguous()
-        feat = torch.cat([feat_fl, feat_fr], dim=-1)
-        return feat
-
-    def bbox_pred_split(self, bbox_pred, num_proposals_per_img):
-        """Split batch bbox prediction back to each image."""
-        bucket_cls_preds, bucket_offset_preds = bbox_pred
-        bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0)
-        bucket_offset_preds = bucket_offset_preds.split(
-            num_proposals_per_img, 0)
-        bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds))
-        return bbox_pred
-
-    def reg_forward(self, reg_x):
-        outs = self.side_aware_feature_extractor(reg_x)
-        edge_offset_preds = []
-        edge_cls_preds = []
-        reg_fx = outs[0]
-        reg_fy = outs[1]
-        offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs,
-                                                  self.reg_cls_fcs)
-        offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs,
-                                                  self.reg_cls_fcs)
-        offset_pred_x = self.side_aware_split(offset_pred_x)
-        offset_pred_y = self.side_aware_split(offset_pred_y)
-        cls_pred_x = self.side_aware_split(cls_pred_x)
-        cls_pred_y = self.side_aware_split(cls_pred_y)
-        edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1)
-        edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1)
-
-        return (edge_cls_preds, edge_offset_preds)
-
-    def forward(self, x):
-
-        bbox_pred = self.reg_forward(x)
-        cls_score = self.cls_forward(x)
-
-        return cls_score, bbox_pred
-
-    def get_targets(self, sampling_results, gt_bboxes, gt_labels,
-                    rcnn_train_cfg):
-        pos_proposals = [res.pos_bboxes for res in sampling_results]
-        neg_proposals = [res.neg_bboxes for res in sampling_results]
-        pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
-        pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
-        cls_reg_targets = self.bucket_target(pos_proposals, neg_proposals,
-                                             pos_gt_bboxes, pos_gt_labels,
-                                             rcnn_train_cfg)
-        (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-         bucket_offset_targets, bucket_offset_weights) = cls_reg_targets
-        return (labels, label_weights, (bucket_cls_targets,
-                                        bucket_offset_targets),
-                (bucket_cls_weights, bucket_offset_weights))
-
-    def bucket_target(self,
-                      pos_proposals_list,
-                      neg_proposals_list,
-                      pos_gt_bboxes_list,
-                      pos_gt_labels_list,
-                      rcnn_train_cfg,
-                      concat=True):
-        (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-         bucket_offset_targets, bucket_offset_weights) = multi_apply(
-             self._bucket_target_single,
-             pos_proposals_list,
-             neg_proposals_list,
-             pos_gt_bboxes_list,
-             pos_gt_labels_list,
-             cfg=rcnn_train_cfg)
-
-        if concat:
-            labels = torch.cat(labels, 0)
-            label_weights = torch.cat(label_weights, 0)
-            bucket_cls_targets = torch.cat(bucket_cls_targets, 0)
-            bucket_cls_weights = torch.cat(bucket_cls_weights, 0)
-            bucket_offset_targets = torch.cat(bucket_offset_targets, 0)
-            bucket_offset_weights = torch.cat(bucket_offset_weights, 0)
-        return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-                bucket_offset_targets, bucket_offset_weights)
-
-    def _bucket_target_single(self, pos_proposals, neg_proposals,
-                              pos_gt_bboxes, pos_gt_labels, cfg):
-        """Compute bucketing estimation targets and fine regression targets for
-        a single image.
-
-        Args:
-            pos_proposals (Tensor): positive proposals of a single image,
-                 Shape (n_pos, 4)
-            neg_proposals (Tensor): negative proposals of a single image,
-                 Shape (n_neg, 4).
-            pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals
-                 of a single image, Shape (n_pos, 4).
-            pos_gt_labels (Tensor): gt labels assigned to positive proposals
-                 of a single image, Shape (n_pos, ).
-            cfg (dict): Config of calculating targets
-
-        Returns:
-            tuple:
-
-                - labels (Tensor): Labels in a single image. \
-                    Shape (n,).
-                - label_weights (Tensor): Label weights in a single image.\
-                    Shape (n,)
-                - bucket_cls_targets (Tensor): Bucket cls targets in \
-                    a single image. Shape (n, num_buckets*2).
-                - bucket_cls_weights (Tensor): Bucket cls weights in \
-                    a single image. Shape (n, num_buckets*2).
-                - bucket_offset_targets (Tensor): Bucket offset targets \
-                    in a single image. Shape (n, num_buckets*2).
-                - bucket_offset_targets (Tensor): Bucket offset weights \
-                    in a single image. Shape (n, num_buckets*2).
-        """
-        num_pos = pos_proposals.size(0)
-        num_neg = neg_proposals.size(0)
-        num_samples = num_pos + num_neg
-        labels = pos_gt_bboxes.new_full((num_samples, ),
-                                        self.num_classes,
-                                        dtype=torch.long)
-        label_weights = pos_proposals.new_zeros(num_samples)
-        bucket_cls_targets = pos_proposals.new_zeros(num_samples,
-                                                     4 * self.side_num)
-        bucket_cls_weights = pos_proposals.new_zeros(num_samples,
-                                                     4 * self.side_num)
-        bucket_offset_targets = pos_proposals.new_zeros(
-            num_samples, 4 * self.side_num)
-        bucket_offset_weights = pos_proposals.new_zeros(
-            num_samples, 4 * self.side_num)
-        if num_pos > 0:
-            labels[:num_pos] = pos_gt_labels
-            label_weights[:num_pos] = 1.0
-            (pos_bucket_offset_targets, pos_bucket_offset_weights,
-             pos_bucket_cls_targets,
-             pos_bucket_cls_weights) = self.bbox_coder.encode(
-                 pos_proposals, pos_gt_bboxes)
-            bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets
-            bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights
-            bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets
-            bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights
-        if num_neg > 0:
-            label_weights[-num_neg:] = 1.0
-        return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-                bucket_offset_targets, bucket_offset_weights)
-
-    def loss(self,
-             cls_score,
-             bbox_pred,
-             rois,
-             labels,
-             label_weights,
-             bbox_targets,
-             bbox_weights,
-             reduction_override=None):
-        losses = dict()
-        if cls_score is not None:
-            avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
-            losses['loss_cls'] = self.loss_cls(
-                cls_score,
-                labels,
-                label_weights,
-                avg_factor=avg_factor,
-                reduction_override=reduction_override)
-            losses['acc'] = accuracy(cls_score, labels)
-
-        if bbox_pred is not None:
-            bucket_cls_preds, bucket_offset_preds = bbox_pred
-            bucket_cls_targets, bucket_offset_targets = bbox_targets
-            bucket_cls_weights, bucket_offset_weights = bbox_weights
-            # edge cls
-            bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num)
-            bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num)
-            bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num)
-            losses['loss_bbox_cls'] = self.loss_bbox_cls(
-                bucket_cls_preds,
-                bucket_cls_targets,
-                bucket_cls_weights,
-                avg_factor=bucket_cls_targets.size(0),
-                reduction_override=reduction_override)
-
-            losses['loss_bbox_reg'] = self.loss_bbox_reg(
-                bucket_offset_preds,
-                bucket_offset_targets,
-                bucket_offset_weights,
-                avg_factor=bucket_offset_targets.size(0),
-                reduction_override=reduction_override)
-
-        return losses
-
-    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
-    def get_bboxes(self,
-                   rois,
-                   cls_score,
-                   bbox_pred,
-                   img_shape,
-                   scale_factor,
-                   rescale=False,
-                   cfg=None):
-        if isinstance(cls_score, list):
-            cls_score = sum(cls_score) / float(len(cls_score))
-        scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
-
-        if bbox_pred is not None:
-            bboxes, confids = self.bbox_coder.decode(rois[:, 1:], bbox_pred,
-                                                     img_shape)
-        else:
-            bboxes = rois[:, 1:].clone()
-            confids = None
-            if img_shape is not None:
-                bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
-                bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)
-
-        if rescale and bboxes.size(0) > 0:
-            if isinstance(scale_factor, float):
-                bboxes /= scale_factor
-            else:
-                bboxes /= torch.from_numpy(scale_factor).to(bboxes.device)
-
-        if cfg is None:
-            return bboxes, scores
-        else:
-            det_bboxes, det_labels = multiclass_nms(
-                bboxes,
-                scores,
-                cfg.score_thr,
-                cfg.nms,
-                cfg.max_per_img,
-                score_factors=confids)
-
-            return det_bboxes, det_labels
-
-    @force_fp32(apply_to=('bbox_preds', ))
-    def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
-        """Refine bboxes during training.
-
-        Args:
-            rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
-                and bs is the sampled RoIs per image.
-            labels (Tensor): Shape (n*bs, ).
-            bbox_preds (list[Tensor]): Shape [(n*bs, num_buckets*2), \
-                (n*bs, num_buckets*2)].
-            pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
-                is a gt bbox.
-            img_metas (list[dict]): Meta info of each image.
-
-        Returns:
-            list[Tensor]: Refined bboxes of each image in a mini-batch.
-        """
-        img_ids = rois[:, 0].long().unique(sorted=True)
-        assert img_ids.numel() == len(img_metas)
-
-        bboxes_list = []
-        for i in range(len(img_metas)):
-            inds = torch.nonzero(
-                rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
-            num_rois = inds.numel()
-
-            bboxes_ = rois[inds, 1:]
-            label_ = labels[inds]
-            edge_cls_preds, edge_offset_preds = bbox_preds
-            edge_cls_preds_ = edge_cls_preds[inds]
-            edge_offset_preds_ = edge_offset_preds[inds]
-            bbox_pred_ = [edge_cls_preds_, edge_offset_preds_]
-            img_meta_ = img_metas[i]
-            pos_is_gts_ = pos_is_gts[i]
-
-            bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
-                                           img_meta_)
-            # filter gt bboxes
-            pos_keep = 1 - pos_is_gts_
-            keep_inds = pos_is_gts_.new_ones(num_rois)
-            keep_inds[:len(pos_is_gts_)] = pos_keep
-
-            bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
-
-        return bboxes_list
-
-    @force_fp32(apply_to=('bbox_pred', ))
-    def regress_by_class(self, rois, label, bbox_pred, img_meta):
-        """Regress the bbox for the predicted class. Used in Cascade R-CNN.
-
-        Args:
-            rois (Tensor): shape (n, 4) or (n, 5)
-            label (Tensor): shape (n, )
-            bbox_pred (list[Tensor]): shape [(n, num_buckets *2), \
-                (n, num_buckets *2)]
-            img_meta (dict): Image meta info.
-
-        Returns:
-            Tensor: Regressed bboxes, the same shape as input rois.
-        """
-        assert rois.size(1) == 4 or rois.size(1) == 5
-
-        if rois.size(1) == 4:
-            new_rois, _ = self.bbox_coder.decode(rois, bbox_pred,
-                                                 img_meta['img_shape'])
-        else:
-            bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred,
-                                               img_meta['img_shape'])
-            new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
-
-        return new_rois
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/dense_heads/gfl_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/dense_heads/gfl_head.py
deleted file mode 100644
index 961bc92237663ad5343d3d08eb9c0e4e811ada05..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/dense_heads/gfl_head.py
+++ /dev/null
@@ -1,647 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps,
-                        build_assigner, build_sampler, distance2bbox,
-                        images_to_levels, multi_apply, multiclass_nms,
-                        reduce_mean, unmap)
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-
-class Integral(nn.Module):
-    """A fixed layer for calculating integral result from distribution.
-
-    This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
-    P(y_i) denotes the softmax vector that represents the discrete distribution
-    y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}
-
-    Args:
-        reg_max (int): The maximal value of the discrete set. Default: 16. You
-            may want to reset it according to your new dataset or related
-            settings.
-    """
-
-    def __init__(self, reg_max=16):
-        super(Integral, self).__init__()
-        self.reg_max = reg_max
-        self.register_buffer('project',
-                             torch.linspace(0, self.reg_max, self.reg_max + 1))
-
-    def forward(self, x):
-        """Forward feature from the regression head to get integral result of
-        bounding box location.
-
-        Args:
-            x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
-                n is self.reg_max.
-
-        Returns:
-            x (Tensor): Integral result of box locations, i.e., distance
-                offsets from the box center in four directions, shape (N, 4).
-        """
-        x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
-        x = F.linear(x, self.project.type_as(x)).reshape(-1, 4)
-        return x
-
-
-@HEADS.register_module()
-class GFLHead(AnchorHead):
-    """Generalized Focal Loss: Learning Qualified and Distributed Bounding
-    Boxes for Dense Object Detection.
-
-    GFL head structure is similar with ATSS, however GFL uses
-    1) joint representation for classification and localization quality, and
-    2) flexible General distribution for bounding box locations,
-    which are supervised by
-    Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively
-
-    https://arxiv.org/abs/2006.04388
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        stacked_convs (int): Number of conv layers in cls and reg tower.
-            Default: 4.
-        conv_cfg (dict): dictionary to construct and config conv layer.
-            Default: None.
-        norm_cfg (dict): dictionary to construct and config norm layer.
-            Default: dict(type='GN', num_groups=32, requires_grad=True).
-        loss_qfl (dict): Config of Quality Focal Loss (QFL).
-        reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`
-            in QFL setting. Default: 16.
-    Example:
-        >>> self = GFLHead(11, 7)
-        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
-        >>> cls_quality_score, bbox_pred = self.forward(feats)
-        >>> assert len(cls_quality_score) == len(self.scales)
-    """
-
-    def __init__(self,
-                 num_classes,
-                 in_channels,
-                 stacked_convs=4,
-                 conv_cfg=None,
-                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
-                 loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
-                 reg_max=16,
-                 **kwargs):
-        self.stacked_convs = stacked_convs
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.reg_max = reg_max
-        super(GFLHead, self).__init__(num_classes, in_channels, **kwargs)
-
-        self.sampling = False
-        if self.train_cfg:
-            self.assigner = build_assigner(self.train_cfg.assigner)
-            # SSD sampling=False so use PseudoSampler
-            sampler_cfg = dict(type='PseudoSampler')
-            self.sampler = build_sampler(sampler_cfg, context=self)
-
-        self.integral = Integral(self.reg_max)
-        self.loss_dfl = build_loss(loss_dfl)
-
-    def _init_layers(self):
-        """Initialize layers of the head."""
-        self.relu = nn.ReLU(inplace=True)
-        self.cls_convs = nn.ModuleList()
-        self.reg_convs = nn.ModuleList()
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            self.cls_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-            self.reg_convs.append(
-                ConvModule(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=self.conv_cfg,
-                    norm_cfg=self.norm_cfg))
-        assert self.num_anchors == 1, 'anchor free version'
-        self.gfl_cls = nn.Conv2d(
-            self.feat_channels, self.cls_out_channels, 3, padding=1)
-        self.gfl_reg = nn.Conv2d(
-            self.feat_channels, 4 * (self.reg_max + 1), 3, padding=1)
-        self.scales = nn.ModuleList(
-            [Scale(1.0) for _ in self.anchor_generator.strides])
-
-    def init_weights(self):
-        """Initialize weights of the head."""
-        for m in self.cls_convs:
-            normal_init(m.conv, std=0.01)
-        for m in self.reg_convs:
-            normal_init(m.conv, std=0.01)
-        bias_cls = bias_init_with_prob(0.01)
-        normal_init(self.gfl_cls, std=0.01, bias=bias_cls)
-        normal_init(self.gfl_reg, std=0.01)
-
-    def forward(self, feats):
-        """Forward features from the upstream network.
-
-        Args:
-            feats (tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-
-        Returns:
-            tuple: Usually a tuple of classification scores and bbox prediction
-                cls_scores (list[Tensor]): Classification and quality (IoU)
-                    joint scores for all scale levels, each is a 4D-tensor,
-                    the channel number is num_classes.
-                bbox_preds (list[Tensor]): Box distribution logits for all
-                    scale levels, each is a 4D-tensor, the channel number is
-                    4*(n+1), n is max value of integral set.
-        """
-        return multi_apply(self.forward_single, feats, self.scales)
-
-    def forward_single(self, x, scale):
-        """Forward feature of a single scale level.
-
-        Args:
-            x (Tensor): Features of a single scale level.
-            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
-                the bbox prediction.
-
-        Returns:
-            tuple:
-                cls_score (Tensor): Cls and quality joint scores for a single
-                    scale level the channel number is num_classes.
-                bbox_pred (Tensor): Box distribution logits for a single scale
-                    level, the channel number is 4*(n+1), n is max value of
-                    integral set.
-        """
-        cls_feat = x
-        reg_feat = x
-        for cls_conv in self.cls_convs:
-            cls_feat = cls_conv(cls_feat)
-        for reg_conv in self.reg_convs:
-            reg_feat = reg_conv(reg_feat)
-        cls_score = self.gfl_cls(cls_feat)
-        bbox_pred = scale(self.gfl_reg(reg_feat)).float()
-        return cls_score, bbox_pred
-
-    def anchor_center(self, anchors):
-        """Get anchor centers from anchors.
-
-        Args:
-            anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
-
-        Returns:
-            Tensor: Anchor centers with shape (N, 2), "xy" format.
-        """
-        anchors_cx = (anchors[..., 2] + anchors[..., 0]) / 2
-        anchors_cy = (anchors[..., 3] + anchors[..., 1]) / 2
-        return torch.stack([anchors_cx, anchors_cy], dim=-1)
-
-    def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
-                    bbox_targets, stride, num_total_samples):
-        """Compute loss of a single scale level.
-
-        Args:
-            anchors (Tensor): Box reference for each scale level with shape
-                (N, num_total_anchors, 4).
-            cls_score (Tensor): Cls and quality joint scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_pred (Tensor): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            labels (Tensor): Labels of each anchors with shape
-                (N, num_total_anchors).
-            label_weights (Tensor): Label weights of each anchor with shape
-                (N, num_total_anchors)
-            bbox_targets (Tensor): BBox regression targets of each anchor wight
-                shape (N, num_total_anchors, 4).
-            stride (tuple): Stride in this scale level.
-            num_total_samples (int): Number of positive samples that is
-                reduced over all GPUs.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-        assert stride[0] == stride[1], 'h stride is not equal to w stride!'
-        anchors = anchors.reshape(-1, 4)
-        cls_score = cls_score.permute(0, 2, 3,
-                                      1).reshape(-1, self.cls_out_channels)
-        bbox_pred = bbox_pred.permute(0, 2, 3,
-                                      1).reshape(-1, 4 * (self.reg_max + 1))
-        bbox_targets = bbox_targets.reshape(-1, 4)
-        labels = labels.reshape(-1)
-        label_weights = label_weights.reshape(-1)
-
-        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
-        bg_class_ind = self.num_classes
-        pos_inds = ((labels >= 0)
-                    & (labels < bg_class_ind)).nonzero().squeeze(1)
-        score = label_weights.new_zeros(labels.shape)
-
-        if len(pos_inds) > 0:
-            pos_bbox_targets = bbox_targets[pos_inds]
-            pos_bbox_pred = bbox_pred[pos_inds]
-            pos_anchors = anchors[pos_inds]
-            pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
-
-            weight_targets = cls_score.detach().sigmoid()
-            weight_targets = weight_targets.max(dim=1)[0][pos_inds]
-            pos_bbox_pred_corners = self.integral(pos_bbox_pred)
-            pos_decode_bbox_pred = distance2bbox(pos_anchor_centers,
-                                                 pos_bbox_pred_corners)
-            pos_decode_bbox_targets = pos_bbox_targets / stride[0]
-            score[pos_inds] = bbox_overlaps(
-                pos_decode_bbox_pred.detach(),
-                pos_decode_bbox_targets,
-                is_aligned=True)
-            pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
-            target_corners = bbox2distance(pos_anchor_centers,
-                                           pos_decode_bbox_targets,
-                                           self.reg_max).reshape(-1)
-
-            # regression loss
-            loss_bbox = self.loss_bbox(
-                pos_decode_bbox_pred,
-                pos_decode_bbox_targets,
-                weight=weight_targets,
-                avg_factor=1.0)
-
-            # dfl loss
-            loss_dfl = self.loss_dfl(
-                pred_corners,
-                target_corners,
-                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
-                avg_factor=4.0)
-        else:
-            loss_bbox = bbox_pred.sum() * 0
-            loss_dfl = bbox_pred.sum() * 0
-            weight_targets = bbox_pred.new_tensor(0)
-
-        # cls (qfl) loss
-        loss_cls = self.loss_cls(
-            cls_score, (labels, score),
-            weight=label_weights,
-            avg_factor=num_total_samples)
-
-        return loss_cls, loss_bbox, loss_dfl, weight_targets.sum()
-
-    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
-    def loss(self,
-             cls_scores,
-             bbox_preds,
-             gt_bboxes,
-             gt_labels,
-             img_metas,
-             gt_bboxes_ignore=None):
-        """Compute losses of the head.
-
-        Args:
-            cls_scores (list[Tensor]): Cls and quality scores for each scale
-                level has shape (N, num_classes, H, W).
-            bbox_preds (list[Tensor]): Box distribution logits for each scale
-                level with shape (N, 4*(n+1), H, W), n is max value of integral
-                set.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
-                boxes can be ignored when computing the loss.
-
-        Returns:
-            dict[str, Tensor]: A dictionary of loss components.
-        """
-
-        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
-        assert len(featmap_sizes) == self.anchor_generator.num_levels
-
-        device = cls_scores[0].device
-        anchor_list, valid_flag_list = self.get_anchors(
-            featmap_sizes, img_metas, device=device)
-        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
-
-        cls_reg_targets = self.get_targets(
-            anchor_list,
-            valid_flag_list,
-            gt_bboxes,
-            img_metas,
-            gt_bboxes_ignore_list=gt_bboxes_ignore,
-            gt_labels_list=gt_labels,
-            label_channels=label_channels)
-        if cls_reg_targets is None:
-            return None
-
-        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
-         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
-
-        num_total_samples = reduce_mean(
-            torch.tensor(num_total_pos, dtype=torch.float,
-                         device=device)).item()
-        num_total_samples = max(num_total_samples, 1.0)
-
-        losses_cls, losses_bbox, losses_dfl,\
-            avg_factor = multi_apply(
-                self.loss_single,
-                anchor_list,
-                cls_scores,
-                bbox_preds,
-                labels_list,
-                label_weights_list,
-                bbox_targets_list,
-                self.anchor_generator.strides,
-                num_total_samples=num_total_samples)
-
-        avg_factor = sum(avg_factor)
-        avg_factor = reduce_mean(avg_factor).item()
-        losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox))
-        losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl))
-        return dict(
-            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl)
-
-    def _get_bboxes(self,
-                    cls_scores,
-                    bbox_preds,
-                    mlvl_anchors,
-                    img_shapes,
-                    scale_factors,
-                    cfg,
-                    rescale=False,
-                    with_nms=True):
-        """Transform outputs for a single batch item into labeled boxes.
-
-        Args:
-            cls_scores (list[Tensor]): Box scores for a single scale level
-                has shape (N, num_classes, H, W).
-            bbox_preds (list[Tensor]): Box distribution logits for a single
-                scale level with shape (N, 4*(n+1), H, W), n is max value of
-                integral set.
-            mlvl_anchors (list[Tensor]): Box reference for a single scale level
-                with shape (num_total_anchors, 4).
-            img_shapes (list[tuple[int]]): Shape of the input image,
-                list[(height, width, 3)].
-            scale_factors (list[ndarray]): Scale factor of the image arange as
-                (w_scale, h_scale, w_scale, h_scale).
-            cfg (mmcv.Config | None): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-                Default: False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default: True.
-
-        Returns:
-            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
-                The first item is an (n, 5) tensor, where 5 represent
-                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
-                The shape of the second tensor in the tuple is (n,), and
-                each element represents the class label of the corresponding
-                box.
-        """
-        cfg = self.test_cfg if cfg is None else cfg
-        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
-        batch_size = cls_scores[0].shape[0]
-
-        mlvl_bboxes = []
-        mlvl_scores = []
-        for cls_score, bbox_pred, stride, anchors in zip(
-                cls_scores, bbox_preds, self.anchor_generator.strides,
-                mlvl_anchors):
-            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-            assert stride[0] == stride[1]
-            scores = cls_score.permute(0, 2, 3, 1).reshape(
-                batch_size, -1, self.cls_out_channels).sigmoid()
-            bbox_pred = bbox_pred.permute(0, 2, 3, 1)
-
-            bbox_pred = self.integral(bbox_pred) * stride[0]
-            bbox_pred = bbox_pred.reshape(batch_size, -1, 4)
-
-            nms_pre = cfg.get('nms_pre', -1)
-            if nms_pre > 0 and scores.shape[1] > nms_pre:
-                max_scores, _ = scores.max(-1)
-                _, topk_inds = max_scores.topk(nms_pre)
-                batch_inds = torch.arange(batch_size).view(
-                    -1, 1).expand_as(topk_inds).long()
-                anchors = anchors[topk_inds, :]
-                bbox_pred = bbox_pred[batch_inds, topk_inds, :]
-                scores = scores[batch_inds, topk_inds, :]
-            else:
-                anchors = anchors.expand_as(bbox_pred)
-
-            bboxes = distance2bbox(
-                self.anchor_center(anchors), bbox_pred, max_shape=img_shapes)
-            mlvl_bboxes.append(bboxes)
-            mlvl_scores.append(scores)
-
-        batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1)
-        if rescale:
-            batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor(
-                scale_factors).unsqueeze(1)
-
-        batch_mlvl_scores = torch.cat(mlvl_scores, dim=1)
-        # Add a dummy background class to the backend when using sigmoid
-        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
-        # BG cat_id: num_class
-        padding = batch_mlvl_scores.new_zeros(batch_size,
-                                              batch_mlvl_scores.shape[1], 1)
-        batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1)
-
-        if with_nms:
-            det_results = []
-            for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes,
-                                                  batch_mlvl_scores):
-                det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores,
-                                                     cfg.score_thr, cfg.nms,
-                                                     cfg.max_per_img)
-                det_results.append(tuple([det_bbox, det_label]))
-        else:
-            det_results = [
-                tuple(mlvl_bs)
-                for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores)
-            ]
-        return det_results
-
-    def get_targets(self,
-                    anchor_list,
-                    valid_flag_list,
-                    gt_bboxes_list,
-                    img_metas,
-                    gt_bboxes_ignore_list=None,
-                    gt_labels_list=None,
-                    label_channels=1,
-                    unmap_outputs=True):
-        """Get targets for GFL head.
-
-        This method is almost the same as `AnchorHead.get_targets()`. Besides
-        returning the targets as the parent method does, it also returns the
-        anchors as the first element of the returned tuple.
-        """
-        num_imgs = len(img_metas)
-        assert len(anchor_list) == len(valid_flag_list) == num_imgs
-
-        # anchor number of multi levels
-        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
-        num_level_anchors_list = [num_level_anchors] * num_imgs
-
-        # concat all level anchors and flags to a single tensor
-        for i in range(num_imgs):
-            assert len(anchor_list[i]) == len(valid_flag_list[i])
-            anchor_list[i] = torch.cat(anchor_list[i])
-            valid_flag_list[i] = torch.cat(valid_flag_list[i])
-
-        # compute targets for each image
-        if gt_bboxes_ignore_list is None:
-            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
-        if gt_labels_list is None:
-            gt_labels_list = [None for _ in range(num_imgs)]
-        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
-         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
-             self._get_target_single,
-             anchor_list,
-             valid_flag_list,
-             num_level_anchors_list,
-             gt_bboxes_list,
-             gt_bboxes_ignore_list,
-             gt_labels_list,
-             img_metas,
-             label_channels=label_channels,
-             unmap_outputs=unmap_outputs)
-        # no valid anchors
-        if any([labels is None for labels in all_labels]):
-            return None
-        # sampled anchors of all images
-        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
-        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
-        # split targets to a list w.r.t. multiple levels
-        anchors_list = images_to_levels(all_anchors, num_level_anchors)
-        labels_list = images_to_levels(all_labels, num_level_anchors)
-        label_weights_list = images_to_levels(all_label_weights,
-                                              num_level_anchors)
-        bbox_targets_list = images_to_levels(all_bbox_targets,
-                                             num_level_anchors)
-        bbox_weights_list = images_to_levels(all_bbox_weights,
-                                             num_level_anchors)
-        return (anchors_list, labels_list, label_weights_list,
-                bbox_targets_list, bbox_weights_list, num_total_pos,
-                num_total_neg)
-
-    def _get_target_single(self,
-                           flat_anchors,
-                           valid_flags,
-                           num_level_anchors,
-                           gt_bboxes,
-                           gt_bboxes_ignore,
-                           gt_labels,
-                           img_meta,
-                           label_channels=1,
-                           unmap_outputs=True):
-        """Compute regression, classification targets for anchors in a single
-        image.
-
-        Args:
-            flat_anchors (Tensor): Multi-level anchors of the image, which are
-                concatenated into a single tensor of shape (num_anchors, 4)
-            valid_flags (Tensor): Multi level valid flags of the image,
-                which are concatenated into a single tensor of
-                    shape (num_anchors,).
-            num_level_anchors Tensor): Number of anchors of each scale level.
-            gt_bboxes (Tensor): Ground truth bboxes of the image,
-                shape (num_gts, 4).
-            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
-                ignored, shape (num_ignored_gts, 4).
-            gt_labels (Tensor): Ground truth labels of each box,
-                shape (num_gts,).
-            img_meta (dict): Meta info of the image.
-            label_channels (int): Channel of label.
-            unmap_outputs (bool): Whether to map outputs back to the original
-                set of anchors.
-
-        Returns:
-            tuple: N is the number of total anchors in the image.
-                anchors (Tensor): All anchors in the image with shape (N, 4).
-                labels (Tensor): Labels of all anchors in the image with shape
-                    (N,).
-                label_weights (Tensor): Label weights of all anchor in the
-                    image with shape (N,).
-                bbox_targets (Tensor): BBox targets of all anchors in the
-                    image with shape (N, 4).
-                bbox_weights (Tensor): BBox weights of all anchors in the
-                    image with shape (N, 4).
-                pos_inds (Tensor): Indices of positive anchor with shape
-                    (num_pos,).
-                neg_inds (Tensor): Indices of negative anchor with shape
-                    (num_neg,).
-        """
-        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
-                                           img_meta['img_shape'][:2],
-                                           self.train_cfg.allowed_border)
-        if not inside_flags.any():
-            return (None, ) * 7
-        # assign gt and sample anchors
-        anchors = flat_anchors[inside_flags, :]
-
-        num_level_anchors_inside = self.get_num_level_anchors_inside(
-            num_level_anchors, inside_flags)
-        assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
-                                             gt_bboxes, gt_bboxes_ignore,
-                                             gt_labels)
-
-        sampling_result = self.sampler.sample(assign_result, anchors,
-                                              gt_bboxes)
-
-        num_valid_anchors = anchors.shape[0]
-        bbox_targets = torch.zeros_like(anchors)
-        bbox_weights = torch.zeros_like(anchors)
-        labels = anchors.new_full((num_valid_anchors, ),
-                                  self.num_classes,
-                                  dtype=torch.long)
-        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
-
-        pos_inds = sampling_result.pos_inds
-        neg_inds = sampling_result.neg_inds
-        if len(pos_inds) > 0:
-            pos_bbox_targets = sampling_result.pos_gt_bboxes
-            bbox_targets[pos_inds, :] = pos_bbox_targets
-            bbox_weights[pos_inds, :] = 1.0
-            if gt_labels is None:
-                # Only rpn gives gt_labels as None
-                # Foreground is the first class
-                labels[pos_inds] = 0
-            else:
-                labels[pos_inds] = gt_labels[
-                    sampling_result.pos_assigned_gt_inds]
-            if self.train_cfg.pos_weight <= 0:
-                label_weights[pos_inds] = 1.0
-            else:
-                label_weights[pos_inds] = self.train_cfg.pos_weight
-        if len(neg_inds) > 0:
-            label_weights[neg_inds] = 1.0
-
-        # map up to original set of anchors
-        if unmap_outputs:
-            num_total_anchors = flat_anchors.size(0)
-            anchors = unmap(anchors, num_total_anchors, inside_flags)
-            labels = unmap(
-                labels, num_total_anchors, inside_flags, fill=self.num_classes)
-            label_weights = unmap(label_weights, num_total_anchors,
-                                  inside_flags)
-            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
-            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
-
-        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
-                pos_inds, neg_inds)
-
-    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
-        split_inside_flags = torch.split(inside_flags, num_level_anchors)
-        num_level_anchors_inside = [
-            int(flags.sum()) for flags in split_inside_flags
-        ]
-        return num_level_anchors_inside
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/decode_heads/ann_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/decode_heads/ann_head.py
deleted file mode 100644
index 30aaacc2cafc568d3de71d1477b4de0dc0fea9d3..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmseg/models/decode_heads/ann_head.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import torch
-import torch.nn as nn
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from ..builder import HEADS
-from ..utils import SelfAttentionBlock as _SelfAttentionBlock
-from .decode_head import BaseDecodeHead
-
-
-class PPMConcat(nn.ModuleList):
-    """Pyramid Pooling Module that only concat the features of each layer.
-
-    Args:
-        pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module.
-    """
-
-    def __init__(self, pool_scales=(1, 3, 6, 8)):
-        super(PPMConcat, self).__init__(
-            [nn.AdaptiveAvgPool2d(pool_scale) for pool_scale in pool_scales])
-
-    def forward(self, feats):
-        """Forward function."""
-        ppm_outs = []
-        for ppm in self:
-            ppm_out = ppm(feats)
-            ppm_outs.append(ppm_out.view(*feats.shape[:2], -1))
-        concat_outs = torch.cat(ppm_outs, dim=2)
-        return concat_outs
-
-
-class SelfAttentionBlock(_SelfAttentionBlock):
-    """Make a ANN used SelfAttentionBlock.
-
-    Args:
-        low_in_channels (int): Input channels of lower level feature,
-            which is the key feature for self-attention.
-        high_in_channels (int): Input channels of higher level feature,
-            which is the query feature for self-attention.
-        channels (int): Output channels of key/query transform.
-        out_channels (int): Output channels.
-        share_key_query (bool): Whether share projection weight between key
-            and query projection.
-        query_scale (int): The scale of query feature map.
-        key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module of key feature.
-        conv_cfg (dict|None): Config of conv layers.
-        norm_cfg (dict|None): Config of norm layers.
-        act_cfg (dict|None): Config of activation layers.
-    """
-
-    def __init__(self, low_in_channels, high_in_channels, channels,
-                 out_channels, share_key_query, query_scale, key_pool_scales,
-                 conv_cfg, norm_cfg, act_cfg):
-        key_psp = PPMConcat(key_pool_scales)
-        if query_scale > 1:
-            query_downsample = nn.MaxPool2d(kernel_size=query_scale)
-        else:
-            query_downsample = None
-        super(SelfAttentionBlock, self).__init__(
-            key_in_channels=low_in_channels,
-            query_in_channels=high_in_channels,
-            channels=channels,
-            out_channels=out_channels,
-            share_key_query=share_key_query,
-            query_downsample=query_downsample,
-            key_downsample=key_psp,
-            key_query_num_convs=1,
-            key_query_norm=True,
-            value_out_num_convs=1,
-            value_out_norm=False,
-            matmul_norm=True,
-            with_out=True,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-
-
-class AFNB(nn.Module):
-    """Asymmetric Fusion Non-local Block(AFNB)
-
-    Args:
-        low_in_channels (int): Input channels of lower level feature,
-            which is the key feature for self-attention.
-        high_in_channels (int): Input channels of higher level feature,
-            which is the query feature for self-attention.
-        channels (int): Output channels of key/query transform.
-        out_channels (int): Output channels.
-            and query projection.
-        query_scales (tuple[int]): The scales of query feature map.
-            Default: (1,)
-        key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module of key feature.
-        conv_cfg (dict|None): Config of conv layers.
-        norm_cfg (dict|None): Config of norm layers.
-        act_cfg (dict|None): Config of activation layers.
-    """
-
-    def __init__(self, low_in_channels, high_in_channels, channels,
-                 out_channels, query_scales, key_pool_scales, conv_cfg,
-                 norm_cfg, act_cfg):
-        super(AFNB, self).__init__()
-        self.stages = nn.ModuleList()
-        for query_scale in query_scales:
-            self.stages.append(
-                SelfAttentionBlock(
-                    low_in_channels=low_in_channels,
-                    high_in_channels=high_in_channels,
-                    channels=channels,
-                    out_channels=out_channels,
-                    share_key_query=False,
-                    query_scale=query_scale,
-                    key_pool_scales=key_pool_scales,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-        self.bottleneck = ConvModule(
-            out_channels + high_in_channels,
-            out_channels,
-            1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-    def forward(self, low_feats, high_feats):
-        """Forward function."""
-        priors = [stage(high_feats, low_feats) for stage in self.stages]
-        context = torch.stack(priors, dim=0).sum(dim=0)
-        output = self.bottleneck(torch.cat([context, high_feats], 1))
-        return output
-
-
-class APNB(nn.Module):
-    """Asymmetric Pyramid Non-local Block (APNB)
-
-    Args:
-        in_channels (int): Input channels of key/query feature,
-            which is the key feature for self-attention.
-        channels (int): Output channels of key/query transform.
-        out_channels (int): Output channels.
-        query_scales (tuple[int]): The scales of query feature map.
-            Default: (1,)
-        key_pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
-            Module of key feature.
-        conv_cfg (dict|None): Config of conv layers.
-        norm_cfg (dict|None): Config of norm layers.
-        act_cfg (dict|None): Config of activation layers.
-    """
-
-    def __init__(self, in_channels, channels, out_channels, query_scales,
-                 key_pool_scales, conv_cfg, norm_cfg, act_cfg):
-        super(APNB, self).__init__()
-        self.stages = nn.ModuleList()
-        for query_scale in query_scales:
-            self.stages.append(
-                SelfAttentionBlock(
-                    low_in_channels=in_channels,
-                    high_in_channels=in_channels,
-                    channels=channels,
-                    out_channels=out_channels,
-                    share_key_query=True,
-                    query_scale=query_scale,
-                    key_pool_scales=key_pool_scales,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-        self.bottleneck = ConvModule(
-            2 * in_channels,
-            out_channels,
-            1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=act_cfg)
-
-    def forward(self, feats):
-        """Forward function."""
-        priors = [stage(feats, feats) for stage in self.stages]
-        context = torch.stack(priors, dim=0).sum(dim=0)
-        output = self.bottleneck(torch.cat([context, feats], 1))
-        return output
-
-
-@HEADS.register_module()
-class ANNHead(BaseDecodeHead):
-    """Asymmetric Non-local Neural Networks for Semantic Segmentation.
-
-    This head is the implementation of `ANNNet
-    <https://arxiv.org/abs/1908.07678>`_.
-
-    Args:
-        project_channels (int): Projection channels for Nonlocal.
-        query_scales (tuple[int]): The scales of query feature map.
-            Default: (1,)
-        key_pool_scales (tuple[int]): The pooling scales of key feature map.
-            Default: (1, 3, 6, 8).
-    """
-
-    def __init__(self,
-                 project_channels,
-                 query_scales=(1, ),
-                 key_pool_scales=(1, 3, 6, 8),
-                 **kwargs):
-        super(ANNHead, self).__init__(
-            input_transform='multiple_select', **kwargs)
-        assert len(self.in_channels) == 2
-        low_in_channels, high_in_channels = self.in_channels
-        self.project_channels = project_channels
-        self.fusion = AFNB(
-            low_in_channels=low_in_channels,
-            high_in_channels=high_in_channels,
-            out_channels=high_in_channels,
-            channels=project_channels,
-            query_scales=query_scales,
-            key_pool_scales=key_pool_scales,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        self.bottleneck = ConvModule(
-            high_in_channels,
-            self.channels,
-            3,
-            padding=1,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-        self.context = APNB(
-            in_channels=self.channels,
-            out_channels=self.channels,
-            channels=project_channels,
-            query_scales=query_scales,
-            key_pool_scales=key_pool_scales,
-            conv_cfg=self.conv_cfg,
-            norm_cfg=self.norm_cfg,
-            act_cfg=self.act_cfg)
-
-    def forward(self, inputs):
-        """Forward function."""
-        low_feats, high_feats = self._transform_inputs(inputs)
-        output = self.fusion(low_feats, high_feats)
-        output = self.dropout(output)
-        output = self.bottleneck(output)
-        output = self.context(output)
-        output = self.cls_seg(output)
-
-        return output
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/lraspp_m-v3-d8.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/lraspp_m-v3-d8.py
deleted file mode 100644
index 93258242a90695cc94a7c6bd41562d6a75988771..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/configs/_base_/models/lraspp_m-v3-d8.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', eps=0.001, requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    backbone=dict(
-        type='MobileNetV3',
-        arch='large',
-        out_indices=(1, 3, 16),
-        norm_cfg=norm_cfg),
-    decode_head=dict(
-        type='LRASPPHead',
-        in_channels=(16, 24, 960),
-        in_index=(0, 1, 2),
-        channels=128,
-        input_transform='multiple_select',
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        act_cfg=dict(type='ReLU'),
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/akhaliq/animeganv2-onnx/README.md b/spaces/akhaliq/animeganv2-onnx/README.md
deleted file mode 100644
index 91f49bdfeaffb8d5dd540d43c7a4923dc07b0869..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/animeganv2-onnx/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Animeganv2 Onnx
-emoji: 📉
-colorFrom: pink
-colorTo: green
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/alexrame/rewardedsoups/streamlit_app/data/locomotion/trajectories/0.html b/spaces/alexrame/rewardedsoups/streamlit_app/data/locomotion/trajectories/0.html
deleted file mode 100644
index d57fc73a56f75bfccf59adf1325cb36d67a7a017..0000000000000000000000000000000000000000
--- a/spaces/alexrame/rewardedsoups/streamlit_app/data/locomotion/trajectories/0.html
+++ /dev/null
@@ -1,48 +0,0 @@
-
-<html>
-    <head>
-        <title>brax visualizer</title>
-        <style>
-            body {
-              margin: 0;
-              padding: 0;
-            }
-            #brax-viewer {
-              margin: 0;
-              padding: 0;
-              height: 300px;
-              width: 150px;
-            }
-        </style>
-    </head>
-    <body>
-        <script type="application/javascript">
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0.08221428096294403, 0.01953011564910412], [0.9871552586555481, 0.06860533356666565, -0.14343677461147308, 0.015608428046107292], [0.980735182762146, 0.12053894251585007, -0.15188495814800262, 0.023660283535718918], [0.991090714931488, 0.03141210973262787, 0.07552830129861832, 0.10510887950658798], [0.971835196018219, 0.016709458082914352, 0.2081802487373352, 0.10917019844055176], [0.9077779650688171, 0.0579720139503479, -0.41527560353279114, 0.01116385031491518], [0.9239506125450134, 0.05742297321557999, -0.37793898582458496, 0.013420219533145428], [0.8499069213867188, -0.4141913652420044, -0.26337626576423645, 0.19166849553585052], [0.6352585554122925, -0.3973064124584198, -0.36810624599456787, 0.5505379438400269], [0.9202458262443542, -0.2446541041135788, -0.12778867781162262, -0.2774205803871155], [0.853182315826416, -0.09749200195074081, 0.4925275146961212, 0.1413927972316742], [1.0, 0.0, 0.0, 0.0]], [[0.9950999021530151, 0.05896925553679466, 0.07246077060699463, 0.03237597644329071], [0.9838575124740601, 0.062126148492097855, -0.16782301664352417, 0.00032534750062040985], [0.9798604846000671, 0.11562729626893997, -0.1623930037021637, 0.011501342989504337], [0.9881083369255066, 0.047047071158885956, 0.1151687428355217, 0.09035942703485489], [0.9528862237930298, 0.031149625778198242, 0.2853147089481354, 0.09814800322055817], [0.906320333480835, 0.07021746784448624, -0.4159335494041443, 0.025542352348566055], [0.913189709186554, 0.06632248312234879, -0.4009796977043152, 0.03001941367983818], [0.8462113738059998, -0.4152738153934479, -0.2759752869606018, 0.1879139542579651], [0.6289370059967041, -0.4018026292324066, -0.3797549605369568, 0.5466068387031555], [0.9153035879135132, -0.25173044204711914, -0.15500853955745697, -0.27353885769844055], [0.860835075378418, -0.12043365836143494, 0.47465240955352783, 0.13843411207199097], [1.0, 0.0, 0.0, 0.0]], [[0.9953768253326416, 0.06101972982287407, 0.05541860684752464, 0.049298521131277084], [0.9840671420097351, 0.061076611280441284, -0.16697366535663605, 0.0011218683794140816], [0.982128918170929, 0.08723394572734833, -0.1665184497833252, 0.009194506332278252], [0.9848251342773438, 0.05401061475276947, 0.15101245045661926, 0.06631527096033096], [0.9401359558105469, 0.04239398613572121, 0.3294309377670288, 0.07630487531423569], [0.9027571678161621, 0.09419270604848862, -0.419045627117157, 0.023622041568160057], [0.9098949432373047, 0.09305892884731293, -0.40339475870132446, 0.02653535269200802], [0.8424861431121826, -0.41181132197380066, -0.2920131981372833, 0.18803447484970093], [0.6197763681411743, -0.4064469039440155, -0.39103594422340393, 0.5456822514533997], [0.9099912047386169, -0.2537921667098999, -0.18252825736999512, -0.2723768353462219], [0.8709279298782349, -0.13436417281627655, 0.45282191038131714, 0.13558481633663177], [1.0, 0.0, 0.0, 0.0]], [[0.9952518343925476, 0.05879954993724823, 0.04139111563563347, 0.0655997171998024], [0.9833135008811951, 0.059859178960323334, -0.1716577410697937, 0.0067152585834264755], [0.9832209944725037, 0.0629003643989563, -0.17103654146194458, 0.008169232867658138], [0.9809185862541199, 0.049114879220724106, 0.1851585954427719, 0.03320765122771263], [0.9333849549293518, 0.04359572008252144, 0.35367512702941895, 0.04249613732099533], [0.8973994255065918, 0.12848711013793945, -0.42198649048805237, 0.009629935957491398], [0.9057490825653076, 0.12725122272968292, -0.40404099225997925, 0.013287504203617573], [0.8400949239730835, -0.4011732339859009, -0.3111714720726013, 0.19097918272018433], [0.6113195419311523, -0.39959466457366943, -0.41355812549591064, 0.5436748266220093], [0.904741108417511, -0.2548816502094269, -0.20968297123908997, -0.2692803740501404], [0.8795920610427856, -0.14941532909870148, 0.4312953054904938, 0.13407936692237854], [1.0, 0.0, 0.0, 0.0]], [[0.9943984150886536, 0.058484189212322235, 0.022496959194540977, 0.08511969447135925], [0.9838411211967468, 0.059136997908353806, 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-        </script>
-        <div id="brax-viewer"></div>
-        <script type="module">
-          import {Viewer} from 'https://cdn.jsdelivr.net/gh/google/brax@v0.1.0/js/viewer.js';
-          const domElement = document.getElementById('brax-viewer');
-          var viewer = new Viewer(domElement, system);
-          viewer.camera.position.set(-4.0, 0.0, 1.0);
-          viewer.camera.follow = true;
-          viewer.camera.freezeAngle = false;
-          viewer.camera.followDistance = 2.5;
-
-          
-          const guiElement = viewer.gui.domElement;
-          guiElement.style.display = 'none';
-
-          for (let c of viewer.scene.children){
-            if (c.name == "ffoot" || c.name == "fshin" || c.name == "fthigh" || c.name == "bfoot" || c.name == "bshin" || c.name == "bthigh" || c.name == "torso" || c.name == "right_shin" || c.name == "left_shin" || c.name == "right_thigh" || c.name == "left_thigh" || c.name == "left_lower_arm" || c.name == "right_lower_arm" || c.name == "right_upper_arm" || c.name == "left_upper_arm" || c.name == "pelvis" || c.name == "lwaist" ){
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diff --git a/spaces/algomuffin/jojo_fork/e4e/criteria/id_loss.py b/spaces/algomuffin/jojo_fork/e4e/criteria/id_loss.py
deleted file mode 100644
index bab806172eff18c0630536ae96817508c3197b8b..0000000000000000000000000000000000000000
--- a/spaces/algomuffin/jojo_fork/e4e/criteria/id_loss.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import torch
-from torch import nn
-from configs.paths_config import model_paths
-from models.encoders.model_irse import Backbone
-
-
-class IDLoss(nn.Module):
-    def __init__(self):
-        super(IDLoss, self).__init__()
-        print('Loading ResNet ArcFace')
-        self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
-        self.facenet.load_state_dict(torch.load(model_paths['ir_se50']))
-        self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
-        self.facenet.eval()
-        for module in [self.facenet, self.face_pool]:
-            for param in module.parameters():
-                param.requires_grad = False
-
-    def extract_feats(self, x):
-        x = x[:, :, 35:223, 32:220]  # Crop interesting region
-        x = self.face_pool(x)
-        x_feats = self.facenet(x)
-        return x_feats
-
-    def forward(self, y_hat, y, x):
-        n_samples = x.shape[0]
-        x_feats = self.extract_feats(x)
-        y_feats = self.extract_feats(y)  # Otherwise use the feature from there
-        y_hat_feats = self.extract_feats(y_hat)
-        y_feats = y_feats.detach()
-        loss = 0
-        sim_improvement = 0
-        id_logs = []
-        count = 0
-        for i in range(n_samples):
-            diff_target = y_hat_feats[i].dot(y_feats[i])
-            diff_input = y_hat_feats[i].dot(x_feats[i])
-            diff_views = y_feats[i].dot(x_feats[i])
-            id_logs.append({'diff_target': float(diff_target),
-                            'diff_input': float(diff_input),
-                            'diff_views': float(diff_views)})
-            loss += 1 - diff_target
-            id_diff = float(diff_target) - float(diff_views)
-            sim_improvement += id_diff
-            count += 1
-
-        return loss / count, sim_improvement / count, id_logs
diff --git a/spaces/ali-ghamdan/deoldify/fastai/general_optimizer.py b/spaces/ali-ghamdan/deoldify/fastai/general_optimizer.py
deleted file mode 100644
index f6a0487d582fe6264627d302d6580364affdf754..0000000000000000000000000000000000000000
--- a/spaces/ali-ghamdan/deoldify/fastai/general_optimizer.py
+++ /dev/null
@@ -1,139 +0,0 @@
-from .torch_core import *
-from torch.optim import Optimizer
-import types
-
-__all__ = ['StatScope', 'Statistic', 'ConstStatistic', 'AvgStatistic', 'AvgSquare', 'GeneralOptimizer']
-
-StatScope = Enum('StatScope', 'Global Group Layer Channel Weight')
-
-@dataclass
-class Statistic():
-    name:str
-    param:float=0.9  # e.g. for exp moving average
-    scope:StatScope=StatScope.Weight
-    init:float=0.  # starting value
-
-    @property
-    def buf(self): return f'{self.name}_buffer'
-
-    def new_step(self):
-        "Set state when computing statistics for Global or Group"
-        raise NotImplementedError
-
-    def accumulate(self, val):
-        "Add `val` to statistic"
-        raise NotImplementedError
-
-    def update(self, state, param, val=None, step=None):
-        "Update state with accumlated, or `val` (if `Weight` or `Layer` scope)"
-        raise NotImplementedError
-
-class ConstStatistic(Statistic):
-    @property
-    def buf(self): return None
-    def new_step(self):   pass
-    def accumulate(self): pass
-    def update(self, state, param, val=None, step=None): return param
-
-@dataclass
-class CounterStat(Statistic):
-    def __post_init__(self): self.init,self._buf,self.name = 0,self.name,None
-    @property
-    def buf(self): return self._buf
-    def new_step(self): pass
-    def accumulate(self, val): pass
-    def update(self, state, param, val=None, step=None): return state + 1
-
-@dataclass
-class AvgStatistic(Statistic):
-    decay:bool=False
-    debias:bool=False
-    def new_step(self): self.val,self.count = 0.,0
-
-    def accumulate(self, val):
-        self.count += 1
-        self.val += self._get_val1(val)
-
-    def _get_val1(self, val): return val.mean()
-    def _get_val2(self, state, val, param): return state.add_(1-param, val) if self.decay else state.add_(val)
-    def _get_val3(self, state, val, param): 
-        v = val.view(val.size(0), -1).mean(1)
-        return state.add_(1-param, v) if self.decay else state.add_(v)
-
-    def update(self, state, param, val=None, step=None):
-        if self.scope == StatScope.Weight:
-            # `state` is a tensor
-            res = self._get_val2(state.mul_(param), val, param)
-        elif self.scope == StatScope.Channel:
-            # `state` is a tensor of size n_channels
-            res = self._get_val3(state.mul_(param), val, param)
-        # For everything else, `state` is a scalar
-        elif self.scope == StatScope.Layer:  res = state*param + self._get_val1(val) * (1-param if self.decay else 1.)
-        elif self.count != 0:                res = state*param + self.val/self.count * (1-param if self.decay else 1.)
-        else: return state
-        if self.debias and step is not None: res /= (1 - param ** step)
-        return res
-
-class AvgSquare(AvgStatistic):
-
-    def __init__(self, name:str, param:float=0.9, scope=StatScope.Weight, init:float=0., decay:bool=True, debias:bool=False):
-        super().__init__(name, param=param, scope=scope, init=init, decay=decay, debias=debias)
-
-    def _get_val1(self, val): return torch.norm(val).pow(2)/val.numel()
-    def _get_val2(self, state, val, param): 
-        return state.addcmul_(1-param, val, val) if self.decay else state.addcmul_(val, val)
-    def _get_val3(self, state, val, param):
-        v = val.view(val.size(0), -1).mean(1)
-        return state.addcmul_(1-param, v, v) if self.decay else state.addcmul_(v, v)
-
-class GeneralOptimizer(Optimizer):
-    def __init__(self, params, stats=None, on_step:Callable=None):
-        defaults = {s.name:s.param for s in listify(stats) if s.name is not None}
-        super().__init__(params, defaults)
-        self.global_stats,self.group_stats,self.layer_stats,self.channel_stats,self.weight_stats = self._split_stats(stats)
-        self.init_stats()
-        if on_step is not None: self.on_step = types.MethodType(on_step, self)
-
-    def step(self, closure=None):
-        self.update_stats()
-        for i,pg in enumerate(self.param_groups):
-            for p in pg['params']:
-                if p.grad is not None: self.on_step(p, pg, i)
-
-    def on_step(self, p, group, group_idx): p.data.add_(-group['lr'], p.grad.data)
-
-    def _split_stats(self, stats):
-        splits = [[stat for stat in listify(stats) if stat.scope==scope] for scope in StatScope]
-        for split,s in zip([splits[0], splits[1], splits[2]+splits[3]+splits[4]], StatScope):
-            if np.any([getattr(s, 'debias', False) for s in split]): split.insert(0, CounterStat('step', scope=s))
-        return splits
-
-    def _init_stats(self, stats, data=None):
-        return {stat.buf: stat.init if data is None
-                else torch.zeros_like(data) + stat.init for stat in stats if stat.buf is not None}
-
-    def init_stats(self):
-        self.state['global'] = self._init_stats(self.global_stats)
-        for i,pg in enumerate(self.param_groups):
-            self.state[f'group{i}'] = self._init_stats(self.group_stats)
-            for p in pg['params']:
-                self.state[p] = self._init_stats(self.layer_stats)
-                self.state[p].update(self._init_stats(self.channel_stats, p.data.view(p.data.size(0), -1).mean(1)))
-                self.state[p].update(self._init_stats(self.weight_stats, p.data))
-
-    def _set_bufs(self, p, stats, pg, val=None):
-        d = self.state[p]
-        for stat in stats:
-            if stat.buf is not None: d[stat.buf] = stat.update(d[stat.buf], pg[stat.name], val=val, step=d.get('step', None))
-
-    def update_stats(self):
-        for stat in self.global_stats: stat.new_step()
-        for i,pg in enumerate(self.param_groups):
-            for stat in self.group_stats: stat.new_step()
-            for p in pg['params']:
-                if p.grad is not None:
-                    for stat in self.global_stats + self.group_stats: stat.accumulate(p.grad.data)
-                    self._set_bufs(p, self.layer_stats+self.channel_stats+self.weight_stats, pg, p.grad.data)
-            self._set_bufs(f'group{i}', self.group_stats, pg)
-        self._set_bufs('global', self.global_stats, self.param_groups[0])
-
diff --git a/spaces/aliabid94/AutoGPT/autogpt/js/overlay.js b/spaces/aliabid94/AutoGPT/autogpt/js/overlay.js
deleted file mode 100644
index 1c99c72673330b8ea8cf037ef889233f2d4326be..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/js/overlay.js
+++ /dev/null
@@ -1,29 +0,0 @@
-const overlay = document.createElement('div');
-Object.assign(overlay.style, {
-    position: 'fixed',
-    zIndex: 999999,
-    top: 0,
-    left: 0,
-    width: '100%',
-    height: '100%',
-    background: 'rgba(0, 0, 0, 0.7)',
-    color: '#fff',
-    fontSize: '24px',
-    fontWeight: 'bold',
-    display: 'flex',
-    justifyContent: 'center',
-    alignItems: 'center',
-});
-const textContent = document.createElement('div');
-Object.assign(textContent.style, {
-    textAlign: 'center',
-});
-textContent.textContent = 'AutoGPT Analyzing Page';
-overlay.appendChild(textContent);
-document.body.append(overlay);
-document.body.style.overflow = 'hidden';
-let dotCount = 0;
-setInterval(() => {
-    textContent.textContent = 'AutoGPT Analyzing Page' + '.'.repeat(dotCount);
-    dotCount = (dotCount + 1) % 4;
-}, 1000);
diff --git a/spaces/allieannez/NLPContextQASquad2Demo/app.py b/spaces/allieannez/NLPContextQASquad2Demo/app.py
deleted file mode 100644
index ebe8500e5f871271c2b9f7d0b570936b33dbc530..0000000000000000000000000000000000000000
--- a/spaces/allieannez/NLPContextQASquad2Demo/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-title = 'NLP Context QA with Transformers and Roberta Base Squad2'
-
-question1T = "What pressures do teens face?"
-question2T = "What do teens deal with?"
-question3T = "What persistent fears might teens face?"
-
-question1A = "What do half of American adults suffer from?"
-question2A = "What cognitive issues do adults face after COVID?"
-question3A = "What anxiety and changes are faced by adults?"
-
-question1E = "What problems do elderly have due to medical issues?"
-question2E = "What helps mental health for elderly?"
-question3E = "How many older adultsexperience mental disorders?"
-
-context1 = "Pressures teens face: Youth mental health expert have raised concerns about the extreme pressures on children and teens throughout the COVID-19 pandemic. Lingering effects of school closures and COVID-related stressors are key factors in teen stress. Many young people are also dealing with overwhelming pressure to achieve good grades in school or gain admission to elite colleges and universities.  The need to be superstars in sports, the performing arts or other extracurricular activities.  Tough schedules that don't allow enough time for rest, relaxation and unstructured fun.  They deal with Bullying whether in person, via social media or both.  They face persistent fears about climate change, global conflict and other weighty issues.  They may face discrimination based on race, gender, sexual orientation, weight, religion, disability or other factors.  Teens also face problems related to a poverty or lack of money for safe, stable housing and enough nutritious food."
-context2 = "Pressures adults face: Nearly half of Americans surveyed reported recent symptoms of an anxiety or depressive disorder, and 10% feel their mental health needs are not being met. Rates of anxiety, depression, and substance use disorder have increased since the beginning of the pandemic. People who have mental illnesses or disorders and then get COVID-19 are more likely to die than those who don’t have mental illnesses or disorders.  Adults face a number of symptoms related to brain and mental health including cognitive and attention deficits like brain fog, anxiety and depression, seizures, and suicidal behavior.  Stressors caused by the COVID-19 pandemic is not yet fully understood but include changes to daily routines, virtual office and schooling, mask wearing, caregiver absence, loss and grief, and financial instability.  People more likely to experience difficulties include people from racial and ethnic minority groups, mothers and pregnant women, people with finanical or housing insecurity, children, people with disabilities, people with pre-existing mental illnesses or substance use problems and health care workers."
-context3 = "Pressures facing elderly: Anxiety and depression have increased for older adults since the start of the pandemic.  Elders cope with uncertainty better than younger generations, however depression and anxiety have negative impacts on quality of life, function and general health.  Due to medical vulnerability elders face isolation with sacrifices and pain to endure including loneliness.  At least one in four older adults experience mental disorders such as depression, anxiety and dementia.  Number of seniors is expected to double by 2030.  Isolation, affective and anxiety disorders, dementia, and psychosis are common as well as sleep disorders.  Behavioral disorders, cognitive deterioration or confusion states as a result of physical disorders and surgical interventions occur for elderly.  Health care providers including those in primary care can play a key role in promoting mental health by working with mental health professionals, local governments, civil society organizations, families and communities to provide comprehensive mental health care and supportive environments.  Elderly should be encouraged to participate in communities and society while policy makers should ensure health concerns are addressed in national health planning and policies."
-
-# Model (autotrain compatible) https://huggingface.co/deepset/roberta-base-squad2/tree/main
-# Model Card: https://huggingface.co/deepset/roberta-base-squad2
-model_name = "deepset/roberta-base-squad2"
-question_answerer = pipeline("question-answering", model=model_name, tokenizer=model_name)
-
-interface = gr.Interface.from_pipeline(question_answerer,
-    title = title,
-    theme = "peach",
-    examples = [
-        [context1, question1T],[context1, question2T],[context1, question3T],
-        [context2, question1A],[context2, question2A],[context2, question3A],
-        [context3, question1E],[context3, question2E],[context3, question3E]
-    ]).launch()
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test178/README.md b/spaces/allknowingroger/Image-Models-Test178/README.md
deleted file mode 100644
index f91e4b31ab345f987b425de029c057bfb69d9e1b..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test178/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: More Image Models
-emoji: 😻
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: true
-duplicated_from: allknowingroger/Image-Models-Test
----
-
-<!--Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference-->
\ No newline at end of file
diff --git a/spaces/allknowingroger/text-generation-webui-space-1/extensions/silero_tts/script.py b/spaces/allknowingroger/text-generation-webui-space-1/extensions/silero_tts/script.py
deleted file mode 100644
index f611dc27b7480cd357b77c0c407fcc2bd6df2679..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/text-generation-webui-space-1/extensions/silero_tts/script.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import time
-from pathlib import Path
-
-import gradio as gr
-import torch
-
-import modules.chat as chat
-import modules.shared as shared
-
-torch._C._jit_set_profiling_mode(False)
-
-params = {
-    'activate': True,
-    'speaker': 'en_56',
-    'language': 'en',
-    'model_id': 'v3_en',
-    'sample_rate': 48000,
-    'device': 'cpu',
-    'show_text': False,
-    'autoplay': True,
-    'voice_pitch': 'medium',
-    'voice_speed': 'medium',
-}
-
-current_params = params.copy()
-voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115']
-voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high']
-voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast']
-
-# Used for making text xml compatible, needed for voice pitch and speed control
-table = str.maketrans({
-    "<": "&lt;",
-    ">": "&gt;",
-    "&": "&amp;",
-    "'": "&apos;",
-    '"': "&quot;",
-})
-
-def xmlesc(txt):
-    return txt.translate(table)
-
-def load_model():
-    model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id'])
-    model.to(params['device'])
-    return model
-model = load_model()
-
-def remove_surrounded_chars(string):
-    new_string = ""
-    in_star = False
-    for char in string:
-        if char == '*':
-            in_star = not in_star
-        elif not in_star:
-            new_string += char
-    return new_string
-
-def remove_tts_from_history(name1, name2):
-    for i, entry in enumerate(shared.history['internal']):
-        shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]]
-    return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
-
-def toggle_text_in_history(name1, name2):
-    for i, entry in enumerate(shared.history['visible']):
-        visible_reply = entry[1]
-        if visible_reply.startswith('<audio'):
-            if params['show_text']:
-                reply = shared.history['internal'][i][1]
-                shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>\n\n{reply}"]
-            else:
-                shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('</audio>')[0]}</audio>"]
-    return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character)
-
-def input_modifier(string):
-    """
-    This function is applied to your text inputs before
-    they are fed into the model.
-    """
-
-    # Remove autoplay from the last reply
-    if (shared.args.chat or shared.args.cai_chat) and len(shared.history['internal']) > 0:
-        shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>','controls>')]
-
-    shared.processing_message = "*Is recording a voice message...*"
-    return string
-
-def output_modifier(string):
-    """
-    This function is applied to the model outputs.
-    """
-
-    global model, current_params
-
-    for i in params:
-        if params[i] != current_params[i]:
-            model = load_model()
-            current_params = params.copy()
-            break
-
-    if params['activate'] == False:
-        return string
-
-    original_string = string
-    string = remove_surrounded_chars(string)
-    string = string.replace('"', '')
-    string = string.replace('“', '')
-    string = string.replace('\n', ' ')
-    string = string.strip()
-
-    if string == '':
-        string = '*Empty reply, try regenerating*'
-    else:
-        output_file = Path(f'extensions/silero_tts/outputs/{shared.character}_{int(time.time())}.wav')
-        prosody = '<prosody rate="{}" pitch="{}">'.format(params['voice_speed'], params['voice_pitch'])
-        silero_input = f'<speak>{prosody}{xmlesc(string)}</prosody></speak>'
-        model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file))
-
-        autoplay = 'autoplay' if params['autoplay'] else ''
-        string = f'<audio src="file/{output_file.as_posix()}" controls {autoplay}></audio>'
-        if params['show_text']:
-            string += f'\n\n{original_string}'
-
-    shared.processing_message = "*Is typing...*"
-    return string
-
-def bot_prefix_modifier(string):
-    """
-    This function is only applied in chat mode. It modifies
-    the prefix text for the Bot and can be used to bias its
-    behavior.
-    """
-
-    return string
-
-def ui():
-    # Gradio elements
-    with gr.Accordion("Silero TTS"):
-        with gr.Row():
-            activate = gr.Checkbox(value=params['activate'], label='Activate TTS')
-            autoplay = gr.Checkbox(value=params['autoplay'], label='Play TTS automatically')
-        show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player')
-        voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice')
-        with gr.Row():
-            v_pitch = gr.Dropdown(value=params['voice_pitch'], choices=voice_pitches, label='Voice pitch')
-            v_speed = gr.Dropdown(value=params['voice_speed'], choices=voice_speeds, label='Voice speed')
-        with gr.Row():
-            convert = gr.Button('Permanently replace audios with the message texts')
-            convert_cancel = gr.Button('Cancel', visible=False)
-            convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False)
-
-    # Convert history with confirmation
-    convert_arr = [convert_confirm, convert, convert_cancel]
-    convert.click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr)
-    convert_confirm.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
-    convert_confirm.click(remove_tts_from_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
-    convert_confirm.click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
-    convert_cancel.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr)
-
-    # Toggle message text in history
-    show_text.change(lambda x: params.update({"show_text": x}), show_text, None)
-    show_text.change(toggle_text_in_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'])
-    show_text.change(lambda : chat.save_history(timestamp=False), [], [], show_progress=False)
-
-    # Event functions to update the parameters in the backend
-    activate.change(lambda x: params.update({"activate": x}), activate, None)
-    autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None)
-    voice.change(lambda x: params.update({"speaker": x}), voice, None)
-    v_pitch.change(lambda x: params.update({"voice_pitch": x}), v_pitch, None)
-    v_speed.change(lambda x: params.update({"voice_speed": x}), v_speed, None)
diff --git a/spaces/amanatid/Adi_The_ArxivGPT_with_Voice/faq.py b/spaces/amanatid/Adi_The_ArxivGPT_with_Voice/faq.py
deleted file mode 100644
index 59592c5dd7088da2edc1eedd05a12b04e307735d..0000000000000000000000000000000000000000
--- a/spaces/amanatid/Adi_The_ArxivGPT_with_Voice/faq.py
+++ /dev/null
@@ -1,31 +0,0 @@
-# flake8: noqa
-import streamlit as st
-
-
-def faq():
-    st.markdown(
-        """
-# FAQ
-## How does ArxivGPT works work?
-When you  load do it will be divided into smaller chunks 
-and stored in a special type of database called a vector index 
-that allows for semantic search and retrieval.
-
-When you ask a question, ArxivGPT will search through the
-pdf chunks and find the most relevant ones using the vector index.
-Then, it will use the powerful language model GPT4 to generate a final answer.
-
-## Why does ArxivGPT take time to index my document?
-The reason is the following one. In the case of a free OpenAI API key
-takes time to index the loaded pdf files since a free API key has a 
-restricted [rate limits](https://platform.openai.com/docs/guides/rate-limits/overview).
-To make  the process fast, you can use a paid API key.
-
-
-## How accurate is  ArxivGPT?
-To our experience and our tests, it seems impressive accurate but keep in mind the 
-following since GPT-4 is language model  is keen to mistakes. It is 
-based on semantic search  and extracts the most relevant chuncks from the pdf 
-files. 
-"""
-    )
diff --git a/spaces/anaclaudia13ct/insect_detection/models/yolo.py b/spaces/anaclaudia13ct/insect_detection/models/yolo.py
deleted file mode 100644
index ed21c067ee9337bf534bfc908574362a61ad3207..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/models/yolo.py
+++ /dev/null
@@ -1,391 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-YOLO-specific modules
-
-Usage:
-    $ python models/yolo.py --cfg yolov5s.yaml
-"""
-
-import argparse
-import contextlib
-import os
-import platform
-import sys
-from copy import deepcopy
-from pathlib import Path
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[1]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-if platform.system() != 'Windows':
-    ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import *
-from models.experimental import *
-from utils.autoanchor import check_anchor_order
-from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
-from utils.plots import feature_visualization
-from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
-                               time_sync)
-
-try:
-    import thop  # for FLOPs computation
-except ImportError:
-    thop = None
-
-
-class Detect(nn.Module):
-    # YOLOv5 Detect head for detection models
-    stride = None  # strides computed during build
-    dynamic = False  # force grid reconstruction
-    export = False  # export mode
-
-    def __init__(self, nc=80, anchors=(), ch=(), inplace=True):  # detection layer
-        super().__init__()
-        self.nc = nc  # number of classes
-        self.no = nc + 5  # number of outputs per anchor
-        self.nl = len(anchors)  # number of detection layers
-        self.na = len(anchors[0]) // 2  # number of anchors
-        self.grid = [torch.empty(0) for _ in range(self.nl)]  # init grid
-        self.anchor_grid = [torch.empty(0) for _ in range(self.nl)]  # init anchor grid
-        self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2))  # shape(nl,na,2)
-        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
-        self.inplace = inplace  # use inplace ops (e.g. slice assignment)
-
-    def forward(self, x):
-        z = []  # inference output
-        for i in range(self.nl):
-            x[i] = self.m[i](x[i])  # conv
-            bs, _, ny, nx = x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
-            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
-
-            if not self.training:  # inference
-                if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
-                    self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
-
-                if isinstance(self, Segment):  # (boxes + masks)
-                    xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
-                    xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i]  # xy
-                    wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i]  # wh
-                    y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
-                else:  # Detect (boxes only)
-                    xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
-                    xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
-                    wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
-                    y = torch.cat((xy, wh, conf), 4)
-                z.append(y.view(bs, self.na * nx * ny, self.no))
-
-        return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
-
-    def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
-        d = self.anchors[i].device
-        t = self.anchors[i].dtype
-        shape = 1, self.na, ny, nx, 2  # grid shape
-        y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
-        yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x)  # torch>=0.7 compatibility
-        grid = torch.stack((xv, yv), 2).expand(shape) - 0.5  # add grid offset, i.e. y = 2.0 * x - 0.5
-        anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
-        return grid, anchor_grid
-
-
-class Segment(Detect):
-    # YOLOv5 Segment head for segmentation models
-    def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
-        super().__init__(nc, anchors, ch, inplace)
-        self.nm = nm  # number of masks
-        self.npr = npr  # number of protos
-        self.no = 5 + nc + self.nm  # number of outputs per anchor
-        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv
-        self.proto = Proto(ch[0], self.npr, self.nm)  # protos
-        self.detect = Detect.forward
-
-    def forward(self, x):
-        p = self.proto(x[0])
-        x = self.detect(self, x)
-        return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])
-
-
-class BaseModel(nn.Module):
-    # YOLOv5 base model
-    def forward(self, x, profile=False, visualize=False):
-        return self._forward_once(x, profile, visualize)  # single-scale inference, train
-
-    def _forward_once(self, x, profile=False, visualize=False):
-        y, dt = [], []  # outputs
-        for m in self.model:
-            if m.f != -1:  # if not from previous layer
-                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
-            if profile:
-                self._profile_one_layer(m, x, dt)
-            x = m(x)  # run
-            y.append(x if m.i in self.save else None)  # save output
-            if visualize:
-                feature_visualization(x, m.type, m.i, save_dir=visualize)
-        return x
-
-    def _profile_one_layer(self, m, x, dt):
-        c = m == self.model[-1]  # is final layer, copy input as inplace fix
-        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
-        t = time_sync()
-        for _ in range(10):
-            m(x.copy() if c else x)
-        dt.append((time_sync() - t) * 100)
-        if m == self.model[0]:
-            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
-        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
-        if c:
-            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")
-
-    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
-        LOGGER.info('Fusing layers... ')
-        for m in self.model.modules():
-            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
-                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
-                delattr(m, 'bn')  # remove batchnorm
-                m.forward = m.forward_fuse  # update forward
-        self.info()
-        return self
-
-    def info(self, verbose=False, img_size=640):  # print model information
-        model_info(self, verbose, img_size)
-
-    def _apply(self, fn):
-        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
-        self = super()._apply(fn)
-        m = self.model[-1]  # Detect()
-        if isinstance(m, (Detect, Segment)):
-            m.stride = fn(m.stride)
-            m.grid = list(map(fn, m.grid))
-            if isinstance(m.anchor_grid, list):
-                m.anchor_grid = list(map(fn, m.anchor_grid))
-        return self
-
-
-class DetectionModel(BaseModel):
-    # YOLOv5 detection model
-    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
-        super().__init__()
-        if isinstance(cfg, dict):
-            self.yaml = cfg  # model dict
-        else:  # is *.yaml
-            import yaml  # for torch hub
-            self.yaml_file = Path(cfg).name
-            with open(cfg, encoding='ascii', errors='ignore') as f:
-                self.yaml = yaml.safe_load(f)  # model dict
-
-        # Define model
-        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
-        if nc and nc != self.yaml['nc']:
-            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
-            self.yaml['nc'] = nc  # override yaml value
-        if anchors:
-            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
-            self.yaml['anchors'] = round(anchors)  # override yaml value
-        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
-        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
-        self.inplace = self.yaml.get('inplace', True)
-
-        # Build strides, anchors
-        m = self.model[-1]  # Detect()
-        if isinstance(m, (Detect, Segment)):
-            s = 256  # 2x min stride
-            m.inplace = self.inplace
-            forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
-            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
-            check_anchor_order(m)
-            m.anchors /= m.stride.view(-1, 1, 1)
-            self.stride = m.stride
-            self._initialize_biases()  # only run once
-
-        # Init weights, biases
-        initialize_weights(self)
-        self.info()
-        LOGGER.info('')
-
-    def forward(self, x, augment=False, profile=False, visualize=False):
-        if augment:
-            return self._forward_augment(x)  # augmented inference, None
-        return self._forward_once(x, profile, visualize)  # single-scale inference, train
-
-    def _forward_augment(self, x):
-        img_size = x.shape[-2:]  # height, width
-        s = [1, 0.83, 0.67]  # scales
-        f = [None, 3, None]  # flips (2-ud, 3-lr)
-        y = []  # outputs
-        for si, fi in zip(s, f):
-            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
-            yi = self._forward_once(xi)[0]  # forward
-            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
-            yi = self._descale_pred(yi, fi, si, img_size)
-            y.append(yi)
-        y = self._clip_augmented(y)  # clip augmented tails
-        return torch.cat(y, 1), None  # augmented inference, train
-
-    def _descale_pred(self, p, flips, scale, img_size):
-        # de-scale predictions following augmented inference (inverse operation)
-        if self.inplace:
-            p[..., :4] /= scale  # de-scale
-            if flips == 2:
-                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
-            elif flips == 3:
-                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
-        else:
-            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
-            if flips == 2:
-                y = img_size[0] - y  # de-flip ud
-            elif flips == 3:
-                x = img_size[1] - x  # de-flip lr
-            p = torch.cat((x, y, wh, p[..., 4:]), -1)
-        return p
-
-    def _clip_augmented(self, y):
-        # Clip YOLOv5 augmented inference tails
-        nl = self.model[-1].nl  # number of detection layers (P3-P5)
-        g = sum(4 ** x for x in range(nl))  # grid points
-        e = 1  # exclude layer count
-        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
-        y[0] = y[0][:, :-i]  # large
-        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
-        y[-1] = y[-1][:, i:]  # small
-        return y
-
-    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
-        # https://arxiv.org/abs/1708.02002 section 3.3
-        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
-        m = self.model[-1]  # Detect() module
-        for mi, s in zip(m.m, m.stride):  # from
-            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
-            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
-            b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum())  # cls
-            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
-
-
-Model = DetectionModel  # retain YOLOv5 'Model' class for backwards compatibility
-
-
-class SegmentationModel(DetectionModel):
-    # YOLOv5 segmentation model
-    def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None):
-        super().__init__(cfg, ch, nc, anchors)
-
-
-class ClassificationModel(BaseModel):
-    # YOLOv5 classification model
-    def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):  # yaml, model, number of classes, cutoff index
-        super().__init__()
-        self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
-
-    def _from_detection_model(self, model, nc=1000, cutoff=10):
-        # Create a YOLOv5 classification model from a YOLOv5 detection model
-        if isinstance(model, DetectMultiBackend):
-            model = model.model  # unwrap DetectMultiBackend
-        model.model = model.model[:cutoff]  # backbone
-        m = model.model[-1]  # last layer
-        ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels  # ch into module
-        c = Classify(ch, nc)  # Classify()
-        c.i, c.f, c.type = m.i, m.f, 'models.common.Classify'  # index, from, type
-        model.model[-1] = c  # replace
-        self.model = model.model
-        self.stride = model.stride
-        self.save = []
-        self.nc = nc
-
-    def _from_yaml(self, cfg):
-        # Create a YOLOv5 classification model from a *.yaml file
-        self.model = None
-
-
-def parse_model(d, ch):  # model_dict, input_channels(3)
-    # Parse a YOLOv5 model.yaml dictionary
-    LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
-    anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
-    if act:
-        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
-        LOGGER.info(f"{colorstr('activation:')} {act}")  # print
-    na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
-    no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
-
-    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
-        m = eval(m) if isinstance(m, str) else m  # eval strings
-        for j, a in enumerate(args):
-            with contextlib.suppress(NameError):
-                args[j] = eval(a) if isinstance(a, str) else a  # eval strings
-
-        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
-        if m in {
-                Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
-                BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
-            c1, c2 = ch[f], args[0]
-            if c2 != no:  # if not output
-                c2 = make_divisible(c2 * gw, 8)
-
-            args = [c1, c2, *args[1:]]
-            if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
-                args.insert(2, n)  # number of repeats
-                n = 1
-        elif m is nn.BatchNorm2d:
-            args = [ch[f]]
-        elif m is Concat:
-            c2 = sum(ch[x] for x in f)
-        # TODO: channel, gw, gd
-        elif m in {Detect, Segment}:
-            args.append([ch[x] for x in f])
-            if isinstance(args[1], int):  # number of anchors
-                args[1] = [list(range(args[1] * 2))] * len(f)
-            if m is Segment:
-                args[3] = make_divisible(args[3] * gw, 8)
-        elif m is Contract:
-            c2 = ch[f] * args[0] ** 2
-        elif m is Expand:
-            c2 = ch[f] // args[0] ** 2
-        else:
-            c2 = ch[f]
-
-        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
-        t = str(m)[8:-2].replace('__main__.', '')  # module type
-        np = sum(x.numel() for x in m_.parameters())  # number params
-        m_.i, m_.f, m_.type, m_.np = i, f, t, np  # attach index, 'from' index, type, number params
-        LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f}  {t:<40}{str(args):<30}')  # print
-        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
-        layers.append(m_)
-        if i == 0:
-            ch = []
-        ch.append(c2)
-    return nn.Sequential(*layers), sorted(save)
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
-    parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--profile', action='store_true', help='profile model speed')
-    parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer')
-    parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
-    opt = parser.parse_args()
-    opt.cfg = check_yaml(opt.cfg)  # check YAML
-    print_args(vars(opt))
-    device = select_device(opt.device)
-
-    # Create model
-    im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
-    model = Model(opt.cfg).to(device)
-
-    # Options
-    if opt.line_profile:  # profile layer by layer
-        model(im, profile=True)
-
-    elif opt.profile:  # profile forward-backward
-        results = profile(input=im, ops=[model], n=3)
-
-    elif opt.test:  # test all models
-        for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
-            try:
-                _ = Model(cfg)
-            except Exception as e:
-                print(f'Error in {cfg}: {e}')
-
-    else:  # report fused model summary
-        model.fuse()
diff --git a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/css/options.css b/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/css/options.css
deleted file mode 100644
index fb015a54e0a7f7ac521517357d812c994621592e..0000000000000000000000000000000000000000
--- a/spaces/andryMLOPS/ASTA-GPT-3.8_web_ui/client/css/options.css
+++ /dev/null
@@ -1,10 +0,0 @@
-.options-container {
-	display: flex;
-	flex-wrap: wrap;
-}
-
-@media screen and (max-width: 990px) {
-	.options-container {
-		justify-content: space-between;
-	}
-}
diff --git a/spaces/antonovmaxim/text-generation-webui-space/extensions/sd_api_pictures/script.py b/spaces/antonovmaxim/text-generation-webui-space/extensions/sd_api_pictures/script.py
deleted file mode 100644
index 949531c9ea3578c67bad7be7fe694836e04ef03c..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/extensions/sd_api_pictures/script.py
+++ /dev/null
@@ -1,332 +0,0 @@
-import base64
-import io
-import re
-import time
-from datetime import date
-from pathlib import Path
-
-import gradio as gr
-import requests
-import torch
-from PIL import Image
-
-import modules.shared as shared
-from modules.models import reload_model, unload_model
-
-torch._C._jit_set_profiling_mode(False)
-
-# parameters which can be customized in settings.json of webui
-params = {
-    'address': 'http://127.0.0.1:7860',
-    'mode': 0,  # modes of operation: 0 (Manual only), 1 (Immersive/Interactive - looks for words to trigger), 2 (Picturebook Adventure - Always on)
-    'manage_VRAM': False,
-    'save_img': False,
-    'SD_model': 'NeverEndingDream',  # not used right now
-    'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful',
-    'negative_prompt': '(worst quality, low quality:1.3)',
-    'width': 512,
-    'height': 512,
-    'denoising_strength': 0.61,
-    'restore_faces': False,
-    'enable_hr': False,
-    'hr_upscaler': 'ESRGAN_4x',
-    'hr_scale': '1.0',
-    'seed': -1,
-    'sampler_name': 'DDIM',
-    'steps': 32,
-    'cfg_scale': 7
-}
-
-
-def give_VRAM_priority(actor):
-    global shared, params
-
-    if actor == 'SD':
-        unload_model()
-        print("Requesting Auto1111 to re-load last checkpoint used...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
-        response.raise_for_status()
-
-    elif actor == 'LLM':
-        print("Requesting Auto1111 to vacate VRAM...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
-        response.raise_for_status()
-        reload_model()
-
-    elif actor == 'set':
-        print("VRAM mangement activated -- requesting Auto1111 to vacate VRAM...")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='')
-        response.raise_for_status()
-
-    elif actor == 'reset':
-        print("VRAM mangement deactivated -- requesting Auto1111 to reload checkpoint")
-        response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='')
-        response.raise_for_status()
-
-    else:
-        raise RuntimeError(f'Managing VRAM: "{actor}" is not a known state!')
-
-    response.raise_for_status()
-    del response
-
-
-if params['manage_VRAM']:
-    give_VRAM_priority('set')
-
-samplers = ['DDIM', 'DPM++ 2M Karras']  # TODO: get the availible samplers with http://{address}}/sdapi/v1/samplers
-SD_models = ['NeverEndingDream']  # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select
-
-picture_response = False  # specifies if the next model response should appear as a picture
-
-
-def remove_surrounded_chars(string):
-    # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR
-    # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string'
-    return re.sub('\*[^\*]*?(\*|$)', '', string)
-
-
-def triggers_are_in(string):
-    string = remove_surrounded_chars(string)
-    # regex searches for send|main|message|me (at the end of the word) followed by
-    # a whole word of image|pic|picture|photo|snap|snapshot|selfie|meme(s),
-    # (?aims) are regex parser flags
-    return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string))
-
-
-def state_modifier(state):
-    if picture_response:
-        state['stream'] = False
-
-    return state
-
-
-def input_modifier(string):
-    """
-    This function is applied to your text inputs before
-    they are fed into the model.
-    """
-
-    global params
-
-    if not params['mode'] == 1:  # if not in immersive/interactive mode, do nothing
-        return string
-
-    if triggers_are_in(string):  # if we're in it, check for trigger words
-        toggle_generation(True)
-        string = string.lower()
-        if "of" in string:
-            subject = string.split('of', 1)[1]  # subdivide the string once by the first 'of' instance and get what's coming after it
-            string = "Please provide a detailed and vivid description of " + subject
-        else:
-            string = "Please provide a detailed description of your appearance, your surroundings and what you are doing right now"
-
-    return string
-
-# Get and save the Stable Diffusion-generated picture
-def get_SD_pictures(description):
-    global params
-
-    if params['manage_VRAM']:
-        give_VRAM_priority('SD')
-
-    payload = {
-        "prompt": params['prompt_prefix'] + description,
-        "seed": params['seed'],
-        "sampler_name": params['sampler_name'],
-        "enable_hr": params['enable_hr'],
-        "hr_scale": params['hr_scale'],
-        "hr_upscaler": params['hr_upscaler'],
-        "denoising_strength": params['denoising_strength'],
-        "steps": params['steps'],
-        "cfg_scale": params['cfg_scale'],
-        "width": params['width'],
-        "height": params['height'],
-        "restore_faces": params['restore_faces'],
-        "override_settings_restore_afterwards": True,
-        "negative_prompt": params['negative_prompt']
-    }
-
-    print(f'Prompting the image generator via the API on {params["address"]}...')
-    response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload)
-    response.raise_for_status()
-    r = response.json()
-
-    visible_result = ""
-    for img_str in r['images']:
-        if params['save_img']:
-            img_data = base64.b64decode(img_str)
-
-            variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}'
-            output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png')
-            output_file.parent.mkdir(parents=True, exist_ok=True)
-
-            with open(output_file.as_posix(), 'wb') as f:
-                f.write(img_data)
-
-            visible_result = visible_result + f'<img src="/file/extensions/sd_api_pictures/outputs/{variadic}.png" alt="{description}" style="max-width: unset; max-height: unset;">\n'
-        else:
-            image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0])))
-            # lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history
-            image.thumbnail((300, 300))
-            buffered = io.BytesIO()
-            image.save(buffered, format="JPEG")
-            buffered.seek(0)
-            image_bytes = buffered.getvalue()
-            img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode()
-            visible_result = visible_result + f'<img src="{img_str}" alt="{description}">\n'
-
-    if params['manage_VRAM']:
-        give_VRAM_priority('LLM')
-
-    return visible_result
-
-# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
-# and replace it with 'text' for the purposes of logging?
-def output_modifier(string):
-    """
-    This function is applied to the model outputs.
-    """
-
-    global picture_response, params
-
-    if not picture_response:
-        return string
-
-    string = remove_surrounded_chars(string)
-    string = string.replace('"', '')
-    string = string.replace('“', '')
-    string = string.replace('\n', ' ')
-    string = string.strip()
-
-    if string == '':
-        string = 'no viable description in reply, try regenerating'
-        return string
-
-    text = ""
-    if (params['mode'] < 2):
-        toggle_generation(False)
-        text = f'*Sends a picture which portrays: “{string}”*'
-    else:
-        text = string
-
-    string = get_SD_pictures(string) + "\n" + text
-
-    return string
-
-
-def bot_prefix_modifier(string):
-    """
-    This function is only applied in chat mode. It modifies
-    the prefix text for the Bot and can be used to bias its
-    behavior.
-    """
-
-    return string
-
-
-def toggle_generation(*args):
-    global picture_response, shared
-
-    if not args:
-        picture_response = not picture_response
-    else:
-        picture_response = args[0]
-
-    shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*"
-
-
-def filter_address(address):
-    address = address.strip()
-    # address = re.sub('http(s)?:\/\/|\/$','',address) # remove starting http:// OR https:// OR trailing slash
-    address = re.sub('\/$', '', address)  # remove trailing /s
-    if not address.startswith('http'):
-        address = 'http://' + address
-    return address
-
-
-def SD_api_address_update(address):
-
-    global params
-
-    msg = "✔️ SD API is found on:"
-    address = filter_address(address)
-    params.update({"address": address})
-    try:
-        response = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models')
-        response.raise_for_status()
-        # r = response.json()
-    except:
-        msg = "❌ No SD API endpoint on:"
-
-    return gr.Textbox.update(label=msg)
-
-
-def custom_css():
-    path_to_css = Path(__file__).parent.resolve() / 'style.css'
-    return open(path_to_css, 'r').read()
-
-
-def ui():
-
-    # Gradio elements
-    # gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title
-    with gr.Accordion("Parameters", open=True, elem_classes="SDAP"):
-        with gr.Row():
-            address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address')
-            modes_list = ["Manual", "Immersive/Interactive", "Picturebook/Adventure"]
-            mode = gr.Dropdown(modes_list, value=modes_list[params['mode']], label="Mode of operation", type="index")
-            with gr.Column(scale=1, min_width=300):
-                manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM')
-                save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat')
-
-            force_pic = gr.Button("Force the picture response")
-            suppr_pic = gr.Button("Suppress the picture response")
-
-        with gr.Accordion("Generation parameters", open=False):
-            prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)')
-            negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt')
-            with gr.Row():
-                with gr.Column():
-                    width = gr.Slider(256, 768, value=params['width'], step=64, label='Width')
-                    height = gr.Slider(256, 768, value=params['height'], step=64, label='Height')
-                with gr.Column():
-                    sampler_name = gr.Textbox(placeholder=params['sampler_name'], value=params['sampler_name'], label='Sampling method', elem_id="sampler_box")
-                    steps = gr.Slider(1, 150, value=params['steps'], step=1, label="Sampling steps")
-            with gr.Row():
-                seed = gr.Number(label="Seed", value=params['seed'], elem_id="seed_box")
-                cfg_scale = gr.Number(label="CFG Scale", value=params['cfg_scale'], elem_id="cfg_box")
-                with gr.Column() as hr_options:
-                    restore_faces = gr.Checkbox(value=params['restore_faces'], label='Restore faces')
-                    enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix')
-            with gr.Row(visible=params['enable_hr'], elem_classes="hires_opts") as hr_options:
-                hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by')
-                denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength')
-                hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler')
-
-    # Event functions to update the parameters in the backend
-    address.change(lambda x: params.update({"address": filter_address(x)}), address, None)
-    mode.select(lambda x: params.update({"mode": x}), mode, None)
-    mode.select(lambda x: toggle_generation(x > 1), inputs=mode, outputs=None)
-    manage_VRAM.change(lambda x: params.update({"manage_VRAM": x}), manage_VRAM, None)
-    manage_VRAM.change(lambda x: give_VRAM_priority('set' if x else 'reset'), inputs=manage_VRAM, outputs=None)
-    save_img.change(lambda x: params.update({"save_img": x}), save_img, None)
-
-    address.submit(fn=SD_api_address_update, inputs=address, outputs=address)
-    prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None)
-    negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None)
-    width.change(lambda x: params.update({"width": x}), width, None)
-    height.change(lambda x: params.update({"height": x}), height, None)
-    hr_scale.change(lambda x: params.update({"hr_scale": x}), hr_scale, None)
-    denoising_strength.change(lambda x: params.update({"denoising_strength": x}), denoising_strength, None)
-    restore_faces.change(lambda x: params.update({"restore_faces": x}), restore_faces, None)
-    hr_upscaler.change(lambda x: params.update({"hr_upscaler": x}), hr_upscaler, None)
-    enable_hr.change(lambda x: params.update({"enable_hr": x}), enable_hr, None)
-    enable_hr.change(lambda x: hr_options.update(visible=params["enable_hr"]), enable_hr, hr_options)
-
-    sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None)
-    steps.change(lambda x: params.update({"steps": x}), steps, None)
-    seed.change(lambda x: params.update({"seed": x}), seed, None)
-    cfg_scale.change(lambda x: params.update({"cfg_scale": x}), cfg_scale, None)
-
-    force_pic.click(lambda x: toggle_generation(True), inputs=force_pic, outputs=None)
-    suppr_pic.click(lambda x: toggle_generation(False), inputs=suppr_pic, outputs=None)
diff --git a/spaces/antonovmaxim/text-generation-webui-space/extensions/sd_api_pictures/style.css b/spaces/antonovmaxim/text-generation-webui-space/extensions/sd_api_pictures/style.css
deleted file mode 100644
index a10e6397a1a0f5e0fbd98b98d65a572a2290807b..0000000000000000000000000000000000000000
--- a/spaces/antonovmaxim/text-generation-webui-space/extensions/sd_api_pictures/style.css
+++ /dev/null
@@ -1,25 +0,0 @@
-/* Align the elements for SD_api_picture extension */
-.SDAP #sampler_box {
-  padding-top: var(--spacing-sm);
-  padding-bottom: var(--spacing-sm);
-}
-
-.SDAP #seed_box,
-.SDAP #cfg_box {
-  padding-top: var(--spacing-md);
-}
-
-.SDAP #sampler_box span,
-.SDAP #seed_box span,
-.SDAP #cfg_box span{
-  margin-bottom: var(--spacing-sm);
-}
-
-.SDAP svg.dropdown-arrow {
-  flex-shrink: 0 !important;
-  margin: 0px !important;
-}
-
-.SDAP .hires_opts input[type="number"] {
-  width: 6em !important;
-}
diff --git a/spaces/apsys/hetfit/nets/opti/__init__.py b/spaces/apsys/hetfit/nets/opti/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/arnavkartikeya/SCRIPture-final/pretrain.py b/spaces/arnavkartikeya/SCRIPture-final/pretrain.py
deleted file mode 100644
index c9490ec8eb8ff5f074b5772ada55cd27ec673a12..0000000000000000000000000000000000000000
--- a/spaces/arnavkartikeya/SCRIPture-final/pretrain.py
+++ /dev/null
@@ -1,173 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-'''
-import argparse
-import os
-import ruamel_yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.blip_pretrain import blip_pretrain
-import utils
-from utils import warmup_lr_schedule, step_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-
-def train(model, data_loader, optimizer, epoch, device, config):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss_ita', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
-    metric_logger.add_meter('loss_itm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))    
-    metric_logger.add_meter('loss_lm', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
-    
-    header = 'Train Epoch: [{}]'.format(epoch)
-    print_freq = 50   
-
-    if config['laion_path']:
-        data_loader.dataset.reload_laion(epoch)
-    
-    data_loader.sampler.set_epoch(epoch)
-
-    for i, (image, caption) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-        
-        if epoch==0:
-            warmup_lr_schedule(optimizer, i, config['warmup_steps'], config['warmup_lr'], config['init_lr'])
-            
-        optimizer.zero_grad()
-        
-        image = image.to(device,non_blocking=True)
-        
-        # ramp up alpha in the first 2 epochs
-        alpha = config['alpha']*min(1,(epoch*len(data_loader)+i)/(2*len(data_loader))) 
-
-        loss_ita, loss_itm, loss_lm = model(image, caption, alpha = alpha)  
-        loss = loss_ita + loss_itm + loss_lm  
-
-        loss.backward()
-        optimizer.step()    
-
-        metric_logger.update(loss_ita=loss_ita.item())
-        metric_logger.update(loss_itm=loss_itm.item())
-        metric_logger.update(loss_lm=loss_lm.item())
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])  
-
-        
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}  
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)    
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating dataset")
-    datasets = [create_dataset('pretrain', config, min_scale=0.2)]
-    print('number of training samples: %d'%len(datasets[0]))
-
-    num_tasks = utils.get_world_size()
-    global_rank = utils.get_rank()            
-    samplers = create_sampler(datasets, [True], num_tasks, global_rank)         
-
-    data_loader = create_loader(datasets,samplers,batch_size=[config['batch_size']], num_workers=[4], is_trains=[True], collate_fns=[None])[0]      
-
-    #### Model #### 
-    print("Creating model")
-    model = blip_pretrain(image_size=config['image_size'], vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], 
-                            vit_ckpt_layer=config['vit_ckpt_layer'], queue_size=config['queue_size'])
-
-    model = model.to(device)   
-
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
-    
-    start_epoch = 0
-    if args.checkpoint:    
-        checkpoint = torch.load(args.checkpoint, map_location='cpu') 
-        state_dict = checkpoint['model']    
-        model.load_state_dict(state_dict)    
-        
-        optimizer.load_state_dict(checkpoint['optimizer'])
-        start_epoch = checkpoint['epoch']+1                
-        print('resume checkpoint from %s'%args.checkpoint)    
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-        
-    print("Start training")
-    start_time = time.time()    
-    for epoch in range(start_epoch, config['max_epoch']):
-        
-        step_lr_schedule(optimizer, epoch, config['init_lr'], config['min_lr'], config['lr_decay_rate'])
-                
-        train_stats = train(model, data_loader, optimizer, epoch, device, config) 
-        if utils.is_main_process():  
-            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
-                         'epoch': epoch,
-                        }                     
-            save_obj = {
-                'model': model_without_ddp.state_dict(),
-                'optimizer': optimizer.state_dict(),
-                'config': config,
-                'epoch': epoch,
-            }
-            torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth'%epoch))  
-            
-            with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                f.write(json.dumps(log_stats) + "\n")
-
-        dist.barrier()        
-                
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/pretrain.yaml')
-    parser.add_argument('--output_dir', default='output/Pretrain')  
-    parser.add_argument('--checkpoint', default='')    
-    parser.add_argument('--evaluate', action='store_true')    
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/audio/processor.py b/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/audio/processor.py
deleted file mode 100644
index 4ceb7da4b37f65510c0e7977ba43eb0f8bfde4b6..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/TTS/utils/audio/processor.py
+++ /dev/null
@@ -1,777 +0,0 @@
-from io import BytesIO
-from typing import Dict, Tuple
-
-import librosa
-import numpy as np
-import scipy.io.wavfile
-import scipy.signal
-import soundfile as sf
-
-from TTS.tts.utils.helpers import StandardScaler
-from TTS.utils.audio.numpy_transforms import compute_f0
-
-# pylint: disable=too-many-public-methods
-
-
-class AudioProcessor(object):
-    """Audio Processor for TTS.
-
-    Note:
-        All the class arguments are set to default values to enable a flexible initialization
-        of the class with the model config. They are not meaningful for all the arguments.
-
-    Args:
-        sample_rate (int, optional):
-            target audio sampling rate. Defaults to None.
-
-        resample (bool, optional):
-            enable/disable resampling of the audio clips when the target sampling rate does not match the original sampling rate. Defaults to False.
-
-        num_mels (int, optional):
-            number of melspectrogram dimensions. Defaults to None.
-
-        log_func (int, optional):
-            log exponent used for converting spectrogram aplitude to DB.
-
-        min_level_db (int, optional):
-            minimum db threshold for the computed melspectrograms. Defaults to None.
-
-        frame_shift_ms (int, optional):
-            milliseconds of frames between STFT columns. Defaults to None.
-
-        frame_length_ms (int, optional):
-            milliseconds of STFT window length. Defaults to None.
-
-        hop_length (int, optional):
-            number of frames between STFT columns. Used if ```frame_shift_ms``` is None. Defaults to None.
-
-        win_length (int, optional):
-            STFT window length. Used if ```frame_length_ms``` is None. Defaults to None.
-
-        ref_level_db (int, optional):
-            reference DB level to avoid background noise. In general <20DB corresponds to the air noise. Defaults to None.
-
-        fft_size (int, optional):
-            FFT window size for STFT. Defaults to 1024.
-
-        power (int, optional):
-            Exponent value applied to the spectrogram before GriffinLim. Defaults to None.
-
-        preemphasis (float, optional):
-            Preemphasis coefficient. Preemphasis is disabled if == 0.0. Defaults to 0.0.
-
-        signal_norm (bool, optional):
-            enable/disable signal normalization. Defaults to None.
-
-        symmetric_norm (bool, optional):
-            enable/disable symmetric normalization. If set True normalization is performed in the range [-k, k] else [0, k], Defaults to None.
-
-        max_norm (float, optional):
-            ```k``` defining the normalization range. Defaults to None.
-
-        mel_fmin (int, optional):
-            minimum filter frequency for computing melspectrograms. Defaults to None.
-
-        mel_fmax (int, optional):
-            maximum filter frequency for computing melspectrograms. Defaults to None.
-
-        pitch_fmin (int, optional):
-            minimum filter frequency for computing pitch. Defaults to None.
-
-        pitch_fmax (int, optional):
-            maximum filter frequency for computing pitch. Defaults to None.
-
-        spec_gain (int, optional):
-            gain applied when converting amplitude to DB. Defaults to 20.
-
-        stft_pad_mode (str, optional):
-            Padding mode for STFT. Defaults to 'reflect'.
-
-        clip_norm (bool, optional):
-            enable/disable clipping the our of range values in the normalized audio signal. Defaults to True.
-
-        griffin_lim_iters (int, optional):
-            Number of GriffinLim iterations. Defaults to None.
-
-        do_trim_silence (bool, optional):
-            enable/disable silence trimming when loading the audio signal. Defaults to False.
-
-        trim_db (int, optional):
-            DB threshold used for silence trimming. Defaults to 60.
-
-        do_sound_norm (bool, optional):
-            enable/disable signal normalization. Defaults to False.
-
-        do_amp_to_db_linear (bool, optional):
-            enable/disable amplitude to dB conversion of linear spectrograms. Defaults to True.
-
-        do_amp_to_db_mel (bool, optional):
-            enable/disable amplitude to dB conversion of mel spectrograms. Defaults to True.
-
-        do_rms_norm (bool, optional):
-            enable/disable RMS volume normalization when loading an audio file. Defaults to False.
-
-        db_level (int, optional):
-            dB level used for rms normalization. The range is -99 to 0. Defaults to None.
-
-        stats_path (str, optional):
-            Path to the computed stats file. Defaults to None.
-
-        verbose (bool, optional):
-            enable/disable logging. Defaults to True.
-
-    """
-
-    def __init__(
-        self,
-        sample_rate=None,
-        resample=False,
-        num_mels=None,
-        log_func="np.log10",
-        min_level_db=None,
-        frame_shift_ms=None,
-        frame_length_ms=None,
-        hop_length=None,
-        win_length=None,
-        ref_level_db=None,
-        fft_size=1024,
-        power=None,
-        preemphasis=0.0,
-        signal_norm=None,
-        symmetric_norm=None,
-        max_norm=None,
-        mel_fmin=None,
-        mel_fmax=None,
-        pitch_fmax=None,
-        pitch_fmin=None,
-        spec_gain=20,
-        stft_pad_mode="reflect",
-        clip_norm=True,
-        griffin_lim_iters=None,
-        do_trim_silence=False,
-        trim_db=60,
-        do_sound_norm=False,
-        do_amp_to_db_linear=True,
-        do_amp_to_db_mel=True,
-        do_rms_norm=False,
-        db_level=None,
-        stats_path=None,
-        verbose=True,
-        **_,
-    ):
-        # setup class attributed
-        self.sample_rate = sample_rate
-        self.resample = resample
-        self.num_mels = num_mels
-        self.log_func = log_func
-        self.min_level_db = min_level_db or 0
-        self.frame_shift_ms = frame_shift_ms
-        self.frame_length_ms = frame_length_ms
-        self.ref_level_db = ref_level_db
-        self.fft_size = fft_size
-        self.power = power
-        self.preemphasis = preemphasis
-        self.griffin_lim_iters = griffin_lim_iters
-        self.signal_norm = signal_norm
-        self.symmetric_norm = symmetric_norm
-        self.mel_fmin = mel_fmin or 0
-        self.mel_fmax = mel_fmax
-        self.pitch_fmin = pitch_fmin
-        self.pitch_fmax = pitch_fmax
-        self.spec_gain = float(spec_gain)
-        self.stft_pad_mode = stft_pad_mode
-        self.max_norm = 1.0 if max_norm is None else float(max_norm)
-        self.clip_norm = clip_norm
-        self.do_trim_silence = do_trim_silence
-        self.trim_db = trim_db
-        self.do_sound_norm = do_sound_norm
-        self.do_amp_to_db_linear = do_amp_to_db_linear
-        self.do_amp_to_db_mel = do_amp_to_db_mel
-        self.do_rms_norm = do_rms_norm
-        self.db_level = db_level
-        self.stats_path = stats_path
-        # setup exp_func for db to amp conversion
-        if log_func == "np.log":
-            self.base = np.e
-        elif log_func == "np.log10":
-            self.base = 10
-        else:
-            raise ValueError(" [!] unknown `log_func` value.")
-        # setup stft parameters
-        if hop_length is None:
-            # compute stft parameters from given time values
-            self.hop_length, self.win_length = self._stft_parameters()
-        else:
-            # use stft parameters from config file
-            self.hop_length = hop_length
-            self.win_length = win_length
-        assert min_level_db != 0.0, " [!] min_level_db is 0"
-        assert (
-            self.win_length <= self.fft_size
-        ), f" [!] win_length cannot be larger than fft_size - {self.win_length} vs {self.fft_size}"
-        members = vars(self)
-        if verbose:
-            print(" > Setting up Audio Processor...")
-            for key, value in members.items():
-                print(" | > {}:{}".format(key, value))
-        # create spectrogram utils
-        self.mel_basis = self._build_mel_basis()
-        self.inv_mel_basis = np.linalg.pinv(self._build_mel_basis())
-        # setup scaler
-        if stats_path and signal_norm:
-            mel_mean, mel_std, linear_mean, linear_std, _ = self.load_stats(stats_path)
-            self.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)
-            self.signal_norm = True
-            self.max_norm = None
-            self.clip_norm = None
-            self.symmetric_norm = None
-
-    @staticmethod
-    def init_from_config(config: "Coqpit", verbose=True):
-        if "audio" in config:
-            return AudioProcessor(verbose=verbose, **config.audio)
-        return AudioProcessor(verbose=verbose, **config)
-
-    ### setting up the parameters ###
-    def _build_mel_basis(
-        self,
-    ) -> np.ndarray:
-        """Build melspectrogram basis.
-
-        Returns:
-            np.ndarray: melspectrogram basis.
-        """
-        if self.mel_fmax is not None:
-            assert self.mel_fmax <= self.sample_rate // 2
-        return librosa.filters.mel(
-            sr=self.sample_rate, n_fft=self.fft_size, n_mels=self.num_mels, fmin=self.mel_fmin, fmax=self.mel_fmax
-        )
-
-    def _stft_parameters(
-        self,
-    ) -> Tuple[int, int]:
-        """Compute the real STFT parameters from the time values.
-
-        Returns:
-            Tuple[int, int]: hop length and window length for STFT.
-        """
-        factor = self.frame_length_ms / self.frame_shift_ms
-        assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
-        hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
-        win_length = int(hop_length * factor)
-        return hop_length, win_length
-
-    ### normalization ###
-    def normalize(self, S: np.ndarray) -> np.ndarray:
-        """Normalize values into `[0, self.max_norm]` or `[-self.max_norm, self.max_norm]`
-
-        Args:
-            S (np.ndarray): Spectrogram to normalize.
-
-        Raises:
-            RuntimeError: Mean and variance is computed from incompatible parameters.
-
-        Returns:
-            np.ndarray: Normalized spectrogram.
-        """
-        # pylint: disable=no-else-return
-        S = S.copy()
-        if self.signal_norm:
-            # mean-var scaling
-            if hasattr(self, "mel_scaler"):
-                if S.shape[0] == self.num_mels:
-                    return self.mel_scaler.transform(S.T).T
-                elif S.shape[0] == self.fft_size / 2:
-                    return self.linear_scaler.transform(S.T).T
-                else:
-                    raise RuntimeError(" [!] Mean-Var stats does not match the given feature dimensions.")
-            # range normalization
-            S -= self.ref_level_db  # discard certain range of DB assuming it is air noise
-            S_norm = (S - self.min_level_db) / (-self.min_level_db)
-            if self.symmetric_norm:
-                S_norm = ((2 * self.max_norm) * S_norm) - self.max_norm
-                if self.clip_norm:
-                    S_norm = np.clip(
-                        S_norm, -self.max_norm, self.max_norm  # pylint: disable=invalid-unary-operand-type
-                    )
-                return S_norm
-            else:
-                S_norm = self.max_norm * S_norm
-                if self.clip_norm:
-                    S_norm = np.clip(S_norm, 0, self.max_norm)
-                return S_norm
-        else:
-            return S
-
-    def denormalize(self, S: np.ndarray) -> np.ndarray:
-        """Denormalize spectrogram values.
-
-        Args:
-            S (np.ndarray): Spectrogram to denormalize.
-
-        Raises:
-            RuntimeError: Mean and variance are incompatible.
-
-        Returns:
-            np.ndarray: Denormalized spectrogram.
-        """
-        # pylint: disable=no-else-return
-        S_denorm = S.copy()
-        if self.signal_norm:
-            # mean-var scaling
-            if hasattr(self, "mel_scaler"):
-                if S_denorm.shape[0] == self.num_mels:
-                    return self.mel_scaler.inverse_transform(S_denorm.T).T
-                elif S_denorm.shape[0] == self.fft_size / 2:
-                    return self.linear_scaler.inverse_transform(S_denorm.T).T
-                else:
-                    raise RuntimeError(" [!] Mean-Var stats does not match the given feature dimensions.")
-            if self.symmetric_norm:
-                if self.clip_norm:
-                    S_denorm = np.clip(
-                        S_denorm, -self.max_norm, self.max_norm  # pylint: disable=invalid-unary-operand-type
-                    )
-                S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db / (2 * self.max_norm)) + self.min_level_db
-                return S_denorm + self.ref_level_db
-            else:
-                if self.clip_norm:
-                    S_denorm = np.clip(S_denorm, 0, self.max_norm)
-                S_denorm = (S_denorm * -self.min_level_db / self.max_norm) + self.min_level_db
-                return S_denorm + self.ref_level_db
-        else:
-            return S_denorm
-
-    ### Mean-STD scaling ###
-    def load_stats(self, stats_path: str) -> Tuple[np.array, np.array, np.array, np.array, Dict]:
-        """Loading mean and variance statistics from a `npy` file.
-
-        Args:
-            stats_path (str): Path to the `npy` file containing
-
-        Returns:
-            Tuple[np.array, np.array, np.array, np.array, Dict]: loaded statistics and the config used to
-                compute them.
-        """
-        stats = np.load(stats_path, allow_pickle=True).item()  # pylint: disable=unexpected-keyword-arg
-        mel_mean = stats["mel_mean"]
-        mel_std = stats["mel_std"]
-        linear_mean = stats["linear_mean"]
-        linear_std = stats["linear_std"]
-        stats_config = stats["audio_config"]
-        # check all audio parameters used for computing stats
-        skip_parameters = ["griffin_lim_iters", "stats_path", "do_trim_silence", "ref_level_db", "power"]
-        for key in stats_config.keys():
-            if key in skip_parameters:
-                continue
-            if key not in ["sample_rate", "trim_db"]:
-                assert (
-                    stats_config[key] == self.__dict__[key]
-                ), f" [!] Audio param {key} does not match the value used for computing mean-var stats. {stats_config[key]} vs {self.__dict__[key]}"
-        return mel_mean, mel_std, linear_mean, linear_std, stats_config
-
-    # pylint: disable=attribute-defined-outside-init
-    def setup_scaler(
-        self, mel_mean: np.ndarray, mel_std: np.ndarray, linear_mean: np.ndarray, linear_std: np.ndarray
-    ) -> None:
-        """Initialize scaler objects used in mean-std normalization.
-
-        Args:
-            mel_mean (np.ndarray): Mean for melspectrograms.
-            mel_std (np.ndarray): STD for melspectrograms.
-            linear_mean (np.ndarray): Mean for full scale spectrograms.
-            linear_std (np.ndarray): STD for full scale spectrograms.
-        """
-        self.mel_scaler = StandardScaler()
-        self.mel_scaler.set_stats(mel_mean, mel_std)
-        self.linear_scaler = StandardScaler()
-        self.linear_scaler.set_stats(linear_mean, linear_std)
-
-    ### DB and AMP conversion ###
-    # pylint: disable=no-self-use
-    def _amp_to_db(self, x: np.ndarray) -> np.ndarray:
-        """Convert amplitude values to decibels.
-
-        Args:
-            x (np.ndarray): Amplitude spectrogram.
-
-        Returns:
-            np.ndarray: Decibels spectrogram.
-        """
-        return self.spec_gain * _log(np.maximum(1e-5, x), self.base)
-
-    # pylint: disable=no-self-use
-    def _db_to_amp(self, x: np.ndarray) -> np.ndarray:
-        """Convert decibels spectrogram to amplitude spectrogram.
-
-        Args:
-            x (np.ndarray): Decibels spectrogram.
-
-        Returns:
-            np.ndarray: Amplitude spectrogram.
-        """
-        return _exp(x / self.spec_gain, self.base)
-
-    ### Preemphasis ###
-    def apply_preemphasis(self, x: np.ndarray) -> np.ndarray:
-        """Apply pre-emphasis to the audio signal. Useful to reduce the correlation between neighbouring signal values.
-
-        Args:
-            x (np.ndarray): Audio signal.
-
-        Raises:
-            RuntimeError: Preemphasis coeff is set to 0.
-
-        Returns:
-            np.ndarray: Decorrelated audio signal.
-        """
-        if self.preemphasis == 0:
-            raise RuntimeError(" [!] Preemphasis is set 0.0.")
-        return scipy.signal.lfilter([1, -self.preemphasis], [1], x)
-
-    def apply_inv_preemphasis(self, x: np.ndarray) -> np.ndarray:
-        """Reverse pre-emphasis."""
-        if self.preemphasis == 0:
-            raise RuntimeError(" [!] Preemphasis is set 0.0.")
-        return scipy.signal.lfilter([1], [1, -self.preemphasis], x)
-
-    ### SPECTROGRAMs ###
-    def _linear_to_mel(self, spectrogram: np.ndarray) -> np.ndarray:
-        """Project a full scale spectrogram to a melspectrogram.
-
-        Args:
-            spectrogram (np.ndarray): Full scale spectrogram.
-
-        Returns:
-            np.ndarray: Melspectrogram
-        """
-        return np.dot(self.mel_basis, spectrogram)
-
-    def _mel_to_linear(self, mel_spec: np.ndarray) -> np.ndarray:
-        """Convert a melspectrogram to full scale spectrogram."""
-        return np.maximum(1e-10, np.dot(self.inv_mel_basis, mel_spec))
-
-    def spectrogram(self, y: np.ndarray) -> np.ndarray:
-        """Compute a spectrogram from a waveform.
-
-        Args:
-            y (np.ndarray): Waveform.
-
-        Returns:
-            np.ndarray: Spectrogram.
-        """
-        if self.preemphasis != 0:
-            D = self._stft(self.apply_preemphasis(y))
-        else:
-            D = self._stft(y)
-        if self.do_amp_to_db_linear:
-            S = self._amp_to_db(np.abs(D))
-        else:
-            S = np.abs(D)
-        return self.normalize(S).astype(np.float32)
-
-    def melspectrogram(self, y: np.ndarray) -> np.ndarray:
-        """Compute a melspectrogram from a waveform."""
-        if self.preemphasis != 0:
-            D = self._stft(self.apply_preemphasis(y))
-        else:
-            D = self._stft(y)
-        if self.do_amp_to_db_mel:
-            S = self._amp_to_db(self._linear_to_mel(np.abs(D)))
-        else:
-            S = self._linear_to_mel(np.abs(D))
-        return self.normalize(S).astype(np.float32)
-
-    def inv_spectrogram(self, spectrogram: np.ndarray) -> np.ndarray:
-        """Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
-        S = self.denormalize(spectrogram)
-        S = self._db_to_amp(S)
-        # Reconstruct phase
-        if self.preemphasis != 0:
-            return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
-        return self._griffin_lim(S**self.power)
-
-    def inv_melspectrogram(self, mel_spectrogram: np.ndarray) -> np.ndarray:
-        """Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
-        D = self.denormalize(mel_spectrogram)
-        S = self._db_to_amp(D)
-        S = self._mel_to_linear(S)  # Convert back to linear
-        if self.preemphasis != 0:
-            return self.apply_inv_preemphasis(self._griffin_lim(S**self.power))
-        return self._griffin_lim(S**self.power)
-
-    def out_linear_to_mel(self, linear_spec: np.ndarray) -> np.ndarray:
-        """Convert a full scale linear spectrogram output of a network to a melspectrogram.
-
-        Args:
-            linear_spec (np.ndarray): Normalized full scale linear spectrogram.
-
-        Returns:
-            np.ndarray: Normalized melspectrogram.
-        """
-        S = self.denormalize(linear_spec)
-        S = self._db_to_amp(S)
-        S = self._linear_to_mel(np.abs(S))
-        S = self._amp_to_db(S)
-        mel = self.normalize(S)
-        return mel
-
-    ### STFT and ISTFT ###
-    def _stft(self, y: np.ndarray) -> np.ndarray:
-        """Librosa STFT wrapper.
-
-        Args:
-            y (np.ndarray): Audio signal.
-
-        Returns:
-            np.ndarray: Complex number array.
-        """
-        return librosa.stft(
-            y=y,
-            n_fft=self.fft_size,
-            hop_length=self.hop_length,
-            win_length=self.win_length,
-            pad_mode=self.stft_pad_mode,
-            window="hann",
-            center=True,
-        )
-
-    def _istft(self, y: np.ndarray) -> np.ndarray:
-        """Librosa iSTFT wrapper."""
-        return librosa.istft(y, hop_length=self.hop_length, win_length=self.win_length)
-
-    def _griffin_lim(self, S):
-        angles = np.exp(2j * np.pi * np.random.rand(*S.shape))
-        try:
-            S_complex = np.abs(S).astype(np.complex)
-        except AttributeError:  # np.complex is deprecated since numpy 1.20.0
-            S_complex = np.abs(S).astype(complex)
-        y = self._istft(S_complex * angles)
-        if not np.isfinite(y).all():
-            print(" [!] Waveform is not finite everywhere. Skipping the GL.")
-            return np.array([0.0])
-        for _ in range(self.griffin_lim_iters):
-            angles = np.exp(1j * np.angle(self._stft(y)))
-            y = self._istft(S_complex * angles)
-        return y
-
-    def compute_stft_paddings(self, x, pad_sides=1):
-        """Compute paddings used by Librosa's STFT. Compute right padding (final frame) or both sides padding
-        (first and final frames)"""
-        assert pad_sides in (1, 2)
-        pad = (x.shape[0] // self.hop_length + 1) * self.hop_length - x.shape[0]
-        if pad_sides == 1:
-            return 0, pad
-        return pad // 2, pad // 2 + pad % 2
-
-    def compute_f0(self, x: np.ndarray) -> np.ndarray:
-        """Compute pitch (f0) of a waveform using the same parameters used for computing melspectrogram.
-
-        Args:
-            x (np.ndarray): Waveform.
-
-        Returns:
-            np.ndarray: Pitch.
-
-        Examples:
-            >>> WAV_FILE = filename = librosa.example('vibeace')
-            >>> from TTS.config import BaseAudioConfig
-            >>> from TTS.utils.audio import AudioProcessor
-            >>> conf = BaseAudioConfig(pitch_fmax=640, pitch_fmin=1)
-            >>> ap = AudioProcessor(**conf)
-            >>> wav = ap.load_wav(WAV_FILE, sr=ap.sample_rate)[:5 * ap.sample_rate]
-            >>> pitch = ap.compute_f0(wav)
-        """
-        assert self.pitch_fmax is not None, " [!] Set `pitch_fmax` before caling `compute_f0`."
-        assert self.pitch_fmin is not None, " [!] Set `pitch_fmin` before caling `compute_f0`."
-        # align F0 length to the spectrogram length
-        if len(x) % self.hop_length == 0:
-            x = np.pad(x, (0, self.hop_length // 2), mode=self.stft_pad_mode)
-
-        f0 = compute_f0(
-            x=x,
-            pitch_fmax=self.pitch_fmax,
-            pitch_fmin=self.pitch_fmin,
-            hop_length=self.hop_length,
-            win_length=self.win_length,
-            sample_rate=self.sample_rate,
-            stft_pad_mode=self.stft_pad_mode,
-            center=True,
-        )
-
-        return f0
-
-    ### Audio Processing ###
-    def find_endpoint(self, wav: np.ndarray, min_silence_sec=0.8) -> int:
-        """Find the last point without silence at the end of a audio signal.
-
-        Args:
-            wav (np.ndarray): Audio signal.
-            threshold_db (int, optional): Silence threshold in decibels. Defaults to -40.
-            min_silence_sec (float, optional): Ignore silences that are shorter then this in secs. Defaults to 0.8.
-
-        Returns:
-            int: Last point without silence.
-        """
-        window_length = int(self.sample_rate * min_silence_sec)
-        hop_length = int(window_length / 4)
-        threshold = self._db_to_amp(-self.trim_db)
-        for x in range(hop_length, len(wav) - window_length, hop_length):
-            if np.max(wav[x : x + window_length]) < threshold:
-                return x + hop_length
-        return len(wav)
-
-    def trim_silence(self, wav):
-        """Trim silent parts with a threshold and 0.01 sec margin"""
-        margin = int(self.sample_rate * 0.01)
-        wav = wav[margin:-margin]
-        return librosa.effects.trim(wav, top_db=self.trim_db, frame_length=self.win_length, hop_length=self.hop_length)[
-            0
-        ]
-
-    @staticmethod
-    def sound_norm(x: np.ndarray) -> np.ndarray:
-        """Normalize the volume of an audio signal.
-
-        Args:
-            x (np.ndarray): Raw waveform.
-
-        Returns:
-            np.ndarray: Volume normalized waveform.
-        """
-        return x / abs(x).max() * 0.95
-
-    @staticmethod
-    def _rms_norm(wav, db_level=-27):
-        r = 10 ** (db_level / 20)
-        a = np.sqrt((len(wav) * (r**2)) / np.sum(wav**2))
-        return wav * a
-
-    def rms_volume_norm(self, x: np.ndarray, db_level: float = None) -> np.ndarray:
-        """Normalize the volume based on RMS of the signal.
-
-        Args:
-            x (np.ndarray): Raw waveform.
-
-        Returns:
-            np.ndarray: RMS normalized waveform.
-        """
-        if db_level is None:
-            db_level = self.db_level
-        assert -99 <= db_level <= 0, " [!] db_level should be between -99 and 0"
-        wav = self._rms_norm(x, db_level)
-        return wav
-
-    ### save and load ###
-    def load_wav(self, filename: str, sr: int = None) -> np.ndarray:
-        """Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
-
-        Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.
-
-        Args:
-            filename (str): Path to the wav file.
-            sr (int, optional): Sampling rate for resampling. Defaults to None.
-
-        Returns:
-            np.ndarray: Loaded waveform.
-        """
-        if self.resample:
-            # loading with resampling. It is significantly slower.
-            x, sr = librosa.load(filename, sr=self.sample_rate)
-        elif sr is None:
-            # SF is faster than librosa for loading files
-            x, sr = sf.read(filename)
-            assert self.sample_rate == sr, "%s vs %s" % (self.sample_rate, sr)
-        else:
-            x, sr = librosa.load(filename, sr=sr)
-        if self.do_trim_silence:
-            try:
-                x = self.trim_silence(x)
-            except ValueError:
-                print(f" [!] File cannot be trimmed for silence - {filename}")
-        if self.do_sound_norm:
-            x = self.sound_norm(x)
-        if self.do_rms_norm:
-            x = self.rms_volume_norm(x, self.db_level)
-        return x
-
-    def save_wav(self, wav: np.ndarray, path: str, sr: int = None, pipe_out=None) -> None:
-        """Save a waveform to a file using Scipy.
-
-        Args:
-            wav (np.ndarray): Waveform to save.
-            path (str): Path to a output file.
-            sr (int, optional): Sampling rate used for saving to the file. Defaults to None.
-            pipe_out (BytesIO, optional): Flag to stdout the generated TTS wav file for shell pipe.
-        """
-        if self.do_rms_norm:
-            wav_norm = self.rms_volume_norm(wav, self.db_level) * 32767
-        else:
-            wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
-
-        wav_norm = wav_norm.astype(np.int16)
-        if pipe_out:
-            wav_buffer = BytesIO()
-            scipy.io.wavfile.write(wav_buffer, sr if sr else self.sample_rate, wav_norm)
-            wav_buffer.seek(0)
-            pipe_out.buffer.write(wav_buffer.read())
-        scipy.io.wavfile.write(path, sr if sr else self.sample_rate, wav_norm)
-
-    def get_duration(self, filename: str) -> float:
-        """Get the duration of a wav file using Librosa.
-
-        Args:
-            filename (str): Path to the wav file.
-        """
-        return librosa.get_duration(filename=filename)
-
-    @staticmethod
-    def mulaw_encode(wav: np.ndarray, qc: int) -> np.ndarray:
-        mu = 2**qc - 1
-        # wav_abs = np.minimum(np.abs(wav), 1.0)
-        signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1.0 + mu)
-        # Quantize signal to the specified number of levels.
-        signal = (signal + 1) / 2 * mu + 0.5
-        return np.floor(
-            signal,
-        )
-
-    @staticmethod
-    def mulaw_decode(wav, qc):
-        """Recovers waveform from quantized values."""
-        mu = 2**qc - 1
-        x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
-        return x
-
-    @staticmethod
-    def encode_16bits(x):
-        return np.clip(x * 2**15, -(2**15), 2**15 - 1).astype(np.int16)
-
-    @staticmethod
-    def quantize(x: np.ndarray, bits: int) -> np.ndarray:
-        """Quantize a waveform to a given number of bits.
-
-        Args:
-            x (np.ndarray): Waveform to quantize. Must be normalized into the range `[-1, 1]`.
-            bits (int): Number of quantization bits.
-
-        Returns:
-            np.ndarray: Quantized waveform.
-        """
-        return (x + 1.0) * (2**bits - 1) / 2
-
-    @staticmethod
-    def dequantize(x, bits):
-        """Dequantize a waveform from the given number of bits."""
-        return 2 * x / (2**bits - 1) - 1
-
-
-def _log(x, base):
-    if base == 10:
-        return np.log10(x)
-    return np.log(x)
-
-
-def _exp(x, base):
-    if base == 10:
-        return np.power(10, x)
-    return np.exp(x)
diff --git a/spaces/ashuonnet/skillrecommender/skill_recommender.py b/spaces/ashuonnet/skillrecommender/skill_recommender.py
deleted file mode 100644
index 528ba457c4d14a09914255844cac5d25bb0ea30b..0000000000000000000000000000000000000000
--- a/spaces/ashuonnet/skillrecommender/skill_recommender.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# Import the necessary libraries
-import pandas as pd
-
-class skills_recommender:
-  
-  # Constructor of the class
-  def __init__(self):
-    # Read the data from an excel file and store it in a pandas dataframe
-    self.df = pd.read_excel('./job_market_skills.xlsx')
-    
-    # Create a dictionary to map each vertical with its name
-    self.vertical_mapping = {
-        "vertical_0":"Embedded Development",
-        "vertical_1":"Cloud Computing",
-        "vertical_2":"Project Management",
-        "vertical_3":"DevOps",
-        "vertical_4":"Hardware Asic",
-        "vertical_5":"IT Support",
-        "vertical_6":"Networking",
-        "vertical_7":"Web Development",
-        "vertical_8":"Information Security"
-    }
-
-  # Method to get the matching vertical skills
-  def get_matching_vertical_skills(self, skill_list):
-    # Create an empty pandas dataframe to store the matching vertical skills
-    return_df = pd.DataFrame(columns=['vertical','skill','score'])
-    # Initialize a row index variable
-    row_index=0
-    # Loop through the skill_list
-    for skill in skill_list:
-      # Remove leading/trailing whitespaces from the skill
-      skill.strip()
-
-      # If the skill is not empty, then check if it matches with any of the skills in the dataframe
-      if skill != '':
-        for index, row in self.df.iterrows():
-          # Get the list of skills in the current row and loop through them
-          v_skills = row['skill'].split(' ')
-          for _skill in v_skills:
-            # Check if the skill matches with the current skill in the row
-            if _skill.lower().strip() == skill.lower().strip():
-              # If the skill matches, then add the corresponding vertical, skill, and score to the return dataframe
-              return_df.loc[row_index] = [row['vertical'],row['skill'],row['score']]
-              row_index = row_index+1
-              #values_list.append([row['vertical'],row['skill'],row['score']])  
-    # Return the dataframe containing the matching vertical skills
-    return return_df
-
-  # Method to get the top 10 skills for a given vertical
-  def get_top_10_skills(self,vertical,org_skill_list):
-    # Create an empty list to store the recommended skills
-    skill_list = []
-    # Initialize a skill count variable
-    skill_count = 0
-    # Get the rows from the dataframe corresponding to the given vertical and sort them by score in descending order
-    temp_df = self.df[self.df['vertical'] == vertical]
-    temp_df = temp_df.sort_values(['score'], ascending=[False])
-
-    # Loop through the rows in the dataframe
-    for index, row in temp_df.iterrows():
-      # Get the current skill from the row
-      new_skill = row['skill']
-      # Check if the current skill is not already present in the org_skill_list and add it to the skill_list
-      if new_skill not in org_skill_list:
-        skill_list.append(new_skill)
-        skill_count = skill_count+1
-      # If the skill_count reaches 10, then break the loop
-      if skill_count == 10:
-        break
-    # Return the recommended skill_list
-    return skill_list
-
-  # Method to get the name of a vertical given its code
-  def get_vertical_name(self, vertical):
-    return self.vertical_mapping[vertical]
-        
-  # Method to suggest skills based on a given list of skills
-  def suggest_skills(self, org_skill_list):
-    # Clean skill list if needed
-    skill_list = []
-    for _skill in org_skill_list:
-        # If there are newline characters in the skill, replace them with commas
-        if "\n" in _skill:
-            _skill = _skill.replace("\n", ",")
-            # Add each resulting skill to the skill_list
-            skill_list = skill_list + _skill.split(',')
-        else:
-            skill_list.append(_skill)
-
-    # Print the cleaned skill list for debugging purposes
-    print("checking for skill list = ", skill_list)
-
-    # Get a DataFrame with matching skills for each vertical
-    df = self.get_matching_vertical_skills(skill_list)
-
-    # Get the count of how many times each vertical appears in the DataFrame
-    vertical_counts = df['vertical'].value_counts()
-
-    # Get the unique list of verticals in the DataFrame
-    verticals = df['vertical'].unique()
-
-    # Create an empty dictionary to store scores for each vertical
-    scores = {}
-
-    # Calculate a score for each vertical based on the sum of the scores of the matching skills
-    for vertical in verticals:
-        # Initialize the vertical score to 0
-        vertical_score = 0
-        # Get a DataFrame with only the rows corresponding to the current vertical
-        temp_df = df[df['vertical'] == vertical]
-        # Calculate the sum of the scores for the matching skills
-        vertical_score = temp_df['score'].sum()
-        # Store the vertical score in the scores dictionary
-        scores[vertical] = vertical_score
-
-    # Sort the scores dictionary in descending order by value
-    _scores = dict(sorted(scores.items(), key=lambda item: item[1], reverse=True))
-
-    # Create a string that lists the original skills
-    my_string = ','.join(skill_list)
-    text = "Your current skills are: " + my_string
-    text = text + "\n\n"
-
-    # Define a dictionary to map index to recommended domain text
-    rec_dict = {
-        1: "Highest Recommended Domain: ",
-        2: "\nSecond Best Recommended Domain: ",
-        3: "\nAlternate Recommended Domain: ",
-        4: "\nLess Preferable Recommended Domain: ",
-        5: "\nVery Less Preferable Domain, But Is An Option: "
-    }
-
-    # Print the sorted scores for debugging purposes
-    print(_scores)
-
-    # Initialize the recommended domain index to 0
-    rec_index = 0
-
-    # Loop over the recommended domains and print them along with the top ten missing skills for each domain
-    for key in _scores:
-        # Increment the recommended domain index
-        rec_index = rec_index + 1
-        # If we have already printed the top five recommended domains, exit the loop
-        if rec_index == 6:
-            break
-        # Add the recommended domain text to the output string
-        text = text + rec_dict[rec_index] + self.get_vertical_name(key) + "\n"
-        # Get the top ten skills missing from the original skill list for the current domain
-        rec_list = self.get_top_10_skills(key, skill_list)
-        i = 1
-        # Print each missing skill along with its rank
-        for _skill in rec_list:
-            text = text + "   " + str(i) + ". " + _skill + "\n"
-            i = i + 1
-
-    # Return the output string
-    return text
-
-
-def print_db(self):
-    # Print the DataFrame containing skill information
-    print(self.df)
-
-   
diff --git a/spaces/atticus/image-text-retrival-huster/pred_retrieval.py b/spaces/atticus/image-text-retrival-huster/pred_retrieval.py
deleted file mode 100644
index 5ce063f1b8d1410f64c0dee32e8ae55f2f8a3eeb..0000000000000000000000000000000000000000
--- a/spaces/atticus/image-text-retrival-huster/pred_retrieval.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""
-****************** COPYRIGHT AND CONFIDENTIALITY INFORMATION ******************
-Copyright (c) 2018 [Thomson Licensing]
-All Rights Reserved
-This program contains proprietary information which is a trade secret/business \
-secret of [Thomson Licensing] and is protected, even if unpublished, under \
-applicable Copyright laws (including French droit d'auteur) and/or may be \
-subject to one or more patent(s).
-Recipient is to retain this program in confidence and is not permitted to use \
-or make copies thereof other than as permitted in a written agreement with \
-[Thomson Licensing] unless otherwise expressly allowed by applicable laws or \
-by [Thomson Licensing] under express agreement.
-Thomson Licensing is a company of the group TECHNICOLOR
-*******************************************************************************
-This scripts permits one to reproduce training and experiments of:
-    Engilberge, M., Chevallier, L., Pérez, P., & Cord, M. (2018, April).
-    Finding beans in burgers: Deep semantic-visual embedding with localization.
-    In Proceedings of CVPR (pp. 3984-3993)
-
-Author: Martin Engilberge
-"""
-
-import argparse
-import re
-import time
-
-import numpy as np
-from numpy.__config__ import show
-import torch
-
-
-from misc.model import img_embedding, joint_embedding
-from torch.utils.data import DataLoader, dataset
-
-from misc.dataset import TextDataset
-from misc.utils import collate_fn_cap_padded
-from torch.utils.data import DataLoader
-from misc.utils import load_obj 
-from misc.evaluation import recallTopK 
-
-from misc.utils import show_imgs
-import sys 
-from misc.dataset import TextEncoder 
-
-device = torch.device("cuda")
-# device = torch.device("cpu") # uncomment to run with cpu
-
-if __name__ == '__main__':
-
-    parser = argparse.ArgumentParser(description='Extract embedding representation for images')
-    parser.add_argument("-p", '--path', dest="model_path", help='Path to the weights of the model to evaluate')
-    parser.add_argument("-d", '--data', dest="data_path", help='path to the file containing the sentence to embed')
-    parser.add_argument("-bs", "--batch_size", help="The size of the batches", type=int, default=1)
-
-    args = parser.parse_args()
-
-    print("Loading model from:", args.model_path)
-    checkpoint = torch.load(args.model_path, map_location=lambda storage, loc: storage)
-
-    join_emb = joint_embedding(checkpoint['args_dict'])
-    join_emb.load_state_dict(checkpoint["state_dict"])
-
-    for param in join_emb.parameters():
-        param.requires_grad = False
-
-    join_emb.to(device)
-    join_emb.eval()
-
-    encoder = TextEncoder()
-    print("Loading model done")
-    # (4) design intersection mode.
-    print("Please input your description of the image that you wanna search >>>")
-    for line in sys.stdin:
-        
-        t0 = time.time() 
-        cap_str = line.strip() 
-        # with open(args.data_path, 'w') as cap_file:
-        #     cap_file.writelines(cap_str)
-        t1 = time.time()
-        print("text is embedding ...")
-        dataset = torch.Tensor(encoder.encode(cap_str)).unsqueeze(dim=0)
-        t111 = time.time()
-        dataset_loader = DataLoader(dataset, batch_size=args.batch_size, num_workers=1, pin_memory=True, collate_fn=collate_fn_cap_padded)
-        t11 = time.time() 
-        caps_enc = list()
-        for i, (caps, length) in enumerate(dataset_loader, 0):
-            input_caps = caps.to(device)
-            with torch.no_grad():
-                _, output_emb = join_emb(None, input_caps, length)
-            caps_enc.append(output_emb.cpu().data.numpy())
-
-        t12 = time.time() 
-        caps_stack = np.vstack(caps_enc)
-        # print(t11 - t1, t12 - t11, t111 - t1)
-        
-        t2 = time.time()
-        print("recall from resources ...")
-        # (1) load candidate imgs from saved embeding pkl file. 
-        imgs_emb_file_path = "/home/atticus/proj/matching/DSVE/imgs_embed/v20210915_01_9408/allImg"
-        # imgs_emb(40775, 2400)
-        imgs_emb, imgs_path = load_obj(imgs_emb_file_path)
-        # (2) calculate the sim between cap and imgs. 
-        # (3) rank imgs and display the searching result. 
-        recall_imgs = recallTopK(caps_stack, imgs_emb, imgs_path, ks=5)
-
-        t3 = time.time() 
-        show_imgs(imgs_path=recall_imgs)
-        
-        # print("input stage time: {} \n text embedding stage time: {} \n recall stage time: {}".format(t1 - t0, t2 - t1, t3 - t2))
-
-        print("======== current epoch done ========")
-        print("Please input your description of the image that you wanna search >>>")
diff --git a/spaces/autoevaluate/model-evaluator/app.py b/spaces/autoevaluate/model-evaluator/app.py
deleted file mode 100644
index 42e73e70ab3f4da543a14eb5fc26e60b4462804e..0000000000000000000000000000000000000000
--- a/spaces/autoevaluate/model-evaluator/app.py
+++ /dev/null
@@ -1,693 +0,0 @@
-import os
-import time
-from pathlib import Path
-
-import pandas as pd
-import streamlit as st
-import yaml
-from datasets import get_dataset_config_names
-from dotenv import load_dotenv
-from huggingface_hub import list_datasets
-
-from evaluation import filter_evaluated_models
-from utils import (
-    AUTOTRAIN_TASK_TO_HUB_TASK,
-    commit_evaluation_log,
-    create_autotrain_project_name,
-    format_col_mapping,
-    get_compatible_models,
-    get_config_metadata,
-    get_dataset_card_url,
-    get_key,
-    get_metadata,
-    http_get,
-    http_post,
-)
-
-if Path(".env").is_file():
-    load_dotenv(".env")
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-AUTOTRAIN_USERNAME = os.getenv("AUTOTRAIN_USERNAME")
-AUTOTRAIN_BACKEND_API = os.getenv("AUTOTRAIN_BACKEND_API")
-DATASETS_PREVIEW_API = os.getenv("DATASETS_PREVIEW_API")
-
-# Put image tasks on top
-TASK_TO_ID = {
-    "image_binary_classification": 17,
-    "image_multi_class_classification": 18,
-    "binary_classification": 1,
-    "multi_class_classification": 2,
-    "natural_language_inference": 22,
-    "entity_extraction": 4,
-    "extractive_question_answering": 5,
-    "translation": 6,
-    "summarization": 8,
-    "text_zero_shot_classification": 23,
-}
-
-TASK_TO_DEFAULT_METRICS = {
-    "binary_classification": ["f1", "precision", "recall", "auc", "accuracy"],
-    "multi_class_classification": [
-        "f1",
-        "precision",
-        "recall",
-        "accuracy",
-    ],
-    "natural_language_inference": ["f1", "precision", "recall", "auc", "accuracy"],
-    "entity_extraction": ["precision", "recall", "f1", "accuracy"],
-    "extractive_question_answering": ["f1", "exact_match"],
-    "translation": ["sacrebleu"],
-    "summarization": ["rouge1", "rouge2", "rougeL", "rougeLsum"],
-    "image_binary_classification": ["f1", "precision", "recall", "auc", "accuracy"],
-    "image_multi_class_classification": [
-        "f1",
-        "precision",
-        "recall",
-        "accuracy",
-    ],
-    "text_zero_shot_classification": ["accuracy", "loss"],
-}
-
-AUTOTRAIN_TASK_TO_LANG = {
-    "translation": "en2de",
-    "image_binary_classification": "unk",
-    "image_multi_class_classification": "unk",
-}
-
-AUTOTRAIN_MACHINE = {"text_zero_shot_classification": "r5.16x"}
-
-
-SUPPORTED_TASKS = list(TASK_TO_ID.keys())
-
-# Extracted from utils.get_supported_metrics
-# Hardcoded for now due to speed / caching constraints
-SUPPORTED_METRICS = [
-    "accuracy",
-    "bertscore",
-    "bleu",
-    "cer",
-    "chrf",
-    "code_eval",
-    "comet",
-    "competition_math",
-    "coval",
-    "cuad",
-    "exact_match",
-    "f1",
-    "frugalscore",
-    "google_bleu",
-    "mae",
-    "mahalanobis",
-    "matthews_correlation",
-    "mean_iou",
-    "meteor",
-    "mse",
-    "pearsonr",
-    "perplexity",
-    "precision",
-    "recall",
-    "roc_auc",
-    "rouge",
-    "sacrebleu",
-    "sari",
-    "seqeval",
-    "spearmanr",
-    "squad",
-    "squad_v2",
-    "ter",
-    "trec_eval",
-    "wer",
-    "wiki_split",
-    "xnli",
-    "angelina-wang/directional_bias_amplification",
-    "jordyvl/ece",
-    "lvwerra/ai4code",
-    "lvwerra/amex",
-]
-
-
-#######
-# APP #
-#######
-st.title("Evaluation on the Hub")
-st.markdown(
-    """
-    Welcome to Hugging Face's automatic model evaluator 👋!
-
-    This application allows you to evaluate 🤗 Transformers
-    [models](https://huggingface.co/models?library=transformers&sort=downloads)
-    across a wide variety of [datasets](https://huggingface.co/datasets) on the
-    Hub. Please select the dataset and configuration below. The results of your
-    evaluation will be displayed on the [public
-    leaderboards](https://huggingface.co/spaces/autoevaluate/leaderboards). For
-    more details, check out out our [blog
-    post](https://huggingface.co/blog/eval-on-the-hub).
-    """
-)
-
-all_datasets = [d.id for d in list_datasets()]
-query_params = st.experimental_get_query_params()
-if "first_query_params" not in st.session_state:
-    st.session_state.first_query_params = query_params
-first_query_params = st.session_state.first_query_params
-default_dataset = all_datasets[0]
-if "dataset" in first_query_params:
-    if len(first_query_params["dataset"]) > 0 and first_query_params["dataset"][0] in all_datasets:
-        default_dataset = first_query_params["dataset"][0]
-
-selected_dataset = st.selectbox(
-    "Select a dataset",
-    all_datasets,
-    index=all_datasets.index(default_dataset),
-    help="""Datasets with metadata can be evaluated with 1-click. Configure an evaluation job to add \
-        new metadata to a dataset card.""",
-)
-st.experimental_set_query_params(**{"dataset": [selected_dataset]})
-
-# Check if selected dataset can be streamed
-is_valid_dataset = http_get(
-    path="/is-valid",
-    domain=DATASETS_PREVIEW_API,
-    params={"dataset": selected_dataset},
-).json()
-if is_valid_dataset["viewer"] is False and is_valid_dataset["preview"] is False:
-    st.error(
-        """The dataset you selected is not currently supported. Open a \
-            [discussion](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions) for support."""
-    )
-
-metadata = get_metadata(selected_dataset, token=HF_TOKEN)
-print(f"INFO -- Dataset metadata: {metadata}")
-if metadata is None:
-    st.warning("No evaluation metadata found. Please configure the evaluation job below.")
-
-with st.expander("Advanced configuration"):
-    # Select task
-    selected_task = st.selectbox(
-        "Select a task",
-        SUPPORTED_TASKS,
-        index=SUPPORTED_TASKS.index(metadata[0]["task_id"]) if metadata is not None else 0,
-        help="""Don't see your favourite task here? Open a \
-            [discussion](https://huggingface.co/spaces/autoevaluate/model-evaluator/discussions) to request it!""",
-    )
-    # Select config
-    configs = get_dataset_config_names(selected_dataset)
-    selected_config = st.selectbox(
-        "Select a config",
-        configs,
-        help="""Some datasets contain several sub-datasets, known as _configurations_. \
-            Select one to evaluate your models on. \
-            See the [docs](https://huggingface.co/docs/datasets/master/en/load_hub#configurations) for more details.
-            """,
-    )
-    # Some datasets have multiple metadata (one per config), so we grab the one associated with the selected config
-    config_metadata = get_config_metadata(selected_config, metadata)
-    print(f"INFO -- Config metadata: {config_metadata}")
-
-    # Select splits
-    splits_resp = http_get(
-        path="/splits",
-        domain=DATASETS_PREVIEW_API,
-        params={"dataset": selected_dataset},
-    )
-    if splits_resp.status_code == 200:
-        split_names = []
-        all_splits = splits_resp.json()
-        for split in all_splits["splits"]:
-            if split["config"] == selected_config:
-                split_names.append(split["split"])
-
-        if config_metadata is not None:
-            eval_split = config_metadata["splits"].get("eval_split", None)
-        else:
-            eval_split = None
-        selected_split = st.selectbox(
-            "Select a split",
-            split_names,
-            index=split_names.index(eval_split) if eval_split is not None else 0,
-            help="Be wary when evaluating models on the `train` split.",
-        )
-
-    # Select columns
-    rows_resp = http_get(
-        path="/first-rows",
-        domain=DATASETS_PREVIEW_API,
-        params={
-            "dataset": selected_dataset,
-            "config": selected_config,
-            "split": selected_split,
-        },
-    ).json()
-    col_names = list(pd.json_normalize(rows_resp["rows"][0]["row"]).columns)
-
-    st.markdown("**Map your dataset columns**")
-    st.markdown(
-        """The model evaluator uses a standardised set of column names for the input examples and labels. \
-        Please define the mapping between your dataset columns (right) and the standardised column names (left)."""
-    )
-    col1, col2 = st.columns(2)
-
-    # TODO: find a better way to layout these items
-    # TODO: need graceful way of handling dataset <--> task mismatch for datasets with metadata
-    col_mapping = {}
-    if selected_task in ["binary_classification", "multi_class_classification"]:
-        with col1:
-            st.markdown("`text` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`target` column")
-        with col2:
-            text_col = st.selectbox(
-                "This column should contain the text to be classified",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "text"))
-                if config_metadata is not None
-                else 0,
-            )
-            target_col = st.selectbox(
-                "This column should contain the labels associated with the text",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "target"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[text_col] = "text"
-            col_mapping[target_col] = "target"
-
-    elif selected_task == "text_zero_shot_classification":
-        with col1:
-            st.markdown("`text` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`classes` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`target` column")
-        with col2:
-            text_col = st.selectbox(
-                "This column should contain the text to be classified",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "text"))
-                if config_metadata is not None
-                else 0,
-            )
-            classes_col = st.selectbox(
-                "This column should contain the classes associated with the text",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "classes"))
-                if config_metadata is not None
-                else 0,
-            )
-            target_col = st.selectbox(
-                "This column should contain the index of the correct class",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "target"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[text_col] = "text"
-            col_mapping[classes_col] = "classes"
-            col_mapping[target_col] = "target"
-
-    if selected_task in ["natural_language_inference"]:
-        config_metadata = get_config_metadata(selected_config, metadata)
-        with col1:
-            st.markdown("`text1` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`text2` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`target` column")
-        with col2:
-            text1_col = st.selectbox(
-                "This column should contain the first text passage to be classified",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "text1"))
-                if config_metadata is not None
-                else 0,
-            )
-            text2_col = st.selectbox(
-                "This column should contain the second text passage to be classified",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "text2"))
-                if config_metadata is not None
-                else 0,
-            )
-            target_col = st.selectbox(
-                "This column should contain the labels associated with the text",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "target"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[text1_col] = "text1"
-            col_mapping[text2_col] = "text2"
-            col_mapping[target_col] = "target"
-
-    elif selected_task == "entity_extraction":
-        with col1:
-            st.markdown("`tokens` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`tags` column")
-        with col2:
-            tokens_col = st.selectbox(
-                "This column should contain the array of tokens to be classified",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "tokens"))
-                if config_metadata is not None
-                else 0,
-            )
-            tags_col = st.selectbox(
-                "This column should contain the labels associated with each part of the text",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "tags"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[tokens_col] = "tokens"
-            col_mapping[tags_col] = "tags"
-
-    elif selected_task == "translation":
-        with col1:
-            st.markdown("`source` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`target` column")
-        with col2:
-            text_col = st.selectbox(
-                "This column should contain the text to be translated",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "source"))
-                if config_metadata is not None
-                else 0,
-            )
-            target_col = st.selectbox(
-                "This column should contain the target translation",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "target"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[text_col] = "source"
-            col_mapping[target_col] = "target"
-
-    elif selected_task == "summarization":
-        with col1:
-            st.markdown("`text` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`target` column")
-        with col2:
-            text_col = st.selectbox(
-                "This column should contain the text to be summarized",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "text"))
-                if config_metadata is not None
-                else 0,
-            )
-            target_col = st.selectbox(
-                "This column should contain the target summary",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "target"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[text_col] = "text"
-            col_mapping[target_col] = "target"
-
-    elif selected_task == "extractive_question_answering":
-        if config_metadata is not None:
-            col_mapping = config_metadata["col_mapping"]
-            # Hub YAML parser converts periods to hyphens, so we remap them here
-            col_mapping = format_col_mapping(col_mapping)
-        with col1:
-            st.markdown("`context` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`question` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`answers.text` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`answers.answer_start` column")
-        with col2:
-            context_col = st.selectbox(
-                "This column should contain the question's context",
-                col_names,
-                index=col_names.index(get_key(col_mapping, "context")) if config_metadata is not None else 0,
-            )
-            question_col = st.selectbox(
-                "This column should contain the question to be answered, given the context",
-                col_names,
-                index=col_names.index(get_key(col_mapping, "question")) if config_metadata is not None else 0,
-            )
-            answers_text_col = st.selectbox(
-                "This column should contain example answers to the question, extracted from the context",
-                col_names,
-                index=col_names.index(get_key(col_mapping, "answers.text")) if config_metadata is not None else 0,
-            )
-            answers_start_col = st.selectbox(
-                "This column should contain the indices in the context of the first character of each `answers.text`",
-                col_names,
-                index=col_names.index(get_key(col_mapping, "answers.answer_start"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[context_col] = "context"
-            col_mapping[question_col] = "question"
-            col_mapping[answers_text_col] = "answers.text"
-            col_mapping[answers_start_col] = "answers.answer_start"
-    elif selected_task in ["image_binary_classification", "image_multi_class_classification"]:
-        with col1:
-            st.markdown("`image` column")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.text("")
-            st.markdown("`target` column")
-        with col2:
-            image_col = st.selectbox(
-                "This column should contain the images to be classified",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "image"))
-                if config_metadata is not None
-                else 0,
-            )
-            target_col = st.selectbox(
-                "This column should contain the labels associated with the images",
-                col_names,
-                index=col_names.index(get_key(config_metadata["col_mapping"], "target"))
-                if config_metadata is not None
-                else 0,
-            )
-            col_mapping[image_col] = "image"
-            col_mapping[target_col] = "target"
-
-    # Select metrics
-    st.markdown("**Select metrics**")
-    st.markdown("The following metrics will be computed")
-    html_string = " ".join(
-        [
-            '<div style="padding-right:5px;padding-left:5px;padding-top:5px;padding-bottom:5px;float:left">'
-            + '<div style="background-color:#D3D3D3;border-radius:5px;display:inline-block;padding-right:5px;'
-            + 'padding-left:5px;color:white">'
-            + metric
-            + "</div></div>"
-            for metric in TASK_TO_DEFAULT_METRICS[selected_task]
-        ]
-    )
-    st.markdown(html_string, unsafe_allow_html=True)
-    selected_metrics = st.multiselect(
-        "(Optional) Select additional metrics",
-        sorted(list(set(SUPPORTED_METRICS) - set(TASK_TO_DEFAULT_METRICS[selected_task]))),
-        help="""User-selected metrics will be computed with their default arguments. \
-            For example, `f1` will report results for binary labels. \
-            Check out the [available metrics](https://huggingface.co/metrics) for more details.""",
-    )
-
-with st.form(key="form"):
-    compatible_models = get_compatible_models(selected_task, [selected_dataset])
-    selected_models = st.multiselect(
-        "Select the models you wish to evaluate",
-        compatible_models,
-        help="""Don't see your favourite model in this list? Add the dataset and task it was trained on to the \
-            [model card metadata.](https://huggingface.co/docs/hub/models-cards#model-card-metadata)""",
-    )
-    print("INFO -- Selected models before filter:", selected_models)
-
-    hf_username = st.text_input("Enter your 🤗 Hub username to be notified when the evaluation is finished")
-
-    submit_button = st.form_submit_button("Evaluate models 🚀")
-
-    if submit_button:
-        if len(hf_username) == 0:
-            st.warning("No 🤗 Hub username provided! Please enter your username and try again.")
-        elif len(selected_models) == 0:
-            st.warning("⚠️ No models were selected for evaluation! Please select at least one model and try again.")
-        elif len(selected_models) > 10:
-            st.warning("Only 10 models can be evaluated at once. Please select fewer models and try again.")
-        else:
-            # Filter out previously evaluated models
-            selected_models = filter_evaluated_models(
-                selected_models,
-                selected_task,
-                selected_dataset,
-                selected_config,
-                selected_split,
-                selected_metrics,
-            )
-            print("INFO -- Selected models after filter:", selected_models)
-            if len(selected_models) > 0:
-                project_payload = {
-                    "username": AUTOTRAIN_USERNAME,
-                    "proj_name": create_autotrain_project_name(selected_dataset, selected_config),
-                    "task": TASK_TO_ID[selected_task],
-                    "config": {
-                        "language": AUTOTRAIN_TASK_TO_LANG[selected_task]
-                        if selected_task in AUTOTRAIN_TASK_TO_LANG
-                        else "en",
-                        "max_models": 5,
-                        "instance": {
-                            "provider": "sagemaker" if selected_task in AUTOTRAIN_MACHINE.keys() else "ovh",
-                            "instance_type": AUTOTRAIN_MACHINE[selected_task]
-                            if selected_task in AUTOTRAIN_MACHINE.keys()
-                            else "p3",
-                            "max_runtime_seconds": 172800,
-                            "num_instances": 1,
-                            "disk_size_gb": 200,
-                        },
-                        "evaluation": {
-                            "metrics": selected_metrics,
-                            "models": selected_models,
-                            "hf_username": hf_username,
-                        },
-                    },
-                }
-                print(f"INFO -- Payload: {project_payload}")
-                project_json_resp = http_post(
-                    path="/projects/create",
-                    payload=project_payload,
-                    token=HF_TOKEN,
-                    domain=AUTOTRAIN_BACKEND_API,
-                ).json()
-                print(f"INFO -- Project creation response: {project_json_resp}")
-
-                if project_json_resp["created"]:
-                    data_payload = {
-                        "split": 4,  # use "auto" split choice in AutoTrain
-                        "col_mapping": col_mapping,
-                        "load_config": {"max_size_bytes": 0, "shuffle": False},
-                        "dataset_id": selected_dataset,
-                        "dataset_config": selected_config,
-                        "dataset_split": selected_split,
-                    }
-                    data_json_resp = http_post(
-                        path=f"/projects/{project_json_resp['id']}/data/dataset",
-                        payload=data_payload,
-                        token=HF_TOKEN,
-                        domain=AUTOTRAIN_BACKEND_API,
-                    ).json()
-                    print(f"INFO -- Dataset creation response: {data_json_resp}")
-                    if data_json_resp["download_status"] == 1:
-                        train_json_resp = http_post(
-                            path=f"/projects/{project_json_resp['id']}/data/start_processing",
-                            token=HF_TOKEN,
-                            domain=AUTOTRAIN_BACKEND_API,
-                        ).json()
-                        # For local development we process and approve projects on-the-fly
-                        if "localhost" in AUTOTRAIN_BACKEND_API:
-                            with st.spinner("⏳ Waiting for data processing to complete ..."):
-                                is_data_processing_success = False
-                                while is_data_processing_success is not True:
-                                    project_status = http_get(
-                                        path=f"/projects/{project_json_resp['id']}",
-                                        token=HF_TOKEN,
-                                        domain=AUTOTRAIN_BACKEND_API,
-                                    ).json()
-                                    if project_status["status"] == 3:
-                                        is_data_processing_success = True
-                                    time.sleep(10)
-
-                            # Approve training job
-                            train_job_resp = http_post(
-                                path=f"/projects/{project_json_resp['id']}/start_training",
-                                token=HF_TOKEN,
-                                domain=AUTOTRAIN_BACKEND_API,
-                            ).json()
-                            st.success("✅  Data processing and project approval complete - go forth and evaluate!")
-                        else:
-                            # Prod/staging submissions are evaluated in a cron job via run_evaluation_jobs.py
-                            print(f"INFO -- AutoTrain job response: {train_json_resp}")
-                            if train_json_resp["success"]:
-                                train_eval_index = {
-                                    "train-eval-index": [
-                                        {
-                                            "config": selected_config,
-                                            "task": AUTOTRAIN_TASK_TO_HUB_TASK[selected_task],
-                                            "task_id": selected_task,
-                                            "splits": {"eval_split": selected_split},
-                                            "col_mapping": col_mapping,
-                                        }
-                                    ]
-                                }
-                                selected_metadata = yaml.dump(train_eval_index, sort_keys=False)
-                                dataset_card_url = get_dataset_card_url(selected_dataset)
-                                st.success("✅ Successfully submitted evaluation job!")
-                                st.markdown(
-                                    f"""
-                                Evaluation can take up to 1 hour to complete, so grab a ☕️ or 🍵 while you wait:
-
-                                * 🔔 A [Hub pull request](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) with the evaluation results will be opened for each model you selected. Check your email for notifications.
-                                * 📊 Click [here](https://hf.co/spaces/autoevaluate/leaderboards?dataset={selected_dataset}) to view the results from your submission once the Hub pull request is merged.
-                                * 🥱 Tired of configuring evaluations? Add the following metadata to the [dataset card]({dataset_card_url}) to enable 1-click evaluations:
-                                """  # noqa
-                                )
-                                st.markdown(
-                                    f"""
-                                ```yaml
-                                {selected_metadata}
-                                """
-                                )
-                                print("INFO -- Pushing evaluation job logs to the Hub")
-                                evaluation_log = {}
-                                evaluation_log["project_id"] = project_json_resp["id"]
-                                evaluation_log["autotrain_env"] = (
-                                    "staging" if "staging" in AUTOTRAIN_BACKEND_API else "prod"
-                                )
-                                evaluation_log["payload"] = project_payload
-                                evaluation_log["project_creation_response"] = project_json_resp
-                                evaluation_log["dataset_creation_response"] = data_json_resp
-                                evaluation_log["autotrain_job_response"] = train_json_resp
-                                commit_evaluation_log(evaluation_log, hf_access_token=HF_TOKEN)
-                            else:
-                                st.error("🙈 Oh no, there was an error submitting your evaluation job!")
-            else:
-                st.warning("⚠️ No models left to evaluate! Please select other models and try again.")
diff --git a/spaces/autonomous019/Story_Generator_v2/spaces_info.py b/spaces/autonomous019/Story_Generator_v2/spaces_info.py
deleted file mode 100644
index 649c3185305b49bc3fe3fad8bc6be26719f599fb..0000000000000000000000000000000000000000
--- a/spaces/autonomous019/Story_Generator_v2/spaces_info.py
+++ /dev/null
@@ -1,76 +0,0 @@
-description = """Gradio Demo for BLOOM. To use it, simply add your text, or click one of the examples to load them.
-Tips:
-- Do NOT talk to BLOOM as an entity, it's not a chatbot but a webpage/blog/article completion model.
-- For the best results: MIMIC a few sentences of a webpage similar to the content you want to generate.
-Start a paragraph as if YOU were writing a blog, webpage, math post, coding article and BLOOM will generate a coherent follow-up. Longer prompts usually give more interesting results.
-- Content: Please see our [content disclaimer](https://hf.co/spaces/bigscience/bloom-book) before using the model, as it may sometimes behave in unexpected ways.
-
-Options:
-- sampling: imaginative completions (may be not super accurate e.g. math/history)
-- greedy: accurate completions (may be more boring or have repetitions)
-"""
-
-wip_description = """JAX / Flax Gradio Demo for BLOOM. The 176B BLOOM model running on a TPU v3-256 pod, with 2D model parallelism and custom mesh axes.
-Note:
-1. For this WIP demo, only **sampling** is supported.
-2. Rendering of the screenshot is currently not optimised. To experience the true speed of JAX / Flax, tick 'just output raw text'.
-"""
-
-examples = [
-    [
-        'A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is: We were traveling in Africa and we saw these very cute whatpus. To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:',
-        32,
-        "Sample",
-        "Sample 1",
-    ],
-    [
-        "A poem about the beauty of science by Alfred Edgar Brittle\nTitle: The Magic Craft\nIn the old times",
-        50,
-        "Sample",
-        "Sample 1",
-    ],
-    ["استخراج العدد العاملي في لغة بايثون:", 30, "Greedy", "Sample 1"],
-    ["Pour déguster un ortolan, il faut tout d'abord", 32, "Sample", "Sample 1"],
-    [
-        "Traduce español de España a español de Argentina\nEl coche es rojo - el auto es rojo\nEl ordenador es nuevo - la computadora es nueva\nel boligrafo es negro -",
-        16,
-        "Sample",
-        "Sample 1",
-    ],
-    [
-        "Estos ejemplos quitan vocales de las palabras\nEjemplos:\nhola - hl\nmanzana - mnzn\npapas - pps\nalacran - lcrn\npapa -",
-        16,
-        "Sample",
-        "Sample 1",
-    ],
-    [
-        "Question: If I put cheese into the fridge, will it melt?\nAnswer:",
-        32,
-        "Sample",
-        "Sample 1",
-    ],
-    ["Math exercise - answers:\n34+10=44\n54+20=", 16, "Greedy", "Sample 1"],
-    [
-        "Question: Where does the Greek Goddess Persephone spend half of the year when she is not with her mother?\nAnswer:",
-        24,
-        "Greedy",
-        "Sample 1",
-    ],
-    [
-        "spelling test answers.\nWhat are the letters in « language »?\nAnswer: l-a-n-g-u-a-g-e\nWhat are the letters in « Romanian »?\nAnswer:",
-        24,
-        "Greedy",
-        "Sample 1",
-    ],
-]
-
-initial_prompt_value = """استخراج العدد العاملي في لغة بايثون :
-def factorial(n):
-    if n == 0:
-        return 1
-    else:
-        result = 1
-        for i in range(1, n + 1) :
-            result *= i
-    return result
-print(factorial(5))"""
\ No newline at end of file
diff --git a/spaces/avivdm1/AutoGPT/autogpt/commands/execute_code.py b/spaces/avivdm1/AutoGPT/autogpt/commands/execute_code.py
deleted file mode 100644
index 11266f852727f2f8aedbc995b1e504a17acbfb77..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/commands/execute_code.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""Execute code in a Docker container"""
-import os
-import subprocess
-
-import docker
-from docker.errors import ImageNotFound
-
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-
-def execute_python_file(file: str) -> str:
-    """Execute a Python file in a Docker container and return the output
-
-    Args:
-        file (str): The name of the file to execute
-
-    Returns:
-        str: The output of the file
-    """
-
-    print(f"Executing file '{file}' in workspace '{WORKSPACE_PATH}'")
-
-    if not file.endswith(".py"):
-        return "Error: Invalid file type. Only .py files are allowed."
-
-    file_path = path_in_workspace(file)
-
-    if not os.path.isfile(file_path):
-        return f"Error: File '{file}' does not exist."
-
-    if we_are_running_in_a_docker_container():
-        result = subprocess.run(
-            f"python {file_path}", capture_output=True, encoding="utf8", shell=True
-        )
-        if result.returncode == 0:
-            return result.stdout
-        else:
-            return f"Error: {result.stderr}"
-
-    try:
-        client = docker.from_env()
-
-        # You can replace this with the desired Python image/version
-        # You can find available Python images on Docker Hub:
-        # https://hub.docker.com/_/python
-        image_name = "python:3-alpine"
-        try:
-            client.images.get(image_name)
-            print(f"Image '{image_name}' found locally")
-        except ImageNotFound:
-            print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
-            # Use the low-level API to stream the pull response
-            low_level_client = docker.APIClient()
-            for line in low_level_client.pull(image_name, stream=True, decode=True):
-                # Print the status and progress, if available
-                status = line.get("status")
-                progress = line.get("progress")
-                if status and progress:
-                    print(f"{status}: {progress}")
-                elif status:
-                    print(status)
-
-        container = client.containers.run(
-            image_name,
-            f"python {file}",
-            volumes={
-                os.path.abspath(WORKSPACE_PATH): {
-                    "bind": "/workspace",
-                    "mode": "ro",
-                }
-            },
-            working_dir="/workspace",
-            stderr=True,
-            stdout=True,
-            detach=True,
-        )
-
-        container.wait()
-        logs = container.logs().decode("utf-8")
-        container.remove()
-
-        # print(f"Execution complete. Output: {output}")
-        # print(f"Logs: {logs}")
-
-        return logs
-
-    except docker.errors.DockerException as e:
-        print(
-            "Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/"
-        )
-        return f"Error: {str(e)}"
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def execute_shell(command_line: str) -> str:
-    """Execute a shell command and return the output
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: The output of the command
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    result = subprocess.run(command_line, capture_output=True, shell=True)
-    output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return output
-
-
-def execute_shell_popen(command_line) -> str:
-    """Execute a shell command with Popen and returns an english description
-    of the event and the process id
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: Description of the fact that the process started and its id
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    do_not_show_output = subprocess.DEVNULL
-    process = subprocess.Popen(
-        command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output
-    )
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return f"Subprocess started with PID:'{str(process.pid)}'"
-
-
-def we_are_running_in_a_docker_container() -> bool:
-    """Check if we are running in a Docker container
-
-    Returns:
-        bool: True if we are running in a Docker container, False otherwise
-    """
-    return os.path.exists("/.dockerenv")
diff --git a/spaces/awaawawawa/iurf7irfuyytruyyugb/optimizedSD/img2img_gradio.py b/spaces/awaawawawa/iurf7irfuyytruyyugb/optimizedSD/img2img_gradio.py
deleted file mode 100644
index 7fd4678380cd957b7c4b2b0ab752db9006e95015..0000000000000000000000000000000000000000
--- a/spaces/awaawawawa/iurf7irfuyytruyyugb/optimizedSD/img2img_gradio.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from torchvision.utils import make_grid
-import os, re
-from PIL import Image
-import torch
-import numpy as np
-from random import randint
-from omegaconf import OmegaConf
-from PIL import Image
-from tqdm import tqdm, trange
-from itertools import islice
-from einops import rearrange
-from torchvision.utils import make_grid
-import time
-from pytorch_lightning import seed_everything
-from torch import autocast
-from einops import rearrange, repeat
-from contextlib import nullcontext
-from ldmlib.util import instantiate_from_config
-from transformers import logging
-import pandas as pd
-from optimUtils import split_weighted_subprompts, logger
-logging.set_verbosity_error()
-import mimetypes
-mimetypes.init()
-mimetypes.add_type("application/javascript", ".js")
-
-
-def chunk(it, size):
-    it = iter(it)
-    return iter(lambda: tuple(islice(it, size)), ())
-
-
-def load_model_from_config(ckpt, verbose=False):
-    print(f"Loading model from {ckpt}")
-    pl_sd = torch.load(ckpt, map_location="cpu")
-    if "global_step" in pl_sd:
-        print(f"Global Step: {pl_sd['global_step']}")
-    sd = pl_sd["state_dict"]
-    return sd
-
-
-def load_img(image, h0, w0):
-
-    image = image.convert("RGB")
-    w, h = image.size
-    print(f"loaded input image of size ({w}, {h})")
-    if h0 is not None and w0 is not None:
-        h, w = h0, w0
-
-    w, h = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 32
-
-    print(f"New image size ({w}, {h})")
-    image = image.resize((w, h), resample=Image.LANCZOS)
-    image = np.array(image).astype(np.float32) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return 2.0 * image - 1.0
-
-config = "optimizedSD/v1-inference.yaml"
-ckpt = "models/ldm/stable-diffusion-v1/model.ckpt"
-sd = load_model_from_config(f"{ckpt}")
-li, lo = [], []
-for key, v_ in sd.items():
-    sp = key.split(".")
-    if (sp[0]) == "model":
-        if "input_blocks" in sp:
-            li.append(key)
-        elif "middle_block" in sp:
-            li.append(key)
-        elif "time_embed" in sp:
-            li.append(key)
-        else:
-            lo.append(key)
-for key in li:
-    sd["model1." + key[6:]] = sd.pop(key)
-for key in lo:
-    sd["model2." + key[6:]] = sd.pop(key)
-
-config = OmegaConf.load(f"{config}")
-
-model = instantiate_from_config(config.modelUNet)
-_, _ = model.load_state_dict(sd, strict=False)
-model.eval()
-
-modelCS = instantiate_from_config(config.modelCondStage)
-_, _ = modelCS.load_state_dict(sd, strict=False)
-modelCS.eval()
-
-modelFS = instantiate_from_config(config.modelFirstStage)
-_, _ = modelFS.load_state_dict(sd, strict=False)
-modelFS.eval()
-del sd
-
-def generate(
-    image,
-    prompt,
-    strength,
-    ddim_steps,
-    n_iter,
-    batch_size,
-    Height,
-    Width,
-    scale,
-    ddim_eta,
-    unet_bs,
-    device,
-    seed,
-    outdir,
-    img_format,
-    turbo,
-    full_precision,
-):
-
-    if seed == "":
-        seed = randint(0, 1000000)
-    seed = int(seed)
-    seed_everything(seed)
-
-    # Logging
-    sampler = "ddim"
-    logger(locals(), log_csv = "logs/img2img_gradio_logs.csv")
-
-    init_image = load_img(image, Height, Width).to(device)
-    model.unet_bs = unet_bs
-    model.turbo = turbo
-    model.cdevice = device
-    modelCS.cond_stage_model.device = device
-
-    if device != "cpu" and full_precision == False:
-        model.half()
-        modelCS.half()
-        modelFS.half()
-        init_image = init_image.half()
-
-    tic = time.time()
-    os.makedirs(outdir, exist_ok=True)
-    outpath = outdir
-    sample_path = os.path.join(outpath, "_".join(re.split(":| ", prompt)))[:150]
-    os.makedirs(sample_path, exist_ok=True)
-    base_count = len(os.listdir(sample_path))
-
-    # n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
-    assert prompt is not None
-    data = [batch_size * [prompt]]
-
-    modelFS.to(device)
-
-    init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
-    init_latent = modelFS.get_first_stage_encoding(modelFS.encode_first_stage(init_image))  # move to latent space
-
-    if device != "cpu":
-        mem = torch.cuda.memory_allocated() / 1e6
-        modelFS.to("cpu")
-        while torch.cuda.memory_allocated() / 1e6 >= mem:
-            time.sleep(1)
-
-    assert 0.0 <= strength <= 1.0, "can only work with strength in [0.0, 1.0]"
-    t_enc = int(strength * ddim_steps)
-    print(f"target t_enc is {t_enc} steps")
-
-    if full_precision == False and device != "cpu":
-        precision_scope = autocast
-    else:
-        precision_scope = nullcontext
-
-    all_samples = []
-    seeds = ""
-    with torch.no_grad():
-        all_samples = list()
-        for _ in trange(n_iter, desc="Sampling"):
-            for prompts in tqdm(data, desc="data"):
-                with precision_scope("cuda"):
-                    modelCS.to(device)
-                    uc = None
-                    if scale != 1.0:
-                        uc = modelCS.get_learned_conditioning(batch_size * [""])
-                    if isinstance(prompts, tuple):
-                        prompts = list(prompts)
-
-                    subprompts, weights = split_weighted_subprompts(prompts[0])
-                    if len(subprompts) > 1:
-                        c = torch.zeros_like(uc)
-                        totalWeight = sum(weights)
-                        # normalize each "sub prompt" and add it
-                        for i in range(len(subprompts)):
-                            weight = weights[i]
-                            # if not skip_normalize:
-                            weight = weight / totalWeight
-                            c = torch.add(c, modelCS.get_learned_conditioning(subprompts[i]), alpha=weight)
-                    else:
-                        c = modelCS.get_learned_conditioning(prompts)
-
-                    if device != "cpu":
-                        mem = torch.cuda.memory_allocated() / 1e6
-                        modelCS.to("cpu")
-                        while torch.cuda.memory_allocated() / 1e6 >= mem:
-                            time.sleep(1)
-
-                    # encode (scaled latent)
-                    z_enc = model.stochastic_encode(
-                        init_latent, torch.tensor([t_enc] * batch_size).to(device), seed, ddim_eta, ddim_steps
-                    )
-                    # decode it
-                    samples_ddim = model.sample(
-                                    t_enc,
-                                    c,
-                                    z_enc,
-                                    unconditional_guidance_scale=scale,
-                                    unconditional_conditioning=uc,
-                                    sampler = sampler
-                    )
-
-                    modelFS.to(device)
-                    print("saving images")
-                    for i in range(batch_size):
-
-                        x_samples_ddim = modelFS.decode_first_stage(samples_ddim[i].unsqueeze(0))
-                        x_sample = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
-                        all_samples.append(x_sample.to("cpu"))
-                        x_sample = 255.0 * rearrange(x_sample[0].cpu().numpy(), "c h w -> h w c")
-                        Image.fromarray(x_sample.astype(np.uint8)).save(
-                            os.path.join(sample_path, "seed_" + str(seed) + "_" + f"{base_count:05}.{img_format}")
-                        )
-                        seeds += str(seed) + ","
-                        seed += 1
-                        base_count += 1
-
-                    if device != "cpu":
-                        mem = torch.cuda.memory_allocated() / 1e6
-                        modelFS.to("cpu")
-                        while torch.cuda.memory_allocated() / 1e6 >= mem:
-                            time.sleep(1)
-
-                    del samples_ddim
-                    del x_sample
-                    del x_samples_ddim
-                    print("memory_final = ", torch.cuda.memory_allocated() / 1e6)
-
-    toc = time.time()
-
-    time_taken = (toc - tic) / 60.0
-    grid = torch.cat(all_samples, 0)
-    grid = make_grid(grid, nrow=n_iter)
-    grid = 255.0 * rearrange(grid, "c h w -> h w c").cpu().numpy()
-
-    txt = (
-        "Samples finished in "
-        + str(round(time_taken, 3))
-        + " minutes and exported to \n"
-        + sample_path
-        + "\nSeeds used = "
-        + seeds[:-1]
-    )
-    return Image.fromarray(grid.astype(np.uint8)), txt
-
-
-demo = gr.Interface(
-    fn=generate,
-    inputs=[
-        gr.Image(tool="editor", type="pil"),
-        "text",
-        gr.Slider(0, 1, value=0.75),
-        gr.Slider(1, 1000, value=50),
-        gr.Slider(1, 100, step=1),
-        gr.Slider(1, 100, step=1),
-        gr.Slider(64, 4096, value=512, step=64),
-        gr.Slider(64, 4096, value=512, step=64),
-        gr.Slider(0, 50, value=7.5, step=0.1),
-        gr.Slider(0, 1, step=0.01),
-        gr.Slider(1, 2, value=1, step=1),
-        gr.Text(value="cuda"),
-        "text",
-        gr.Text(value="outputs/img2img-samples"),
-        gr.Radio(["png", "jpg"], value='png'),
-        "checkbox",
-        "checkbox",
-    ],
-    outputs=["image", "text"],
-)
-demo.launch()
diff --git a/spaces/awacke1/Gradio-Blocks-Demo-2/README.md b/spaces/awacke1/Gradio-Blocks-Demo-2/README.md
deleted file mode 100644
index 124b205b616fd02da9d1973cd8f3529eb5b087e6..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Gradio-Blocks-Demo-2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: GradioBlocksTextGenerator
-emoji: 🚀
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/GradioAutoCSVLoaderToPlotly/README.md b/spaces/awacke1/GradioAutoCSVLoaderToPlotly/README.md
deleted file mode 100644
index 492dcf5a30e6d7bbbcfc4669a836fa6c8711a2e0..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioAutoCSVLoaderToPlotly/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GradioAutoCSVLoaderToPlotly
-emoji: 🚀
-colorFrom: yellow
-colorTo: purple
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/SKLearnSkopsTabularEditor/app.py b/spaces/awacke1/SKLearnSkopsTabularEditor/app.py
deleted file mode 100644
index 9d6eb9ad30b0d326a97a3d36f2844fad831eb29e..0000000000000000000000000000000000000000
--- a/spaces/awacke1/SKLearnSkopsTabularEditor/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-
-# title and description are optional
-title = "Top Ten Fun Facts"
-description = "This model predicts questions and answers with top ten topics most likely to help with fun facts from consensus. Drag and drop any column of the below example datasets to edit values as you wish and test your inputs to tabular processing AI models.."
-
-gr.Interface.load("huggingface/scikit-learn/tabular-playground", title=title, description=description).launch()
\ No newline at end of file
diff --git a/spaces/awacke1/Three.JS-TheCube-Game/style.css b/spaces/awacke1/Three.JS-TheCube-Game/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Three.JS-TheCube-Game/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/azusarang/so-vits-svc-models-ba_P/vdecoder/nsf_hifigan/nvSTFT.py b/spaces/azusarang/so-vits-svc-models-ba_P/vdecoder/nsf_hifigan/nvSTFT.py
deleted file mode 100644
index 62bd5a008f81929054f036c81955d5d73377f772..0000000000000000000000000000000000000000
--- a/spaces/azusarang/so-vits-svc-models-ba_P/vdecoder/nsf_hifigan/nvSTFT.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import math
-import os
-os.environ["LRU_CACHE_CAPACITY"] = "3"
-import random
-import torch
-import torch.utils.data
-import numpy as np
-import librosa
-from librosa.util import normalize
-from librosa.filters import mel as librosa_mel_fn
-from scipy.io.wavfile import read
-import soundfile as sf
-import torch.nn.functional as F
-
-def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
-    sampling_rate = None
-    try:
-        data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
-    except Exception as ex:
-        print(f"'{full_path}' failed to load.\nException:")
-        print(ex)
-        if return_empty_on_exception:
-            return [], sampling_rate or target_sr or 48000
-        else:
-            raise Exception(ex)
-    
-    if len(data.shape) > 1:
-        data = data[:, 0]
-        assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
-    
-    if np.issubdtype(data.dtype, np.integer): # if audio data is type int
-        max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
-    else: # if audio data is type fp32
-        max_mag = max(np.amax(data), -np.amin(data))
-        max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
-    
-    data = torch.FloatTensor(data.astype(np.float32))/max_mag
-    
-    if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
-        return [], sampling_rate or target_sr or 48000
-    if target_sr is not None and sampling_rate != target_sr:
-        data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
-        sampling_rate = target_sr
-    
-    return data, sampling_rate
-
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
-
-def dynamic_range_decompression(x, C=1):
-    return np.exp(x) / C
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-def dynamic_range_decompression_torch(x, C=1):
-    return torch.exp(x) / C
-
-class STFT():
-    def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
-        self.target_sr = sr
-        
-        self.n_mels     = n_mels
-        self.n_fft      = n_fft
-        self.win_size   = win_size
-        self.hop_length = hop_length
-        self.fmin     = fmin
-        self.fmax     = fmax
-        self.clip_val = clip_val
-        self.mel_basis = {}
-        self.hann_window = {}
-    
-    def get_mel(self, y, keyshift=0, speed=1, center=False):
-        sampling_rate = self.target_sr
-        n_mels     = self.n_mels
-        n_fft      = self.n_fft
-        win_size   = self.win_size
-        hop_length = self.hop_length
-        fmin       = self.fmin
-        fmax       = self.fmax
-        clip_val   = self.clip_val
-        
-        factor = 2 ** (keyshift / 12)       
-        n_fft_new = int(np.round(n_fft * factor))
-        win_size_new = int(np.round(win_size * factor))
-        hop_length_new = int(np.round(hop_length * speed))
-        
-        if torch.min(y) < -1.:
-            print('min value is ', torch.min(y))
-        if torch.max(y) > 1.:
-            print('max value is ', torch.max(y))
-        
-        mel_basis_key = str(fmax)+'_'+str(y.device)
-        if mel_basis_key not in self.mel_basis:
-            mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
-            self.mel_basis[mel_basis_key] = torch.from_numpy(mel).float().to(y.device)
-        
-        keyshift_key = str(keyshift)+'_'+str(y.device)
-        if keyshift_key not in self.hann_window:
-            self.hann_window[keyshift_key] = torch.hann_window(win_size_new).to(y.device)
-        
-        pad_left = (win_size_new - hop_length_new) //2
-        pad_right = max((win_size_new- hop_length_new + 1) //2, win_size_new - y.size(-1) - pad_left)
-        if pad_right < y.size(-1):
-            mode = 'reflect'
-        else:
-            mode = 'constant'
-        y = torch.nn.functional.pad(y.unsqueeze(1), (pad_left, pad_right), mode = mode)
-        y = y.squeeze(1)
-        
-        spec = torch.stft(y, n_fft_new, hop_length=hop_length_new, win_length=win_size_new, window=self.hann_window[keyshift_key],
-                          center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-        # print(111,spec)
-        spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
-        if keyshift != 0:
-            size = n_fft // 2 + 1
-            resize = spec.size(1)
-            if resize < size:
-                spec = F.pad(spec, (0, 0, 0, size-resize))
-            spec = spec[:, :size, :] * win_size / win_size_new
-            
-        # print(222,spec)
-        spec = torch.matmul(self.mel_basis[mel_basis_key], spec)
-        # print(333,spec)
-        spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
-        # print(444,spec)
-        return spec
-    
-    def __call__(self, audiopath):
-        audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
-        spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
-        return spect
-
-stft = STFT()
diff --git a/spaces/badayvedat/LLaVA/llava/model/multimodal_encoder/builder.py b/spaces/badayvedat/LLaVA/llava/model/multimodal_encoder/builder.py
deleted file mode 100644
index 2b13589d4e55af529fe0838c4130c2033ac10478..0000000000000000000000000000000000000000
--- a/spaces/badayvedat/LLaVA/llava/model/multimodal_encoder/builder.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import os
-from .clip_encoder import CLIPVisionTower
-
-
-def build_vision_tower(vision_tower_cfg, **kwargs):
-    vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None))
-    is_absolute_path_exists = os.path.exists(vision_tower)
-    if is_absolute_path_exists or vision_tower.startswith("openai") or vision_tower.startswith("laion"):
-        return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
-
-    raise ValueError(f'Unknown vision tower: {vision_tower}')
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/crossfade/gui.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/crossfade/gui.js
deleted file mode 100644
index 40184cf4c9f43d1c469760f42aa7d70f8586aabb..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/crossfade/gui.js
+++ /dev/null
@@ -1,39 +0,0 @@
-var transitionParams = {
-	"useTexture": true,
-	"transition": 0.5,
-	"transitionSpeed": 2.0,
-	"texture": 5,
-	"loopTexture": true,
-	"animateTransition": true,
-	"textureThreshold": 0.3
-};
-
-function initGUI() {
-	
-	var gui = new dat.GUI();
-
-	gui.add( transitionParams, "useTexture" ).onChange( function( value ) {
-		
-		transition.useTexture( value );
-		
-	} );
-	
-	gui.add( transitionParams, 'loopTexture' );
-	
-	gui.add( transitionParams, 'texture', { Perlin: 0, Squares: 1, Cells: 2, Distort: 3, Gradient: 4, Radial: 5 } ).onChange( function( value ) {
-		
-		transition.setTexture( value );
-		
-	} ).listen();
-		
-	gui.add( transitionParams, "textureThreshold", 0, 1, 0.01 ).onChange( function( value ) {
-		
-		transition.setTextureThreshold( value );
-		
-	} );
-
-	gui.add( transitionParams, "animateTransition" );
-	gui.add( transitionParams, "transition", 0, 1, 0.01 ).listen();
-	gui.add( transitionParams, "transitionSpeed", 0.5, 5, 0.01 );
-	
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/distanceRGBA_frag.glsl.js b/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/distanceRGBA_frag.glsl.js
deleted file mode 100644
index 7c78545b38e00f34644e6576e4e99228dbfc614a..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/renderers/shaders/ShaderLib/distanceRGBA_frag.glsl.js
+++ /dev/null
@@ -1,33 +0,0 @@
-export default /* glsl */`
-#define DISTANCE
-
-uniform vec3 referencePosition;
-uniform float nearDistance;
-uniform float farDistance;
-varying vec3 vWorldPosition;
-
-#include <common>
-#include <packing>
-#include <uv_pars_fragment>
-#include <map_pars_fragment>
-#include <alphamap_pars_fragment>
-#include <clipping_planes_pars_fragment>
-
-void main () {
-
-	#include <clipping_planes_fragment>
-
-	vec4 diffuseColor = vec4( 1.0 );
-
-	#include <map_fragment>
-	#include <alphamap_fragment>
-	#include <alphatest_fragment>
-
-	float dist = length( vWorldPosition - referencePosition );
-	dist = ( dist - nearDistance ) / ( farDistance - nearDistance );
-	dist = saturate( dist ); // clamp to [ 0, 1 ]
-
-	gl_FragColor = packDepthToRGBA( dist );
-
-}
-`;
diff --git a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620231624.py b/spaces/beihai/PDF-Table-Extractor/.history/app_20220620231624.py
deleted file mode 100644
index b694f26981ce076a89361a2c7c94a91a502b9e03..0000000000000000000000000000000000000000
--- a/spaces/beihai/PDF-Table-Extractor/.history/app_20220620231624.py
+++ /dev/null
@@ -1,42 +0,0 @@
-#-*- coding : utf-8-*-
-import os,subprocess,base64
-from subprocess import STDOUT #os process manipuation
-import streamlit as st
-# @st.cache
-# def gh():
-#     """install ghostscript on the linux machine"""
-    
-#     proc = subprocess.Popen('apt-get update', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-#     proc = subprocess.Popen('apt-get install sudo', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-#     proc = subprocess.Popen('sudo apt update', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-#     proc = subprocess.Popen('apt install ghostscript python3-tk', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-#     proc = subprocess.Popen('apt-get install -y libgl1-mesa-glx', shell=True, stdin=None, stdout=open(os.devnull,"wb"), stderr=STDOUT, executable="/bin/bash")
-#     proc.wait()
-# gh()
-import pandas as pd 
-import camelot as cam # extracting tables from PDFs 
-
-st.title("PDF Table Extractor")
-
-input_pdf = st.file_uploader(label = "", type = 'pdf')
-
-page_number = st.text_input("请填写表格所在PDF页码，eg: 3", value = 1)
-
-if input_pdf is not None:
-    # byte object into a PDF file 
-    with open("input.pdf", "wb") as f:
-        base64_pdf = base64.b64encode(input_pdf.read()).decode('utf-8')
-        f.write(base64.b64decode(base64_pdf))
-    f.close()
-
-    # read the pdf and parse it using stream
-    tables = cam.read_pdf("input.pdf", pages=page_number)
-    result = pd.ExcelWriter('result.xlsx', engine='xlsxwriter') 
-    tables[0].to_excel(result,index=False) 
-    # for i in range(0,len(tables)):
-    #     table = tables[i].df
-    #     sheetname = str(i)
-    #     table.to_excel(result, sheetname,index=False) 
-
-    with open('result.xlsx','rb') as f:
-       st.download_button('提取完成，点击下载！', f,file_name='result.xlsx',mime="application/vnd.ms-excel")
\ No newline at end of file
diff --git a/spaces/bigjoker/stable-diffusion-webui/modules/prompt_parser.py b/spaces/bigjoker/stable-diffusion-webui/modules/prompt_parser.py
deleted file mode 100644
index a7bbfa4ea73cbfcb6da0e1012ac166042b6fae08..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/modules/prompt_parser.py
+++ /dev/null
@@ -1,373 +0,0 @@
-import re
-from collections import namedtuple
-from typing import List
-import lark
-
-# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
-# will be represented with prompt_schedule like this (assuming steps=100):
-# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
-# [50, 'fantasy landscape with a lake and an oak in foreground in background shoddy']
-# [60, 'fantasy landscape with a lake and an oak in foreground in background masterful']
-# [75, 'fantasy landscape with a lake and an oak in background masterful']
-# [100, 'fantasy landscape with a lake and a christmas tree in background masterful']
-
-schedule_parser = lark.Lark(r"""
-!start: (prompt | /[][():]/+)*
-prompt: (emphasized | scheduled | alternate | plain | WHITESPACE)*
-!emphasized: "(" prompt ")"
-        | "(" prompt ":" prompt ")"
-        | "[" prompt "]"
-scheduled: "[" [prompt ":"] prompt ":" [WHITESPACE] NUMBER "]"
-alternate: "[" prompt ("|" prompt)+ "]"
-WHITESPACE: /\s+/
-plain: /([^\\\[\]():|]|\\.)+/
-%import common.SIGNED_NUMBER -> NUMBER
-""")
-
-def get_learned_conditioning_prompt_schedules(prompts, steps):
-    """
-    >>> g = lambda p: get_learned_conditioning_prompt_schedules([p], 10)[0]
-    >>> g("test")
-    [[10, 'test']]
-    >>> g("a [b:3]")
-    [[3, 'a '], [10, 'a b']]
-    >>> g("a [b: 3]")
-    [[3, 'a '], [10, 'a b']]
-    >>> g("a [[[b]]:2]")
-    [[2, 'a '], [10, 'a [[b]]']]
-    >>> g("[(a:2):3]")
-    [[3, ''], [10, '(a:2)']]
-    >>> g("a [b : c : 1] d")
-    [[1, 'a b  d'], [10, 'a  c  d']]
-    >>> g("a[b:[c:d:2]:1]e")
-    [[1, 'abe'], [2, 'ace'], [10, 'ade']]
-    >>> g("a [unbalanced")
-    [[10, 'a [unbalanced']]
-    >>> g("a [b:.5] c")
-    [[5, 'a  c'], [10, 'a b c']]
-    >>> g("a [{b|d{:.5] c")  # not handling this right now
-    [[5, 'a  c'], [10, 'a {b|d{ c']]
-    >>> g("((a][:b:c [d:3]")
-    [[3, '((a][:b:c '], [10, '((a][:b:c d']]
-    >>> g("[a|(b:1.1)]")
-    [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
-    """
-
-    def collect_steps(steps, tree):
-        l = [steps]
-        class CollectSteps(lark.Visitor):
-            def scheduled(self, tree):
-                tree.children[-1] = float(tree.children[-1])
-                if tree.children[-1] < 1:
-                    tree.children[-1] *= steps
-                tree.children[-1] = min(steps, int(tree.children[-1]))
-                l.append(tree.children[-1])
-            def alternate(self, tree):
-                l.extend(range(1, steps+1))
-        CollectSteps().visit(tree)
-        return sorted(set(l))
-
-    def at_step(step, tree):
-        class AtStep(lark.Transformer):
-            def scheduled(self, args):
-                before, after, _, when = args
-                yield before or () if step <= when else after
-            def alternate(self, args):
-                yield next(args[(step - 1)%len(args)])
-            def start(self, args):
-                def flatten(x):
-                    if type(x) == str:
-                        yield x
-                    else:
-                        for gen in x:
-                            yield from flatten(gen)
-                return ''.join(flatten(args))
-            def plain(self, args):
-                yield args[0].value
-            def __default__(self, data, children, meta):
-                for child in children:
-                    yield child
-        return AtStep().transform(tree)
-
-    def get_schedule(prompt):
-        try:
-            tree = schedule_parser.parse(prompt)
-        except lark.exceptions.LarkError as e:
-            if 0:
-                import traceback
-                traceback.print_exc()
-            return [[steps, prompt]]
-        return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
-
-    promptdict = {prompt: get_schedule(prompt) for prompt in set(prompts)}
-    return [promptdict[prompt] for prompt in prompts]
-
-
-ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
-
-
-def get_learned_conditioning(model, prompts, steps):
-    """converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
-    and the sampling step at which this condition is to be replaced by the next one.
-
-    Input:
-    (model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
-
-    Output:
-    [
-        [
-            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0523,  ..., -0.4901, -0.3066,  0.0674], ..., [ 0.3317, -0.5102, -0.4066,  ...,  0.4119, -0.7647, -1.0160]], device='cuda:0'))
-        ],
-        [
-            ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.0192,  0.3867, -0.4644,  ...,  0.1135, -0.3696, -0.4625]], device='cuda:0')),
-            ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886,  0.0229, -0.0522,  ..., -0.4901, -0.3067,  0.0673], ..., [-0.7352, -0.4356, -0.7888,  ...,  0.6994, -0.4312, -1.2593]], device='cuda:0'))
-        ]
-    ]
-    """
-    res = []
-
-    prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
-    cache = {}
-
-    for prompt, prompt_schedule in zip(prompts, prompt_schedules):
-
-        cached = cache.get(prompt, None)
-        if cached is not None:
-            res.append(cached)
-            continue
-
-        texts = [x[1] for x in prompt_schedule]
-        conds = model.get_learned_conditioning(texts)
-
-        cond_schedule = []
-        for i, (end_at_step, text) in enumerate(prompt_schedule):
-            cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
-
-        cache[prompt] = cond_schedule
-        res.append(cond_schedule)
-
-    return res
-
-
-re_AND = re.compile(r"\bAND\b")
-re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
-
-def get_multicond_prompt_list(prompts):
-    res_indexes = []
-
-    prompt_flat_list = []
-    prompt_indexes = {}
-
-    for prompt in prompts:
-        subprompts = re_AND.split(prompt)
-
-        indexes = []
-        for subprompt in subprompts:
-            match = re_weight.search(subprompt)
-
-            text, weight = match.groups() if match is not None else (subprompt, 1.0)
-
-            weight = float(weight) if weight is not None else 1.0
-
-            index = prompt_indexes.get(text, None)
-            if index is None:
-                index = len(prompt_flat_list)
-                prompt_flat_list.append(text)
-                prompt_indexes[text] = index
-
-            indexes.append((index, weight))
-
-        res_indexes.append(indexes)
-
-    return res_indexes, prompt_flat_list, prompt_indexes
-
-
-class ComposableScheduledPromptConditioning:
-    def __init__(self, schedules, weight=1.0):
-        self.schedules: List[ScheduledPromptConditioning] = schedules
-        self.weight: float = weight
-
-
-class MulticondLearnedConditioning:
-    def __init__(self, shape, batch):
-        self.shape: tuple = shape  # the shape field is needed to send this object to DDIM/PLMS
-        self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
-
-def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
-    """same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
-    For each prompt, the list is obtained by splitting the prompt using the AND separator.
-
-    https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
-    """
-
-    res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
-
-    learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
-
-    res = []
-    for indexes in res_indexes:
-        res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
-
-    return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
-
-
-def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
-    param = c[0][0].cond
-    res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
-    for i, cond_schedule in enumerate(c):
-        target_index = 0
-        for current, (end_at, cond) in enumerate(cond_schedule):
-            if current_step <= end_at:
-                target_index = current
-                break
-        res[i] = cond_schedule[target_index].cond
-
-    return res
-
-
-def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
-    param = c.batch[0][0].schedules[0].cond
-
-    tensors = []
-    conds_list = []
-
-    for batch_no, composable_prompts in enumerate(c.batch):
-        conds_for_batch = []
-
-        for cond_index, composable_prompt in enumerate(composable_prompts):
-            target_index = 0
-            for current, (end_at, cond) in enumerate(composable_prompt.schedules):
-                if current_step <= end_at:
-                    target_index = current
-                    break
-
-            conds_for_batch.append((len(tensors), composable_prompt.weight))
-            tensors.append(composable_prompt.schedules[target_index].cond)
-
-        conds_list.append(conds_for_batch)
-
-    # if prompts have wildly different lengths above the limit we'll get tensors fo different shapes
-    # and won't be able to torch.stack them. So this fixes that.
-    token_count = max([x.shape[0] for x in tensors])
-    for i in range(len(tensors)):
-        if tensors[i].shape[0] != token_count:
-            last_vector = tensors[i][-1:]
-            last_vector_repeated = last_vector.repeat([token_count - tensors[i].shape[0], 1])
-            tensors[i] = torch.vstack([tensors[i], last_vector_repeated])
-
-    return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
-
-
-re_attention = re.compile(r"""
-\\\(|
-\\\)|
-\\\[|
-\\]|
-\\\\|
-\\|
-\(|
-\[|
-:([+-]?[.\d]+)\)|
-\)|
-]|
-[^\\()\[\]:]+|
-:
-""", re.X)
-
-re_break = re.compile(r"\s*\bBREAK\b\s*", re.S)
-
-def parse_prompt_attention(text):
-    """
-    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
-    Accepted tokens are:
-      (abc) - increases attention to abc by a multiplier of 1.1
-      (abc:3.12) - increases attention to abc by a multiplier of 3.12
-      [abc] - decreases attention to abc by a multiplier of 1.1
-      \( - literal character '('
-      \[ - literal character '['
-      \) - literal character ')'
-      \] - literal character ']'
-      \\ - literal character '\'
-      anything else - just text
-
-    >>> parse_prompt_attention('normal text')
-    [['normal text', 1.0]]
-    >>> parse_prompt_attention('an (important) word')
-    [['an ', 1.0], ['important', 1.1], [' word', 1.0]]
-    >>> parse_prompt_attention('(unbalanced')
-    [['unbalanced', 1.1]]
-    >>> parse_prompt_attention('\(literal\]')
-    [['(literal]', 1.0]]
-    >>> parse_prompt_attention('(unnecessary)(parens)')
-    [['unnecessaryparens', 1.1]]
-    >>> parse_prompt_attention('a (((house:1.3)) [on] a (hill:0.5), sun, (((sky))).')
-    [['a ', 1.0],
-     ['house', 1.5730000000000004],
-     [' ', 1.1],
-     ['on', 1.0],
-     [' a ', 1.1],
-     ['hill', 0.55],
-     [', sun, ', 1.1],
-     ['sky', 1.4641000000000006],
-     ['.', 1.1]]
-    """
-
-    res = []
-    round_brackets = []
-    square_brackets = []
-
-    round_bracket_multiplier = 1.1
-    square_bracket_multiplier = 1 / 1.1
-
-    def multiply_range(start_position, multiplier):
-        for p in range(start_position, len(res)):
-            res[p][1] *= multiplier
-
-    for m in re_attention.finditer(text):
-        text = m.group(0)
-        weight = m.group(1)
-
-        if text.startswith('\\'):
-            res.append([text[1:], 1.0])
-        elif text == '(':
-            round_brackets.append(len(res))
-        elif text == '[':
-            square_brackets.append(len(res))
-        elif weight is not None and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), float(weight))
-        elif text == ')' and len(round_brackets) > 0:
-            multiply_range(round_brackets.pop(), round_bracket_multiplier)
-        elif text == ']' and len(square_brackets) > 0:
-            multiply_range(square_brackets.pop(), square_bracket_multiplier)
-        else:
-            parts = re.split(re_break, text)
-            for i, part in enumerate(parts):
-                if i > 0:
-                    res.append(["BREAK", -1])
-                res.append([part, 1.0])
-
-    for pos in round_brackets:
-        multiply_range(pos, round_bracket_multiplier)
-
-    for pos in square_brackets:
-        multiply_range(pos, square_bracket_multiplier)
-
-    if len(res) == 0:
-        res = [["", 1.0]]
-
-    # merge runs of identical weights
-    i = 0
-    while i + 1 < len(res):
-        if res[i][1] == res[i + 1][1]:
-            res[i][0] += res[i + 1][0]
-            res.pop(i + 1)
-        else:
-            i += 1
-
-    return res
-
-if __name__ == "__main__":
-    import doctest
-    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
-else:
-    import torch  # doctest faster
diff --git a/spaces/bioriAsaeru/text-to-voice/Avira Antivirus Pro 2020 15.0.2002.1755 Crack Activation Key The Best Antivirus Solution.md b/spaces/bioriAsaeru/text-to-voice/Avira Antivirus Pro 2020 15.0.2002.1755 Crack Activation Key The Best Antivirus Solution.md
deleted file mode 100644
index f1b675cf6a161b927205ae306ed44722cd3bdfa5..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Avira Antivirus Pro 2020 15.0.2002.1755 Crack Activation Key The Best Antivirus Solution.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Avira Antivirus Pro 2020 15.0.2002.1755 Crack Activation Key Download</h2><br /><p><b><b>Download</b> &#10031; <a href="https://urloso.com/2uyRix">https://urloso.com/2uyRix</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/grids/_base_explorers.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/grids/_base_explorers.py
deleted file mode 100644
index d3f26666aa596f7bd2e8695c4f00e7963e978ceb..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/grids/_base_explorers.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-import time
-import typing as tp
-from dora import Explorer
-import treetable as tt
-
-
-def get_sheep_ping(sheep) -> tp.Optional[str]:
-    """Return the amount of time since the Sheep made some update
-    to its log. Returns a str using the relevant time unit."""
-    ping = None
-    if sheep.log is not None and sheep.log.exists():
-        delta = time.time() - sheep.log.stat().st_mtime
-        if delta > 3600 * 24:
-            ping = f'{delta / (3600 * 24):.1f}d'
-        elif delta > 3600:
-            ping = f'{delta / (3600):.1f}h'
-        elif delta > 60:
-            ping = f'{delta / 60:.1f}m'
-        else:
-            ping = f'{delta:.1f}s'
-    return ping
-
-
-class BaseExplorer(ABC, Explorer):
-    """Base explorer for AudioCraft grids.
-
-    All task specific solvers are expected to implement the `get_grid_metrics`
-    method to specify logic about metrics to display for a given task.
-
-    If additional stages are used, the child explorer must define how to handle
-    these new stages in the `process_history` and `process_sheep` methods.
-    """
-    def stages(self):
-        return ["train", "valid", "evaluate"]
-
-    def get_grid_meta(self):
-        """Returns the list of Meta information to display for each XP/job.
-        """
-        return [
-            tt.leaf("index", align=">"),
-            tt.leaf("name", wrap=140),
-            tt.leaf("state"),
-            tt.leaf("sig", align=">"),
-            tt.leaf("sid", align="<"),
-        ]
-
-    @abstractmethod
-    def get_grid_metrics(self):
-        """Return the metrics that should be displayed in the tracking table.
-        """
-        ...
-
-    def process_sheep(self, sheep, history):
-        train = {
-            "epoch": len(history),
-        }
-        parts = {"train": train}
-        for metrics in history:
-            for key, sub in metrics.items():
-                part = parts.get(key, {})
-                if 'duration' in sub:
-                    # Convert to minutes for readability.
-                    sub['duration'] = sub['duration'] / 60.
-                part.update(sub)
-                parts[key] = part
-        ping = get_sheep_ping(sheep)
-        if ping is not None:
-            for name in self.stages():
-                if name not in parts:
-                    parts[name] = {}
-                # Add the ping to each part for convenience.
-                parts[name]['ping'] = ping
-        return parts
diff --git a/spaces/camel-ai/camel-data-explorer/README.md b/spaces/camel-ai/camel-data-explorer/README.md
deleted file mode 100644
index ae030b6bea2ca505c98c584da175ee649e6942bd..0000000000000000000000000000000000000000
--- a/spaces/camel-ai/camel-data-explorer/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Camel Data Explorer
-emoji: 🦀
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PyAccess.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PyAccess.py
deleted file mode 100644
index 99b46a4a66c013afc08edf134384e7a1d4dc200a..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PyAccess.py
+++ /dev/null
@@ -1,363 +0,0 @@
-#
-# The Python Imaging Library
-# Pillow fork
-#
-# Python implementation of the PixelAccess Object
-#
-# Copyright (c) 1997-2009 by Secret Labs AB.  All rights reserved.
-# Copyright (c) 1995-2009 by Fredrik Lundh.
-# Copyright (c) 2013 Eric Soroos
-#
-# See the README file for information on usage and redistribution
-#
-
-# Notes:
-#
-#  * Implements the pixel access object following Access.c
-#  * Taking only the tuple form, which is used from python.
-#    * Fill.c uses the integer form, but it's still going to use the old
-#      Access.c implementation.
-#
-
-import logging
-import sys
-
-from ._deprecate import deprecate
-
-try:
-    from cffi import FFI
-
-    defs = """
-    struct Pixel_RGBA {
-        unsigned char r,g,b,a;
-    };
-    struct Pixel_I16 {
-        unsigned char l,r;
-    };
-    """
-    ffi = FFI()
-    ffi.cdef(defs)
-except ImportError as ex:
-    # Allow error import for doc purposes, but error out when accessing
-    # anything in core.
-    from ._util import DeferredError
-
-    FFI = ffi = DeferredError(ex)
-
-logger = logging.getLogger(__name__)
-
-
-class PyAccess:
-    def __init__(self, img, readonly=False):
-        deprecate("PyAccess", 11)
-        vals = dict(img.im.unsafe_ptrs)
-        self.readonly = readonly
-        self.image8 = ffi.cast("unsigned char **", vals["image8"])
-        self.image32 = ffi.cast("int **", vals["image32"])
-        self.image = ffi.cast("unsigned char **", vals["image"])
-        self.xsize, self.ysize = img.im.size
-        self._img = img
-
-        # Keep pointer to im object to prevent dereferencing.
-        self._im = img.im
-        if self._im.mode in ("P", "PA"):
-            self._palette = img.palette
-
-        # Debugging is polluting test traces, only useful here
-        # when hacking on PyAccess
-        # logger.debug("%s", vals)
-        self._post_init()
-
-    def _post_init(self):
-        pass
-
-    def __setitem__(self, xy, color):
-        """
-        Modifies the pixel at x,y. The color is given as a single
-        numerical value for single band images, and a tuple for
-        multi-band images
-
-        :param xy: The pixel coordinate, given as (x, y). See
-           :ref:`coordinate-system`.
-        :param color: The pixel value.
-        """
-        if self.readonly:
-            msg = "Attempt to putpixel a read only image"
-            raise ValueError(msg)
-        (x, y) = xy
-        if x < 0:
-            x = self.xsize + x
-        if y < 0:
-            y = self.ysize + y
-        (x, y) = self.check_xy((x, y))
-
-        if (
-            self._im.mode in ("P", "PA")
-            and isinstance(color, (list, tuple))
-            and len(color) in [3, 4]
-        ):
-            # RGB or RGBA value for a P or PA image
-            if self._im.mode == "PA":
-                alpha = color[3] if len(color) == 4 else 255
-                color = color[:3]
-            color = self._palette.getcolor(color, self._img)
-            if self._im.mode == "PA":
-                color = (color, alpha)
-
-        return self.set_pixel(x, y, color)
-
-    def __getitem__(self, xy):
-        """
-        Returns the pixel at x,y. The pixel is returned as a single
-        value for single band images or a tuple for multiple band
-        images
-
-        :param xy: The pixel coordinate, given as (x, y). See
-          :ref:`coordinate-system`.
-        :returns: a pixel value for single band images, a tuple of
-          pixel values for multiband images.
-        """
-        (x, y) = xy
-        if x < 0:
-            x = self.xsize + x
-        if y < 0:
-            y = self.ysize + y
-        (x, y) = self.check_xy((x, y))
-        return self.get_pixel(x, y)
-
-    putpixel = __setitem__
-    getpixel = __getitem__
-
-    def check_xy(self, xy):
-        (x, y) = xy
-        if not (0 <= x < self.xsize and 0 <= y < self.ysize):
-            msg = "pixel location out of range"
-            raise ValueError(msg)
-        return xy
-
-
-class _PyAccess32_2(PyAccess):
-    """PA, LA, stored in first and last bytes of a 32 bit word"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.r, pixel.a
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        # tuple
-        pixel.r = min(color[0], 255)
-        pixel.a = min(color[1], 255)
-
-
-class _PyAccess32_3(PyAccess):
-    """RGB and friends, stored in the first three bytes of a 32 bit word"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.r, pixel.g, pixel.b
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        # tuple
-        pixel.r = min(color[0], 255)
-        pixel.g = min(color[1], 255)
-        pixel.b = min(color[2], 255)
-        pixel.a = 255
-
-
-class _PyAccess32_4(PyAccess):
-    """RGBA etc, all 4 bytes of a 32 bit word"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_RGBA **", self.image32)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.r, pixel.g, pixel.b, pixel.a
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        # tuple
-        pixel.r = min(color[0], 255)
-        pixel.g = min(color[1], 255)
-        pixel.b = min(color[2], 255)
-        pixel.a = min(color[3], 255)
-
-
-class _PyAccess8(PyAccess):
-    """1, L, P, 8 bit images stored as uint8"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = self.image8
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        try:
-            # integer
-            self.pixels[y][x] = min(color, 255)
-        except TypeError:
-            # tuple
-            self.pixels[y][x] = min(color[0], 255)
-
-
-class _PyAccessI16_N(PyAccess):
-    """I;16 access, native bitendian without conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("unsigned short **", self.image)
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        try:
-            # integer
-            self.pixels[y][x] = min(color, 65535)
-        except TypeError:
-            # tuple
-            self.pixels[y][x] = min(color[0], 65535)
-
-
-class _PyAccessI16_L(PyAccess):
-    """I;16L access, with conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_I16 **", self.image)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.l + pixel.r * 256
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        try:
-            color = min(color, 65535)
-        except TypeError:
-            color = min(color[0], 65535)
-
-        pixel.l = color & 0xFF  # noqa: E741
-        pixel.r = color >> 8
-
-
-class _PyAccessI16_B(PyAccess):
-    """I;16B access, with conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("struct Pixel_I16 **", self.image)
-
-    def get_pixel(self, x, y):
-        pixel = self.pixels[y][x]
-        return pixel.l * 256 + pixel.r
-
-    def set_pixel(self, x, y, color):
-        pixel = self.pixels[y][x]
-        try:
-            color = min(color, 65535)
-        except Exception:
-            color = min(color[0], 65535)
-
-        pixel.l = color >> 8  # noqa: E741
-        pixel.r = color & 0xFF
-
-
-class _PyAccessI32_N(PyAccess):
-    """Signed Int32 access, native endian"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = self.image32
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        self.pixels[y][x] = color
-
-
-class _PyAccessI32_Swap(PyAccess):
-    """I;32L/B access, with byteswapping conversion"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = self.image32
-
-    def reverse(self, i):
-        orig = ffi.new("int *", i)
-        chars = ffi.cast("unsigned char *", orig)
-        chars[0], chars[1], chars[2], chars[3] = chars[3], chars[2], chars[1], chars[0]
-        return ffi.cast("int *", chars)[0]
-
-    def get_pixel(self, x, y):
-        return self.reverse(self.pixels[y][x])
-
-    def set_pixel(self, x, y, color):
-        self.pixels[y][x] = self.reverse(color)
-
-
-class _PyAccessF(PyAccess):
-    """32 bit float access"""
-
-    def _post_init(self, *args, **kwargs):
-        self.pixels = ffi.cast("float **", self.image32)
-
-    def get_pixel(self, x, y):
-        return self.pixels[y][x]
-
-    def set_pixel(self, x, y, color):
-        try:
-            # not a tuple
-            self.pixels[y][x] = color
-        except TypeError:
-            # tuple
-            self.pixels[y][x] = color[0]
-
-
-mode_map = {
-    "1": _PyAccess8,
-    "L": _PyAccess8,
-    "P": _PyAccess8,
-    "I;16N": _PyAccessI16_N,
-    "LA": _PyAccess32_2,
-    "La": _PyAccess32_2,
-    "PA": _PyAccess32_2,
-    "RGB": _PyAccess32_3,
-    "LAB": _PyAccess32_3,
-    "HSV": _PyAccess32_3,
-    "YCbCr": _PyAccess32_3,
-    "RGBA": _PyAccess32_4,
-    "RGBa": _PyAccess32_4,
-    "RGBX": _PyAccess32_4,
-    "CMYK": _PyAccess32_4,
-    "F": _PyAccessF,
-    "I": _PyAccessI32_N,
-}
-
-if sys.byteorder == "little":
-    mode_map["I;16"] = _PyAccessI16_N
-    mode_map["I;16L"] = _PyAccessI16_N
-    mode_map["I;16B"] = _PyAccessI16_B
-
-    mode_map["I;32L"] = _PyAccessI32_N
-    mode_map["I;32B"] = _PyAccessI32_Swap
-else:
-    mode_map["I;16"] = _PyAccessI16_L
-    mode_map["I;16L"] = _PyAccessI16_L
-    mode_map["I;16B"] = _PyAccessI16_N
-
-    mode_map["I;32L"] = _PyAccessI32_Swap
-    mode_map["I;32B"] = _PyAccessI32_N
-
-
-def new(img, readonly=False):
-    access_type = mode_map.get(img.mode, None)
-    if not access_type:
-        logger.debug("PyAccess Not Implemented: %s", img.mode)
-        return None
-    return access_type(img, readonly)
diff --git a/spaces/candlend/vits-hoshimi/vits/transforms.py b/spaces/candlend/vits-hoshimi/vits/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/candlend/vits-hoshimi/vits/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/export/caffe2_modeling.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/export/caffe2_modeling.py
deleted file mode 100644
index e00de4ad28fd81483c9e1161394b7b508fdad91f..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/detectron2/export/caffe2_modeling.py
+++ /dev/null
@@ -1,419 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import functools
-import io
-import struct
-import types
-import torch
-
-from detectron2.modeling import meta_arch
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.roi_heads import keypoint_head
-from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
-
-from .c10 import Caffe2Compatible
-from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn
-from .shared import (
-    alias,
-    check_set_pb_arg,
-    get_pb_arg_floats,
-    get_pb_arg_valf,
-    get_pb_arg_vali,
-    get_pb_arg_vals,
-    mock_torch_nn_functional_interpolate,
-)
-
-
-def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False):
-    """
-    A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor])
-    to detectron2's format (i.e. list of Instances instance).
-    This only works when the model follows the Caffe2 detectron's naming convention.
-
-    Args:
-        image_sizes (List[List[int, int]]): [H, W] of every image.
-        tensor_outputs (Dict[str, Tensor]): external_output to its tensor.
-
-        force_mask_on (Bool): if true, the it make sure there'll be pred_masks even
-            if the mask is not found from tensor_outputs (usually due to model crash)
-    """
-
-    results = [Instances(image_size) for image_size in image_sizes]
-
-    batch_splits = tensor_outputs.get("batch_splits", None)
-    if batch_splits:
-        raise NotImplementedError()
-    assert len(image_sizes) == 1
-    result = results[0]
-
-    bbox_nms = tensor_outputs["bbox_nms"]
-    score_nms = tensor_outputs["score_nms"]
-    class_nms = tensor_outputs["class_nms"]
-    # Detection will always success because Conv support 0-batch
-    assert bbox_nms is not None
-    assert score_nms is not None
-    assert class_nms is not None
-    if bbox_nms.shape[1] == 5:
-        result.pred_boxes = RotatedBoxes(bbox_nms)
-    else:
-        result.pred_boxes = Boxes(bbox_nms)
-    result.scores = score_nms
-    result.pred_classes = class_nms.to(torch.int64)
-
-    mask_fcn_probs = tensor_outputs.get("mask_fcn_probs", None)
-    if mask_fcn_probs is not None:
-        # finish the mask pred
-        mask_probs_pred = mask_fcn_probs
-        num_masks = mask_probs_pred.shape[0]
-        class_pred = result.pred_classes
-        indices = torch.arange(num_masks, device=class_pred.device)
-        mask_probs_pred = mask_probs_pred[indices, class_pred][:, None]
-        result.pred_masks = mask_probs_pred
-    elif force_mask_on:
-        # NOTE: there's no way to know the height/width of mask here, it won't be
-        # used anyway when batch size is 0, so just set them to 0.
-        result.pred_masks = torch.zeros([0, 1, 0, 0], dtype=torch.uint8)
-
-    keypoints_out = tensor_outputs.get("keypoints_out", None)
-    kps_score = tensor_outputs.get("kps_score", None)
-    if keypoints_out is not None:
-        # keypoints_out: [N, 4, #kypoints], where 4 is in order of (x, y, score, prob)
-        keypoints_tensor = keypoints_out
-        # NOTE: it's possible that prob is not calculated if "should_output_softmax"
-        # is set to False in HeatmapMaxKeypoint, so just using raw score, seems
-        # it doesn't affect mAP. TODO: check more carefully.
-        keypoint_xyp = keypoints_tensor.transpose(1, 2)[:, :, [0, 1, 2]]
-        result.pred_keypoints = keypoint_xyp
-    elif kps_score is not None:
-        # keypoint heatmap to sparse data structure
-        pred_keypoint_logits = kps_score
-        keypoint_head.keypoint_rcnn_inference(pred_keypoint_logits, [result])
-
-    return results
-
-
-def _cast_to_f32(f64):
-    return struct.unpack("f", struct.pack("f", f64))[0]
-
-
-def set_caffe2_compatible_tensor_mode(model, enable=True):
-    def _fn(m):
-        if isinstance(m, Caffe2Compatible):
-            m.tensor_mode = enable
-
-    model.apply(_fn)
-
-
-def convert_batched_inputs_to_c2_format(batched_inputs, size_divisibility, device):
-    """
-    See get_caffe2_inputs() below.
-    """
-    assert all(isinstance(x, dict) for x in batched_inputs)
-    assert all(x["image"].dim() == 3 for x in batched_inputs)
-
-    images = [x["image"] for x in batched_inputs]
-    images = ImageList.from_tensors(images, size_divisibility)
-
-    im_info = []
-    for input_per_image, image_size in zip(batched_inputs, images.image_sizes):
-        target_height = input_per_image.get("height", image_size[0])
-        target_width = input_per_image.get("width", image_size[1])  # noqa
-        # NOTE: The scale inside im_info is kept as convention and for providing
-        # post-processing information if further processing is needed. For
-        # current Caffe2 model definitions that don't include post-processing inside
-        # the model, this number is not used.
-        # NOTE: There can be a slight difference between width and height
-        # scales, using a single number can results in numerical difference
-        # compared with D2's post-processing.
-        scale = target_height / image_size[0]
-        im_info.append([image_size[0], image_size[1], scale])
-    im_info = torch.Tensor(im_info)
-
-    return images.tensor.to(device), im_info.to(device)
-
-
-class Caffe2MetaArch(Caffe2Compatible, torch.nn.Module):
-    """
-    Base class for caffe2-compatible implementation of a meta architecture.
-    The forward is traceable and its traced graph can be converted to caffe2
-    graph through ONNX.
-    """
-
-    def __init__(self, cfg, torch_model):
-        """
-        Args:
-            cfg (CfgNode):
-            torch_model (nn.Module): the detectron2 model (meta_arch) to be
-                converted.
-        """
-        super().__init__()
-        self._wrapped_model = torch_model
-        self.eval()
-        set_caffe2_compatible_tensor_mode(self, True)
-
-    def get_caffe2_inputs(self, batched_inputs):
-        """
-        Convert pytorch-style structured inputs to caffe2-style inputs that
-        are tuples of tensors.
-
-        Args:
-            batched_inputs (list[dict]): inputs to a detectron2 model
-                in its standard format. Each dict has "image" (CHW tensor), and optionally
-                "height" and "width".
-
-        Returns:
-            tuple[Tensor]:
-                tuple of tensors that will be the inputs to the
-                :meth:`forward` method. For existing models, the first
-                is an NCHW tensor (padded and batched); the second is
-                a im_info Nx3 tensor, where the rows are
-                (height, width, unused legacy parameter)
-        """
-        return convert_batched_inputs_to_c2_format(
-            batched_inputs,
-            self._wrapped_model.backbone.size_divisibility,
-            self._wrapped_model.device,
-        )
-
-    def encode_additional_info(self, predict_net, init_net):
-        """
-        Save extra metadata that will be used by inference in the output protobuf.
-        """
-        pass
-
-    def forward(self, inputs):
-        """
-        Run the forward in caffe2-style. It has to use caffe2-compatible ops
-        and the method will be used for tracing.
-
-        Args:
-            inputs (tuple[Tensor]): inputs defined by :meth:`get_caffe2_input`.
-                They will be the inputs of the converted caffe2 graph.
-
-        Returns:
-            tuple[Tensor]: output tensors. They will be the outputs of the
-                converted caffe2 graph.
-        """
-        raise NotImplementedError
-
-    def _caffe2_preprocess_image(self, inputs):
-        """
-        Caffe2 implementation of preprocess_image, which is called inside each MetaArch's forward.
-        It normalizes the input images, and the final caffe2 graph assumes the
-        inputs have been batched already.
-        """
-        data, im_info = inputs
-        data = alias(data, "data")
-        im_info = alias(im_info, "im_info")
-        mean, std = self._wrapped_model.pixel_mean, self._wrapped_model.pixel_std
-        normalized_data = (data - mean) / std
-        normalized_data = alias(normalized_data, "normalized_data")
-
-        # Pack (data, im_info) into ImageList which is recognized by self.inference.
-        images = ImageList(tensor=normalized_data, image_sizes=im_info)
-        return images
-
-    @staticmethod
-    def get_outputs_converter(predict_net, init_net):
-        """
-        Creates a function that converts outputs of the caffe2 model to
-        detectron2's standard format.
-        The function uses information in `predict_net` and `init_net` that are
-        available at inferene time. Therefore the function logic can be used in inference.
-
-        The returned function has the following signature:
-
-            def convert(batched_inputs, c2_inputs, c2_results) -> detectron2_outputs
-
-        Where
-
-            * batched_inputs (list[dict]): the original input format of the meta arch
-            * c2_inputs (tuple[Tensor]): the caffe2 inputs.
-            * c2_results (dict[str, Tensor]): the caffe2 output format,
-                corresponding to the outputs of the :meth:`forward` function.
-            * detectron2_outputs: the original output format of the meta arch.
-
-        This function can be used to compare the outputs of the original meta arch and
-        the converted caffe2 graph.
-
-        Returns:
-            callable: a callable of the above signature.
-        """
-        raise NotImplementedError
-
-
-class Caffe2GeneralizedRCNN(Caffe2MetaArch):
-    def __init__(self, cfg, torch_model):
-        assert isinstance(torch_model, meta_arch.GeneralizedRCNN)
-        torch_model = patch_generalized_rcnn(torch_model)
-        super().__init__(cfg, torch_model)
-
-        try:
-            use_heatmap_max_keypoint = cfg.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT
-        except AttributeError:
-            use_heatmap_max_keypoint = False
-        self.roi_heads_patcher = ROIHeadsPatcher(
-            self._wrapped_model.roi_heads, use_heatmap_max_keypoint
-        )
-
-    def encode_additional_info(self, predict_net, init_net):
-        size_divisibility = self._wrapped_model.backbone.size_divisibility
-        check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility)
-        check_set_pb_arg(
-            predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii")
-        )
-        check_set_pb_arg(predict_net, "meta_architecture", "s", b"GeneralizedRCNN")
-
-    @mock_torch_nn_functional_interpolate()
-    def forward(self, inputs):
-        if not self.tensor_mode:
-            return self._wrapped_model.inference(inputs)
-        images = self._caffe2_preprocess_image(inputs)
-        features = self._wrapped_model.backbone(images.tensor)
-        proposals, _ = self._wrapped_model.proposal_generator(images, features)
-        with self.roi_heads_patcher.mock_roi_heads():
-            detector_results, _ = self._wrapped_model.roi_heads(images, features, proposals)
-        return tuple(detector_results[0].flatten())
-
-    @staticmethod
-    def get_outputs_converter(predict_net, init_net):
-        def f(batched_inputs, c2_inputs, c2_results):
-            _, im_info = c2_inputs
-            image_sizes = [[int(im[0]), int(im[1])] for im in im_info]
-            results = assemble_rcnn_outputs_by_name(image_sizes, c2_results)
-            return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes)
-
-        return f
-
-
-class Caffe2RetinaNet(Caffe2MetaArch):
-    def __init__(self, cfg, torch_model):
-        assert isinstance(torch_model, meta_arch.RetinaNet)
-        super().__init__(cfg, torch_model)
-
-    @mock_torch_nn_functional_interpolate()
-    def forward(self, inputs):
-        assert self.tensor_mode
-        images = self._caffe2_preprocess_image(inputs)
-
-        # explicitly return the images sizes to avoid removing "im_info" by ONNX
-        # since it's not used in the forward path
-        return_tensors = [images.image_sizes]
-
-        features = self._wrapped_model.backbone(images.tensor)
-        features = [features[f] for f in self._wrapped_model.head_in_features]
-        for i, feature_i in enumerate(features):
-            features[i] = alias(feature_i, "feature_{}".format(i), is_backward=True)
-            return_tensors.append(features[i])
-
-        pred_logits, pred_anchor_deltas = self._wrapped_model.head(features)
-        for i, (box_cls_i, box_delta_i) in enumerate(zip(pred_logits, pred_anchor_deltas)):
-            return_tensors.append(alias(box_cls_i, "box_cls_{}".format(i)))
-            return_tensors.append(alias(box_delta_i, "box_delta_{}".format(i)))
-
-        return tuple(return_tensors)
-
-    def encode_additional_info(self, predict_net, init_net):
-        size_divisibility = self._wrapped_model.backbone.size_divisibility
-        check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility)
-        check_set_pb_arg(
-            predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii")
-        )
-        check_set_pb_arg(predict_net, "meta_architecture", "s", b"RetinaNet")
-
-        # Inference parameters:
-        check_set_pb_arg(
-            predict_net, "score_threshold", "f", _cast_to_f32(self._wrapped_model.test_score_thresh)
-        )
-        check_set_pb_arg(
-            predict_net, "topk_candidates", "i", self._wrapped_model.test_topk_candidates
-        )
-        check_set_pb_arg(
-            predict_net, "nms_threshold", "f", _cast_to_f32(self._wrapped_model.test_nms_thresh)
-        )
-        check_set_pb_arg(
-            predict_net,
-            "max_detections_per_image",
-            "i",
-            self._wrapped_model.max_detections_per_image,
-        )
-
-        check_set_pb_arg(
-            predict_net,
-            "bbox_reg_weights",
-            "floats",
-            [_cast_to_f32(w) for w in self._wrapped_model.box2box_transform.weights],
-        )
-        self._encode_anchor_generator_cfg(predict_net)
-
-    def _encode_anchor_generator_cfg(self, predict_net):
-        # serialize anchor_generator for future use
-        serialized_anchor_generator = io.BytesIO()
-        torch.save(self._wrapped_model.anchor_generator, serialized_anchor_generator)
-        # Ideally we can put anchor generating inside the model, then we don't
-        # need to store this information.
-        bytes = serialized_anchor_generator.getvalue()
-        check_set_pb_arg(predict_net, "serialized_anchor_generator", "s", bytes)
-
-    @staticmethod
-    def get_outputs_converter(predict_net, init_net):
-        self = types.SimpleNamespace()
-        serialized_anchor_generator = io.BytesIO(
-            get_pb_arg_vals(predict_net, "serialized_anchor_generator", None)
-        )
-        self.anchor_generator = torch.load(serialized_anchor_generator)
-        bbox_reg_weights = get_pb_arg_floats(predict_net, "bbox_reg_weights", None)
-        self.box2box_transform = Box2BoxTransform(weights=tuple(bbox_reg_weights))
-        self.test_score_thresh = get_pb_arg_valf(predict_net, "score_threshold", None)
-        self.test_topk_candidates = get_pb_arg_vali(predict_net, "topk_candidates", None)
-        self.test_nms_thresh = get_pb_arg_valf(predict_net, "nms_threshold", None)
-        self.max_detections_per_image = get_pb_arg_vali(
-            predict_net, "max_detections_per_image", None
-        )
-
-        # hack to reuse inference code from RetinaNet
-        for meth in [
-            "forward_inference",
-            "inference_single_image",
-            "_transpose_dense_predictions",
-            "_decode_multi_level_predictions",
-            "_decode_per_level_predictions",
-        ]:
-            setattr(self, meth, functools.partial(getattr(meta_arch.RetinaNet, meth), self))
-
-        def f(batched_inputs, c2_inputs, c2_results):
-            _, im_info = c2_inputs
-            image_sizes = [[int(im[0]), int(im[1])] for im in im_info]
-            dummy_images = ImageList(
-                torch.randn(
-                    (
-                        len(im_info),
-                        3,
-                    )
-                    + tuple(image_sizes[0])
-                ),
-                image_sizes,
-            )
-
-            num_features = len([x for x in c2_results.keys() if x.startswith("box_cls_")])
-            pred_logits = [c2_results["box_cls_{}".format(i)] for i in range(num_features)]
-            pred_anchor_deltas = [c2_results["box_delta_{}".format(i)] for i in range(num_features)]
-
-            # For each feature level, feature should have the same batch size and
-            # spatial dimension as the box_cls and box_delta.
-            dummy_features = [x.clone()[:, 0:0, :, :] for x in pred_logits]
-            # self.num_classess can be inferred
-            self.num_classes = pred_logits[0].shape[1] // (pred_anchor_deltas[0].shape[1] // 4)
-
-            results = self.forward_inference(
-                dummy_images, dummy_features, [pred_logits, pred_anchor_deltas]
-            )
-            return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes)
-
-        return f
-
-
-META_ARCH_CAFFE2_EXPORT_TYPE_MAP = {
-    "GeneralizedRCNN": Caffe2GeneralizedRCNN,
-    "RetinaNet": Caffe2RetinaNet,
-}
diff --git a/spaces/cffl/Exploring_Intelligent_Writing_Assistance/apps/data_utils.py b/spaces/cffl/Exploring_Intelligent_Writing_Assistance/apps/data_utils.py
deleted file mode 100644
index 6ff20283c938dbfcdbed483af7ca384476c56598..0000000000000000000000000000000000000000
--- a/spaces/cffl/Exploring_Intelligent_Writing_Assistance/apps/data_utils.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# ###########################################################################
-#
-#  CLOUDERA APPLIED MACHINE LEARNING PROTOTYPE (AMP)
-#  (C) Cloudera, Inc. 2022
-#  All rights reserved.
-#
-#  Applicable Open Source License: Apache 2.0
-#
-#  NOTE: Cloudera open source products are modular software products
-#  made up of hundreds of individual components, each of which was
-#  individually copyrighted.  Each Cloudera open source product is a
-#  collective work under U.S. Copyright Law. Your license to use the
-#  collective work is as provided in your written agreement with
-#  Cloudera.  Used apart from the collective work, this file is
-#  licensed for your use pursuant to the open source license
-#  identified above.
-#
-#  This code is provided to you pursuant a written agreement with
-#  (i) Cloudera, Inc. or (ii) a third-party authorized to distribute
-#  this code. If you do not have a written agreement with Cloudera nor
-#  with an authorized and properly licensed third party, you do not
-#  have any rights to access nor to use this code.
-#
-#  Absent a written agreement with Cloudera, Inc. (“Cloudera”) to the
-#  contrary, A) CLOUDERA PROVIDES THIS CODE TO YOU WITHOUT WARRANTIES OF ANY
-#  KIND; (B) CLOUDERA DISCLAIMS ANY AND ALL EXPRESS AND IMPLIED
-#  WARRANTIES WITH RESPECT TO THIS CODE, INCLUDING BUT NOT LIMITED TO
-#  IMPLIED WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY AND
-#  FITNESS FOR A PARTICULAR PURPOSE; (C) CLOUDERA IS NOT LIABLE TO YOU,
-#  AND WILL NOT DEFEND, INDEMNIFY, NOR HOLD YOU HARMLESS FOR ANY CLAIMS
-#  ARISING FROM OR RELATED TO THE CODE; AND (D)WITH RESPECT TO YOUR EXERCISE
-#  OF ANY RIGHTS GRANTED TO YOU FOR THE CODE, CLOUDERA IS NOT LIABLE FOR ANY
-#  DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, PUNITIVE OR
-#  CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES
-#  RELATED TO LOST REVENUE, LOST PROFITS, LOSS OF INCOME, LOSS OF
-#  BUSINESS ADVANTAGE OR UNAVAILABILITY, OR LOSS OR CORRUPTION OF
-#  DATA.
-#
-# ###########################################################################
-
-import os
-from typing import List
-from collections import defaultdict
-from dataclasses import dataclass
-
-import numpy as np
-
-
-@dataclass
-class StyleAttributeData:
-    source_attribute: str
-    target_attribute: str
-    examples: List[str]
-    cls_model_path: str
-    seq2seq_model_path: str
-    sbert_model_path: str = "sentence-transformers/all-MiniLM-L6-v2"
-    hf_base_url: str = "https://huggingface.co/"
-
-    def __post_init__(self):
-        self._make_attribute_selection_string()
-        self._make_attribute_AND_string()
-        self._make_attribute_THAN_string()
-
-    def _make_attribute_selection_string(self):
-        self.attribute_selecting_string = (
-            f"{self.source_attribute}-{self.target_attribute}"
-        )
-
-    def _make_attribute_AND_string(self):
-        self.attribute_AND_string = (
-            f"**{self.source_attribute}** and **{self.target_attribute}**"
-        )
-
-    def _make_attribute_THAN_string(self):
-        self.attribute_THAN_string = (
-            f"**{self.source_attribute}** than **{self.target_attribute}**"
-        )
-
-    def build_model_url(self, model_type: str):
-        """
-        Build a complete HuggingFace url for the given `model_type`.
-
-        Args:
-            model_type (str): "cls", "seq2seq", "sbert"
-        """
-        attr_name = f"{model_type}_model_path"
-        return os.path.join(self.hf_base_url, getattr(self, attr_name))
-
-
-# instantiate data classes & collect all data class instances
-DATA_PACKET = {
-    "subjective-to-neutral": StyleAttributeData(
-        source_attribute="subjective",
-        target_attribute="neutral",
-        examples=[
-            "another strikingly elegant four-door design for the bentley s3 continental came from james.",
-            "the band plays an engaging and contagious rhythm known as brega pop and calypso.",
-            "chemical abstracts service (cas), a prominent division of the american chemical society, is the world's leading source of chemical information.",
-            "the final fight scene is with the martial arts great, master ninja sho kosugi.",
-        ],
-        cls_model_path="cffl/bert-base-styleclassification-subjective-neutral",
-        seq2seq_model_path="cffl/bart-base-styletransfer-subjective-to-neutral",
-    ),
-    "informal-to-formal": StyleAttributeData(
-        source_attribute="informal",
-        target_attribute="formal",
-        examples=[
-            "that was funny LOL",
-            "btw - ur avatar looks familiar",
-            "i loooooooooooooooooooooooove going to the movies.",
-            "haha, thatd be dope",
-        ],
-        cls_model_path="cointegrated/roberta-base-formality",
-        seq2seq_model_path="prithivida/informal_to_formal_styletransfer",
-    ),
-}
-
-
-def format_classification_results(id2label: dict, cls_result):
-    """
-    Formats classification output to be plotted using Altair.
-
-    Args:
-        id2label (dict): Transformer model's label dictionary
-        cls_result (List): Classification pipeline output
-    """
-
-    labels = [v for k, v in id2label.items()]
-
-    format_cls_result = []
-
-    for i in range(len(labels)):
-        temp = defaultdict()
-        temp["type"] = labels[i].capitalize()
-        temp["value"] = round(cls_result[0]["distribution"][i], 4)
-
-        if i == 0:
-            temp["percentage_start"] = 0
-            temp["percentage_end"] = temp["value"]
-        else:
-            temp["percentage_start"] = 1 - temp["value"]
-            temp["percentage_end"] = 1
-
-        format_cls_result.append(temp)
-
-    return format_cls_result
-
-
-def string_to_list_string(text: str):
-    return np.expand_dims(np.array(text), axis=0).tolist()
diff --git a/spaces/chendl/compositional_test/transformers/src/transformers/models/align/modeling_align.py b/spaces/chendl/compositional_test/transformers/src/transformers/models/align/modeling_align.py
deleted file mode 100644
index 09ee6eca62650efad7818c9cfdc8a0a83b8aa4f6..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/transformers/src/transformers/models/align/modeling_align.py
+++ /dev/null
@@ -1,1642 +0,0 @@
-# coding=utf-8
-# Copyright 2023 The Google Research Team Authors and 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.
-""" PyTorch ALIGN model."""
-
-import math
-from dataclasses import dataclass
-from typing import Any, Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from torch import nn
-
-from ...activations import ACT2FN
-from ...modeling_outputs import (
-    BaseModelOutputWithNoAttention,
-    BaseModelOutputWithPastAndCrossAttentions,
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    BaseModelOutputWithPoolingAndNoAttention,
-)
-from ...modeling_utils import PreTrainedModel
-from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
-from ...utils import (
-    ModelOutput,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
-from .configuration_align import AlignConfig, AlignTextConfig, AlignVisionConfig
-
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "kakaobrain/align-base"
-_CONFIG_FOR_DOC = "AlignConfig"
-
-
-ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "kakaobrain/align-base",
-    # See all ALIGN models at https://huggingface.co/models?filter=align
-]
-
-
-ALIGN_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`AlignConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the
-            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-ALIGN_TEXT_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-ALIGN_VISION_INPUTS_DOCSTRING = r"""
-    Args:
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__call__`] for details.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-ALIGN_INPUTS_DOCSTRING = r"""
-    Args:
-       input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
-            1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
-            [`AutoImageProcessor`]. See [`EfficientNetImageProcessor.__call__`] for details.
-        return_loss (`bool`, *optional*):
-            Whether or not to return the contrastive loss.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@dataclass
-class AlignVisionModelOutput(ModelOutput):
-    """
-    Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
-
-    Args:
-        image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
-            The image embeddings obtained by applying the projection layer to the pooler_output.
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-    """
-
-    image_embeds: Optional[torch.FloatTensor] = None
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class AlignTextModelOutput(ModelOutput):
-    """
-    Base class for text model's outputs that also contains a pooling of the last hidden states.
-
-    Args:
-        text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
-            The text embeddings obtained by applying the projection layer to the pooler_output.
-        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
-            Sequence of hidden-states at the output of the last layer of the model.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-    """
-
-    text_embeds: Optional[torch.FloatTensor] = None
-    last_hidden_state: torch.FloatTensor = None
-    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
-    attentions: Optional[Tuple[torch.FloatTensor]] = None
-
-
-@dataclass
-class AlignOutput(ModelOutput):
-    """
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
-            Contrastive loss for image-text similarity.
-        logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`):
-            The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
-            similarity scores.
-        logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`):
-            The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
-            similarity scores.
-        text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The text embeddings obtained by applying the projection layer to the pooled output of [`AlignTextModel`].
-        image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`):
-            The output of [`AlignVisionModel`].
-        text_model_output(`BaseModelOutputWithPoolingAndCrossAttentions`):
-            The output of the [`AlignTextModel`].
-        vision_model_output(`BaseModelOutputWithPoolingAndNoAttention`):
-            The output of the [`AlignVisionModel`].
-    """
-
-    loss: Optional[torch.FloatTensor] = None
-    logits_per_image: torch.FloatTensor = None
-    logits_per_text: torch.FloatTensor = None
-    text_embeds: torch.FloatTensor = None
-    image_embeds: torch.FloatTensor = None
-    text_model_output: BaseModelOutputWithPoolingAndCrossAttentions = None
-    vision_model_output: BaseModelOutputWithPoolingAndNoAttention = None
-
-    def to_tuple(self) -> Tuple[Any]:
-        return tuple(
-            self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
-            for k in self.keys()
-        )
-
-
-# contrastive loss function, adapted from
-# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
-def contrastive_loss(logits: torch.Tensor) -> torch.Tensor:
-    return nn.functional.cross_entropy(logits, torch.arange(len(logits), device=logits.device), label_smoothing=0.1)
-
-
-def align_loss(similarity: torch.Tensor) -> torch.Tensor:
-    caption_loss = contrastive_loss(similarity)
-    image_loss = contrastive_loss(similarity.t())
-    return (caption_loss + image_loss) / 2.0
-
-
-# Copied from transformers.models.efficientnet.modeling_efficientnet.round_filters with EfficientNet -> AlignVision
-def round_filters(config: AlignVisionConfig, num_channels: int):
-    r"""
-    Round number of filters based on depth multiplier.
-    """
-    divisor = config.depth_divisor
-    num_channels *= config.width_coefficient
-    new_dim = max(divisor, int(num_channels + divisor / 2) // divisor * divisor)
-
-    # Make sure that round down does not go down by more than 10%.
-    if new_dim < 0.9 * num_channels:
-        new_dim += divisor
-
-    return int(new_dim)
-
-
-# Copied from transformers.models.efficientnet.modeling_efficientnet.correct_pad
-def correct_pad(kernel_size: Union[int, Tuple], adjust: bool = True):
-    r"""
-    Utility function to get the tuple padding value for the depthwise convolution.
-
-    Args:
-        kernel_size (`int` or `tuple`):
-            Kernel size of the convolution layers.
-        adjust (`bool`, *optional*, defaults to `True`):
-            Adjusts padding value to apply to right and bottom sides of the input.
-    """
-    if isinstance(kernel_size, int):
-        kernel_size = (kernel_size, kernel_size)
-
-    correct = (kernel_size[0] // 2, kernel_size[1] // 2)
-    if adjust:
-        return (correct[1] - 1, correct[1], correct[0] - 1, correct[0])
-    else:
-        return (correct[1], correct[1], correct[0], correct[0])
-
-
-# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetEmbeddings with EfficientNet->AlignVision
-class AlignVisionEmbeddings(nn.Module):
-    r"""
-    A module that corresponds to the stem module of the original work.
-    """
-
-    def __init__(self, config: AlignVisionConfig):
-        super().__init__()
-
-        self.out_dim = round_filters(config, 32)
-        self.padding = nn.ZeroPad2d(padding=(0, 1, 0, 1))
-        self.convolution = nn.Conv2d(
-            config.num_channels, self.out_dim, kernel_size=3, stride=2, padding="valid", bias=False
-        )
-        self.batchnorm = nn.BatchNorm2d(self.out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum)
-        self.activation = ACT2FN[config.hidden_act]
-
-    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
-        features = self.padding(pixel_values)
-        features = self.convolution(features)
-        features = self.batchnorm(features)
-        features = self.activation(features)
-
-        return features
-
-
-# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseConv2d with EfficientNet->AlignVision
-class AlignVisionDepthwiseConv2d(nn.Conv2d):
-    def __init__(
-        self,
-        in_channels,
-        depth_multiplier=1,
-        kernel_size=3,
-        stride=1,
-        padding=0,
-        dilation=1,
-        bias=True,
-        padding_mode="zeros",
-    ):
-        out_channels = in_channels * depth_multiplier
-        super().__init__(
-            in_channels=in_channels,
-            out_channels=out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            groups=in_channels,
-            bias=bias,
-            padding_mode=padding_mode,
-        )
-
-
-# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetExpansionLayer with EfficientNet->AlignVision
-class AlignVisionExpansionLayer(nn.Module):
-    r"""
-    This corresponds to the expansion phase of each block in the original implementation.
-    """
-
-    def __init__(self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int):
-        super().__init__()
-        self.expand_conv = nn.Conv2d(
-            in_channels=in_dim,
-            out_channels=out_dim,
-            kernel_size=1,
-            padding="same",
-            bias=False,
-        )
-        self.expand_bn = nn.BatchNorm2d(num_features=out_dim, eps=config.batch_norm_eps)
-        self.expand_act = ACT2FN[config.hidden_act]
-
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
-        # Expand phase
-        hidden_states = self.expand_conv(hidden_states)
-        hidden_states = self.expand_bn(hidden_states)
-        hidden_states = self.expand_act(hidden_states)
-
-        return hidden_states
-
-
-# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetDepthwiseLayer with with EfficientNet->AlignVision
-class AlignVisionDepthwiseLayer(nn.Module):
-    r"""
-    This corresponds to the depthwise convolution phase of each block in the original implementation.
-    """
-
-    def __init__(
-        self,
-        config: AlignVisionConfig,
-        in_dim: int,
-        stride: int,
-        kernel_size: int,
-        adjust_padding: bool,
-    ):
-        super().__init__()
-        self.stride = stride
-        conv_pad = "valid" if self.stride == 2 else "same"
-        padding = correct_pad(kernel_size, adjust=adjust_padding)
-
-        self.depthwise_conv_pad = nn.ZeroPad2d(padding=padding)
-        self.depthwise_conv = AlignVisionDepthwiseConv2d(
-            in_dim, kernel_size=kernel_size, stride=stride, padding=conv_pad, bias=False
-        )
-        self.depthwise_norm = nn.BatchNorm2d(
-            num_features=in_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
-        )
-        self.depthwise_act = ACT2FN[config.hidden_act]
-
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
-        # Depthwise convolution
-        if self.stride == 2:
-            hidden_states = self.depthwise_conv_pad(hidden_states)
-
-        hidden_states = self.depthwise_conv(hidden_states)
-        hidden_states = self.depthwise_norm(hidden_states)
-        hidden_states = self.depthwise_act(hidden_states)
-
-        return hidden_states
-
-
-# Copied from transformers.models.efficientnet.modeling_efficientnet.EfficientNetSqueezeExciteLayer with with EfficientNet->AlignVision
-class AlignVisionSqueezeExciteLayer(nn.Module):
-    r"""
-    This corresponds to the Squeeze and Excitement phase of each block in the original implementation.
-    """
-
-    def __init__(self, config: AlignVisionConfig, in_dim: int, expand_dim: int, expand: bool = False):
-        super().__init__()
-        self.dim = expand_dim if expand else in_dim
-        self.dim_se = max(1, int(in_dim * config.squeeze_expansion_ratio))
-
-        self.squeeze = nn.AdaptiveAvgPool2d(output_size=1)
-        self.reduce = nn.Conv2d(
-            in_channels=self.dim,
-            out_channels=self.dim_se,
-            kernel_size=1,
-            padding="same",
-        )
-        self.expand = nn.Conv2d(
-            in_channels=self.dim_se,
-            out_channels=self.dim,
-            kernel_size=1,
-            padding="same",
-        )
-        self.act_reduce = ACT2FN[config.hidden_act]
-        self.act_expand = nn.Sigmoid()
-
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
-        inputs = hidden_states
-        hidden_states = self.squeeze(hidden_states)
-        hidden_states = self.reduce(hidden_states)
-        hidden_states = self.act_reduce(hidden_states)
-
-        hidden_states = self.expand(hidden_states)
-        hidden_states = self.act_expand(hidden_states)
-        hidden_states = torch.mul(inputs, hidden_states)
-
-        return hidden_states
-
-
-class AlignVisionFinalBlockLayer(nn.Module):
-    r"""
-    This corresponds to the final phase of each block in the original implementation.
-    """
-
-    def __init__(
-        self, config: AlignVisionConfig, in_dim: int, out_dim: int, stride: int, drop_rate: float, id_skip: bool
-    ):
-        super().__init__()
-        self.apply_dropout = stride == 1 and not id_skip
-        self.project_conv = nn.Conv2d(
-            in_channels=in_dim,
-            out_channels=out_dim,
-            kernel_size=1,
-            padding="same",
-            bias=False,
-        )
-        self.project_bn = nn.BatchNorm2d(
-            num_features=out_dim, eps=config.batch_norm_eps, momentum=config.batch_norm_momentum
-        )
-        self.dropout = nn.Dropout(p=drop_rate)
-
-    def forward(self, embeddings: torch.FloatTensor, hidden_states: torch.FloatTensor) -> torch.Tensor:
-        hidden_states = self.project_conv(hidden_states)
-        hidden_states = self.project_bn(hidden_states)
-
-        if self.apply_dropout:
-            hidden_states = self.dropout(hidden_states)
-            hidden_states = hidden_states + embeddings
-
-        return hidden_states
-
-
-class AlignVisionBlock(nn.Module):
-    r"""
-    This corresponds to the block module of original the EfficientNet vision encoder implementation.
-
-    Args:
-        config ([`AlignVisionConfig`]):
-            Model configuration class.
-        in_dim (`int`):
-            Number of input channels.
-        out_dim (`int`):
-            Number of output channels.
-        stride (`int`):
-            Stride size to be used in convolution layers.
-        expand_ratio (`int`):
-            Expand ratio to set the output dimensions for the expansion and squeeze-excite layers.
-        kernel_size (`int`):
-            Kernel size for the depthwise convolution layer.
-        drop_rate (`float`):
-            Dropout rate to be used in the final phase of each block.
-        id_skip (`bool`):
-            Whether to apply dropout and sum the final hidden states with the input embeddings during the final phase
-            of each block. Set to `True` for the first block of each stage.
-        adjust_padding (`bool`):
-            Whether to apply padding to only right and bottom side of the input kernel before the depthwise convolution
-            operation, set to `True` for inputs with odd input sizes.
-    """
-
-    def __init__(
-        self,
-        config: AlignVisionConfig,
-        in_dim: int,
-        out_dim: int,
-        stride: int,
-        expand_ratio: int,
-        kernel_size: int,
-        drop_rate: float,
-        id_skip: bool,
-        adjust_padding: bool,
-    ):
-        super().__init__()
-        self.expand_ratio = expand_ratio
-        self.expand = True if self.expand_ratio != 1 else False
-        expand_in_dim = in_dim * expand_ratio
-
-        if self.expand:
-            self.expansion = AlignVisionExpansionLayer(
-                config=config, in_dim=in_dim, out_dim=expand_in_dim, stride=stride
-            )
-
-        self.depthwise_conv = AlignVisionDepthwiseLayer(
-            config=config,
-            in_dim=expand_in_dim if self.expand else in_dim,
-            stride=stride,
-            kernel_size=kernel_size,
-            adjust_padding=adjust_padding,
-        )
-        self.squeeze_excite = AlignVisionSqueezeExciteLayer(
-            config=config, in_dim=in_dim, expand_dim=expand_in_dim, expand=self.expand
-        )
-        self.projection = AlignVisionFinalBlockLayer(
-            config=config,
-            in_dim=expand_in_dim if self.expand else in_dim,
-            out_dim=out_dim,
-            stride=stride,
-            drop_rate=drop_rate,
-            id_skip=id_skip,
-        )
-
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
-        embeddings = hidden_states
-        # Expansion and depthwise convolution phase
-        if self.expand_ratio != 1:
-            hidden_states = self.expansion(hidden_states)
-        hidden_states = self.depthwise_conv(hidden_states)
-
-        # Squeeze and excite phase
-        hidden_states = self.squeeze_excite(hidden_states)
-        hidden_states = self.projection(embeddings, hidden_states)
-        return hidden_states
-
-
-class AlignVisionEncoder(nn.Module):
-    r"""
-    Forward propogates the embeddings through each vision encoder (EfficientNet) block.
-
-    Args:
-        config ([`AlignVisionConfig`]):
-            Model configuration class.
-    """
-
-    def __init__(self, config: AlignVisionConfig):
-        super().__init__()
-        self.depth_coefficient = config.depth_coefficient
-
-        def round_repeats(repeats):
-            # Round number of block repeats based on depth multiplier.
-            return int(math.ceil(self.depth_coefficient * repeats))
-
-        num_base_blocks = len(config.in_channels)
-        num_blocks = sum(round_repeats(n) for n in config.num_block_repeats)
-
-        curr_block_num = 0
-        blocks = []
-        for i in range(num_base_blocks):
-            in_dim = round_filters(config, config.in_channels[i])
-            out_dim = round_filters(config, config.out_channels[i])
-            stride = config.strides[i]
-            kernel_size = config.kernel_sizes[i]
-            expand_ratio = config.expand_ratios[i]
-
-            for j in range(round_repeats(config.num_block_repeats[i])):
-                id_skip = True if j == 0 else False
-                stride = 1 if j > 0 else stride
-                in_dim = out_dim if j > 0 else in_dim
-                adjust_padding = False if curr_block_num in config.depthwise_padding else True
-                drop_rate = config.drop_connect_rate * curr_block_num / num_blocks
-
-                block = AlignVisionBlock(
-                    config=config,
-                    in_dim=in_dim,
-                    out_dim=out_dim,
-                    stride=stride,
-                    kernel_size=kernel_size,
-                    expand_ratio=expand_ratio,
-                    drop_rate=drop_rate,
-                    id_skip=id_skip,
-                    adjust_padding=adjust_padding,
-                )
-                blocks.append(block)
-                curr_block_num += 1
-
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(
-        self,
-        hidden_states: torch.FloatTensor,
-        output_hidden_states: Optional[bool] = False,
-        return_dict: Optional[bool] = True,
-    ) -> BaseModelOutputWithPoolingAndNoAttention:
-        all_hidden_states = (hidden_states,) if output_hidden_states else None
-
-        for block in self.blocks:
-            hidden_states = block(hidden_states)
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
-
-        return BaseModelOutputWithNoAttention(
-            last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
-        )
-
-
-# Copied from transformers.models.bert.modeling_bert.BertEmbeddings with Bert->AlignText
-class AlignTextEmbeddings(nn.Module):
-    """Construct the embeddings from word, position and token_type embeddings."""
-
-    def __init__(self, config):
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
-        # any TensorFlow checkpoint file
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.register_buffer(
-            "token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long), persistent=False
-        )
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        token_type_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        past_key_values_length: int = 0,
-    ) -> torch.Tensor:
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        else:
-            input_shape = inputs_embeds.size()[:-1]
-
-        seq_length = input_shape[1]
-
-        if position_ids is None:
-            position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
-
-        # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
-        # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves
-        # issue #5664
-        if token_type_ids is None:
-            if hasattr(self, "token_type_ids"):
-                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-        token_type_embeddings = self.token_type_embeddings(token_type_ids)
-
-        embeddings = inputs_embeds + token_type_embeddings
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
-        embeddings = self.LayerNorm(embeddings)
-        embeddings = self.dropout(embeddings)
-        return embeddings
-
-
-# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->AlignText
-class AlignTextSelfAttention(nn.Module):
-    def __init__(self, config, position_embedding_type=None):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
-                f"heads ({config.num_attention_heads})"
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(config.hidden_size, self.all_head_size)
-        self.value = nn.Linear(config.hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = position_embedding_type or getattr(
-            config, "position_embedding_type", "absolute"
-        )
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-
-        self.is_decoder = config.is_decoder
-
-    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        is_cross_attention = encoder_hidden_states is not None
-
-        if is_cross_attention and past_key_value is not None:
-            # reuse k,v, cross_attentions
-            key_layer = past_key_value[0]
-            value_layer = past_key_value[1]
-            attention_mask = encoder_attention_mask
-        elif is_cross_attention:
-            key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
-            value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
-            attention_mask = encoder_attention_mask
-        elif past_key_value is not None:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-            key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
-            value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
-        else:
-            key_layer = self.transpose_for_scores(self.key(hidden_states))
-            value_layer = self.transpose_for_scores(self.value(hidden_states))
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-
-        use_cache = past_key_value is not None
-        if self.is_decoder:
-            # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
-            # Further calls to cross_attention layer can then reuse all cross-attention
-            # key/value_states (first "if" case)
-            # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
-            # all previous decoder key/value_states. Further calls to uni-directional self-attention
-            # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
-            # if encoder bi-directional self-attention `past_key_value` is always `None`
-            past_key_value = (key_layer, value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            query_length, key_length = query_layer.shape[2], key_layer.shape[2]
-            if use_cache:
-                position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-                    -1, 1
-                )
-            else:
-                position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in AlignTextModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(new_context_layer_shape)
-
-        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
-
-        if self.is_decoder:
-            outputs = outputs + (past_key_value,)
-        return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->AlignText
-class AlignTextSelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->AlignText
-class AlignTextAttention(nn.Module):
-    def __init__(self, config, position_embedding_type=None):
-        super().__init__()
-        self.self = AlignTextSelfAttention(config, position_embedding_type=position_embedding_type)
-        self.output = AlignTextSelfOutput(config)
-        self.pruned_heads = set()
-
-    def prune_heads(self, heads):
-        if len(heads) == 0:
-            return
-        heads, index = find_pruneable_heads_and_indices(
-            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
-        )
-
-        # Prune linear layers
-        self.self.query = prune_linear_layer(self.self.query, index)
-        self.self.key = prune_linear_layer(self.self.key, index)
-        self.self.value = prune_linear_layer(self.self.value, index)
-        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
-
-        # Update hyper params and store pruned heads
-        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
-        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
-        self.pruned_heads = self.pruned_heads.union(heads)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            past_key_value,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->AlignText
-class AlignTextIntermediate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
-        if isinstance(config.hidden_act, str):
-            self.intermediate_act_fn = ACT2FN[config.hidden_act]
-        else:
-            self.intermediate_act_fn = config.hidden_act
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.intermediate_act_fn(hidden_states)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->AlignText
-class AlignTextOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->AlignText
-class AlignTextLayer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.chunk_size_feed_forward = config.chunk_size_feed_forward
-        self.seq_len_dim = 1
-        self.attention = AlignTextAttention(config)
-        self.is_decoder = config.is_decoder
-        self.add_cross_attention = config.add_cross_attention
-        if self.add_cross_attention:
-            if not self.is_decoder:
-                raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
-            self.crossattention = AlignTextAttention(config, position_embedding_type="absolute")
-        self.intermediate = AlignTextIntermediate(config)
-        self.output = AlignTextOutput(config)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        output_attentions: Optional[bool] = False,
-    ) -> Tuple[torch.Tensor]:
-        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
-        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
-        self_attention_outputs = self.attention(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            output_attentions=output_attentions,
-            past_key_value=self_attn_past_key_value,
-        )
-        attention_output = self_attention_outputs[0]
-
-        # if decoder, the last output is tuple of self-attn cache
-        if self.is_decoder:
-            outputs = self_attention_outputs[1:-1]
-            present_key_value = self_attention_outputs[-1]
-        else:
-            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
-
-        cross_attn_present_key_value = None
-        if self.is_decoder and encoder_hidden_states is not None:
-            if not hasattr(self, "crossattention"):
-                raise ValueError(
-                    f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
-                    " by setting `config.add_cross_attention=True`"
-                )
-
-            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
-            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
-            cross_attention_outputs = self.crossattention(
-                attention_output,
-                attention_mask,
-                head_mask,
-                encoder_hidden_states,
-                encoder_attention_mask,
-                cross_attn_past_key_value,
-                output_attentions,
-            )
-            attention_output = cross_attention_outputs[0]
-            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
-
-            # add cross-attn cache to positions 3,4 of present_key_value tuple
-            cross_attn_present_key_value = cross_attention_outputs[-1]
-            present_key_value = present_key_value + cross_attn_present_key_value
-
-        layer_output = apply_chunking_to_forward(
-            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
-        )
-        outputs = (layer_output,) + outputs
-
-        # if decoder, return the attn key/values as the last output
-        if self.is_decoder:
-            outputs = outputs + (present_key_value,)
-
-        return outputs
-
-    def feed_forward_chunk(self, attention_output):
-        intermediate_output = self.intermediate(attention_output)
-        layer_output = self.output(intermediate_output, attention_output)
-        return layer_output
-
-
-# Copied from transformers.models.bert.modeling_bert.BertEncoder with Bert->AlignText
-class AlignTextEncoder(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.layer = nn.ModuleList([AlignTextLayer(config) for _ in range(config.num_hidden_layers)])
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.FloatTensor] = None,
-        head_mask: Optional[torch.FloatTensor] = None,
-        encoder_hidden_states: Optional[torch.FloatTensor] = None,
-        encoder_attention_mask: Optional[torch.FloatTensor] = None,
-        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = False,
-        output_hidden_states: Optional[bool] = False,
-        return_dict: Optional[bool] = True,
-    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attentions = () if output_attentions else None
-        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        next_decoder_cache = () if use_cache else None
-        for i, layer_module in enumerate(self.layer):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            layer_head_mask = head_mask[i] if head_mask is not None else None
-            past_key_value = past_key_values[i] if past_key_values is not None else None
-
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        return module(*inputs, past_key_value, output_attentions)
-
-                    return custom_forward
-
-                layer_outputs = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer_module),
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                )
-            else:
-                layer_outputs = layer_module(
-                    hidden_states,
-                    attention_mask,
-                    layer_head_mask,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    past_key_value,
-                    output_attentions,
-                )
-
-            hidden_states = layer_outputs[0]
-            if use_cache:
-                next_decoder_cache += (layer_outputs[-1],)
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_outputs[1],)
-                if self.config.add_cross_attention:
-                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [
-                    hidden_states,
-                    next_decoder_cache,
-                    all_hidden_states,
-                    all_self_attentions,
-                    all_cross_attentions,
-                ]
-                if v is not None
-            )
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=next_decoder_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-            cross_attentions=all_cross_attentions,
-        )
-
-
-# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert -> AlignText
-class AlignTextPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0]
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class AlignPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = AlignConfig
-    base_model_prefix = "align"
-    supports_gradient_checkpointing = True
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
-
-    def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, (nn.Linear, nn.Conv2d)):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, AlignModel):
-            nn.init.xavier_uniform_(module.text_projection.weight)
-            module.text_projection.bias.data.zero_()
-            module.text_projection._is_hf_initialized = True
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        if isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, (AlignTextModel, AlignVisionModel)):
-            module.gradient_checkpointing = value
-
-
-@add_start_docstrings(
-    """The text model from ALIGN without any head or projection on top.""",
-    ALIGN_START_DOCSTRING,
-)
-class AlignTextModel(AlignPreTrainedModel):
-    config_class = AlignTextConfig
-
-    def __init__(self, config: AlignTextConfig, add_pooling_layer: bool = True):
-        super().__init__(config)
-        self.config = config
-
-        self.embeddings = AlignTextEmbeddings(config)
-        self.encoder = AlignTextEncoder(config)
-
-        self.pooler = AlignTextPooler(config) if add_pooling_layer else None
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embeddings.word_embeddings
-
-    def set_input_embeddings(self, value):
-        self.embeddings.word_embeddings = value
-
-    @add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndCrossAttentions, config_class=AlignTextConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPoolingAndCrossAttentions]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, AlignTextModel
-
-        >>> model = AlignTextModel.from_pretrained("kakaobrain/align-base")
-        >>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled (EOS token) states
-        ```"""
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif input_ids is not None:
-            input_shape = input_ids.size()
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        batch_size, seq_length = input_shape
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if attention_mask is None:
-            attention_mask = torch.ones(((batch_size, seq_length)), device=device)
-
-        if token_type_ids is None:
-            if hasattr(self.embeddings, "token_type_ids"):
-                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
-                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
-                token_type_ids = buffered_token_type_ids_expanded
-            else:
-                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
-
-        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
-        # ourselves in which case we just need to make it broadcastable to all heads.
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
-
-        # Prepare head mask if needed
-        # 1.0 in head_mask indicate we keep the head
-        # attention_probs has shape bsz x n_heads x N x N
-        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
-        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-
-        embedding_output = self.embeddings(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            inputs_embeds=inputs_embeds,
-        )
-        encoder_outputs = self.encoder(
-            embedding_output,
-            attention_mask=extended_attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        sequence_output = encoder_outputs[0]
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
-            cross_attentions=encoder_outputs.cross_attentions,
-        )
-
-
-@add_start_docstrings(
-    """The vision model from ALIGN without any head or projection on top.""",
-    ALIGN_START_DOCSTRING,
-)
-class AlignVisionModel(AlignPreTrainedModel):
-    config_class = AlignVisionConfig
-    main_input_name = "pixel_values"
-
-    def __init__(self, config: AlignVisionConfig):
-        super().__init__(config)
-        self.config = config
-        self.embeddings = AlignVisionEmbeddings(config)
-        self.encoder = AlignVisionEncoder(config)
-
-        # Final pooling layer
-        if config.pooling_type == "mean":
-            self.pooler = nn.AvgPool2d(config.hidden_dim, ceil_mode=True)
-        elif config.pooling_type == "max":
-            self.pooler = nn.MaxPool2d(config.hidden_dim, ceil_mode=True)
-        else:
-            raise ValueError(f"config.pooling must be one of ['mean', 'max'] got {config.pooling}")
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Module:
-        return self.vision_model.embeddings.convolution
-
-    @add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutputWithPoolingAndNoAttention, config_class=AlignVisionConfig)
-    def forward(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, AlignVisionModel
-
-        >>> model = AlignVisionModel.from_pretrained("kakaobrain/align-base")
-        >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(images=image, return_tensors="pt")
-
-        >>> outputs = model(**inputs)
-        >>> last_hidden_state = outputs.last_hidden_state
-        >>> pooled_output = outputs.pooler_output  # pooled CLS states
-        ```"""
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if pixel_values is None:
-            raise ValueError("You have to specify pixel_values")
-
-        embedding_output = self.embeddings(pixel_values)
-        encoder_outputs = self.encoder(
-            embedding_output,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        # Apply pooling
-        last_hidden_state = encoder_outputs[0]
-        pooled_output = self.pooler(last_hidden_state)
-        # Reshape (batch_size, projection_dim, 1 , 1) -> (batch_size, projection_dim)
-        pooled_output = pooled_output.reshape(pooled_output.shape[:2])
-
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
-        return BaseModelOutputWithPoolingAndNoAttention(
-            last_hidden_state=last_hidden_state,
-            pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-        )
-
-
-@add_start_docstrings(ALIGN_START_DOCSTRING)
-class AlignModel(AlignPreTrainedModel):
-    config_class = AlignConfig
-
-    def __init__(self, config: AlignConfig):
-        super().__init__(config)
-
-        if not isinstance(config.text_config, AlignTextConfig):
-            raise ValueError(
-                "config.text_config is expected to be of type AlignTextConfig but is of type"
-                f" {type(config.text_config)}."
-            )
-
-        if not isinstance(config.vision_config, AlignVisionConfig):
-            raise ValueError(
-                "config.vision_config is expected to be of type AlignVisionConfig but is of type"
-                f" {type(config.vision_config)}."
-            )
-
-        text_config = config.text_config
-        vision_config = config.vision_config
-
-        self.projection_dim = config.projection_dim
-        self.text_embed_dim = text_config.hidden_size
-
-        self.text_model = AlignTextModel(text_config)
-        self.vision_model = AlignVisionModel(vision_config)
-
-        self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim)
-        self.temperature = nn.Parameter(torch.ones([]) * self.config.temperature_init_value)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    @add_start_docstrings_to_model_forward(ALIGN_TEXT_INPUTS_DOCSTRING)
-    def get_text_features(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-        r"""
-        Returns:
-            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
-            applying the projection layer to the pooled output of [`AlignTextModel`].
-
-        Examples:
-
-        ```python
-        >>> from transformers import AutoTokenizer, AlignModel
-
-        >>> model = AlignModel.from_pretrained("kakaobrain/align-base")
-        >>> tokenizer = AutoTokenizer.from_pretrained("kakaobrain/align-base")
-
-        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt")
-        >>> text_features = model.get_text_features(**inputs)
-        ```"""
-        # Use ALIGN model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        text_outputs = self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = text_outputs[0][:, 0, :]
-        text_features = self.text_projection(last_hidden_state)
-
-        return text_features
-
-    @add_start_docstrings_to_model_forward(ALIGN_VISION_INPUTS_DOCSTRING)
-    def get_image_features(
-        self,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> torch.FloatTensor:
-        r"""
-        Returns:
-            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
-            applying the projection layer to the pooled output of [`AlignVisionModel`].
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, AlignModel
-
-        >>> model = AlignModel.from_pretrained("kakaobrain/align-base")
-        >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(images=image, return_tensors="pt")
-
-        >>> image_features = model.get_image_features(**inputs)
-        ```"""
-        # Use ALIGN model's config for some fields (if specified) instead of those of vision & text components.
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        image_features = vision_outputs[1]  # pooled_output
-
-        return image_features
-
-    @add_start_docstrings_to_model_forward(ALIGN_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=AlignOutput, config_class=AlignConfig)
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        pixel_values: Optional[torch.FloatTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        return_loss: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, AlignOutput]:
-        r"""
-        Returns:
-
-        Examples:
-
-        ```python
-        >>> from PIL import Image
-        >>> import requests
-        >>> from transformers import AutoProcessor, AlignModel
-
-        >>> model = AlignModel.from_pretrained("kakaobrain/align-base")
-        >>> processor = AutoProcessor.from_pretrained("kakaobrain/align-base")
-
-        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-        >>> image = Image.open(requests.get(url, stream=True).raw)
-
-        >>> inputs = processor(
-        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
-        ... )
-
-        >>> outputs = model(**inputs)
-        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
-        >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
-        ```"""
-        # Use ALIGN model's config for some fields (if specified) instead of those of vision & text components.
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        vision_outputs = self.vision_model(
-            pixel_values=pixel_values,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        text_outputs = self.text_model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        image_embeds = vision_outputs[1]
-        text_embeds = text_outputs[0][:, 0, :]
-        text_embeds = self.text_projection(text_embeds)
-
-        # normalized features
-        image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True)
-        text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True)
-
-        # cosine similarity as logits
-        logits_per_text = torch.matmul(text_embeds, image_embeds.t()) / self.temperature
-        logits_per_image = logits_per_text.t()
-
-        loss = None
-        if return_loss:
-            loss = align_loss(logits_per_text)
-
-        if not return_dict:
-            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
-            return ((loss,) + output) if loss is not None else output
-
-        return AlignOutput(
-            loss=loss,
-            logits_per_image=logits_per_image,
-            logits_per_text=logits_per_text,
-            text_embeds=text_embeds,
-            image_embeds=image_embeds,
-            text_model_output=text_outputs,
-            vision_model_output=vision_outputs,
-        )
diff --git a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/text/chinese_bert.py b/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/text/chinese_bert.py
deleted file mode 100644
index 8159425df4bf7e577008b22f44e84f3147fdce14..0000000000000000000000000000000000000000
--- a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/text/chinese_bert.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import torch
-import sys
-from transformers import AutoTokenizer, AutoModelForMaskedLM
-
-tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large")
-
-models = dict()
-
-
-def get_bert_feature(text, word2ph, device=None):
-    if (
-        sys.platform == "darwin"
-        and torch.backends.mps.is_available()
-        and device == "cpu"
-    ):
-        device = "mps"
-    if not device:
-        device = "cuda"
-    if device not in models.keys():
-        models[device] = AutoModelForMaskedLM.from_pretrained(
-            "./bert/chinese-roberta-wwm-ext-large"
-        ).to(device)
-    with torch.no_grad():
-        inputs = tokenizer(text, return_tensors="pt")
-        for i in inputs:
-            inputs[i] = inputs[i].to(device)
-        res = models[device](**inputs, output_hidden_states=True)
-        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()
-
-    assert len(word2ph) == len(text) + 2
-    word2phone = word2ph
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        repeat_feature = res[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-
-    return phone_level_feature.T
-
-
-if __name__ == "__main__":
-    import torch
-
-    word_level_feature = torch.rand(38, 1024)  # 12个词,每个词1024维特征
-    word2phone = [
-        1,
-        2,
-        1,
-        2,
-        2,
-        1,
-        2,
-        2,
-        1,
-        2,
-        2,
-        1,
-        2,
-        2,
-        2,
-        2,
-        2,
-        1,
-        1,
-        2,
-        2,
-        1,
-        2,
-        2,
-        2,
-        2,
-        1,
-        2,
-        2,
-        2,
-        2,
-        2,
-        1,
-        2,
-        2,
-        2,
-        2,
-        1,
-    ]
-
-    # 计算总帧数
-    total_frames = sum(word2phone)
-    print(word_level_feature.shape)
-    print(word2phone)
-    phone_level_feature = []
-    for i in range(len(word2phone)):
-        print(word_level_feature[i].shape)
-
-        # 对每个词重复word2phone[i]次
-        repeat_feature = word_level_feature[i].repeat(word2phone[i], 1)
-        phone_level_feature.append(repeat_feature)
-
-    phone_level_feature = torch.cat(phone_level_feature, dim=0)
-    print(phone_level_feature.shape)  # torch.Size([36, 1024])
diff --git a/spaces/choimirai/whisper-large-v3/app.py b/spaces/choimirai/whisper-large-v3/app.py
deleted file mode 100644
index 3251cf43b2a57bf298db41591ae4c5e4286232e3..0000000000000000000000000000000000000000
--- a/spaces/choimirai/whisper-large-v3/app.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import torch
-
-import gradio as gr
-import yt_dlp as youtube_dl
-from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
-
-import tempfile
-import os
-
-MODEL_NAME = "openai/whisper-large-v3"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-YT_LENGTH_LIMIT_S = 3600  # limit to 1 hour YouTube files
-
-device = 0 if torch.cuda.is_available() else "cpu"
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-
-def chunks_to_srt(chunks):
-    srt_format = ""
-    for i, chunk in enumerate(chunks, 1):
-        start_time, end_time = chunk['timestamp']
-        start_time_hms = "{:02}:{:02}:{:02},{:03}".format(int(start_time // 3600), int((start_time % 3600) // 60), int(start_time % 60), int((start_time % 1) * 1000))
-        end_time_hms = "{:02}:{:02}:{:02},{:03}".format(int(end_time // 3600), int((end_time % 3600) // 60), int(end_time % 60), int((end_time % 1) * 1000))
-        srt_format += f"{i}\n{start_time_hms} --> {end_time_hms}\n{chunk['text']}\n\n"
-    return srt_format
-
-def transcribe(inputs, task, language, return_timestamps):
-    if inputs is None:
-        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
-    
-    # Map the language names to their corresponding codes
-    language_codes = {"English": "en", "Korean": "ko", "Japanese": "ja"}
-    language_code = language_codes.get(language, "en")  # Default to "en" if the language is not found
-    result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task, "language": f"<|{language_code}|>"}, return_timestamps=return_timestamps)
-    
-    if return_timestamps:
-        return chunks_to_srt(result['chunks'])
-    else:
-        return result['text']
-
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    except youtube_dl.utils.DownloadError as err:
-        raise gr.Error(str(err))
-    
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
-    
-    if file_length_s > YT_LENGTH_LIMIT_S:
-        yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
-        raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
-    
-    ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        except youtube_dl.utils.ExtractorError as err:
-            raise gr.Error(str(err))
-
-
-def yt_transcribe(yt_url, task, return_timestamps, language, max_filesize=75.0):
-    html_embed_str = _return_yt_html_embed(yt_url)
-
-    with tempfile.TemporaryDirectory() as tmpdirname:
-        filepath = os.path.join(tmpdirname, "video.mp4")
-        download_yt_audio(yt_url, filepath)
-        with open(filepath, "rb") as f:
-            inputs = f.read()
-
-    inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
-    inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
-    
-    # Map the language names to their corresponding codes
-    language_codes = {"English": "en", "Korean": "ko", "Japanese": "ja"}
-    language_code = language_codes.get(language, "en")  # Default to "en" if the language is not found
-    
-    result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task, "language": f"<|{language_code}|>"}, return_timestamps=return_timestamps)
-    
-    if return_timestamps:
-        return html_embed_str, chunks_to_srt(result['chunks'])
-    else:
-        return html_embed_str, result['text']
-
-
-demo = gr.Blocks()
-
-mf_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="microphone", type="filepath", optional=True),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-        gr.inputs.Checkbox(label="Return timestamps"),
-        gr.inputs.Dropdown(choices=["English", "Korean", "Japanese"], label="Language"),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe Audio",
-    description=(
-        "\n\n"
-        "<center>⭐️Brought to you by <a href='https://note.com/sangmin/n/n9813f2064a6a'>Chiomirai School</a>⭐️</center>"
-    ),
-    allow_flagging="never",
-)
-
-file_transcribe = gr.Interface(
-    fn=transcribe,
-    inputs=[
-        gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-        gr.inputs.Checkbox(label="Return timestamps"),
-        gr.inputs.Dropdown(choices=["English", "Korean", "Japanese"], label="Language"),
-    ],
-    outputs="text",
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe Audio File",
-    description=(
-        "\n\n"
-        "<center>⭐️Brought to you by <a href='https://note.com/sangmin/n/n9813f2064a6a'>Chiomirai School</a>⭐️</center>"
-    ),
-    allow_flagging="never",
-)
-
-yt_transcribe = gr.Interface(
-    fn=yt_transcribe,
-    inputs=[
-        gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"),
-        gr.inputs.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
-        gr.inputs.Checkbox(label="Return timestamps"),
-        gr.inputs.Dropdown(choices=["English", "Korean", "Japanese"], label="Language"),
-    ],
-    outputs=["html", "text"],
-    layout="horizontal",
-    theme="huggingface",
-    title="Whisper Large V3: Transcribe YouTube",
-    description=(
-        "\n\n"
-        "<center>⭐️Brought to you by <a href='https://note.com/sangmin/n/n9813f2064a6a'>Chiomirai School</a>⭐️</center>"
-    ),
-    allow_flagging="never",
-)
-
-with demo:
-    gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"])
-
-demo.launch(enable_queue=True)
-
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/middleware/gzip.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/middleware/gzip.py
deleted file mode 100644
index bbeb2cc7861a735d6cd5c0e29aeb6dbf8457023a..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fastapi/middleware/gzip.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.middleware.gzip import GZipMiddleware as GZipMiddleware  # noqa
diff --git a/spaces/cihyFjudo/fairness-paper-search/Download and Play Cabelas Hunting Expeditions Skidrow Crack 16 for PC.md b/spaces/cihyFjudo/fairness-paper-search/Download and Play Cabelas Hunting Expeditions Skidrow Crack 16 for PC.md
deleted file mode 100644
index 7912ef3d6bbd94b132ee566efc0b715160ba8c17..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Download and Play Cabelas Hunting Expeditions Skidrow Crack 16 for PC.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>cabela's hunting expeditions skidrow crack 16</h2><br /><p><b><b>Download Zip</b> &#10001; <a href="https://tinurli.com/2uwhEw">https://tinurli.com/2uwhEw</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/cleanmaster/so-vits-svc-akagi/preprocess_hubert_f0.py b/spaces/cleanmaster/so-vits-svc-akagi/preprocess_hubert_f0.py
deleted file mode 100644
index 4fe7f21541acb01537797f430d53b3c0e63279e1..0000000000000000000000000000000000000000
--- a/spaces/cleanmaster/so-vits-svc-akagi/preprocess_hubert_f0.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import os
-import argparse
-
-import torch
-import json
-from glob import glob
-
-from pyworld import pyworld
-from tqdm import tqdm
-from scipy.io import wavfile
-
-import utils
-from mel_processing import mel_spectrogram_torch
-#import h5py
-import logging
-logging.getLogger('numba').setLevel(logging.WARNING)
-
-import parselmouth
-import librosa
-import numpy as np
-
-
-def get_f0(path,p_len=None, f0_up_key=0):
-    x, _ = librosa.load(path, 32000)
-    if p_len is None:
-        p_len = x.shape[0]//320
-    else:
-        assert abs(p_len-x.shape[0]//320) < 3, (path, p_len, x.shape)
-    time_step = 320 / 32000 * 1000
-    f0_min = 50
-    f0_max = 1100
-    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
-    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
-
-    f0 = parselmouth.Sound(x, 32000).to_pitch_ac(
-        time_step=time_step / 1000, voicing_threshold=0.6,
-        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
-
-    pad_size=(p_len - len(f0) + 1) // 2
-    if(pad_size>0 or p_len - len(f0) - pad_size>0):
-        f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
-
-    f0bak = f0.copy()
-    f0 *= pow(2, f0_up_key / 12)
-    f0_mel = 1127 * np.log(1 + f0 / 700)
-    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
-    f0_mel[f0_mel <= 1] = 1
-    f0_mel[f0_mel > 255] = 255
-    f0_coarse = np.rint(f0_mel).astype(np.int)
-    return f0_coarse, f0bak
-
-def resize2d(x, target_len):
-    source = np.array(x)
-    source[source<0.001] = np.nan
-    target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
-    res = np.nan_to_num(target)
-    return res
-
-def compute_f0(path, c_len):
-    x, sr = librosa.load(path, sr=32000)
-    f0, t = pyworld.dio(
-        x.astype(np.double),
-        fs=sr,
-        f0_ceil=800,
-        frame_period=1000 * 320 / sr,
-    )
-    f0 = pyworld.stonemask(x.astype(np.double), f0, t, 32000)
-    for index, pitch in enumerate(f0):
-        f0[index] = round(pitch, 1)
-    assert abs(c_len - x.shape[0]//320) < 3, (c_len, f0.shape)
-
-    return None, resize2d(f0, c_len)
-
-
-def process(filename):
-    print(filename)
-    save_name = filename+".soft.pt"
-    if not os.path.exists(save_name):
-        devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        wav, _ = librosa.load(filename, sr=16000)
-        wav = torch.from_numpy(wav).unsqueeze(0).to(devive)
-        c = utils.get_hubert_content(hmodel, wav)
-        torch.save(c.cpu(), save_name)
-    else:
-        c = torch.load(save_name)
-    f0path = filename+".f0.npy"
-    if not os.path.exists(f0path):
-        cf0, f0 = compute_f0(filename, c.shape[-1] * 2)
-        np.save(f0path, f0)
-
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--in_dir", type=str, default="dataset/32k", help="path to input dir")
-    args = parser.parse_args()
-
-    print("Loading hubert for content...")
-    hmodel = utils.get_hubert_model(0 if torch.cuda.is_available() else None)
-    print("Loaded hubert.")
-
-    filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True)#[:10]
-    
-    for filename in tqdm(filenames):
-        process(filename)
-    
\ No newline at end of file
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ExifTags.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ExifTags.py
deleted file mode 100644
index 2347c6d4c2768b6c946a386bba9f1325ed91193f..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/ExifTags.py
+++ /dev/null
@@ -1,380 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# EXIF tags
-#
-# Copyright (c) 2003 by Secret Labs AB
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-This module provides constants and clear-text names for various
-well-known EXIF tags.
-"""
-
-from enum import IntEnum
-
-
-class Base(IntEnum):
-    # possibly incomplete
-    InteropIndex = 0x0001
-    ProcessingSoftware = 0x000B
-    NewSubfileType = 0x00FE
-    SubfileType = 0x00FF
-    ImageWidth = 0x0100
-    ImageLength = 0x0101
-    BitsPerSample = 0x0102
-    Compression = 0x0103
-    PhotometricInterpretation = 0x0106
-    Thresholding = 0x0107
-    CellWidth = 0x0108
-    CellLength = 0x0109
-    FillOrder = 0x010A
-    DocumentName = 0x010D
-    ImageDescription = 0x010E
-    Make = 0x010F
-    Model = 0x0110
-    StripOffsets = 0x0111
-    Orientation = 0x0112
-    SamplesPerPixel = 0x0115
-    RowsPerStrip = 0x0116
-    StripByteCounts = 0x0117
-    MinSampleValue = 0x0118
-    MaxSampleValue = 0x0119
-    XResolution = 0x011A
-    YResolution = 0x011B
-    PlanarConfiguration = 0x011C
-    PageName = 0x011D
-    FreeOffsets = 0x0120
-    FreeByteCounts = 0x0121
-    GrayResponseUnit = 0x0122
-    GrayResponseCurve = 0x0123
-    T4Options = 0x0124
-    T6Options = 0x0125
-    ResolutionUnit = 0x0128
-    PageNumber = 0x0129
-    TransferFunction = 0x012D
-    Software = 0x0131
-    DateTime = 0x0132
-    Artist = 0x013B
-    HostComputer = 0x013C
-    Predictor = 0x013D
-    WhitePoint = 0x013E
-    PrimaryChromaticities = 0x013F
-    ColorMap = 0x0140
-    HalftoneHints = 0x0141
-    TileWidth = 0x0142
-    TileLength = 0x0143
-    TileOffsets = 0x0144
-    TileByteCounts = 0x0145
-    SubIFDs = 0x014A
-    InkSet = 0x014C
-    InkNames = 0x014D
-    NumberOfInks = 0x014E
-    DotRange = 0x0150
-    TargetPrinter = 0x0151
-    ExtraSamples = 0x0152
-    SampleFormat = 0x0153
-    SMinSampleValue = 0x0154
-    SMaxSampleValue = 0x0155
-    TransferRange = 0x0156
-    ClipPath = 0x0157
-    XClipPathUnits = 0x0158
-    YClipPathUnits = 0x0159
-    Indexed = 0x015A
-    JPEGTables = 0x015B
-    OPIProxy = 0x015F
-    JPEGProc = 0x0200
-    JpegIFOffset = 0x0201
-    JpegIFByteCount = 0x0202
-    JpegRestartInterval = 0x0203
-    JpegLosslessPredictors = 0x0205
-    JpegPointTransforms = 0x0206
-    JpegQTables = 0x0207
-    JpegDCTables = 0x0208
-    JpegACTables = 0x0209
-    YCbCrCoefficients = 0x0211
-    YCbCrSubSampling = 0x0212
-    YCbCrPositioning = 0x0213
-    ReferenceBlackWhite = 0x0214
-    XMLPacket = 0x02BC
-    RelatedImageFileFormat = 0x1000
-    RelatedImageWidth = 0x1001
-    RelatedImageLength = 0x1002
-    Rating = 0x4746
-    RatingPercent = 0x4749
-    ImageID = 0x800D
-    CFARepeatPatternDim = 0x828D
-    BatteryLevel = 0x828F
-    Copyright = 0x8298
-    ExposureTime = 0x829A
-    FNumber = 0x829D
-    IPTCNAA = 0x83BB
-    ImageResources = 0x8649
-    ExifOffset = 0x8769
-    InterColorProfile = 0x8773
-    ExposureProgram = 0x8822
-    SpectralSensitivity = 0x8824
-    GPSInfo = 0x8825
-    ISOSpeedRatings = 0x8827
-    OECF = 0x8828
-    Interlace = 0x8829
-    TimeZoneOffset = 0x882A
-    SelfTimerMode = 0x882B
-    SensitivityType = 0x8830
-    StandardOutputSensitivity = 0x8831
-    RecommendedExposureIndex = 0x8832
-    ISOSpeed = 0x8833
-    ISOSpeedLatitudeyyy = 0x8834
-    ISOSpeedLatitudezzz = 0x8835
-    ExifVersion = 0x9000
-    DateTimeOriginal = 0x9003
-    DateTimeDigitized = 0x9004
-    OffsetTime = 0x9010
-    OffsetTimeOriginal = 0x9011
-    OffsetTimeDigitized = 0x9012
-    ComponentsConfiguration = 0x9101
-    CompressedBitsPerPixel = 0x9102
-    ShutterSpeedValue = 0x9201
-    ApertureValue = 0x9202
-    BrightnessValue = 0x9203
-    ExposureBiasValue = 0x9204
-    MaxApertureValue = 0x9205
-    SubjectDistance = 0x9206
-    MeteringMode = 0x9207
-    LightSource = 0x9208
-    Flash = 0x9209
-    FocalLength = 0x920A
-    Noise = 0x920D
-    ImageNumber = 0x9211
-    SecurityClassification = 0x9212
-    ImageHistory = 0x9213
-    TIFFEPStandardID = 0x9216
-    MakerNote = 0x927C
-    UserComment = 0x9286
-    SubsecTime = 0x9290
-    SubsecTimeOriginal = 0x9291
-    SubsecTimeDigitized = 0x9292
-    AmbientTemperature = 0x9400
-    Humidity = 0x9401
-    Pressure = 0x9402
-    WaterDepth = 0x9403
-    Acceleration = 0x9404
-    CameraElevationAngle = 0x9405
-    XPTitle = 0x9C9B
-    XPComment = 0x9C9C
-    XPAuthor = 0x9C9D
-    XPKeywords = 0x9C9E
-    XPSubject = 0x9C9F
-    FlashPixVersion = 0xA000
-    ColorSpace = 0xA001
-    ExifImageWidth = 0xA002
-    ExifImageHeight = 0xA003
-    RelatedSoundFile = 0xA004
-    ExifInteroperabilityOffset = 0xA005
-    FlashEnergy = 0xA20B
-    SpatialFrequencyResponse = 0xA20C
-    FocalPlaneXResolution = 0xA20E
-    FocalPlaneYResolution = 0xA20F
-    FocalPlaneResolutionUnit = 0xA210
-    SubjectLocation = 0xA214
-    ExposureIndex = 0xA215
-    SensingMethod = 0xA217
-    FileSource = 0xA300
-    SceneType = 0xA301
-    CFAPattern = 0xA302
-    CustomRendered = 0xA401
-    ExposureMode = 0xA402
-    WhiteBalance = 0xA403
-    DigitalZoomRatio = 0xA404
-    FocalLengthIn35mmFilm = 0xA405
-    SceneCaptureType = 0xA406
-    GainControl = 0xA407
-    Contrast = 0xA408
-    Saturation = 0xA409
-    Sharpness = 0xA40A
-    DeviceSettingDescription = 0xA40B
-    SubjectDistanceRange = 0xA40C
-    ImageUniqueID = 0xA420
-    CameraOwnerName = 0xA430
-    BodySerialNumber = 0xA431
-    LensSpecification = 0xA432
-    LensMake = 0xA433
-    LensModel = 0xA434
-    LensSerialNumber = 0xA435
-    CompositeImage = 0xA460
-    CompositeImageCount = 0xA461
-    CompositeImageExposureTimes = 0xA462
-    Gamma = 0xA500
-    PrintImageMatching = 0xC4A5
-    DNGVersion = 0xC612
-    DNGBackwardVersion = 0xC613
-    UniqueCameraModel = 0xC614
-    LocalizedCameraModel = 0xC615
-    CFAPlaneColor = 0xC616
-    CFALayout = 0xC617
-    LinearizationTable = 0xC618
-    BlackLevelRepeatDim = 0xC619
-    BlackLevel = 0xC61A
-    BlackLevelDeltaH = 0xC61B
-    BlackLevelDeltaV = 0xC61C
-    WhiteLevel = 0xC61D
-    DefaultScale = 0xC61E
-    DefaultCropOrigin = 0xC61F
-    DefaultCropSize = 0xC620
-    ColorMatrix1 = 0xC621
-    ColorMatrix2 = 0xC622
-    CameraCalibration1 = 0xC623
-    CameraCalibration2 = 0xC624
-    ReductionMatrix1 = 0xC625
-    ReductionMatrix2 = 0xC626
-    AnalogBalance = 0xC627
-    AsShotNeutral = 0xC628
-    AsShotWhiteXY = 0xC629
-    BaselineExposure = 0xC62A
-    BaselineNoise = 0xC62B
-    BaselineSharpness = 0xC62C
-    BayerGreenSplit = 0xC62D
-    LinearResponseLimit = 0xC62E
-    CameraSerialNumber = 0xC62F
-    LensInfo = 0xC630
-    ChromaBlurRadius = 0xC631
-    AntiAliasStrength = 0xC632
-    ShadowScale = 0xC633
-    DNGPrivateData = 0xC634
-    MakerNoteSafety = 0xC635
-    CalibrationIlluminant1 = 0xC65A
-    CalibrationIlluminant2 = 0xC65B
-    BestQualityScale = 0xC65C
-    RawDataUniqueID = 0xC65D
-    OriginalRawFileName = 0xC68B
-    OriginalRawFileData = 0xC68C
-    ActiveArea = 0xC68D
-    MaskedAreas = 0xC68E
-    AsShotICCProfile = 0xC68F
-    AsShotPreProfileMatrix = 0xC690
-    CurrentICCProfile = 0xC691
-    CurrentPreProfileMatrix = 0xC692
-    ColorimetricReference = 0xC6BF
-    CameraCalibrationSignature = 0xC6F3
-    ProfileCalibrationSignature = 0xC6F4
-    AsShotProfileName = 0xC6F6
-    NoiseReductionApplied = 0xC6F7
-    ProfileName = 0xC6F8
-    ProfileHueSatMapDims = 0xC6F9
-    ProfileHueSatMapData1 = 0xC6FA
-    ProfileHueSatMapData2 = 0xC6FB
-    ProfileToneCurve = 0xC6FC
-    ProfileEmbedPolicy = 0xC6FD
-    ProfileCopyright = 0xC6FE
-    ForwardMatrix1 = 0xC714
-    ForwardMatrix2 = 0xC715
-    PreviewApplicationName = 0xC716
-    PreviewApplicationVersion = 0xC717
-    PreviewSettingsName = 0xC718
-    PreviewSettingsDigest = 0xC719
-    PreviewColorSpace = 0xC71A
-    PreviewDateTime = 0xC71B
-    RawImageDigest = 0xC71C
-    OriginalRawFileDigest = 0xC71D
-    SubTileBlockSize = 0xC71E
-    RowInterleaveFactor = 0xC71F
-    ProfileLookTableDims = 0xC725
-    ProfileLookTableData = 0xC726
-    OpcodeList1 = 0xC740
-    OpcodeList2 = 0xC741
-    OpcodeList3 = 0xC74E
-    NoiseProfile = 0xC761
-
-
-"""Maps EXIF tags to tag names."""
-TAGS = {
-    **{i.value: i.name for i in Base},
-    0x920C: "SpatialFrequencyResponse",
-    0x9214: "SubjectLocation",
-    0x9215: "ExposureIndex",
-    0x828E: "CFAPattern",
-    0x920B: "FlashEnergy",
-    0x9216: "TIFF/EPStandardID",
-}
-
-
-class GPS(IntEnum):
-    GPSVersionID = 0
-    GPSLatitudeRef = 1
-    GPSLatitude = 2
-    GPSLongitudeRef = 3
-    GPSLongitude = 4
-    GPSAltitudeRef = 5
-    GPSAltitude = 6
-    GPSTimeStamp = 7
-    GPSSatellites = 8
-    GPSStatus = 9
-    GPSMeasureMode = 10
-    GPSDOP = 11
-    GPSSpeedRef = 12
-    GPSSpeed = 13
-    GPSTrackRef = 14
-    GPSTrack = 15
-    GPSImgDirectionRef = 16
-    GPSImgDirection = 17
-    GPSMapDatum = 18
-    GPSDestLatitudeRef = 19
-    GPSDestLatitude = 20
-    GPSDestLongitudeRef = 21
-    GPSDestLongitude = 22
-    GPSDestBearingRef = 23
-    GPSDestBearing = 24
-    GPSDestDistanceRef = 25
-    GPSDestDistance = 26
-    GPSProcessingMethod = 27
-    GPSAreaInformation = 28
-    GPSDateStamp = 29
-    GPSDifferential = 30
-    GPSHPositioningError = 31
-
-
-"""Maps EXIF GPS tags to tag names."""
-GPSTAGS = {i.value: i.name for i in GPS}
-
-
-class Interop(IntEnum):
-    InteropIndex = 1
-    InteropVersion = 2
-    RelatedImageFileFormat = 4096
-    RelatedImageWidth = 4097
-    RleatedImageHeight = 4098
-
-
-class IFD(IntEnum):
-    Exif = 34665
-    GPSInfo = 34853
-    Makernote = 37500
-    Interop = 40965
-    IFD1 = -1
-
-
-class LightSource(IntEnum):
-    Unknown = 0
-    Daylight = 1
-    Fluorescent = 2
-    Tungsten = 3
-    Flash = 4
-    Fine = 9
-    Cloudy = 10
-    Shade = 11
-    DaylightFluorescent = 12
-    DayWhiteFluorescent = 13
-    CoolWhiteFluorescent = 14
-    WhiteFluorescent = 15
-    StandardLightA = 17
-    StandardLightB = 18
-    StandardLightC = 19
-    D55 = 20
-    D65 = 21
-    D75 = 22
-    D50 = 23
-    ISO = 24
-    Other = 255
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/TiffTags.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/TiffTags.py
deleted file mode 100644
index 30b05e4e1d41fa21a7b7bf12c04ee05af6aa5284..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/TiffTags.py
+++ /dev/null
@@ -1,560 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# TIFF tags
-#
-# This module provides clear-text names for various well-known
-# TIFF tags.  the TIFF codec works just fine without it.
-#
-# Copyright (c) Secret Labs AB 1999.
-#
-# See the README file for information on usage and redistribution.
-#
-
-##
-# This module provides constants and clear-text names for various
-# well-known TIFF tags.
-##
-
-from collections import namedtuple
-
-
-class TagInfo(namedtuple("_TagInfo", "value name type length enum")):
-    __slots__ = []
-
-    def __new__(cls, value=None, name="unknown", type=None, length=None, enum=None):
-        return super().__new__(cls, value, name, type, length, enum or {})
-
-    def cvt_enum(self, value):
-        # Using get will call hash(value), which can be expensive
-        # for some types (e.g. Fraction). Since self.enum is rarely
-        # used, it's usually better to test it first.
-        return self.enum.get(value, value) if self.enum else value
-
-
-def lookup(tag, group=None):
-    """
-    :param tag: Integer tag number
-    :param group: Which :py:data:`~PIL.TiffTags.TAGS_V2_GROUPS` to look in
-
-    .. versionadded:: 8.3.0
-
-    :returns: Taginfo namedtuple, From the ``TAGS_V2`` info if possible,
-        otherwise just populating the value and name from ``TAGS``.
-        If the tag is not recognized, "unknown" is returned for the name
-
-    """
-
-    if group is not None:
-        info = TAGS_V2_GROUPS[group].get(tag) if group in TAGS_V2_GROUPS else None
-    else:
-        info = TAGS_V2.get(tag)
-    return info or TagInfo(tag, TAGS.get(tag, "unknown"))
-
-
-##
-# Map tag numbers to tag info.
-#
-#  id: (Name, Type, Length, enum_values)
-#
-# The length here differs from the length in the tiff spec.  For
-# numbers, the tiff spec is for the number of fields returned. We
-# agree here.  For string-like types, the tiff spec uses the length of
-# field in bytes.  In Pillow, we are using the number of expected
-# fields, in general 1 for string-like types.
-
-
-BYTE = 1
-ASCII = 2
-SHORT = 3
-LONG = 4
-RATIONAL = 5
-SIGNED_BYTE = 6
-UNDEFINED = 7
-SIGNED_SHORT = 8
-SIGNED_LONG = 9
-SIGNED_RATIONAL = 10
-FLOAT = 11
-DOUBLE = 12
-IFD = 13
-LONG8 = 16
-
-TAGS_V2 = {
-    254: ("NewSubfileType", LONG, 1),
-    255: ("SubfileType", SHORT, 1),
-    256: ("ImageWidth", LONG, 1),
-    257: ("ImageLength", LONG, 1),
-    258: ("BitsPerSample", SHORT, 0),
-    259: (
-        "Compression",
-        SHORT,
-        1,
-        {
-            "Uncompressed": 1,
-            "CCITT 1d": 2,
-            "Group 3 Fax": 3,
-            "Group 4 Fax": 4,
-            "LZW": 5,
-            "JPEG": 6,
-            "PackBits": 32773,
-        },
-    ),
-    262: (
-        "PhotometricInterpretation",
-        SHORT,
-        1,
-        {
-            "WhiteIsZero": 0,
-            "BlackIsZero": 1,
-            "RGB": 2,
-            "RGB Palette": 3,
-            "Transparency Mask": 4,
-            "CMYK": 5,
-            "YCbCr": 6,
-            "CieLAB": 8,
-            "CFA": 32803,  # TIFF/EP, Adobe DNG
-            "LinearRaw": 32892,  # Adobe DNG
-        },
-    ),
-    263: ("Threshholding", SHORT, 1),
-    264: ("CellWidth", SHORT, 1),
-    265: ("CellLength", SHORT, 1),
-    266: ("FillOrder", SHORT, 1),
-    269: ("DocumentName", ASCII, 1),
-    270: ("ImageDescription", ASCII, 1),
-    271: ("Make", ASCII, 1),
-    272: ("Model", ASCII, 1),
-    273: ("StripOffsets", LONG, 0),
-    274: ("Orientation", SHORT, 1),
-    277: ("SamplesPerPixel", SHORT, 1),
-    278: ("RowsPerStrip", LONG, 1),
-    279: ("StripByteCounts", LONG, 0),
-    280: ("MinSampleValue", SHORT, 0),
-    281: ("MaxSampleValue", SHORT, 0),
-    282: ("XResolution", RATIONAL, 1),
-    283: ("YResolution", RATIONAL, 1),
-    284: ("PlanarConfiguration", SHORT, 1, {"Contiguous": 1, "Separate": 2}),
-    285: ("PageName", ASCII, 1),
-    286: ("XPosition", RATIONAL, 1),
-    287: ("YPosition", RATIONAL, 1),
-    288: ("FreeOffsets", LONG, 1),
-    289: ("FreeByteCounts", LONG, 1),
-    290: ("GrayResponseUnit", SHORT, 1),
-    291: ("GrayResponseCurve", SHORT, 0),
-    292: ("T4Options", LONG, 1),
-    293: ("T6Options", LONG, 1),
-    296: ("ResolutionUnit", SHORT, 1, {"none": 1, "inch": 2, "cm": 3}),
-    297: ("PageNumber", SHORT, 2),
-    301: ("TransferFunction", SHORT, 0),
-    305: ("Software", ASCII, 1),
-    306: ("DateTime", ASCII, 1),
-    315: ("Artist", ASCII, 1),
-    316: ("HostComputer", ASCII, 1),
-    317: ("Predictor", SHORT, 1, {"none": 1, "Horizontal Differencing": 2}),
-    318: ("WhitePoint", RATIONAL, 2),
-    319: ("PrimaryChromaticities", RATIONAL, 6),
-    320: ("ColorMap", SHORT, 0),
-    321: ("HalftoneHints", SHORT, 2),
-    322: ("TileWidth", LONG, 1),
-    323: ("TileLength", LONG, 1),
-    324: ("TileOffsets", LONG, 0),
-    325: ("TileByteCounts", LONG, 0),
-    330: ("SubIFDs", LONG, 0),
-    332: ("InkSet", SHORT, 1),
-    333: ("InkNames", ASCII, 1),
-    334: ("NumberOfInks", SHORT, 1),
-    336: ("DotRange", SHORT, 0),
-    337: ("TargetPrinter", ASCII, 1),
-    338: ("ExtraSamples", SHORT, 0),
-    339: ("SampleFormat", SHORT, 0),
-    340: ("SMinSampleValue", DOUBLE, 0),
-    341: ("SMaxSampleValue", DOUBLE, 0),
-    342: ("TransferRange", SHORT, 6),
-    347: ("JPEGTables", UNDEFINED, 1),
-    # obsolete JPEG tags
-    512: ("JPEGProc", SHORT, 1),
-    513: ("JPEGInterchangeFormat", LONG, 1),
-    514: ("JPEGInterchangeFormatLength", LONG, 1),
-    515: ("JPEGRestartInterval", SHORT, 1),
-    517: ("JPEGLosslessPredictors", SHORT, 0),
-    518: ("JPEGPointTransforms", SHORT, 0),
-    519: ("JPEGQTables", LONG, 0),
-    520: ("JPEGDCTables", LONG, 0),
-    521: ("JPEGACTables", LONG, 0),
-    529: ("YCbCrCoefficients", RATIONAL, 3),
-    530: ("YCbCrSubSampling", SHORT, 2),
-    531: ("YCbCrPositioning", SHORT, 1),
-    532: ("ReferenceBlackWhite", RATIONAL, 6),
-    700: ("XMP", BYTE, 0),
-    33432: ("Copyright", ASCII, 1),
-    33723: ("IptcNaaInfo", UNDEFINED, 1),
-    34377: ("PhotoshopInfo", BYTE, 0),
-    # FIXME add more tags here
-    34665: ("ExifIFD", LONG, 1),
-    34675: ("ICCProfile", UNDEFINED, 1),
-    34853: ("GPSInfoIFD", LONG, 1),
-    36864: ("ExifVersion", UNDEFINED, 1),
-    37724: ("ImageSourceData", UNDEFINED, 1),
-    40965: ("InteroperabilityIFD", LONG, 1),
-    41730: ("CFAPattern", UNDEFINED, 1),
-    # MPInfo
-    45056: ("MPFVersion", UNDEFINED, 1),
-    45057: ("NumberOfImages", LONG, 1),
-    45058: ("MPEntry", UNDEFINED, 1),
-    45059: ("ImageUIDList", UNDEFINED, 0),  # UNDONE, check
-    45060: ("TotalFrames", LONG, 1),
-    45313: ("MPIndividualNum", LONG, 1),
-    45569: ("PanOrientation", LONG, 1),
-    45570: ("PanOverlap_H", RATIONAL, 1),
-    45571: ("PanOverlap_V", RATIONAL, 1),
-    45572: ("BaseViewpointNum", LONG, 1),
-    45573: ("ConvergenceAngle", SIGNED_RATIONAL, 1),
-    45574: ("BaselineLength", RATIONAL, 1),
-    45575: ("VerticalDivergence", SIGNED_RATIONAL, 1),
-    45576: ("AxisDistance_X", SIGNED_RATIONAL, 1),
-    45577: ("AxisDistance_Y", SIGNED_RATIONAL, 1),
-    45578: ("AxisDistance_Z", SIGNED_RATIONAL, 1),
-    45579: ("YawAngle", SIGNED_RATIONAL, 1),
-    45580: ("PitchAngle", SIGNED_RATIONAL, 1),
-    45581: ("RollAngle", SIGNED_RATIONAL, 1),
-    40960: ("FlashPixVersion", UNDEFINED, 1),
-    50741: ("MakerNoteSafety", SHORT, 1, {"Unsafe": 0, "Safe": 1}),
-    50780: ("BestQualityScale", RATIONAL, 1),
-    50838: ("ImageJMetaDataByteCounts", LONG, 0),  # Can be more than one
-    50839: ("ImageJMetaData", UNDEFINED, 1),  # see Issue #2006
-}
-TAGS_V2_GROUPS = {
-    # ExifIFD
-    34665: {
-        36864: ("ExifVersion", UNDEFINED, 1),
-        40960: ("FlashPixVersion", UNDEFINED, 1),
-        40965: ("InteroperabilityIFD", LONG, 1),
-        41730: ("CFAPattern", UNDEFINED, 1),
-    },
-    # GPSInfoIFD
-    34853: {
-        0: ("GPSVersionID", BYTE, 4),
-        1: ("GPSLatitudeRef", ASCII, 2),
-        2: ("GPSLatitude", RATIONAL, 3),
-        3: ("GPSLongitudeRef", ASCII, 2),
-        4: ("GPSLongitude", RATIONAL, 3),
-        5: ("GPSAltitudeRef", BYTE, 1),
-        6: ("GPSAltitude", RATIONAL, 1),
-        7: ("GPSTimeStamp", RATIONAL, 3),
-        8: ("GPSSatellites", ASCII, 0),
-        9: ("GPSStatus", ASCII, 2),
-        10: ("GPSMeasureMode", ASCII, 2),
-        11: ("GPSDOP", RATIONAL, 1),
-        12: ("GPSSpeedRef", ASCII, 2),
-        13: ("GPSSpeed", RATIONAL, 1),
-        14: ("GPSTrackRef", ASCII, 2),
-        15: ("GPSTrack", RATIONAL, 1),
-        16: ("GPSImgDirectionRef", ASCII, 2),
-        17: ("GPSImgDirection", RATIONAL, 1),
-        18: ("GPSMapDatum", ASCII, 0),
-        19: ("GPSDestLatitudeRef", ASCII, 2),
-        20: ("GPSDestLatitude", RATIONAL, 3),
-        21: ("GPSDestLongitudeRef", ASCII, 2),
-        22: ("GPSDestLongitude", RATIONAL, 3),
-        23: ("GPSDestBearingRef", ASCII, 2),
-        24: ("GPSDestBearing", RATIONAL, 1),
-        25: ("GPSDestDistanceRef", ASCII, 2),
-        26: ("GPSDestDistance", RATIONAL, 1),
-        27: ("GPSProcessingMethod", UNDEFINED, 0),
-        28: ("GPSAreaInformation", UNDEFINED, 0),
-        29: ("GPSDateStamp", ASCII, 11),
-        30: ("GPSDifferential", SHORT, 1),
-    },
-    # InteroperabilityIFD
-    40965: {1: ("InteropIndex", ASCII, 1), 2: ("InteropVersion", UNDEFINED, 1)},
-}
-
-# Legacy Tags structure
-# these tags aren't included above, but were in the previous versions
-TAGS = {
-    347: "JPEGTables",
-    700: "XMP",
-    # Additional Exif Info
-    32932: "Wang Annotation",
-    33434: "ExposureTime",
-    33437: "FNumber",
-    33445: "MD FileTag",
-    33446: "MD ScalePixel",
-    33447: "MD ColorTable",
-    33448: "MD LabName",
-    33449: "MD SampleInfo",
-    33450: "MD PrepDate",
-    33451: "MD PrepTime",
-    33452: "MD FileUnits",
-    33550: "ModelPixelScaleTag",
-    33723: "IptcNaaInfo",
-    33918: "INGR Packet Data Tag",
-    33919: "INGR Flag Registers",
-    33920: "IrasB Transformation Matrix",
-    33922: "ModelTiepointTag",
-    34264: "ModelTransformationTag",
-    34377: "PhotoshopInfo",
-    34735: "GeoKeyDirectoryTag",
-    34736: "GeoDoubleParamsTag",
-    34737: "GeoAsciiParamsTag",
-    34850: "ExposureProgram",
-    34852: "SpectralSensitivity",
-    34855: "ISOSpeedRatings",
-    34856: "OECF",
-    34864: "SensitivityType",
-    34865: "StandardOutputSensitivity",
-    34866: "RecommendedExposureIndex",
-    34867: "ISOSpeed",
-    34868: "ISOSpeedLatitudeyyy",
-    34869: "ISOSpeedLatitudezzz",
-    34908: "HylaFAX FaxRecvParams",
-    34909: "HylaFAX FaxSubAddress",
-    34910: "HylaFAX FaxRecvTime",
-    36864: "ExifVersion",
-    36867: "DateTimeOriginal",
-    36868: "DateTimeDigitized",
-    37121: "ComponentsConfiguration",
-    37122: "CompressedBitsPerPixel",
-    37724: "ImageSourceData",
-    37377: "ShutterSpeedValue",
-    37378: "ApertureValue",
-    37379: "BrightnessValue",
-    37380: "ExposureBiasValue",
-    37381: "MaxApertureValue",
-    37382: "SubjectDistance",
-    37383: "MeteringMode",
-    37384: "LightSource",
-    37385: "Flash",
-    37386: "FocalLength",
-    37396: "SubjectArea",
-    37500: "MakerNote",
-    37510: "UserComment",
-    37520: "SubSec",
-    37521: "SubSecTimeOriginal",
-    37522: "SubsecTimeDigitized",
-    40960: "FlashPixVersion",
-    40961: "ColorSpace",
-    40962: "PixelXDimension",
-    40963: "PixelYDimension",
-    40964: "RelatedSoundFile",
-    40965: "InteroperabilityIFD",
-    41483: "FlashEnergy",
-    41484: "SpatialFrequencyResponse",
-    41486: "FocalPlaneXResolution",
-    41487: "FocalPlaneYResolution",
-    41488: "FocalPlaneResolutionUnit",
-    41492: "SubjectLocation",
-    41493: "ExposureIndex",
-    41495: "SensingMethod",
-    41728: "FileSource",
-    41729: "SceneType",
-    41730: "CFAPattern",
-    41985: "CustomRendered",
-    41986: "ExposureMode",
-    41987: "WhiteBalance",
-    41988: "DigitalZoomRatio",
-    41989: "FocalLengthIn35mmFilm",
-    41990: "SceneCaptureType",
-    41991: "GainControl",
-    41992: "Contrast",
-    41993: "Saturation",
-    41994: "Sharpness",
-    41995: "DeviceSettingDescription",
-    41996: "SubjectDistanceRange",
-    42016: "ImageUniqueID",
-    42032: "CameraOwnerName",
-    42033: "BodySerialNumber",
-    42034: "LensSpecification",
-    42035: "LensMake",
-    42036: "LensModel",
-    42037: "LensSerialNumber",
-    42112: "GDAL_METADATA",
-    42113: "GDAL_NODATA",
-    42240: "Gamma",
-    50215: "Oce Scanjob Description",
-    50216: "Oce Application Selector",
-    50217: "Oce Identification Number",
-    50218: "Oce ImageLogic Characteristics",
-    # Adobe DNG
-    50706: "DNGVersion",
-    50707: "DNGBackwardVersion",
-    50708: "UniqueCameraModel",
-    50709: "LocalizedCameraModel",
-    50710: "CFAPlaneColor",
-    50711: "CFALayout",
-    50712: "LinearizationTable",
-    50713: "BlackLevelRepeatDim",
-    50714: "BlackLevel",
-    50715: "BlackLevelDeltaH",
-    50716: "BlackLevelDeltaV",
-    50717: "WhiteLevel",
-    50718: "DefaultScale",
-    50719: "DefaultCropOrigin",
-    50720: "DefaultCropSize",
-    50721: "ColorMatrix1",
-    50722: "ColorMatrix2",
-    50723: "CameraCalibration1",
-    50724: "CameraCalibration2",
-    50725: "ReductionMatrix1",
-    50726: "ReductionMatrix2",
-    50727: "AnalogBalance",
-    50728: "AsShotNeutral",
-    50729: "AsShotWhiteXY",
-    50730: "BaselineExposure",
-    50731: "BaselineNoise",
-    50732: "BaselineSharpness",
-    50733: "BayerGreenSplit",
-    50734: "LinearResponseLimit",
-    50735: "CameraSerialNumber",
-    50736: "LensInfo",
-    50737: "ChromaBlurRadius",
-    50738: "AntiAliasStrength",
-    50740: "DNGPrivateData",
-    50778: "CalibrationIlluminant1",
-    50779: "CalibrationIlluminant2",
-    50784: "Alias Layer Metadata",
-}
-
-
-def _populate():
-    for k, v in TAGS_V2.items():
-        # Populate legacy structure.
-        TAGS[k] = v[0]
-        if len(v) == 4:
-            for sk, sv in v[3].items():
-                TAGS[(k, sv)] = sk
-
-        TAGS_V2[k] = TagInfo(k, *v)
-
-    for group, tags in TAGS_V2_GROUPS.items():
-        for k, v in tags.items():
-            tags[k] = TagInfo(k, *v)
-
-
-_populate()
-##
-# Map type numbers to type names -- defined in ImageFileDirectory.
-
-TYPES = {}
-
-# was:
-# TYPES = {
-#     1: "byte",
-#     2: "ascii",
-#     3: "short",
-#     4: "long",
-#     5: "rational",
-#     6: "signed byte",
-#     7: "undefined",
-#     8: "signed short",
-#     9: "signed long",
-#     10: "signed rational",
-#     11: "float",
-#     12: "double",
-# }
-
-#
-# These tags are handled by default in libtiff, without
-# adding to the custom dictionary. From tif_dir.c, searching for
-# case TIFFTAG in the _TIFFVSetField function:
-# Line: item.
-# 148: case TIFFTAG_SUBFILETYPE:
-# 151: case TIFFTAG_IMAGEWIDTH:
-# 154: case TIFFTAG_IMAGELENGTH:
-# 157: case TIFFTAG_BITSPERSAMPLE:
-# 181: case TIFFTAG_COMPRESSION:
-# 202: case TIFFTAG_PHOTOMETRIC:
-# 205: case TIFFTAG_THRESHHOLDING:
-# 208: case TIFFTAG_FILLORDER:
-# 214: case TIFFTAG_ORIENTATION:
-# 221: case TIFFTAG_SAMPLESPERPIXEL:
-# 228: case TIFFTAG_ROWSPERSTRIP:
-# 238: case TIFFTAG_MINSAMPLEVALUE:
-# 241: case TIFFTAG_MAXSAMPLEVALUE:
-# 244: case TIFFTAG_SMINSAMPLEVALUE:
-# 247: case TIFFTAG_SMAXSAMPLEVALUE:
-# 250: case TIFFTAG_XRESOLUTION:
-# 256: case TIFFTAG_YRESOLUTION:
-# 262: case TIFFTAG_PLANARCONFIG:
-# 268: case TIFFTAG_XPOSITION:
-# 271: case TIFFTAG_YPOSITION:
-# 274: case TIFFTAG_RESOLUTIONUNIT:
-# 280: case TIFFTAG_PAGENUMBER:
-# 284: case TIFFTAG_HALFTONEHINTS:
-# 288: case TIFFTAG_COLORMAP:
-# 294: case TIFFTAG_EXTRASAMPLES:
-# 298: case TIFFTAG_MATTEING:
-# 305: case TIFFTAG_TILEWIDTH:
-# 316: case TIFFTAG_TILELENGTH:
-# 327: case TIFFTAG_TILEDEPTH:
-# 333: case TIFFTAG_DATATYPE:
-# 344: case TIFFTAG_SAMPLEFORMAT:
-# 361: case TIFFTAG_IMAGEDEPTH:
-# 364: case TIFFTAG_SUBIFD:
-# 376: case TIFFTAG_YCBCRPOSITIONING:
-# 379: case TIFFTAG_YCBCRSUBSAMPLING:
-# 383: case TIFFTAG_TRANSFERFUNCTION:
-# 389: case TIFFTAG_REFERENCEBLACKWHITE:
-# 393: case TIFFTAG_INKNAMES:
-
-# Following pseudo-tags are also handled by default in libtiff:
-# TIFFTAG_JPEGQUALITY 65537
-
-# some of these are not in our TAGS_V2 dict and were included from tiff.h
-
-# This list also exists in encode.c
-LIBTIFF_CORE = {
-    255,
-    256,
-    257,
-    258,
-    259,
-    262,
-    263,
-    266,
-    274,
-    277,
-    278,
-    280,
-    281,
-    340,
-    341,
-    282,
-    283,
-    284,
-    286,
-    287,
-    296,
-    297,
-    321,
-    320,
-    338,
-    32995,
-    322,
-    323,
-    32998,
-    32996,
-    339,
-    32997,
-    330,
-    531,
-    530,
-    301,
-    532,
-    333,
-    # as above
-    269,  # this has been in our tests forever, and works
-    65537,
-}
-
-LIBTIFF_CORE.remove(255)  # We don't have support for subfiletypes
-LIBTIFF_CORE.remove(322)  # We don't have support for writing tiled images with libtiff
-LIBTIFF_CORE.remove(323)  # Tiled images
-LIBTIFF_CORE.remove(333)  # Ink Names either
-
-# Note to advanced users: There may be combinations of these
-# parameters and values that when added properly, will work and
-# produce valid tiff images that may work in your application.
-# It is safe to add and remove tags from this set from Pillow's point
-# of view so long as you test against libtiff.
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/__init__.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/__init__.py
deleted file mode 100644
index 0bd8ec5e3b566d8a2d43a0904fd49db7862a21eb..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/altair/utils/__init__.py
+++ /dev/null
@@ -1,30 +0,0 @@
-from .core import (
-    infer_vegalite_type,
-    infer_encoding_types,
-    sanitize_dataframe,
-    parse_shorthand,
-    use_signature,
-    update_nested,
-    display_traceback,
-    SchemaBase,
-)
-from .html import spec_to_html
-from .plugin_registry import PluginRegistry
-from .deprecation import AltairDeprecationWarning
-from .schemapi import Undefined
-
-
-__all__ = (
-    "infer_vegalite_type",
-    "infer_encoding_types",
-    "sanitize_dataframe",
-    "spec_to_html",
-    "parse_shorthand",
-    "use_signature",
-    "update_nested",
-    "display_traceback",
-    "AltairDeprecationWarning",
-    "SchemaBase",
-    "Undefined",
-    "PluginRegistry",
-)
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/assdec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/assdec.c
deleted file mode 100644
index 89d7b51894b267801536ddb16896032f9d2ce7c9..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/assdec.c
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * SSA/ASS decoder
- * Copyright (c) 2010  Aurelien Jacobs <aurel@gnuage.org>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include <string.h>
-
-#include "avcodec.h"
-#include "ass.h"
-#include "codec_internal.h"
-#include "config_components.h"
-#include "libavutil/internal.h"
-#include "libavutil/mem.h"
-
-static av_cold int ass_decode_init(AVCodecContext *avctx)
-{
-    avctx->subtitle_header = av_malloc(avctx->extradata_size + 1);
-    if (!avctx->subtitle_header)
-        return AVERROR(ENOMEM);
-    if (avctx->extradata_size)
-        memcpy(avctx->subtitle_header, avctx->extradata, avctx->extradata_size);
-    avctx->subtitle_header[avctx->extradata_size] = 0;
-    avctx->subtitle_header_size = avctx->extradata_size;
-    return 0;
-}
-
-static int ass_decode_frame(AVCodecContext *avctx, AVSubtitle *sub,
-                            int *got_sub_ptr, const AVPacket *avpkt)
-{
-    if (avpkt->size <= 0)
-        return avpkt->size;
-
-    sub->rects = av_malloc(sizeof(*sub->rects));
-    if (!sub->rects)
-        return AVERROR(ENOMEM);
-    sub->rects[0] = av_mallocz(sizeof(*sub->rects[0]));
-    if (!sub->rects[0])
-        return AVERROR(ENOMEM);
-    sub->num_rects = 1;
-    sub->rects[0]->type = SUBTITLE_ASS;
-    sub->rects[0]->ass  = av_strdup(avpkt->data);
-    if (!sub->rects[0]->ass)
-        return AVERROR(ENOMEM);
-    *got_sub_ptr = 1;
-    return avpkt->size;
-}
-
-#if CONFIG_SSA_DECODER
-const FFCodec ff_ssa_decoder = {
-    .p.name       = "ssa",
-    CODEC_LONG_NAME("ASS (Advanced SubStation Alpha) subtitle"),
-    .p.type       = AVMEDIA_TYPE_SUBTITLE,
-    .p.id         = AV_CODEC_ID_ASS,
-    .init         = ass_decode_init,
-    FF_CODEC_DECODE_SUB_CB(ass_decode_frame),
-};
-#endif
-
-#if CONFIG_ASS_DECODER
-const FFCodec ff_ass_decoder = {
-    .p.name       = "ass",
-    CODEC_LONG_NAME("ASS (Advanced SubStation Alpha) subtitle"),
-    .p.type       = AVMEDIA_TYPE_SUBTITLE,
-    .p.id         = AV_CODEC_ID_ASS,
-    .init         = ass_decode_init,
-    FF_CODEC_DECODE_SUB_CB(ass_decode_frame),
-};
-#endif
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/g723_1.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/g723_1.c
deleted file mode 100644
index d19d09dd899be76be1ec36e9528bb87c215403d7..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/g723_1.c
+++ /dev/null
@@ -1,1333 +0,0 @@
-/*
- * G.723.1 compatible decoder
- * Copyright (c) 2006 Benjamin Larsson
- * Copyright (c) 2010 Mohamed Naufal Basheer
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include <stdint.h>
-#include <string.h>
-
-#include "libavutil/common.h"
-#include "libavutil/intmath.h"
-
-#include "acelp_vectors.h"
-#include "celp_math.h"
-#include "g723_1.h"
-
-const int16_t ff_g723_1_cos_tab[COS_TBL_SIZE + 1] = {
-    16384,  16383,  16379,  16373,  16364,  16353,  16340,  16324,
-    16305,  16284,  16261,  16235,  16207,  16176,  16143,  16107,
-    16069,  16029,  15986,  15941,  15893,  15843,  15791,  15736,
-    15679,  15619,  15557,  15493,  15426,  15357,  15286,  15213,
-    15137,  15059,  14978,  14896,  14811,  14724,  14635,  14543,
-    14449,  14354,  14256,  14155,  14053,  13949,  13842,  13733,
-    13623,  13510,  13395,  13279,  13160,  13039,  12916,  12792,
-    12665,  12537,  12406,  12274,  12140,  12004,  11866,  11727,
-    11585,  11442,  11297,  11151,  11003,  10853,  10702,  10549,
-    10394,  10238,  10080,   9921,   9760,   9598,   9434,   9269,
-     9102,   8935,   8765,   8595,   8423,   8250,   8076,   7900,
-     7723,   7545,   7366,   7186,   7005,   6823,   6639,   6455,
-     6270,   6084,   5897,   5708,   5520,   5330,   5139,   4948,
-     4756,   4563,   4370,   4176,   3981,   3786,   3590,   3393,
-     3196,   2999,   2801,   2603,   2404,   2205,   2006,   1806,
-     1606,   1406,   1205,   1005,    804,    603,    402,    201,
-        0,   -201,   -402,   -603,   -804,  -1005,  -1205,  -1406,
-    -1606,  -1806,  -2006,  -2205,  -2404,  -2603,  -2801,  -2999,
-    -3196,  -3393,  -3590,  -3786,  -3981,  -4176,  -4370,  -4563,
-    -4756,  -4948,  -5139,  -5330,  -5520,  -5708,  -5897,  -6084,
-    -6270,  -6455,  -6639,  -6823,  -7005,  -7186,  -7366,  -7545,
-    -7723,  -7900,  -8076,  -8250,  -8423,  -8595,  -8765,  -8935,
-    -9102,  -9269,  -9434,  -9598,  -9760,  -9921, -10080, -10238,
-   -10394, -10549, -10702, -10853, -11003, -11151, -11297, -11442,
-   -11585, -11727, -11866, -12004, -12140, -12274, -12406, -12537,
-   -12665, -12792, -12916, -13039, -13160, -13279, -13395, -13510,
-   -13623, -13733, -13842, -13949, -14053, -14155, -14256, -14354,
-   -14449, -14543, -14635, -14724, -14811, -14896, -14978, -15059,
-   -15137, -15213, -15286, -15357, -15426, -15493, -15557, -15619,
-   -15679, -15736, -15791, -15843, -15893, -15941, -15986, -16029,
-   -16069, -16107, -16143, -16176, -16207, -16235, -16261, -16284,
-   -16305, -16324, -16340, -16353, -16364, -16373, -16379, -16383,
-   -16384, -16383, -16379, -16373, -16364, -16353, -16340, -16324,
-   -16305, -16284, -16261, -16235, -16207, -16176, -16143, -16107,
-   -16069, -16029, -15986, -15941, -15893, -15843, -15791, -15736,
-   -15679, -15619, -15557, -15493, -15426, -15357, -15286, -15213,
-   -15137, -15059, -14978, -14896, -14811, -14724, -14635, -14543,
-   -14449, -14354, -14256, -14155, -14053, -13949, -13842, -13733,
-   -13623, -13510, -13395, -13279, -13160, -13039, -12916, -12792,
-   -12665, -12537, -12406, -12274, -12140, -12004, -11866, -11727,
-   -11585, -11442, -11297, -11151, -11003, -10853, -10702, -10549,
-   -10394, -10238, -10080,  -9921,  -9760,  -9598,  -9434,  -9269,
-    -9102,  -8935,  -8765,  -8595,  -8423,  -8250,  -8076,  -7900,
-    -7723,  -7545,  -7366,  -7186,  -7005,  -6823,  -6639,  -6455,
-    -6270,  -6084,  -5897,  -5708,  -5520,  -5330,  -5139,  -4948,
-    -4756,  -4563,  -4370,  -4176,  -3981,  -3786,  -3590,  -3393,
-    -3196,  -2999,  -2801,  -2603,  -2404,  -2205,  -2006,  -1806,
-    -1606,  -1406,  -1205,  -1005,   -804,   -603,   -402,   -201,
-        0,    201,    402,    603,    804,   1005,   1205,   1406,
-     1606,   1806,   2006,   2205,   2404,   2603,   2801,   2999,
-     3196,   3393,   3590,   3786,   3981,   4176,   4370,   4563,
-     4756,   4948,   5139,   5330,   5520,   5708,   5897,   6084,
-     6270,   6455,   6639,   6823,   7005,   7186,   7366,   7545,
-     7723,   7900,   8076,   8250,   8423,   8595,   8765,   8935,
-     9102,   9269,   9434,   9598,   9760,   9921,  10080,  10238,
-    10394,  10549,  10702,  10853,  11003,  11151,  11297,  11442,
-    11585,  11727,  11866,  12004,  12140,  12274,  12406,  12537,
-    12665,  12792,  12916,  13039,  13160,  13279,  13395,  13510,
-    13623,  13733,  13842,  13949,  14053,  14155,  14256,  14354,
-    14449,  14543,  14635,  14724,  14811,  14896,  14978,  15059,
-    15137,  15213,  15286,  15357,  15426,  15493,  15557,  15619,
-    15679,  15736,  15791,  15843,  15893,  15941,  15986,  16029,
-    16069,  16107,  16143,  16176,  16207,  16235,  16261,  16284,
-    16305,  16324,  16340,  16353,  16364,  16373,  16379,  16383,
-    16384
-};
-
-const int16_t ff_g723_1_lsp_band0[LSP_CB_SIZE][3] = {
-    {    0,      0,      0}, { -270,  -1372,  -1032}, { -541,  -1650,  -1382},
-    { -723,  -2011,  -2213}, { -941,  -1122,  -1942}, { -780,  -1145,  -2454},
-    { -884,  -1309,  -1373}, {-1051,  -1523,  -1766}, {-1083,  -1622,  -2300},
-    { -777,  -1377,  -2147}, { -935,  -1467,  -2763}, { -802,  -1327,  -3471},
-    { -935,  -1959,  -3999}, { -240,    -89,    222}, { -661,   -257,   -160},
-    { -994,   -466,   -419}, { -188,   -164,   -278}, { -342,   -512,   -415},
-    { -607,   -511,   -797}, {   16,     19,   -716}, {  374,    425,   -972},
-    { -346,    245,   -282}, { -265,    506,   -754}, { -620,   -147,   1955},
-    { -742,   -860,   2597}, { -150,   -352,   2704}, {  305,    880,   1954},
-    {  123,    731,   2766}, { -348,    765,   3327}, {  618,    221,   3258},
-    { -178,    -47,   4219}, {  393,   1304,   3842}, {  698,   1702,   4801},
-    {   63,   -584,   1229}, { -215,   -732,   1704}, {  172,   -335,   1909},
-    {   -2,    216,   1797}, {  353,    127,   2205}, {-1208,    188,     11},
-    { -513,    -75,   -683}, { -973,    222,   -646}, { -616,   -843,   -388},
-    { -950,  -1113,   -359}, {-1431,   -623,   -705}, {-1398,  -1063,   -178},
-    {  -45,   -461,     35}, {   -9,   -657,   -216}, {  127,  -1078,     95},
-    { -950,  -1156,    584}, {-1480,  -1494,    449}, { -120,   -705,    516},
-    { -368,   -961,    727}, { -378,   -526,    973}, { -793,   -614,    676},
-    { -801,   -755,   1287}, {-1476,   -340,   1636}, { -505,  -1254,   1543},
-    {-1243,  -1622,   1532}, { -776,  -1477,   -655}, {-1151,  -1296,   -823},
-    {-1153,  -1672,  -1124}, {-1291,  -2003,  -1702}, { -622,  -1283,     57},
-    { -471,  -1611,    509}, {-1060,  -1570,   -139}, { -873,  -2156,   -536},
-    {-1716,  -2021,   -364}, {-2150,  -3218,  -1291}, {-1248,  -1945,  -2904},
-    {-1215,  -2633,  -2855}, {  167,   -244,     84}, {  349,   -412,   -217},
-    {  -40,   -352,    632}, {  227,   -529,    405}, {   68,   -383,   -443},
-    {  167,   -558,   -706}, { -275,   -854,    -14}, { -351,  -1089,   -449},
-    {  341,    -72,   -289}, {  603,   -106,   -474}, {  322,   -219,   -649},
-    {  179,   -317,   -998}, {  450,   -291,   -996}, {  555,    195,   -525},
-    {  784,    272,   -831}, { -148,   -384,   -849}, {   82,   -536,  -1357},
-    {  238,   -172,  -1354}, {  422,   -268,  -1841}, {  297,   -737,  -2079},
-    { -111,   -801,   -598}, {    1,   -668,   -984}, { -131,   -818,  -1299},
-    { -329,   -521,  -1310}, { -151,   -778,  -1834}, {  -93,   -352,  -1746},
-    { -568,   -640,  -1821}, { -509,   -941,  -2183}, {  464,   -815,  -1250},
-    {   79,  -1133,  -1597}, { -184,  -1353,  -2123}, { -196,   -410,  -2427},
-    { -192,   -833,  -2810}, { -259,  -1382,  -3045}, { -217,      4,  -1166},
-    { -800,   -325,  -1219}, { -363,   -830,   -898}, { -661,  -1134,   -960},
-    { -386,   -980,  -1501}, { -627,  -1159,  -1722}, { -903,   -829,   -855},
-    { -685,   -829,  -1313}, {-1065,   -959,  -1405}, {  441,     25,   -847},
-    {  655,    -27,  -1181}, { 1159,   -110,   -705}, {  856,    253,  -1671},
-    {  415,    404,     -1}, {  322,    903,   -398}, {  670,    499,   -292},
-    {  803,    591,   -610}, { 1144,    591,   -814}, {  717,    183,    393},
-    {  857,    381,    106}, {  609,     62,    -27}, {  792,    198,   -325},
-    {  735,    805,     88}, { 1142,    812,     78}, { 1028,    366,   -292},
-    { 1309,    743,   -237}, { 1615,    589,    -79}, { 1010,    639,   -243},
-    {  999,    964,   -311}, { 1500,   1137,   -615}, {  988,    357,    646},
-    { 1227,    667,    683}, { 1164,   1565,    894}, { 1392,   2015,    477},
-    { 1138,    533,    250}, { 1437,    896,    391}, { 1765,   1118,     99},
-    { 1112,   1090,    802}, { 1596,    846,   1134}, {  937,   1161,    279},
-    { 1719,   1254,    683}, { 1338,   1086,     35}, { 1419,   1324,    428},
-    { 1428,   1524,     40}, { 2108,   1594,     89}, { 1015,    544,   1222},
-    { 1121,    925,   1263}, { 1030,   1318,   1485}, { 1295,    789,   1817},
-    { 1323,   1272,   1909}, { 1724,   1237,   1803}, { 1797,   1689,    858},
-    { 2149,   1367,   1301}, { 2302,   1867,    761}, { 2863,   2351,   1053},
-    {   52,    163,    -76}, {  230,    309,   -492}, {  -71,    619,     39},
-    { -218,    856,    499}, { -654,    736,   -207}, { -535,   1259,    155},
-    { -480,   1476,    643}, {  262,   1081,    102}, {  309,   1592,   -182},
-    {  627,   1629,    534}, {  337,    643,    456}, {  758,    670,    713},
-    {  202,   1126,    658}, {  612,   1131,    666}, {  686,   1223,   1136},
-    { -131,    377,    525}, {   42,    708,    907}, {   87,   1488,   1035},
-    {  432,   2117,    904}, {  137,    981,   1332}, { -447,   1014,   1136},
-    { -839,   1793,   1246}, { -559,    297,    198}, { -850,    685,    446},
-    {-1273,    632,    826}, { -401,   -544,    173}, { -753,   -793,    144},
-    { -436,     -9,    772}, { -115,   -243,   1310}, { -670,   -269,    374},
-    {-1027,    -13,    639}, { -887,    -81,   1137}, {-1277,   -455,    158},
-    {-1411,   -720,    736}, {  172,     88,    403}, {  386,    255,    756},
-    { -500,    522,    910}, { -958,    659,   1388}, { -395,    301,   1344},
-    { -356,    768,   1813}, { -613,    841,   2419}, {  445,   -122,    252},
-    {  629,    -87,    723}, {  283,   -253,    870}, {  456,   -116,   1381},
-    {  757,    180,   1059}, {  532,    408,   1509}, {  947,    288,   1806},
-    { 1325,    994,   2524}, {  892,   1219,   3023}, { 1397,   1596,   3406},
-    { 1143,   1552,   2546}, { 1850,   1433,   2710}, {  -10,    134,   1002},
-    {  154,    499,   1323}, {  508,    792,   1117}, {  509,   1340,   1616},
-    {  762,    862,   1608}, {  787,    740,   2320}, {  794,   1727,   1283},
-    {  465,   2108,   1660}, { -120,   1451,   1613}, { -386,   2016,   2169},
-    {  891,   1225,   2050}, {  456,   1480,   2185}, { 1493,   1283,   1209},
-    { 1397,   1636,   1518}, { 1776,   1738,   1552}, { 1572,   1698,   2141},
-    { 1389,   2126,   1271}, { 1959,   2413,   1119}, { 1365,   2892,   1505},
-    { 2206,   1971,   1623}, { 2076,   1950,   2280}, { 1717,   2291,   1867},
-    { 2366,   2515,   1953}, { 2865,   2838,   2522}, { 2535,   3465,   2011},
-    { 3381,   4127,   2638}, {  836,   2667,   2289}, { 1761,   2773,   2337},
-    { 1415,   3325,   2911}, { 2354,   3138,   3126}, { 2659,   4192,   4010},
-    { 1048,   1786,   1818}, { 1242,   2111,   2240}, { 1512,   2079,   2780},
-    { 1573,   2491,   3138}, { 2230,   2377,   2782}, {  416,   1773,   2704},
-    {  725,   2336,   3297}, { 1252,   2373,   3978}, { 2094,   2268,   3568},
-    { 2011,   2712,   4528}, { 1341,   3507,   3876}, { 1216,   3919,   4922},
-    { 1693,   4793,   6012}
-};
-
-const int16_t ff_g723_1_lsp_band1[LSP_CB_SIZE][3] = {
-    {    0,      0,      0}, {-2114,  -1302,     76}, {-2652,  -1278,  -1368},
-    {-2847,   -828,   -349}, {-3812,  -2190,   -349}, {-3946,   -364,   -449},
-    {-2725,  -4492,  -3607}, {-3495,  -4764,  -1744}, {  -51,   -756,     84},
-    { -153,  -1191,    504}, {  108,  -1418,   1167}, { -835,   -896,    390},
-    { -569,  -1702,     87}, {-1151,  -1818,    933}, {-1826,  -2547,    411},
-    {-1842,  -1818,   1451}, {-2438,  -1611,    781}, {-2747,  -2477,   1311},
-    { -940,   1252,    477}, {-1629,   1688,    602}, {-1202,    617,    280},
-    {-1737,    393,    580}, {-1528,   1077,   1199}, {-2165,   -161,   1408},
-    {-2504,  -1087,   2371}, {-3458,   -175,   1395}, {-1397,    -98,   -843},
-    {-2252,   -177,  -1149}, {-1489,   -726,  -1283}, {-1558,   -265,  -1744},
-    {-1867,   -821,  -1897}, {-2062,  -1516,  -2340}, {-2595,  -1142,  -2861},
-    {  170,     46,   -819}, { -193,   -204,  -1151}, {  326,   -196,  -1532},
-    {  780,    329,   -816}, {  201,    369,  -1243}, {  650,   -209,  -1060},
-    { 1144,    -15,  -1216}, { 1203,   -259,  -1867}, { -890,   -564,  -1430},
-    { -638,   -852,  -1921}, {  177,   -739,  -1358}, { -261,   -526,  -1666},
-    {  206,   -407,  -2255}, {  338,   -526,   -822}, {  421,  -1095,  -1009},
-    {  765,   -607,  -1408}, {  825,  -1295,  -2004}, {  357,   -905,  -1815},
-    {  -58,  -1248,  -1588}, { -596,  -1436,  -2046}, {  -73,  -1159,  -2116},
-    { -115,  -1382,  -2581}, { -160,  -1723,  -1952}, {   -6,  -2196,  -2954},
-    { -649,  -1705,  -2603}, { -617,  -1453,  -3282}, { -949,  -2019,  -3102},
-    { -812,   1544,   1937}, {-1854,    574,   2000}, {-1463,   1140,   2649},
-    {-2683,   1748,   1452}, {-2486,   2241,   2523}, {  783,   1910,   1435},
-    {  581,   2682,   1376}, {  236,   2197,   1885}, { -453,   2943,   2057},
-    { -682,   2178,   2565}, {-1342,   3201,   3328}, { -288,   -184,    262},
-    {  121,   -149,   -183}, {  758,   -412,    206}, { 1038,   -204,    853},
-    { 1577,   -457,    700}, {  937,   -640,   -567}, { 1508,   -528,  -1024},
-    { -225,   -527,   -427}, { -564,  -1095,   -332}, { -742,   -353,   -186},
-    {-1288,   -459,     84}, {-1853,   -484,   -274}, {-1554,   -731,    825},
-    {-2425,   -234,    382}, {-1722,    293,   -271}, {-2515,    425,   -564},
-    {-2599,    818,    464}, { -358,    118,   -375}, { -613,    198,   -874},
-    { -690,    683,   -324}, {-1352,   1155,   -168}, {-1093,    129,   -324},
-    {-1184,    611,   -858}, {  433,    386,   -372}, { -120,    486,   -634},
-    {  234,    851,   -631}, {  602,    128,     46}, { 1099,    410,    159},
-    {  715,   -145,   -424}, { 1198,    -85,   -593}, { 1390,    367,   -358},
-    { 1683,    362,   -964}, { 1711,    622,     45}, { 2033,    833,   -383},
-    { 2890,    549,   -506}, {    7,    401,     52}, {   72,    811,    415},
-    {  566,    668,     41}, {  467,   1218,    130}, {   68,    957,   -187},
-    {  -25,   1649,   -103}, { -661,    260,    214}, { -925,    -94,    612},
-    { -321,   -422,    965}, { -788,   -672,   1783}, {  400,   -673,    779},
-    {  741,   -595,   1635}, { -161,    307,    657}, { -382,    836,    871},
-    { -814,    400,   1223}, {  364,    606,   1247}, {   57,     75,   1571},
-    {  151,    471,   2287}, {  -81,   1021,   1502}, {  227,   1470,   1097},
-    {  658,   1275,   1653}, {  664,   1478,   2377}, {  263,   -127,    444},
-    {  264,     89,    969}, {  794,    171,    576}, {  821,    186,   1226},
-    {  404,    462,    517}, {  339,    918,    794}, { 1280,   1423,    196},
-    { 1453,   2019,    365}, { 1615,   1481,    672}, { 2394,   1708,    508},
-    {  806,   1238,    573}, {  713,   1158,   1078}, { 1285,   1436,   1232},
-    { 1790,   1188,   1141}, {  765,    643,    864}, { 1032,    797,   1279},
-    {  900,    563,   1827}, { 1514,    673,   2312}, { 1544,   1129,   3240},
-    { 1469,   1050,   1594}, { 1945,   1318,   1988}, { 2397,   2026,   2060},
-    { 3538,   2057,   2620}, { 1249,   -118,     74}, { 1727,    194,    421},
-    { 2078,    -50,   -463}, {  970,    688,   -432}, { 1149,    952,   -110},
-    { 1254,   1275,   -651}, { 1386,    929,    401}, { 1960,   1167,    232},
-    {  407,   -752,   -243}, {  859,  -1118,    172}, { -227,   -860,   -992},
-    { -796,  -1175,  -1380}, {    8,  -1282,   -388}, {  353,  -1781,  -1037},
-    { -732,   -397,   -807}, { -853,    -28,  -1342}, {-1229,  -1207,  -1959},
-    {-1015,  -1125,  -2543}, {-1452,  -1791,  -2725}, {-1891,  -2416,  -3269},
-    { -918,  -1629,   -783}, { -580,  -2155,   -698}, {-1097,  -2364,    -96},
-    {-1387,  -1513,      7}, {-1588,  -2076,   -664}, {-1473,  -2740,   -784},
-    {-2378,  -3149,    -56}, {-2856,  -2092,   -169}, {-3391,  -3708,    316},
-    {-1176,   -890,   -614}, {-1944,  -1061,   -800}, { -299,  -1517,  -1000},
-    { -640,  -1850,  -1526}, {-1454,  -1536,  -1233}, {-1890,  -1955,  -1756},
-    {-1086,  -1921,  -2122}, { -750,  -2325,  -2260}, {-1325,  -2413,  -2673},
-    {-1114,  -2542,  -3459}, {-1341,  -2901,  -3963}, {-1160,  -2226,  -1393},
-    {-1001,  -2772,  -1573}, {-1594,  -2641,  -1978}, {-1534,  -3046,  -2624},
-    {-2224,  -2196,   -675}, {-2807,  -3054,  -1102}, {-2008,  -2840,  -1186},
-    {-1980,  -3332,  -1695}, {-1715,  -3562,   -505}, {-2527,  -4000,  -1887},
-    {-2333,  -2734,  -2296}, {-3440,  -2401,  -3211}, {-2008,  -3528,  -3337},
-    {-2247,  -3291,  -4510}, { -475,    949,    155}, { -149,   1365,    545},
-    { -757,   1644,   1083}, { -217,   2053,   1353}, {-1433,   2301,   1462},
-    {  495,   1661,    529}, {   10,   2037,    740}, { 2082,   1898,    978},
-    { 2831,   2294,    911}, {  842,    793,    420}, { 1223,   1023,    863},
-    { 1237,    451,    780}, { 1744,    708,    822}, { 1533,    284,   1384},
-    { 2135,    609,   1538}, { 2305,    626,    540}, { 2368,   1187,    955},
-    { 2586,   1255,     -7}, { 3116,   1131,    726}, { 3431,   1730,    428},
-    { 2734,   1648,   1307}, { 2988,   1231,   2010}, { 3523,   2024,   1488},
-    { 1034,   1657,    871}, { 1206,   2163,   1036}, { 1807,   2372,   1233},
-    { 1808,   1769,   1493}, { 1573,   2332,   1779}, { 1216,   1609,   1866},
-    { 1480,   1898,   2513}, {  465,   2708,   2776}, {  771,   3638,   3338},
-    { 1869,   2599,   2623}, { 2825,   2745,   2468}, { 2638,   2439,   1585},
-    { 2094,   2970,   1308}, { 2022,   3057,   1999}, { 3428,   2912,   1816},
-    { 4536,   2974,   2129}, { 1046,   2563,   2086}, { 1363,   3562,   2318},
-    { 2511,   1891,   2984}, { 1866,   2306,   3986}, { 3272,   2924,   3682},
-    { 3146,   3564,   2272}, { 3592,   3968,   2822}, { 2431,   3369,   3069},
-    { 1931,   4709,   3090}, { 2629,   4220,   3986}, { 4639,   4056,   3664},
-    { 4035,   5334,   4912}
-};
-
-const int16_t ff_g723_1_lsp_band2[LSP_CB_SIZE][4] = {
-    {    0,      0,      0,      0}, {  601,    512,   -542,    334},
-    {  428,   1087,   -484,   -132}, {  652,    622,   -391,   -572},
-    {  378,    799,    141,   -860}, { 1040,    409,    112,   -554},
-    { 1123,    670,    -75,   -847}, { 1421,    494,   -315,  -1095},
-    {  787,   1001,    114,   -460}, {  988,   1672,    216,   -681},
-    { 1007,   1241,   -132,  -1247}, { 1073,    399,    186,     -5},
-    { 1262,    193,   -694,   -129}, {  325,    196,     51,   -641},
-    {  861,    -59,    350,   -458}, { 1261,    567,    586,   -346},
-    { 1532,    885,    210,   -517}, { 2027,    937,    113,   -792},
-    { 1383,   1064,    334,     38}, { 1964,   1468,    459,    133},
-    { 2062,   1186,    -98,   -121}, { 2577,   1445,    506,   -373},
-    { 2310,   1682,     -2,   -960}, { 2876,   1939,    765,    138},
-    { 3581,   2360,    649,   -414}, {  219,    176,   -398,   -309},
-    {  434,    -78,   -435,   -880}, { -344,    301,    265,   -552},
-    { -915,    470,    657,   -380}, {  419,   -432,   -163,   -453},
-    {  351,   -953,      8,   -562}, {  789,    -43,     20,   -958},
-    {  302,   -594,   -352,  -1159}, { 1040,    108,   -668,   -924},
-    { 1333,    210,  -1217,  -1663}, {  483,    589,   -350,  -1140},
-    { 1003,    824,   -802,  -1184}, {  745,     58,   -589,  -1443},
-    {  346,    247,   -915,  -1683}, {  270,    796,   -720,  -2043},
-    { 1208,    722,   -222,   -193}, { 1486,   1180,   -412,   -672},
-    { 1722,    179,    -69,   -521}, { 2047,    860,   -666,  -1410},
-    { -146,    222,   -281,   -805}, { -189,     90,   -114,  -1307},
-    { -152,   1086,   -241,   -764}, { -439,    733,   -601,  -1302},
-    { -833,   -167,   -351,   -601}, { -856,   -422,   -411,  -1059},
-    { -747,   -355,   -582,  -1644}, { -837,    210,   -916,  -1144},
-    {-1800,     32,   -878,  -1687}, {  -48,    -23,  -1146,     52},
-    { -350,   -409,  -1656,   -364}, {  265,   -728,   -858,   -577},
-    {  458,   -247,  -1141,   -997}, {  691,   -407,  -1988,  -1161},
-    {  -66,   -104,   -705,  -1249}, { -431,    -93,  -1191,  -1844},
-    {  203,   -732,  -1000,  -1693}, {   10,   -832,  -1846,  -1819},
-    {  493,   -128,  -1436,  -1768}, {  488,   -311,  -1730,  -2540},
-    { -653,   -532,  -1150,  -1172}, {-1086,   -289,  -1706,  -1533},
-    { -699,  -1205,  -1216,  -1766}, {-1032,  -1481,  -2074,  -1523},
-    { -721,  -1220,  -2277,  -2600}, {   12,   -539,  -1484,  -1131},
-    {  -40,   -911,  -2106,   -441}, { -471,   -484,  -2267,  -1549},
-    { -141,   -988,  -3006,  -1721}, {-1545,  -2102,   -583,    342},
-    {-1383,  -2772,   -386,    -13}, {-2118,  -2589,  -1205,     72},
-    {-2147,  -3231,   -965,    390}, {-2949,  -3300,   -621,    637},
-    {-3907,  -4138,   -865,    803}, {-1287,   -845,   -375,   -548},
-    {-1416,  -1169,   -487,  -1277}, {-1400,  -1690,  -1027,   -418},
-    {-2018,  -1909,  -1188,  -1260}, {-1418,  -2222,  -2029,   -128},
-    {-2067,  -2998,  -2693,   -310}, { -950,  -1028,  -1538,    185},
-    {-1616,   -915,  -2205,   -549}, {   19,   -821,  -1145,    352},
-    {  184,  -1175,  -1356,   -627}, { -547,  -1088,  -1661,   -911},
-    { -216,  -1502,  -2197,   -948}, { -795,  -1306,  -2374,   -451},
-    { -924,  -1889,  -2796,   -680}, { -600,  -1614,  -3609,   -885},
-    {-2392,  -2528,    319,    303}, {-2908,  -2095,   -310,    573},
-    {-3460,  -2141,     49,   -113}, {-2231,   -448,    675,   -146},
-    {-2805,   -532,   1231,    479}, {-2684,   -486,   -200,    611},
-    {-3525,   -971,   -198,    704}, {-3707,    173,    349,    254},
-    {-4734,  -1447,    -34,    880}, {  777,   -512,    114,    -10},
-    { 1250,    -66,    442,     -5}, {  604,    613,    452,   -352},
-    { 1224,    777,    675,  -1014}, {-1372,    -79,  -1208,   -238},
-    {-2389,    -17,  -1157,   -818}, {-1504,   -673,  -1133,  -1060},
-    {-1984,   -799,  -2005,  -1973}, {-2037,   -798,  -1068,   -105},
-    {-3190,   -899,  -1817,   -194}, { -156,   -886,    394,   -318},
-    { -258,  -1283,    551,    202}, { -536,  -1729,    910,    331},
-    { -847,  -1109,    795,   -163}, {-1171,  -1128,    715,    519},
-    {-1080,  -1319,   1685,    668}, {-1000,  -1921,     96,    211},
-    {-1487,  -2148,    831,    174}, {-1139,   -374,    414,     -4},
-    {-1517,  -1383,    396,   -352}, {-1012,    439,    -59,   -967},
-    {-1812,    706,   -440,  -1030}, {-1971,   -329,    -34,   -827},
-    {-2472,  -1588,   -151,   -606}, {-2161,    374,   -281,     76},
-    {-3012,    231,    -15,   -690}, { 1104,    566,    721,    209},
-    { 1685,    564,    383,     98}, { 1898,    750,    792,    -97},
-    {  556,    -64,    561,    -93}, {  876,    162,    913,    -22},
-    {  961,    675,   1296,    140}, {  756,   -396,    851,    544},
-    {  360,   -303,   1341,    396}, {  878,    -22,   1464,    863},
-    { -309,   -273,    642,   -129}, { -686,    -82,    842,    454},
-    {   -5,    -47,   1069,    998}, {  -94,    967,   1277,    298},
-    { -489,    385,   1473,    746}, { -369,   -717,   1333,    242},
-    {  281,   -993,   1726,    924}, {  464,    601,   1575,   1376},
-    { -250,    206,   2339,   1175}, { -438,    377,   -597,   -285},
-    {-1020,    787,   -790,   -287}, { -458,   -410,    215,    295},
-    { -589,   -860,   -121,    797}, {-1175,    122,   -437,    466},
-    {-1480,   -121,    367,    924}, {  234,    323,    770,   -555},
-    {  145,     30,    996,     26}, {   66,    849,     93,   -145},
-    { -117,   1261,    474,   -399}, {-1495,   1051,    218,   -506},
-    {-1390,    694,    994,     88}, {  616,      7,     78,    304},
-    { 1060,     52,    -62,    835}, {  833,    454,    649,   1359},
-    { -770,    464,     47,     93}, { -574,   1199,    -39,    379},
-    {  114,    -98,    488,    485}, {  727,    244,    606,    696},
-    {  -76,    455,    671,    546}, { -565,    -13,    145,    819},
-    { -376,    569,    448,   1128}, {  218,    122,    265,   1167},
-    {  230,    738,    932,   1003}, {  138,    477,     36,    450},
-    {  404,    787,    -73,   1000}, {  497,   1259,    387,   1231},
-    {   17,    207,    195,    -79}, {  562,    358,     53,   -158},
-    {  493,    387,    478,    189}, {  678,    831,    640,    558},
-    { -197,    523,    613,     57}, {  429,    894,    769,    111},
-    {   67,   1174,    568,    511}, { 1242,    824,    251,    840},
-    { 1419,   1074,    864,    481}, {  924,   1474,    669,    724},
-    { 1539,   1879,    654,   1590}, {  445,    337,   1111,    541},
-    {  472,   1421,   1264,   1094}, {  794,    735,   1103,    668},
-    { 1055,    863,   1192,   1020}, {  778,   1105,    806,   1798},
-    { 1052,   1527,   1587,   2151}, {  881,   1552,   1265,    391},
-    {  726,    872,   1812,    601}, { 1469,    280,   1008,    616},
-    { 1403,    577,   1803,   1244}, { 1650,   1314,   1148,   1072},
-    { 1297,   1669,   1911,   1026}, { 2093,   1044,   2115,   1189},
-    { 1644,   1961,   2587,   1512}, {   25,   -315,     -9,   -106},
-    {  290,   -339,    428,   -444}, {  -68,   -783,    735,    772},
-    {  245,   -555,    468,     47}, {  334,   -895,    814,    146},
-    {  235,    368,   -964,   -959}, { -203,    315,  -1566,  -1217},
-    {  801,     17,   -276,   -354}, {  894,   -495,   -789,   -635},
-    {  716,    291,  -1189,   -357}, {  560,   -260,   -733,     -2},
-    {  679,   -508,  -1429,    211}, {  -51,    -62,   -428,    557},
-    {  322,   -638,   -211,    614}, { -878,  -1057,    -84,    -71},
-    { -388,  -1415,   -167,   -318}, { -754,  -1574,    214,   -539},
-    {-1419,  -2004,    -92,   -787}, {  -47,   -856,   -347,   -255},
-    {   23,  -1211,   -173,    320}, { -658,   -487,   -893,    353},
-    { -783,  -1587,   -584,    507}, {-1420,   -859,   -378,    441},
-    {-2095,  -1491,   -137,    439}, { -321,  -1450,  -1288,    -12},
-    { -359,  -2113,   -553,     -8}, { -831,  -1918,  -1561,     32},
-    {-1014,  -2487,  -1359,   -939}, { -475,   -311,   -169,   -236},
-    { -907,   -426,    276,   -611}, {  -96,   -400,     50,   -710},
-    { -426,  -1022,    -10,   -985}, { -197,   -258,   -744,   -575},
-    { -611,   -930,   -771,   -394}, { -267,   -776,   -612,   -939},
-    { -256,  -1346,   -802,  -1122}, { -796,  -1570,   -825,   -754},
-    {  712,    876,    141,    227}, {  981,   1509,     85,    124},
-    { 1462,   1228,    979,    -39}, { 1734,    999,   1481,    440},
-    { 2293,   1116,    769,    440}, { 2504,   1480,   1241,    356},
-    { 2474,   1909,   1558,    810}, {  917,   1134,    607,   -134},
-    {  509,   1809,    781,   -123}, { 1712,   1506,    559,   -423},
-    { 2037,   2317,    726,   -155}, { 3031,   2676,   1203,    331},
-    { 3664,   3274,   1768,    531}, { 1610,   1839,    867,    183},
-    { 1774,   1972,   1538,     97}, { 1822,   2158,   1282,    659},
-    { 2222,   2758,   1818,    900}, { 3251,   2124,   1723,    996},
-    { 3633,   2336,   2408,   1453}, { 2923,   3517,   2567,   1318},
-};
-
-const int32_t ff_g723_1_combinatorial_table[PULSE_MAX][SUBFRAME_LEN/GRID_SIZE] = {
-    {118755, 98280, 80730,  65780, 53130,
-      42504, 33649, 26334,  20349, 15504,
-      11628,  8568,  6188,   4368,  3003,
-       2002,  1287,   792,    462,   252,
-        126,    56,    21,      6,     1,
-          0,     0,     0,      0,     0},
-
-    { 23751, 20475, 17550,  14950, 12650,
-      10626,  8855,  7315,   5985,  4845,
-       3876,  3060,  2380,   1820,  1365,
-       1001,   715,   495,    330,   210,
-        126,    70,    35,     15,     5,
-          1,     0,     0,      0,     0},
-
-    {  3654,  3276,  2925,   2600,  2300,
-       2024,  1771,  1540,   1330,  1140,
-        969,   816,   680,    560,   455,
-        364,   286,   220,    165,   120,
-         84,    56,    35,     20,    10,
-          4,     1,     0,      0,     0},
-
-    {   406,   378,   351,    325,   300,
-        276,   253,   231,    210,   190,
-        171,   153,   136,    120,   105,
-         91,    78,    66,     55,    45,
-         36,    28,    21,     15,    10,
-          6,     3,     1,      0,     0},
-
-    {    29,    28,    27,     26,    25,
-         24,    23,    22,     21,    20,
-         19,    18,    17,     16,    15,
-         14,    13,    12,     11,    10,
-          9,     8,     7,      6,     5,
-          4,     3,     2,      1,     0},
-
-    {     1,     1,     1,      1,     1,
-          1,     1,     1,      1,     1,
-          1,     1,     1,      1,     1,
-          1,     1,     1,      1,     1,
-          1,     1,     1,      1,     1,
-          1,     1,     1,      1,     1},
-};
-
-const int16_t ff_g723_1_fixed_cb_gain[GAIN_LEVELS] = {
-      1,    2,    3,    4,    6,    9,   13,   18,
-     26,   38,   55,   80,  115,  166,  240,  348,
-    502,  726, 1050, 1517, 2193, 3170, 4582, 6623,
-};
-
-const int16_t ff_g723_1_adaptive_cb_gain85[85 * 20] = {
-        0,      0,      0,      0,      0,      0,      0,      0,
-        0,      0,      0,      0,      0,      0,      0,      0,
-        0,      0,      0,      0,    800,   1496,    167,   -256,
-     -338,    -39,   -136,     -1,     -4,     -6,    -73,     -8,
-      -15,     12,     23,      2,     16,     30,      3,     -5,
-     -462,   -686,    493,   2575,    311,    -13,    -28,    -14,
-     -404,     -5,    -19,     13,     20,     72,    107,    -77,
-        8,     13,     -9,    -48,   1483,    144,    784,    928,
-     1243,   -134,     -1,    -37,    -52,    -94,    -13,    -71,
-       -6,    -84,     -8,    -44,   -112,    -10,    -59,    -70,
-      -77,    275,   3522,   1056,  -1254,      0,     -4,   -757,
-      -68,    -95,      1,     16,    -59,      4,    -17,   -227,
-       -5,     21,    269,     80,   -125,    -40,   -264,    381,
-     5027,      0,      0,     -4,     -8,  -1542,      0,     -2,
-        0,      2,      0,      6,     38,     12,     81,   -117,
-      138,    332,   2215,   2574,   1339,     -1,     -6,   -299,
-     -404,   -109,     -2,    -18,    -44,    -21,    -52,   -348,
-      -11,    -27,   -181,   -210,   3685,   2883,   -887,    866,
-    -1639,   -828,   -507,    -48,    -45,   -164,   -648,    199,
-      156,   -194,   -152,     46,    368,    288,    -88,     86,
-     1396,   2146,   2235,    345,    942,   -118,   -281,   -305,
-       -7,    -54,   -182,   -190,   -292,    -29,    -45,    -47,
-      -80,   -123,   -128,    -19,     13,   4475,   3549,   -804,
-     -655,      0,  -1222,   -768,    -39,    -26,     -3,     -2,
-     -969,      0,    219,    174,      0,    179,    141,    -32,
-     -724,    254,    242,   6049,   2462,    -32,     -3,     -3,
-    -2233,   -370,     11,     10,     -3,    267,    -94,    -89,
-      108,    -38,    -36,   -909,    626,  -1713,   6121,   4561,
-    -1061,    -23,   -179,  -2287,  -1270,    -68,     65,   -233,
-      640,   -174,    477,  -1704,     40,   -111,    396,    295,
-     -350,   1391,   7985,    511,   -405,     -7,   -118,  -3892,
-      -15,    -10,     29,    170,   -678,     10,    -43,   -249,
-       -8,     34,    197,     12,   3144,   -529,    608,   2530,
-     3878,   -603,    -17,    -22,   -390,   -918,    101,   -116,
-       19,   -485,     81,    -93,   -744,    125,   -144,   -599,
-     2589,   -689,   3045,   5603,   -404,   -409,    -29,   -566,
-    -1916,    -10,    108,   -481,    128,   -885,    235,  -1041,
-       63,    -17,     75,    138,   3107,    513,   1374,  -3594,
-    -4922,   -589,    -16,   -115,   -788,  -1478,    -97,   -260,
-      -43,    681,    112,    301,    933,    154,    413,  -1079,
-     2468,   6010,   1107,   -390,   1961,   -372,  -2204,    -74,
-       -9,   -234,   -905,   -166,   -406,     58,    143,     26,
-     -295,   -719,   -132,     46,   4773,   2766,   2368,   4862,
-    -4044,  -1390,   -467,   -342,  -1443,   -998,   -806,   -690,
-     -399,  -1416,   -821,   -702,   1178,    682,    584,   1200,
-     1665,  -1879,   1443,   1701,   8562,   -169,   -215,   -127,
-     -176,  -4475,    190,   -146,    165,   -172,    195,   -149,
-     -870,    982,   -754,   -889,   2716,   9011,  -1007,    755,
-    -1785,   -450,  -4956,    -61,    -34,   -194,  -1493,    167,
-      554,   -125,   -415,     46,    296,    982,   -109,     82,
-    -2727,   7548,   1285,    938,   3420,   -453,  -3478,   -100,
-      -53,   -714,   1256,    213,   -592,    156,   -432,    -73,
-      569,  -1576,   -268,   -196,   3677,    882,   4050,   1202,
-     2323,   -825,    -47,  -1001,    -88,   -329,   -198,   -909,
-     -218,   -269,    -64,   -297,   -521,   -125,   -574,   -170,
-     2046,   -753,    122,  10102,    603,   -255,    -34,      0,
-    -6229,    -22,     94,    -15,      5,  -1261,    464,    -75,
-      -75,     27,     -4,   -372,    449,  -1815,  10690,   3870,
-     -527,    -12,   -201,  -6976,   -914,    -16,     49,   -293,
-     1184,   -106,    428,  -2525,     14,    -58,    344,    124,
-     -941,   2352,   5049,   3650,   2637,    -54,   -337,  -1556,
-     -813,   -424,    135,    290,   -725,    209,   -524,  -1125,
-      151,   -378,   -812,   -587,  -1879,    796,   3117,   9569,
-     -404,   -215,    -38,   -593,  -5589,     -9,     91,    357,
-     -151,   1097,   -464,  -1821,    -46,     19,     76,    236,
-    -1715,   2043,  -2096,   9946,   4001,   -179,   -254,   -268,
-    -6038,   -977,    213,   -219,    261,   1041,  -1240,   1272,
-      418,   -498,    511,  -2429,  -5772,   -618,  -3921,    284,
-    -3155,  -2033,    -23,   -938,     -4,   -607,   -218,  -1381,
-     -148,    100,     10,     68,  -1111,   -119,   -755,     54,
-      382,   4748,   8003,  -2064,   2198,     -8,  -1376,  -3909,
-     -260,   -294,   -110,   -186,  -2319,     48,    598,   1008,
-      -51,   -637,  -1073,    277,   -867,   3015,  11926,  -1675,
-      947,    -45,   -555,  -8681,   -171,    -54,    159,    631,
-    -2195,    -88,    308,   1219,     50,   -174,   -690,     96,
-    -4933,   -432,   6757,   3771,   1352,  -1485,    -11,  -2786,
-     -867,   -111,   -130,   2034,    178,   1135,     99,  -1555,
-      407,     35,   -557,   -311,    152,   9726,   4231,  -1928,
-     1490,     -1,  -5774,  -1092,   -226,   -135,    -90,    -39,
-    -2511,     17,   1144,    498,    -13,   -884,   -384,    175,
-     2512,    193,   9033,   5361,  -3148,   -385,     -2,  -4980,
-    -1754,   -605,    -29,  -1385,   -106,   -822,    -63,  -2956,
-      482,     37,   1735,   1030,   8464,   2844,     12,    549,
-     2132,  -4373,   -493,      0,    -18,   -277,  -1469,     -6,
-       -2,   -284,    -95,      0,  -1101,   -370,     -1,    -71,
-     2141,  -2602,   7166,   9046,  -1350,   -279,   -413,  -3134,
-    -4994,   -111,    340,   -936,   1138,  -1182,   1436,  -3957,
-      176,   -214,    590,    745,   -244,    278,  13307,   1227,
-     -161,     -3,     -4, -10808,    -91,     -1,      4,    198,
-     -226,     18,    -20,   -997,     -2,      2,    131,     12,
-    -1947,   8217,   6269,    917,  -2559,   -231,  -4121,  -2399,
-      -51,   -399,    976,    745,  -3144,    108,   -460,   -350,
-     -304,   1283,    979,    143,  -1810,   2061,  -2781,   6056,
-    10058,   -200,   -259,   -472,  -2238,  -6174,    227,   -307,
-      349,    669,   -761,   1028,   1111,  -1265,   1707,  -3717,
-     7827,   9161,  -3409,   2473,  -1510,  -3739,  -5122,   -709,
-     -373,   -139,  -4376,   1628,   1906,  -1181,  -1382,    514,
-      721,    844,   -314,    228,  -1430,   8313,   9541,  -2955,
-     1626,   -124,  -4218,  -5556,   -533,   -161,    725,    832,
-    -4841,   -257,   1499,   1721,    142,   -825,   -947,    293,
-     2819,  -4247,   5391,   8673,   2756,   -485,  -1101,  -1774,
-    -4591,   -463,    730,   -927,   1397,  -1492,   2248,  -2854,
-     -474,    714,   -907,  -1459,    141,  14552,    690,    257,
-     -112,     -1, -12926,    -29,     -4,      0,   -125,     -5,
-     -613,     -2,   -228,    -10,      0,     99,      4,      1,
-    11938,  -1859,   1806,   -962,   -884,  -8699,   -211,   -199,
-      -56,    -47,   1355,  -1316,    205,    701,   -109,    106,
-      644,   -100,     97,    -51,   3728,   1982,   2264,   4584,
-     3131,   -848,   -239,   -312,  -1282,   -598,   -451,   -515,
-     -273,  -1043,   -554,   -633,   -712,   -378,   -432,   -876,
-    -1181,    766,    720,  14303,   -216,    -85,    -35,    -31,
-   -12486,     -2,     55,     51,    -33,   1031,   -668,   -628,
-      -15,     10,      9,    189,  -4385,   4826,  10112,   1569,
-     3388,  -1173,  -1421,  -6242,   -150,   -700,   1291,   2706,
-    -2979,    420,   -462,   -969,    906,   -998,  -2091,   -324,
-     -448,   1932,  15591,  -1842,    657,    -12,   -227, -14837,
-     -207,    -26,     52,    427,  -1838,    -50,    217,   1753,
-       18,    -77,   -626,     74,  -4141,   1844,   3962,   5517,
-     6220,  -1046,   -207,   -958,  -1858,  -2361,    466,   1001,
-     -446,   1394,   -621,  -1334,   1572,   -700,  -1504,  -2094,
-      729,  -2299,  14755,   3657,   -952,    -32,   -322, -13288,
-     -816,    -55,    102,   -656,   2071,   -162,    513,  -3294,
-       42,   -133,    857,    212,  -1385,   5801,  13339,  -3137,
-     1344,   -117,  -2054, -10861,   -600,   -110,    490,   1127,
-    -4723,   -265,   1111,   2554,    113,   -476,  -1094,    257,
-     4710,   9661,   1073,  -2467,   3274,  -1354,  -5697,    -70,
-     -371,   -654,  -2777,   -308,   -633,    709,   1455,    161,
-     -941,  -1930,   -214,    493,   1843,  -3624,  12422,   6898,
-    -1559,   -207,   -802,  -9419,  -2904,   -148,    407,  -1397,
-     2748,   -775,   1526,  -5230,    175,   -344,   1182,    656,
-     1433,   2394,   2507,   1380,   8780,   -125,   -349,   -383,
-     -116,  -4705,   -209,   -219,   -366,   -120,   -201,   -211,
-     -768,  -1283,  -1343,   -740,  -1712,  12915,   5883,  -2197,
-      991,   -179, -10181,  -2112,   -294,    -60,   1350,    615,
-    -4638,   -229,   1732,    789,    103,   -781,   -356,    133,
-    15072,   2158,  -1245,    910,   -496, -13865,   -284,    -94,
-      -50,    -15,  -1986,   1145,    164,   -837,   -119,     69,
-      456,     65,    -37,     27,   4655,   7319,   4916,    586,
-    -3381,  -1322,  -3270,  -1475,    -20,   -697,  -2079,  -1396,
-    -2196,   -166,   -261,   -175,    960,   1510,   1014,    120,
-     1191,  -2140,   5120,  13498,  -1418,    -86,   -279,  -1600,
-   -11121,   -122,    155,   -372,    669,   -981,   1763,  -4218,
-      103,   -185,    443,   1168,  -1530,   -817,   8191,   9632,
-    -1452,   -143,    -40,  -4095,  -5663,   -128,    -76,    765,
-      408,    900,    480,  -4815,   -135,    -72,    726,    854,
-    -3236,    607,   1696,  -2106,  11485,   -639,    -22,   -175,
-     -270,  -8051,    119,    335,    -62,   -416,     78,    218,
-     2268,   -425,  -1189,   1476,   3203,  -1903,   -837,   9679,
-     7057,   -626,   -221,    -42,  -5718,  -3039,    372,    163,
-      -97,  -1892,   1124,    494,  -1380,    819,    360,  -4169,
-      213,   -655,  17015,    620,   -384,     -2,    -26, -17671,
-      -23,     -9,      8,   -221,    681,     -8,     24,   -644,
-        5,    -15,    399,     14,   5088,     35,  -3339,   3726,
-     8488,  -1580,      0,   -680,   -847,  -4397,    -10,   1037,
-        7,  -1157,     -8,    759,  -2636,    -18,   1730,  -1930,
-     -988,   1454,  -2688,  15039,   2682,    -59,   -129,   -441,
-   -13805,   -439,     87,   -162,    238,    907,  -1335,   2467,
-      161,   -238,    440,  -2462,  -4865,  -2842,    -53,   5495,
-     6523,  -1445,   -493,      0,  -1843,  -2597,   -844,    -16,
-       -9,   1632,    953,     18,   1937,   1131,     21,  -2188,
-     3076,  15069,  -2914,   1810,   -971,   -577, -13860,   -518,
-     -200,    -57,  -2829,    547,   2680,   -339,  -1665,    322,
-      182,    893,   -172,    107,   1311,   5355,  11054,   2299,
-    -3654,   -105,  -1750,  -7458,   -322,   -814,   -428,   -885,
-    -3613,   -184,   -751,  -1551,    292,   1194,   2465,    512,
-     4035,   5619,   4618,   1815,   1912,   -994,  -1927,  -1301,
-     -201,   -223,  -1384,  -1137,  -1583,   -447,   -622,   -511,
-     -471,   -656,   -539,   -211,  -2131,   2754,  -4501,  12879,
-     7432,   -277,   -463,  -1236, -10124,  -3371,    358,   -585,
-      756,   1675,  -2165,   3538,    967,  -1249,   2042,  -5842,
-     5618,   -515,   3219,  -4149,   4857,  -1926,    -16,   -632,
-    -1050,  -1440,    176,  -1104,    101,   1422,   -130,    815,
-    -1666,    152,   -954,   1230,   1838,  -1709,   1139,  16867,
-      716,   -206,   -178,    -79, -17366,    -31,    191,   -127,
-      118,  -1892,   1759,  -1173,    -80,     74,    -49,   -737,
-     1978,  -3845,  10050,  11854,  -2492,   -238,   -902,  -6164,
-    -8576,   -379,    464,  -1213,   2358,  -1431,   2782,  -7271,
-      301,   -585,   1529,   1803,  -2600,  11246,  11289,  -3647,
-     1463,   -412,  -7720,  -7778,   -812,   -130,   1784,   1791,
-    -7749,   -578,   2504,   2513,    232,  -1004,  -1008,    325,
-     3442,    907,   2725,   8970,   3638,   -723,    -50,   -453,
-    -4911,   -808,   -190,   -572,   -150,  -1884,   -496,  -1492,
-     -764,   -201,   -605,  -1992,   -126,  17498,   3481,  -2003,
-     1090,      0, -18689,   -739,   -244,    -72,    135,     26,
-    -3717,    -15,   2139,    425,      8,  -1165,   -231,    133,
-    -1814,   1048,  -2164,   4070,  16272,   -200,    -67,   -285,
-    -1011, -16160,    116,   -239,    138,    450,   -260,    537,
-     1801,  -1041,   2149,  -4042,   9354,  12580,  -1883,    962,
-     -617,  -5341,  -9660,   -216,    -56,    -23,  -7183,   1075,
-     1446,   -549,   -738,    110,    352,    474,    -71,     36,
-     1708,   4199,   7387,   6335,   1003,   -178,  -1076,  -3330,
-    -2449,    -61,   -437,   -770,  -1893,   -660,  -1623,  -2856,
-     -104,   -257,   -452,   -388,  -2624,   5623,  17310,  -2353,
-      592,   -420,  -1930, -18288,   -338,    -21,    900,   2772,
-    -5941,   -376,    807,   2486,     94,   -203,   -625,     85,
-     1211,   -850,   1193,  -1926,  15992,    -89,    -44,    -86,
-     -226, -15609,     62,    -88,     61,    142,   -100,    140,
-    -1182,    830,  -1165,   1880,   3983,  -2054,  11506,    -19,
-     3622,   -968,   -257,  -8080,      0,   -801,    499,  -2797,
-     1442,      4,     -2,     13,   -880,    454,  -2544,      4,
-     -786,  -1354,  16092,   7246,  -1665,    -37,   -111, -15805,
-    -3205,   -169,    -65,    772,   1330,    348,    599,  -7117,
-      -80,   -137,   1636,    736,  -4316,   -511,   6674,  11665,
-     4633,  -1137,    -15,  -2719,  -8305,  -1310,   -134,   1758,
-      208,   3073,    364,  -4752,   1220,    144,  -1887,  -3299,
-     7912,   4557,   1937,   1885,   7037,  -3821,  -1267,   -229,
-     -216,  -3022,  -2200,   -935,   -538,   -910,   -524,   -222,
-    -3398,  -1957,   -832,   -809,   3434,   2967,   5867,   8196,
-     8766,   -720,   -537,  -2101,  -4100,  -4690,   -622,  -1230,
-    -1062,  -1718,  -1484,  -2935,  -1837,  -1588,  -3139,  -4385,
-     5881,   9176,   8119,   3934,   3355,  -2111,  -5139,  -4023,
-     -944,   -687,  -3294,  -2914,  -4547,  -1412,  -2203,  -1949,
-    -1204,  -1879,  -1662,   -805
-};
-
-const int16_t ff_g723_1_adaptive_cb_gain170[170 * 20] = {
-        0,      0,      0,      0,      0,      0,      0,      0,
-        0,      0,      0,      0,      0,      0,      0,      0,
-        0,      0,      0,      0,    776,    212,    715,    670,
-      809,    -36,     -2,    -31,    -27,    -39,    -10,    -33,
-       -9,    -31,     -8,    -29,    -38,    -10,    -35,    -33,
-     1296,   1316,   -168,   -320,   -815,   -102,   -105,     -1,
-       -6,    -40,   -104,     13,     13,     25,     25,     -3,
-       64,     65,     -8,    -15,   -589,    680,   2478,    308,
-     -596,    -21,    -28,   -375,     -5,    -21,     24,     89,
-     -102,     11,    -12,    -46,    -21,     24,     90,     11,
-     -735,   -487,     -5,   2948,    468,    -33,    -14,      0,
-     -530,    -13,    -21,      0,      0,    132,     87,      0,
-       21,     13,      0,    -84,   1042,   1730,   1068,    333,
-      626,    -66,   -182,    -69,     -6,    -23,   -110,    -67,
-     -112,    -21,    -35,    -21,    -39,    -66,    -40,    -12,
-      486,   -769,   4074,   2825,  -1107,    -14,    -36,  -1013,
-     -487,    -74,     22,   -120,    191,    -83,    132,   -702,
-       32,    -52,    275,    191,   1521,   -767,   -124,   4320,
-     1026,   -141,    -35,      0,  -1139,    -64,     71,     11,
-       -5,   -401,    202,     32,    -95,     48,      7,   -270,
-     2425,   1267,   3439,    -91,  -1166,   -359,    -98,   -722,
-        0,    -83,   -187,   -509,   -266,     13,      7,     19,
-      172,     90,    244,     -6,  -1251,    975,    173,   4039,
-     2005,    -95,    -58,     -1,   -996,   -245,     74,     13,
-      -10,    308,   -240,    -42,    153,   -119,    -21,   -494,
-     1820,    632,   1322,   2062,   1031,   -202,    -24,   -106,
-     -259,    -64,    -70,   -146,    -51,   -229,    -79,   -166,
-     -114,    -39,    -83,   -129,   -447,   4904,    244,   -315,
-    -2038,    -12,  -1467,     -3,     -6,   -253,    134,      6,
-      -73,     -8,     94,      4,    -55,    610,     30,    -39,
-     -208,  -1102,    463,   -448,   5653,     -2,    -74,    -13,
-      -12,  -1950,    -14,      5,     31,     -5,    -30,     12,
-       71,    380,   -159,    154,   4739,   2600,  -1864,    856,
-    -1554,  -1371,   -412,   -212,    -44,   -147,   -752,    539,
-      295,   -247,   -135,     97,    449,    246,   -176,     81,
-     1894,   3533,     35,    -26,   2145,   -219,   -762,      0,
-        0,   -280,   -408,     -4,     -7,      3,      5,      0,
-     -248,   -462,     -4,      3,  -2699,   1841,   4072,   2443,
-     1582,   -444,   -207,  -1012,   -364,   -152,    303,    670,
-     -457,    402,   -274,   -607,    260,   -177,   -393,   -236,
-     -844,   3358,   6106,  -1059,   -537,    -43,   -688,  -2275,
-      -68,    -17,    173,    314,  -1251,    -54,    217,    395,
-      -27,    110,    200,    -34,   1251,   1016,   3020,   2210,
-     1445,    -95,    -63,   -556,   -298,   -127,    -77,   -230,
-     -187,   -168,   -137,   -407,   -110,    -89,   -266,   -194,
-     2099,   2277,   4038,   3533,  -2870,   -269,   -316,   -995,
-     -762,   -503,   -291,   -517,   -561,   -452,   -491,   -871,
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-     -240,  -2650,   -823,   -209,    -74,   -748,   -264,    -67,
-    -2484,   -878,   -223,   -798,   -492,    391,  17166,   -681,
-      240,    -14,     -9, -17987,    -28,     -3,     11,    515,
-     -410,    -20,     16,    713,      7,     -5,   -252,     10,
-    12628,   5448,  -2630,   3011,  -2695,  -9733,  -1811,   -422,
-     -553,   -443,  -4199,   2027,    874,  -2321,  -1001,    483,
-     2077,    896,   -432,    495,  -3628,   -534,   3447,   7002,
-     6751,   -803,    -17,   -725,  -2992,  -2782,   -118,    763,
-      112,   1550,    228,  -1473,   1495,    220,  -1420,  -2885,
-    -5239,   5901,   8107,   3650,   4846,  -1675,  -2125,  -4012,
-     -813,  -1433,   1887,   2592,  -2920,   1167,  -1315,  -1806,
-     1550,  -1745,  -2398,  -1080,   6157,   6678,   4099,  -1074,
-     2348,  -2314,  -2722,  -1025,    -70,   -336,  -2509,  -1540,
-    -1670,    403,    437,    268,   -882,   -957,   -587,    153,
-     1079,  16099,    242,   -881,   1690,    -71, -15820,     -3,
-      -47,   -174,  -1060,    -16,   -238,     58,    865,     13,
-     -111,  -1661,    -25,     90,   -278,    227,  -1039,   1636,
-    16945,     -4,     -3,    -65,   -163, -17526,      3,    -17,
-       14,     27,    -22,    103,    287,   -234,   1074,  -1693,
-    15778,  -1454,    574,   -603,   -107, -15195,   -129,    -20,
-      -22,      0,   1400,   -553,     51,    581,    -53,     21,
-      103,     -9,      3,     -3,   2406,   -836,  13224,   7993,
-    -4266,   -353,    -42, -10673,  -3899,  -1111,    122,  -1942,
-      674,  -1174,    407,  -6451,    626,   -217,   3443,   2081,
-     3184,  14368,  -3336,   2255,  -1801,   -619, -12600,   -679,
-     -310,   -198,  -2793,    648,   2926,   -438,  -1977,    459,
-      350,   1580,   -366,    247,  -1698,  17076,   2504,   -539,
-     -646,   -176, -17798,   -382,    -17,    -25,   1770,    259,
-    -2610,    -55,    561,     82,    -67,    673,     98,    -21,
-     2375,   -797,  -2696,  14483,   5383,   -344,    -38,   -443,
-   -12803,  -1769,    115,    391,   -131,  -2100,    705,   2384,
-     -780,    262,    886,  -4759,  -2691,   2554,  -4520,   9573,
-    10655,   -442,   -398,  -1247,  -5594,  -6930,    419,   -742,
-      704,   1572,  -1492,   2641,   1750,  -1661,   2939,  -6226,
-    -4332,  -4399,  -1657,   4880,   7375,  -1145,  -1181,   -167,
-    -1453,  -3319,  -1163,   -438,   -444,   1290,   1310,    493,
-     1950,   1980,    745,  -2196,  -3498,   7405,   9955,   2693,
-    -2971,   -746,  -3347,  -6049,   -442,   -538,   1581,   2125,
-    -4499,    575,  -1217,  -1636,   -634,   1342,   1805,    488,
-     6717,  -3792,   7739,   2798,   3489,  -2754,   -877,  -3655,
-     -477,   -743,   1554,  -3173,   1791,  -1147,    647,  -1321,
-    -1430,    807,  -1648,   -595,   5263,   9770,   3463,   1069,
-    -3971,  -1690,  -5826,   -732,    -69,   -962,  -3138,  -1112,
-    -2065,   -343,   -637,   -226,   1275,   2368,    839,    259,
-     1243,  -2634,  16772,   1871,    332,    -94,   -423, -17169,
-     -213,     -6,    199,  -1273,   2696,   -142,    300,  -1915,
-      -25,     53,   -339,    -37,   2691,   2836,   3105,   5711,
-     4817,   -442,   -491,   -588,  -1991,  -1416,   -465,   -510,
-     -537,   -938,   -988,  -1082,   -791,   -834,   -913,  -1679,
-     4366,   2944,   7210,   3627,   1161,  -1163,   -529,  -3172,
-     -803,    -82,   -784,  -1921,  -1295,   -966,   -651,  -1596,
-     -309,   -208,   -511,   -257,  13888,   3951,   -671,  -2305,
-     3354, -11773,   -953,    -27,   -324,   -686,  -3349,    569,
-      161,   1954,    556,    -94,  -2843,   -809,    137,    472,
-     7053,   5847,   2929,   8378,  -4794,  -3036,  -2086,   -523,
-    -4284,  -1403,  -2517,  -1261,  -1045,  -3607,  -2990,  -1498,
-     2064,   1711,    857,   2451,  -2191,  12838,   9182,  -3915,
-     1617,   -293, -10059,  -5146,   -935,   -159,   1717,   1228,
-    -7195,   -523,   3068,   2194,    216,  -1267,   -906,    386,
-    -4881,  13114,   5767,   -435,   4155,  -1454, -10498,  -2030,
-      -11,  -1054,   3907,   1718,  -4616,   -129,    348,    153,
-     1238,  -3326,  -1462,    110,   7843,  -1250,    210,   7106,
-    -5203,  -3754,    -95,     -2,  -3082,  -1652,    598,   -100,
-       16,  -3402,    542,    -91,   2491,   -397,     66,   2257,
-    -2463,   8168,  14551,  -3908,   1828,   -370,  -4072, -12923,
-     -932,   -204,   1228,   2188,  -7254,   -587,   1948,   3471,
-      274,   -911,  -1623,    436,  -1579,    347,   -272,  -2735,
-    16031,   -152,     -7,     -4,   -456, -15686,     33,    -26,
-        5,   -263,     58,    -45,   1545,   -340,    266,   2676,
-    -6327,   1328,   5093,  -5079,   7617,  -2443,   -107,  -1583,
-    -1574,  -3541,    513,   1967,   -413,  -1961,    411,   1578,
-     2941,   -617,  -2367,   2361,   3286,  -4509,  11306,  11025,
-    -2623,   -659,  -1241,  -7802,  -7419,   -420,    904,  -2267,
-     3112,  -2211,   3034,  -7608,    526,   -722,   1810,   1765,
-     5567,  17853,  -3754,   1166,   -519,  -1892, -19455,   -860,
-      -83,    -16,  -6067,   1275,   4090,   -396,  -1271,    267,
-      176,    566,   -119,     37,  -2136,   -424,  15292,   5108,
-    -1648,   -278,    -10, -14273,  -1593,   -165,    -55,   1993,
-      396,    666,    132,  -4768,   -214,    -42,   1538,    514,
-     2267,  -3297,   2549,  16563,   -791,   -313,   -663,   -396,
-   -16745,    -38,    456,   -352,    513,  -2291,   3333,  -2576,
-      109,   -159,    123,    799,   3655,   1899,  -3364,   6279,
-    12510,   -815,   -220,   -690,  -2406,  -9552,   -423,    750,
-      390,  -1400,   -728,   1289,  -2791,  -1450,   2568,  -4794,
-     8052,   2285,  -6193,   5138,   6003,  -3957,   -318,  -2341,
-    -1611,  -2199,  -1123,   3044,    864,  -2525,   -716,   1942,
-    -2950,   -837,   2269,  -1882,   -386,  -2291,   7679,  15387,
-    -2723,     -9,   -320,  -3599, -14452,   -452,    -54,    181,
-     1074,    362,   2152,  -7212,    -64,   -380,   1276,   2557,
-     2777,  -1173,   3984,  13079,   2508,   -470,    -84,   -969,
-   -10440,   -384,    198,   -675,    285,  -2217,    936,  -3180,
-     -425,    179,   -610,  -2002,  -1879,   1771,  -2684,  16705,
-     1833,   -215,   -191,   -439, -17032,   -205,    203,   -308,
-      290,   1916,  -1805,   2736,    210,   -198,    300,  -1869,
-     1052,   4495,  15519,   1467,  -4032,    -67,  -1233, -14700,
-     -131,   -992,   -288,   -997,  -4257,    -94,   -402,  -1389,
-      259,   1106,   3819,    361,   3010,   2544,   6969,   7559,
-     1996,   -553,   -395,  -2964,  -3487,   -243,   -467,  -1280,
-    -1082,  -1388,  -1174,  -3215,   -366,   -310,   -849,   -921,
-    -5209,  -1867,   8713,  10351,   1549,  -1656,   -212,  -4634,
-    -6540,   -146,   -593,   2770,    993,   3291,   1180,  -5505,
-      492,    176,   -824,   -979,  -4314,   8513,    913,   7547,
-    -2723,  -1135,  -4423,    -50,  -3476,   -452,   2241,    240,
-     -474,   1987,  -3921,   -420,   -717,   1415,    151,   1254,
-    12929,  -1219,   2448,   1757,   6303, -10204,    -90,   -365,
-     -188,  -2425,    962,  -1932,    182,  -1386,    130,   -262,
-    -4974,    469,   -941,   -676,   6465,   4132,   3167,   3160,
-     5697,  -2551,  -1042,   -612,   -609,  -1981,  -1630,  -1249,
-     -798,  -1247,   -797,   -611,  -2248,  -1437,  -1101,  -1099,
-    -3636,   4859,  18914,  -1335,    810,   -807,  -1441, -21836,
-     -108,    -40,   1078,   4198,  -5609,   -296,    396,   1541,
-      179,   -240,   -936,     66,   8844,   7864,    654,  -4063,
-    -5680,  -4774,  -3774,    -26,  -1007,  -1969,  -4245,   -353,
-     -314,   2193,   1950,    162,   3066,   2726,    226,  -1408,
-     1859,   2634,   9228,    996,   9464,   -211,   -423,  -5197,
-      -60,  -5467,   -299,  -1047,  -1483,   -113,   -160,   -561,
-    -1074,  -1521,  -5330,   -575,   2949,  12260,  10290,   -497,
-    -3943,   -530,  -9174,  -6463,    -15,   -949,  -2206,  -1852,
-    -7700,     89,    372,    312,    709,   2950,   2476,   -119,
-    -2903,   1552,  14867,   9970,   -496,   -514,   -147, -13491,
-    -6068,    -15,    275,   2634,  -1408,   1766,   -944,  -9047,
-      -87,     47,    450,    302,   3243,   8234,   7586,   3373,
-     2151,   -642,  -4138,  -3512,   -694,   -282,  -1630,  -1501,
-    -3812,   -667,  -1695,  -1561,   -425,  -1081,   -996,   -442,
-    -9631,     60,   3501,   5359,  10150,  -5662,      0,   -748,
-    -1752,  -6288,     35,   2058,    -12,   3150,    -19,  -1145,
-     5967,    -37,  -2169,  -3320,  -6874,  -2553,  -5446,  -2195,
-    -7841,  -2884,   -397,  -1810,   -294,  -3753,  -1071,  -2285,
-     -848,   -921,   -342,   -729,  -3290,  -1221,  -2606,  -1050,
-    -3413,  -1141,   4630,  13612,   7897,   -711,    -79,  -1308,
-   -11310,  -3806,   -237,    964,    322,   2836,    948,  -3847,
-     1645,    550,  -2231,  -6561,   4410,  -5678,   8006,  -3992,
-     3811,  -1187,  -1968,  -3912,   -973,   -886,   1528,  -2155,
-     2775,   1074,  -1383,   1951,  -1025,   1321,  -1862,    928,
-     5659,  11535,   2203,   -452,   7169,  -1954,  -8121,   -296,
-      -12,  -3137,  -3984,   -761,  -1551,    156,    318,     60,
-    -2476,  -5048,   -964,    197,   2914,  -2914,   3485,  -3965,
-    13675,   -518,   -518,   -741,   -959, -11414,    518,   -620,
-      620,    705,   -705,    843,  -2433,   2432,  -2909,   3310,
-     7843,   1907,   1022,   8882,   7972,  -3755,   -222,    -63,
-    -4815,  -3879,   -913,   -489,   -119,  -4252,  -1034,   -554,
-    -3816,   -928,   -497,  -4322,  13807,   9531,   1436,   1612,
-     1779, -11636,  -5544,   -125,   -158,   -193,  -8032,  -1210,
-     -835,  -1358,   -938,   -141,  -1499,  -1035,   -156,   -175,
-    13620,  -5337,   5450,  -2263,   1723, -11322,  -1738,  -1813,
-     -312,   -181,   4436,  -4531,   1775,   1881,   -737,    752,
-    -1432,    561,   -573,    238,   5297,   8374,   8872,   7694,
-     6538,  -1712,  -4280,  -4804,  -3613,  -2609,  -2707,  -2868,
-    -4534,  -2487,  -3932,  -4166,  -2113,  -3341,  -3540,  -3070
-};
-
-int ff_g723_1_scale_vector(int16_t *dst, const int16_t *vector, int length)
-{
-    int bits, max = 0;
-    int i;
-
-    for (i = 0; i < length; i++)
-        max |= FFABS(vector[i]);
-
-    bits= 14 - av_log2_16bit(max);
-    bits= FFMAX(bits, 0);
-
-    for (i = 0; i < length; i++)
-        dst[i] = (vector[i] * (1 << bits)) >> 3;
-
-    return bits - 3;
-}
-
-int ff_g723_1_normalize_bits(int num, int width)
-{
-    return width - av_log2(num) - 1;
-}
-
-int ff_g723_1_dot_product(const int16_t *a, const int16_t *b, int length)
-{
-    int sum = ff_dot_product(a, b, length);
-    return av_sat_add32(sum, sum);
-}
-
-void ff_g723_1_get_residual(int16_t *residual, int16_t *prev_excitation,
-                            int lag)
-{
-    int offset = PITCH_MAX - PITCH_ORDER / 2 - lag;
-    int i;
-
-    residual[0] = prev_excitation[offset];
-    residual[1] = prev_excitation[offset + 1];
-
-    offset += 2;
-    for (i = 2; i < SUBFRAME_LEN + PITCH_ORDER - 1; i++)
-        residual[i] = prev_excitation[offset + (i - 2) % lag];
-}
-
-void ff_g723_1_gen_dirac_train(int16_t *buf, int pitch_lag)
-{
-    int16_t vector[SUBFRAME_LEN];
-    int i, j;
-
-    memcpy(vector, buf, SUBFRAME_LEN * sizeof(*vector));
-    for (i = pitch_lag; i < SUBFRAME_LEN; i += pitch_lag) {
-        for (j = 0; j < SUBFRAME_LEN - i; j++)
-            buf[i + j] += vector[j];
-    }
-}
-
-void ff_g723_1_gen_acb_excitation(int16_t *vector, int16_t *prev_excitation,
-                                  int pitch_lag, G723_1_Subframe *subfrm,
-                                  enum Rate cur_rate)
-{
-    int16_t residual[SUBFRAME_LEN + PITCH_ORDER - 1];
-    const int16_t *cb_ptr;
-    int lag = pitch_lag + subfrm->ad_cb_lag - 1;
-
-    int i;
-    int sum;
-
-    ff_g723_1_get_residual(residual, prev_excitation, lag);
-
-    /* Select quantization table */
-    if (cur_rate == RATE_6300 && pitch_lag < SUBFRAME_LEN - 2) {
-        cb_ptr = ff_g723_1_adaptive_cb_gain85;
-    } else
-        cb_ptr = ff_g723_1_adaptive_cb_gain170;
-
-    /* Calculate adaptive vector */
-    cb_ptr += subfrm->ad_cb_gain * 20;
-    for (i = 0; i < SUBFRAME_LEN; i++) {
-        sum = ff_dot_product(residual + i, cb_ptr, PITCH_ORDER);
-        vector[i] = av_sat_dadd32(1 << 15, av_sat_add32(sum, sum)) >> 16;
-    }
-}
-
-/**
- * Convert LSP frequencies to LPC coefficients.
- *
- * @param lpc buffer for LPC coefficients
- */
-static void lsp2lpc(int16_t *lpc)
-{
-    int f1[LPC_ORDER / 2 + 1];
-    int f2[LPC_ORDER / 2 + 1];
-    int i, j;
-
-    /* Calculate negative cosine */
-    for (j = 0; j < LPC_ORDER; j++) {
-        int index     = (lpc[j] >> 7) & 0x1FF;
-        int offset    = lpc[j] & 0x7f;
-        int temp1  =  ff_g723_1_cos_tab[index] * (1 << 16);
-        int temp2  = (ff_g723_1_cos_tab[index + 1] - ff_g723_1_cos_tab[index]) *
-                          (((offset << 8) + 0x80) << 1);
-
-        lpc[j] = -(av_sat_dadd32(1 << 15, temp1 + temp2) >> 16);
-    }
-
-    /*
-     * Compute sum and difference polynomial coefficients
-     * (bitexact alternative to lsp2poly() in lsp.c)
-     */
-    /* Initialize with values in Q28 */
-    f1[0] = 1 << 28;
-    f1[1] = (lpc[0] + lpc[2]) * (1 << 14);
-    f1[2] = lpc[0] * lpc[2] + (2 << 28);
-
-    f2[0] = 1 << 28;
-    f2[1] = (lpc[1] + lpc[3]) * (1 << 14);
-    f2[2] = lpc[1] * lpc[3] + (2 << 28);
-
-    /*
-     * Calculate and scale the coefficients by 1/2 in
-     * each iteration for a final scaling factor of Q25
-     */
-    for (i = 2; i < LPC_ORDER / 2; i++) {
-        f1[i + 1] = av_clipl_int32(f1[i - 1] + (int64_t)MULL2(f1[i], lpc[2 * i]));
-        f2[i + 1] = av_clipl_int32(f2[i - 1] + (int64_t)MULL2(f2[i], lpc[2 * i + 1]));
-
-        for (j = i; j >= 2; j--) {
-            f1[j] = MULL2(f1[j - 1], lpc[2 * i]) +
-                    (f1[j] >> 1) + (f1[j - 2] >> 1);
-            f2[j] = MULL2(f2[j - 1], lpc[2 * i + 1]) +
-                    (f2[j] >> 1) + (f2[j - 2] >> 1);
-        }
-
-        f1[0] >>= 1;
-        f2[0] >>= 1;
-        f1[1] = ((lpc[2 * i]     * 65536 >> i) + f1[1]) >> 1;
-        f2[1] = ((lpc[2 * i + 1] * 65536 >> i) + f2[1]) >> 1;
-    }
-
-    /* Convert polynomial coefficients to LPC coefficients */
-    for (i = 0; i < LPC_ORDER / 2; i++) {
-        int64_t ff1 = f1[i + 1] + f1[i];
-        int64_t ff2 = f2[i + 1] - f2[i];
-
-        lpc[i] = av_clipl_int32(((ff1 + ff2) * 8) + (1 << 15)) >> 16;
-        lpc[LPC_ORDER - i - 1] = av_clipl_int32(((ff1 - ff2) * 8) +
-                                                (1 << 15)) >> 16;
-    }
-}
-
-void ff_g723_1_lsp_interpolate(int16_t *lpc, int16_t *cur_lsp,
-                               int16_t *prev_lsp)
-{
-    int i;
-    int16_t *lpc_ptr = lpc;
-
-    /* cur_lsp * 0.25 + prev_lsp * 0.75 */
-    ff_acelp_weighted_vector_sum(lpc, cur_lsp, prev_lsp,
-                                 4096, 12288, 1 << 13, 14, LPC_ORDER);
-    ff_acelp_weighted_vector_sum(lpc + LPC_ORDER, cur_lsp, prev_lsp,
-                                 8192, 8192, 1 << 13, 14, LPC_ORDER);
-    ff_acelp_weighted_vector_sum(lpc + 2 * LPC_ORDER, cur_lsp, prev_lsp,
-                                 12288, 4096, 1 << 13, 14, LPC_ORDER);
-    memcpy(lpc + 3 * LPC_ORDER, cur_lsp, LPC_ORDER * sizeof(*lpc));
-
-    for (i = 0; i < SUBFRAMES; i++) {
-        lsp2lpc(lpc_ptr);
-        lpc_ptr += LPC_ORDER;
-    }
-}
-
-void ff_g723_1_inverse_quant(int16_t *cur_lsp, int16_t *prev_lsp,
-                             uint8_t *lsp_index, int bad_frame)
-{
-    int min_dist, pred;
-    int i, j, temp, stable;
-
-    /* Check for frame erasure */
-    if (!bad_frame) {
-        min_dist     = 0x100;
-        pred         = 12288;
-    } else {
-        min_dist     = 0x200;
-        pred         = 23552;
-        lsp_index[0] = lsp_index[1] = lsp_index[2] = 0;
-    }
-
-    /* Get the VQ table entry corresponding to the transmitted index */
-    cur_lsp[0] = ff_g723_1_lsp_band0[lsp_index[0]][0];
-    cur_lsp[1] = ff_g723_1_lsp_band0[lsp_index[0]][1];
-    cur_lsp[2] = ff_g723_1_lsp_band0[lsp_index[0]][2];
-    cur_lsp[3] = ff_g723_1_lsp_band1[lsp_index[1]][0];
-    cur_lsp[4] = ff_g723_1_lsp_band1[lsp_index[1]][1];
-    cur_lsp[5] = ff_g723_1_lsp_band1[lsp_index[1]][2];
-    cur_lsp[6] = ff_g723_1_lsp_band2[lsp_index[2]][0];
-    cur_lsp[7] = ff_g723_1_lsp_band2[lsp_index[2]][1];
-    cur_lsp[8] = ff_g723_1_lsp_band2[lsp_index[2]][2];
-    cur_lsp[9] = ff_g723_1_lsp_band2[lsp_index[2]][3];
-
-    /* Add predicted vector & DC component to the previously quantized vector */
-    for (i = 0; i < LPC_ORDER; i++) {
-        temp        = ((prev_lsp[i] - dc_lsp[i]) * pred + (1 << 14)) >> 15;
-        cur_lsp[i] += dc_lsp[i] + temp;
-    }
-
-    for (i = 0; i < LPC_ORDER; i++) {
-        cur_lsp[0]             = FFMAX(cur_lsp[0],  0x180);
-        cur_lsp[LPC_ORDER - 1] = FFMIN(cur_lsp[LPC_ORDER - 1], 0x7e00);
-
-        /* Stability check */
-        for (j = 1; j < LPC_ORDER; j++) {
-            temp = min_dist + cur_lsp[j - 1] - cur_lsp[j];
-            if (temp > 0) {
-                temp >>= 1;
-                cur_lsp[j - 1] -= temp;
-                cur_lsp[j]     += temp;
-            }
-        }
-        stable = 1;
-        for (j = 1; j < LPC_ORDER; j++) {
-            temp = cur_lsp[j - 1] + min_dist - cur_lsp[j] - 4;
-            if (temp > 0) {
-                stable = 0;
-                break;
-            }
-        }
-        if (stable)
-            break;
-    }
-    if (!stable)
-        memcpy(cur_lsp, prev_lsp, LPC_ORDER * sizeof(*cur_lsp));
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imm4.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imm4.c
deleted file mode 100644
index ccec5dff43a60475a45b0703835aba1eb6b18825..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/imm4.c
+++ /dev/null
@@ -1,540 +0,0 @@
-/*
- * Infinity IMM4 decoder
- *
- * Copyright (c) 2018 Paul B Mahol
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include <stddef.h>
-#include <string.h>
-
-#include "libavutil/mem_internal.h"
-#include "libavutil/thread.h"
-
-#include "avcodec.h"
-#include "bswapdsp.h"
-#include "codec_internal.h"
-#include "decode.h"
-#include "copy_block.h"
-#include "get_bits.h"
-#include "idctdsp.h"
-
-#define CBPLO_VLC_BITS   6
-#define CBPHI_VLC_BITS   6
-#define BLKTYPE_VLC_BITS 9
-#define BLOCK_VLC_BITS  12
-
-typedef struct IMM4Context {
-    BswapDSPContext bdsp;
-    GetBitContext  gb;
-
-    AVFrame *prev_frame;
-    uint8_t *bitstream;
-    int bitstream_size;
-
-    int factor;
-    unsigned lo;
-    unsigned hi;
-
-    IDCTDSPContext idsp;
-    DECLARE_ALIGNED(32, int16_t, block)[6][64];
-} IMM4Context;
-
-static const uint8_t intra_cb[] = {
-    24, 18, 12
-};
-
-static const uint8_t inter_cb[] = {
-    30, 20, 15
-};
-
-static const uint8_t cbplo[][2] = {
-    {    0,-6 }, { 0x01, 6 }, { 0x02, 6 }, { 0x03, 6 }, { 0x00, 4 },
-    { 0x01, 3 }, { 0x02, 3 }, { 0x03, 3 }, { 0x00, 1 },
-};
-
-static const uint8_t cbphi_bits[] = {
-    4, 5, 5, 4, 5, 4, 6, 4, 5, 6, 4, 4, 4, 4, 4, 2
-};
-
-static const uint8_t cbphi_codes[] = {
-    3, 5, 4, 9, 3, 7, 2, 11, 2, 3, 5, 10, 4, 8, 6, 3
-};
-
-static const uint8_t blktype[][2] = {
-    {    0,-8 }, { 0x34, 9 }, {    0,-9 }, { 0x14, 9 }, {    0,-9 },
-    { 0x23, 8 }, { 0x13, 8 }, { 0x32, 8 }, { 0x33, 7 }, { 0x22, 7 },
-    { 0x12, 7 }, { 0x21, 7 }, { 0x11, 7 }, { 0x04, 6 }, { 0x30, 6 },
-    { 0x03, 5 }, { 0x20, 4 }, { 0x10, 4 }, { 0x02, 3 }, { 0x01, 3 },
-    { 0x00, 1 },
-};
-
-static const uint16_t block_symbols[] = {
-         0, 0x4082, 0x4003, 0x000B, 0x000A, 0x4E01, 0x4D81, 0x4D01, 0x4C81,
-    0x0482, 0x0402, 0x0382, 0x0302, 0x0282, 0x0183, 0x0103, 0x0084, 0x000C,
-    0x0085, 0x0B81, 0x0C01, 0x4E81, 0x4F01, 0x4F81, 0x5001, 0x0086, 0x0104,
-    0x0203, 0x0283, 0x0303, 0x0502, 0x0C81, 0x0D01, 0x5081, 0x5101, 0x5181,
-    0x5201, 0x5281, 0x5301, 0x5381, 0x5401, 0x0000, 0x0009, 0x0008, 0x4C01,
-    0x4B81, 0x4B01, 0x4A81, 0x4A01, 0x4981, 0x4901, 0x4881, 0x4002, 0x0B01,
-    0x0A81, 0x0A01, 0x0981, 0x0901, 0x0881, 0x0801, 0x0781, 0x0202, 0x0182,
-    0x0007, 0x0006, 0x4801, 0x4781, 0x4701, 0x4681, 0x4601, 0x4581, 0x4501,
-    0x4481, 0x0701, 0x0681, 0x0102, 0x0083, 0x0005, 0x4401, 0x4381, 0x4301,
-    0x4281, 0x0601, 0x0581, 0x0501, 0x0004, 0x4201, 0x4181, 0x4101, 0x4081,
-    0x0481, 0x0401, 0x0381, 0x0301, 0x0082, 0x0003, 0x0281, 0x0201, 0x0181,
-    0x4001, 0x0001, 0x0081, 0x0101, 0x0002,
-};
-
-static const uint8_t block_bits[] = {
-    -9, 11, 11, 11, 11, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11,
-    11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
-    12, 12, 12,  7, 10, 10,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,
-     9,  9,  9,  9,  9,  9,  9,  9,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,  8,
-     8,  8,  7,  7,  7,  7,  7,  7,  7,  7,  6,  6,  6,  6,  6,  6,  6,  6,  6,
-     6,  5,  5,  5,  4,  2,  3,  4,  4,
-};
-
-static VLC cbplo_tab;
-static VLC cbphi_tab;
-static VLC blktype_tab;
-static VLC block_tab;
-
-static int get_cbphi(GetBitContext *gb, int x)
-{
-    int value;
-
-    value = get_vlc2(gb, cbphi_tab.table, CBPHI_VLC_BITS, 1);
-    if (value < 0)
-        return AVERROR_INVALIDDATA;
-
-    return x ? value : 15 - value;
-}
-
-static int decode_block(AVCodecContext *avctx, GetBitContext *gb,
-                        int block, int factor, int flag, int offset, int flag2)
-{
-    IMM4Context *s = avctx->priv_data;
-    const uint8_t *idct_permutation = s->idsp.idct_permutation;
-    int i, last, len, factor2;
-
-    for (i = !flag; i < 64; i++) {
-        int value;
-
-        value = get_vlc2(gb, block_tab.table, BLOCK_VLC_BITS, 1);
-        if (value < 0)
-            return AVERROR_INVALIDDATA;
-        if (value == 0) {
-            last = get_bits1(gb);
-            len = get_bits(gb, 6);
-            factor2 = get_sbits(gb, 8);
-        } else {
-            factor2 = value & 0x7F;
-            last = (value >> 14) & 1;
-            len = (value >> 7) & 0x3F;
-            if (get_bits1(gb))
-                factor2 = -factor2;
-        }
-        i += len;
-        if (i >= 64)
-            break;
-        s->block[block][idct_permutation[i]] = offset * (factor2 < 0 ? -1 : 1) + factor * factor2;
-        if (last)
-            break;
-    }
-
-    if (s->hi == 2 && flag2 && block < 4) {
-        if (flag)
-            s->block[block][idct_permutation[0]]  *= 2;
-        s->block[block][idct_permutation[1]]  *= 2;
-        s->block[block][idct_permutation[8]]  *= 2;
-        s->block[block][idct_permutation[16]] *= 2;
-    }
-
-    return 0;
-}
-
-static int decode_blocks(AVCodecContext *avctx, GetBitContext *gb,
-                         unsigned cbp, int flag, int offset, unsigned flag2)
-{
-    IMM4Context *s = avctx->priv_data;
-    const uint8_t *idct_permutation = s->idsp.idct_permutation;
-    int ret, i;
-
-    memset(s->block, 0, sizeof(s->block));
-
-    for (i = 0; i < 6; i++) {
-        if (!flag) {
-            int x = get_bits(gb, 8);
-
-            if (x == 255)
-                x = 128;
-            x *= 8;
-
-            s->block[i][idct_permutation[0]] = x;
-        }
-
-        if (cbp & (1 << (5 - i))) {
-            ret = decode_block(avctx, gb, i, s->factor, flag, offset, flag2);
-            if (ret < 0)
-                return ret;
-        }
-    }
-
-    return 0;
-}
-
-static int decode_intra(AVCodecContext *avctx, GetBitContext *gb, AVFrame *frame)
-{
-    IMM4Context *s = avctx->priv_data;
-    int ret, x, y, offset = 0;
-
-    if (s->hi == 0) {
-        if (s->lo > 2)
-            return AVERROR_INVALIDDATA;
-        s->factor = intra_cb[s->lo];
-    } else {
-        s->factor = s->lo * 2;
-    }
-
-    if (s->hi) {
-        offset = s->factor;
-        offset >>= 1;
-        if (!(offset & 1))
-            offset--;
-    }
-
-    for (y = 0; y < avctx->height; y += 16) {
-        for (x = 0; x < avctx->width; x += 16) {
-            unsigned flag, cbphi, cbplo;
-
-            cbplo = get_vlc2(gb, cbplo_tab.table, CBPLO_VLC_BITS, 1);
-            flag = get_bits1(gb);
-
-            cbphi = get_cbphi(gb, 1);
-
-            ret = decode_blocks(avctx, gb, cbplo | (cbphi << 2), 0, offset, flag);
-            if (ret < 0)
-                return ret;
-
-            s->idsp.idct_put(frame->data[0] + y * frame->linesize[0] + x,
-                             frame->linesize[0], s->block[0]);
-            s->idsp.idct_put(frame->data[0] + y * frame->linesize[0] + x + 8,
-                             frame->linesize[0], s->block[1]);
-            s->idsp.idct_put(frame->data[0] + (y + 8) * frame->linesize[0] + x,
-                             frame->linesize[0], s->block[2]);
-            s->idsp.idct_put(frame->data[0] + (y + 8) * frame->linesize[0] + x + 8,
-                             frame->linesize[0], s->block[3]);
-            s->idsp.idct_put(frame->data[1] + (y >> 1) * frame->linesize[1] + (x >> 1),
-                             frame->linesize[1], s->block[4]);
-            s->idsp.idct_put(frame->data[2] + (y >> 1) * frame->linesize[2] + (x >> 1),
-                             frame->linesize[2], s->block[5]);
-        }
-    }
-
-    return 0;
-}
-
-static int decode_inter(AVCodecContext *avctx, GetBitContext *gb,
-                        AVFrame *frame, AVFrame *prev)
-{
-    IMM4Context *s = avctx->priv_data;
-    int ret, x, y, offset = 0;
-
-    if (s->hi == 0) {
-        if (s->lo > 2)
-            return AVERROR_INVALIDDATA;
-        s->factor = inter_cb[s->lo];
-    } else {
-        s->factor = s->lo * 2;
-    }
-
-    if (s->hi) {
-        offset = s->factor;
-        offset >>= 1;
-        if (!(offset & 1))
-            offset--;
-    }
-
-    for (y = 0; y < avctx->height; y += 16) {
-        for (x = 0; x < avctx->width; x += 16) {
-            int reverse, intra_block, value;
-            unsigned cbphi, cbplo, flag2 = 0;
-
-            if (get_bits1(gb)) {
-                copy_block16(frame->data[0] + y * frame->linesize[0] + x,
-                             prev->data[0] + y * prev->linesize[0] + x,
-                             frame->linesize[0], prev->linesize[0], 16);
-                copy_block8(frame->data[1] + (y >> 1) * frame->linesize[1] + (x >> 1),
-                            prev->data[1] + (y >> 1) * prev->linesize[1] + (x >> 1),
-                            frame->linesize[1], prev->linesize[1], 8);
-                copy_block8(frame->data[2] + (y >> 1) * frame->linesize[2] + (x >> 1),
-                            prev->data[2] + (y >> 1) * prev->linesize[2] + (x >> 1),
-                            frame->linesize[2], prev->linesize[2], 8);
-                continue;
-            }
-
-            value = get_vlc2(gb, blktype_tab.table, BLKTYPE_VLC_BITS, 1);
-            if (value < 0)
-                return AVERROR_INVALIDDATA;
-
-            intra_block = value & 0x07;
-            reverse = intra_block == 3;
-            if (reverse)
-                flag2 = get_bits1(gb);
-
-            cbplo = value >> 4;
-            cbphi = get_cbphi(gb, reverse);
-            if (intra_block) {
-                ret = decode_blocks(avctx, gb, cbplo | (cbphi << 2), 0, offset, flag2);
-                if (ret < 0)
-                    return ret;
-
-                s->idsp.idct_put(frame->data[0] + y * frame->linesize[0] + x,
-                                 frame->linesize[0], s->block[0]);
-                s->idsp.idct_put(frame->data[0] + y * frame->linesize[0] + x + 8,
-                                 frame->linesize[0], s->block[1]);
-                s->idsp.idct_put(frame->data[0] + (y + 8) * frame->linesize[0] + x,
-                                 frame->linesize[0], s->block[2]);
-                s->idsp.idct_put(frame->data[0] + (y + 8) * frame->linesize[0] + x + 8,
-                                 frame->linesize[0], s->block[3]);
-                s->idsp.idct_put(frame->data[1] + (y >> 1) * frame->linesize[1] + (x >> 1),
-                                 frame->linesize[1], s->block[4]);
-                s->idsp.idct_put(frame->data[2] + (y >> 1) * frame->linesize[2] + (x >> 1),
-                                 frame->linesize[2], s->block[5]);
-            } else {
-                flag2 = get_bits1(gb);
-                skip_bits1(gb);
-                ret = decode_blocks(avctx, gb, cbplo | (cbphi << 2), 1, offset, flag2);
-                if (ret < 0)
-                    return ret;
-
-                copy_block16(frame->data[0] + y * frame->linesize[0] + x,
-                             prev->data[0] + y * prev->linesize[0] + x,
-                             frame->linesize[0], prev->linesize[0], 16);
-                copy_block8(frame->data[1] + (y >> 1) * frame->linesize[1] + (x >> 1),
-                            prev->data[1] + (y >> 1) * prev->linesize[1] + (x >> 1),
-                            frame->linesize[1], prev->linesize[1], 8);
-                copy_block8(frame->data[2] + (y >> 1) * frame->linesize[2] + (x >> 1),
-                            prev->data[2] + (y >> 1) * prev->linesize[2] + (x >> 1),
-                            frame->linesize[2], prev->linesize[2], 8);
-
-                s->idsp.idct_add(frame->data[0] + y * frame->linesize[0] + x,
-                                 frame->linesize[0], s->block[0]);
-                s->idsp.idct_add(frame->data[0] + y * frame->linesize[0] + x + 8,
-                                 frame->linesize[0], s->block[1]);
-                s->idsp.idct_add(frame->data[0] + (y + 8) * frame->linesize[0] + x,
-                                 frame->linesize[0], s->block[2]);
-                s->idsp.idct_add(frame->data[0] + (y + 8) * frame->linesize[0] + x + 8,
-                                 frame->linesize[0], s->block[3]);
-                s->idsp.idct_add(frame->data[1] + (y >> 1) * frame->linesize[1] + (x >> 1),
-                                 frame->linesize[1], s->block[4]);
-                s->idsp.idct_add(frame->data[2] + (y >> 1) * frame->linesize[2] + (x >> 1),
-                                 frame->linesize[2], s->block[5]);
-            }
-        }
-    }
-
-    return 0;
-}
-
-static int decode_frame(AVCodecContext *avctx, AVFrame *frame,
-                        int *got_frame, AVPacket *avpkt)
-{
-    IMM4Context *s = avctx->priv_data;
-    GetBitContext *gb = &s->gb;
-    int width, height;
-    unsigned type;
-    int ret, scaled;
-
-    if (avpkt->size <= 32)
-        return AVERROR_INVALIDDATA;
-
-    av_fast_padded_malloc(&s->bitstream, &s->bitstream_size,
-                          FFALIGN(avpkt->size, 4));
-    if (!s->bitstream)
-        return AVERROR(ENOMEM);
-
-    s->bdsp.bswap_buf((uint32_t *)s->bitstream,
-                      (uint32_t *)avpkt->data,
-                      (avpkt->size + 3) >> 2);
-
-    if ((ret = init_get_bits8(gb, s->bitstream, FFALIGN(avpkt->size, 4))) < 0)
-        return ret;
-
-    avctx->pix_fmt = AV_PIX_FMT_YUV420P;
-    avctx->color_range = AVCOL_RANGE_JPEG;
-
-    width = avctx->width;
-    height = avctx->height;
-
-    scaled = avpkt->data[8];
-    if (scaled < 2) {
-        int mode = avpkt->data[10];
-
-        switch (mode) {
-        case 1:
-            width = 352;
-            height = 240;
-            break;
-        case 2:
-            width = 704;
-            height = 240;
-            break;
-        case 4:
-            width = 480;
-            height = 704;
-            break;
-        case 17:
-            width = 352;
-            height = 288;
-            break;
-        case 18:
-            width = 704;
-            height = 288;
-            break;
-        default:
-            width = 704;
-            height = 576;
-            break;
-        }
-    }
-
-    skip_bits_long(gb, 24 * 8);
-    type = get_bits_long(gb, 32);
-    s->hi = get_bits(gb, 16);
-    s->lo = get_bits(gb, 16);
-
-    switch (type) {
-    case 0x19781977:
-        frame->key_frame = 1;
-        frame->pict_type = AV_PICTURE_TYPE_I;
-        break;
-    case 0x12250926:
-        frame->key_frame = 0;
-        frame->pict_type = AV_PICTURE_TYPE_P;
-        break;
-    default:
-        avpriv_request_sample(avctx, "type %X", type);
-        return AVERROR_PATCHWELCOME;
-    }
-
-    if (avctx->width  != width ||
-        avctx->height != height) {
-        if (!frame->key_frame) {
-            av_log(avctx, AV_LOG_ERROR, "Frame size change is unsupported.\n");
-            return AVERROR_INVALIDDATA;
-        }
-        av_frame_unref(s->prev_frame);
-    }
-
-    ret = ff_set_dimensions(avctx, width, height);
-    if (ret < 0)
-        return ret;
-
-    if ((ret = ff_get_buffer(avctx, frame, frame->key_frame ? AV_GET_BUFFER_FLAG_REF : 0)) < 0)
-        return ret;
-
-    if (frame->key_frame) {
-        ret = decode_intra(avctx, gb, frame);
-        if (ret < 0)
-            return ret;
-
-        av_frame_unref(s->prev_frame);
-        if ((ret = av_frame_ref(s->prev_frame, frame)) < 0)
-            return ret;
-    } else {
-        if (!s->prev_frame->data[0]) {
-            av_log(avctx, AV_LOG_ERROR, "Missing reference frame.\n");
-            return AVERROR_INVALIDDATA;
-        }
-
-        ret = decode_inter(avctx, gb, frame, s->prev_frame);
-        if (ret < 0)
-            return ret;
-    }
-
-    *got_frame = 1;
-
-    return avpkt->size;
-}
-
-static av_cold void imm4_init_static_data(void)
-{
-    INIT_VLC_STATIC_FROM_LENGTHS(&cbplo_tab, CBPLO_VLC_BITS, FF_ARRAY_ELEMS(cbplo),
-                                 &cbplo[0][1], 2, &cbplo[0][0], 2, 1,
-                                 0, 0, 1 << CBPLO_VLC_BITS);
-
-    INIT_VLC_SPARSE_STATIC(&cbphi_tab, CBPHI_VLC_BITS, FF_ARRAY_ELEMS(cbphi_bits),
-                           cbphi_bits, 1, 1, cbphi_codes, 1, 1, NULL, 0, 0, 64);
-
-    INIT_VLC_STATIC_FROM_LENGTHS(&blktype_tab, BLKTYPE_VLC_BITS, FF_ARRAY_ELEMS(blktype),
-                                 &blktype[0][1], 2, &blktype[0][0], 2, 1,
-                                 0, 0, 1 << BLKTYPE_VLC_BITS);
-
-    INIT_VLC_STATIC_FROM_LENGTHS(&block_tab, BLOCK_VLC_BITS, FF_ARRAY_ELEMS(block_bits),
-                                 block_bits, 1, block_symbols, 2, 2,
-                                 0, 0, 1 << BLOCK_VLC_BITS);
-}
-
-static av_cold int decode_init(AVCodecContext *avctx)
-{
-    static AVOnce init_static_once = AV_ONCE_INIT;
-    IMM4Context *s = avctx->priv_data;
-
-    ff_bswapdsp_init(&s->bdsp);
-    ff_idctdsp_init(&s->idsp, avctx);
-
-    s->prev_frame = av_frame_alloc();
-    if (!s->prev_frame)
-        return AVERROR(ENOMEM);
-
-    ff_thread_once(&init_static_once, imm4_init_static_data);
-
-    return 0;
-}
-
-static void decode_flush(AVCodecContext *avctx)
-{
-    IMM4Context *s = avctx->priv_data;
-
-    av_frame_unref(s->prev_frame);
-}
-
-static av_cold int decode_close(AVCodecContext *avctx)
-{
-    IMM4Context *s = avctx->priv_data;
-
-    av_frame_free(&s->prev_frame);
-    av_freep(&s->bitstream);
-    s->bitstream_size = 0;
-
-    return 0;
-}
-
-const FFCodec ff_imm4_decoder = {
-    .p.name           = "imm4",
-    CODEC_LONG_NAME("Infinity IMM4"),
-    .p.type           = AVMEDIA_TYPE_VIDEO,
-    .p.id             = AV_CODEC_ID_IMM4,
-    .priv_data_size   = sizeof(IMM4Context),
-    .init             = decode_init,
-    .close            = decode_close,
-    FF_CODEC_DECODE_CB(decode_frame),
-    .flush            = decode_flush,
-    .p.capabilities   = AV_CODEC_CAP_DR1,
-    .caps_internal    = FF_CODEC_CAP_INIT_CLEANUP,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jpegls.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jpegls.c
deleted file mode 100644
index cc598f3c177910b40322d664352bf7aa6a0e2f78..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/jpegls.c
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- * JPEG-LS common code
- * Copyright (c) 2003 Michael Niedermayer
- * Copyright (c) 2006 Konstantin Shishkov
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * JPEG-LS common code.
- */
-
-#include <stddef.h>
-#include "libavutil/internal.h"
-#include "libavutil/intmath.h"
-#include "jpegls.h"
-
-void ff_jpegls_init_state(JLSState *state)
-{
-    int i;
-
-    state->twonear = state->near * 2 + 1;
-    state->range   = (state->maxval + state->twonear - 1) / state->twonear + 1;
-
-    // QBPP = ceil(log2(RANGE))
-    for (state->qbpp = 0; (1 << state->qbpp) < state->range; state->qbpp++)
-        ;
-
-    state->bpp   = FFMAX(av_log2(state->maxval) + 1, 2);
-    state->limit = 2*(state->bpp + FFMAX(state->bpp, 8)) - state->qbpp;
-
-    for (i = 0; i < 367; i++) {
-        state->A[i] = FFMAX(state->range + 32 >> 6, 2);
-        state->N[i] = 1;
-    }
-}
-
-/**
- * Custom value clipping function used in T1, T2, T3 calculation
- */
-static inline int iso_clip(int v, int vmin, int vmax)
-{
-    if (v > vmax || v < vmin)
-        return vmin;
-    else
-        return v;
-}
-
-void ff_jpegls_reset_coding_parameters(JLSState *s, int reset_all)
-{
-    const int basic_t1 = 3;
-    const int basic_t2 = 7;
-    const int basic_t3 = 21;
-    int factor;
-
-    if (s->maxval == 0 || reset_all)
-        s->maxval = (1 << s->bpp) - 1;
-
-    if (s->maxval >= 128) {
-        factor = FFMIN(s->maxval, 4095) + 128 >> 8;
-
-        if (s->T1 == 0 || reset_all)
-            s->T1 = iso_clip(factor * (basic_t1 - 2) + 2 + 3 * s->near,
-                             s->near + 1, s->maxval);
-        if (s->T2 == 0 || reset_all)
-            s->T2 = iso_clip(factor * (basic_t2 - 3) + 3 + 5 * s->near,
-                             s->T1, s->maxval);
-        if (s->T3 == 0 || reset_all)
-            s->T3 = iso_clip(factor * (basic_t3 - 4) + 4 + 7 * s->near,
-                             s->T2, s->maxval);
-    } else {
-        factor = 256 / (s->maxval + 1);
-
-        if (s->T1 == 0 || reset_all)
-            s->T1 = iso_clip(FFMAX(2, basic_t1 / factor + 3 * s->near),
-                             s->near + 1, s->maxval);
-        if (s->T2 == 0 || reset_all)
-            s->T2 = iso_clip(FFMAX(3, basic_t2 / factor + 5 * s->near),
-                             s->T1, s->maxval);
-        if (s->T3 == 0 || reset_all)
-            s->T3 = iso_clip(FFMAX(4, basic_t3 / factor + 7 * s->near),
-                             s->T2, s->maxval);
-    }
-
-    if (s->reset == 0 || reset_all)
-        s->reset = 64;
-    ff_dlog(NULL, "[JPEG-LS RESET] T=%i,%i,%i\n", s->T1, s->T2, s->T3);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/me_cmp_mips.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/me_cmp_mips.h
deleted file mode 100644
index 728640102ad9297b08e56bdb0523cd23a6c40618..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/me_cmp_mips.h
+++ /dev/null
@@ -1,60 +0,0 @@
-/*
- * Copyright (c) 2015 Parag Salasakar (Parag.Salasakar@imgtec.com)
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_MIPS_ME_CMP_MIPS_H
-#define AVCODEC_MIPS_ME_CMP_MIPS_H
-
-#include "../mpegvideo.h"
-#include "libavcodec/bit_depth_template.c"
-
-int ff_hadamard8_diff8x8_msa(MpegEncContext *s, const uint8_t *dst, const uint8_t *src,
-                             ptrdiff_t stride, int h);
-int ff_hadamard8_intra8x8_msa(MpegEncContext *s, const uint8_t *dst, const uint8_t *src,
-                              ptrdiff_t stride, int h);
-int ff_hadamard8_diff16_msa(MpegEncContext *s, const uint8_t *dst, const uint8_t *src,
-                            ptrdiff_t stride, int h);
-int ff_hadamard8_intra16_msa(MpegEncContext *s, const uint8_t *dst, const uint8_t *src,
-                             ptrdiff_t stride, int h);
-int ff_pix_abs16_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                     ptrdiff_t stride, int h);
-int ff_pix_abs16_x2_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                        ptrdiff_t stride, int h);
-int ff_pix_abs16_y2_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                        ptrdiff_t stride, int h);
-int ff_pix_abs16_xy2_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                         ptrdiff_t stride, int h);
-int ff_pix_abs8_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                    ptrdiff_t stride, int h);
-int ff_pix_abs8_x2_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                       ptrdiff_t stride, int h);
-int ff_pix_abs8_y2_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                       ptrdiff_t stride, int h);
-int ff_pix_abs8_xy2_msa(MpegEncContext *v, const uint8_t *pix1, const uint8_t *pix2,
-                        ptrdiff_t stride, int h);
-int ff_sse16_msa(MpegEncContext *v, const uint8_t *pu8Src, const uint8_t *pu8Ref,
-                 ptrdiff_t stride, int i32Height);
-int ff_sse8_msa(MpegEncContext *v, const uint8_t *pu8Src, const uint8_t *pu8Ref,
-                ptrdiff_t stride, int i32Height);
-int ff_sse4_msa(MpegEncContext *v, const uint8_t *pu8Src, const uint8_t *pu8Ref,
-                ptrdiff_t stride, int i32Height);
-void ff_add_pixels8_msa(const uint8_t *av_restrict pixels, int16_t *block,
-                        ptrdiff_t stride);
-
-#endif  // #ifndef AVCODEC_MIPS_ME_CMP_MIPS_H
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Arrow.io APK Download and Play the Ultimate Archer Battle Game.md b/spaces/congsaPfin/Manga-OCR/logs/Arrow.io APK Download and Play the Ultimate Archer Battle Game.md
deleted file mode 100644
index bae8ebfa6039a3750daf77a9d6e817c133f60948..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Arrow.io APK Download and Play the Ultimate Archer Battle Game.md	
+++ /dev/null
@@ -1,84 +0,0 @@
-<br />
-<h1>Arrow.io APK: A Fun and Addictive Multiplayer Game</h1>
-<p>Are you looking for a new and exciting game to play on your Android device? Do you love shooting arrows and defeating your opponents in a fast-paced and competitive environment? If yes, then you should try Arrow.io APK, a brand new io game for real players. In this article, we will tell you everything you need to know about this game, including its features, how to play, and why you should play it.</p>
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-<h2>What is Arrow.io APK?</h2>
-<p>Arrow.io APK is an action game developed by Cheetah Games, the same company behind popular games like Piano Tiles 2 and Dancing Line. It is a multiplayer game that pits you against other players from all over the world in an arena where you have to shoot arrows and dodge enemy attacks. You can also play offline mode where you have to survive against waves of bears and other creatures.</p>
-<h3>Features of Arrow.io APK</h3>
-<p>Arrow.io APK has many features that make it an enjoyable and addictive game. Here are some of them:</p>
-<h4>- Control your pixel-man with virtual joystick</h4>
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-<h4>- Upgrade your skills randomly with each level up</h4>
-<p>Every time you level up in the game, you get to choose from three random skills that enhance your abilities. These skills can be offensive, defensive, or supportive, depending on your preference and strategy. For example, you can increase your arrow speed, damage, or range, or you can heal yourself, create shields, or summon minions. There are hundreds of skills to choose from, making the game unpredictable and fun.</p>
-<h4>- Choose from hundreds of pixel-man characters</h4>
-<p>The game also offers a variety of pixel-man characters that you can unlock and use in the game. Each character has a different appearance and personality, such as a ninja, a pirate, a clown, or a robot. You can also customize your character with different hats, masks, glasses, and other accessories. You can collect diamonds and coins in the game to unlock more characters and items.</p>
-<h3>How to play Arrow.io APK?</h3>
-<p>Playing Arrow.io APK is easy and fun. Here are the steps to follow:</p>
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-<h4>- Join the online battle or play offline mode</h4>
-<p>Once you have installed the game, you can choose to join the online battle or play offline mode. The online battle mode allows you to compete with other players in real time in different maps and modes. You can also chat with other players and make friends or enemies. The offline mode allows you to practice your skills and survive against waves of enemies in different levels.</p>
-<h4>- Shoot your arrows at enemies and avoid their attacks</h4>
-<p>The main goal of the game is to shoot your arrows at enemies and avoid their attacks. You have to be careful and strategic, as one hit can kill you. You can also use your skills to gain an advantage over your opponents. You can see your health bar, level, and score at the top of the screen. You can also see the number of players and enemies remaining on the map. The last one standing wins the game.</p>
-<h4>- Collect diamonds and coins to unlock more skills and characters</h4>
-<p>As you play the game, you can collect diamonds and coins that are scattered around the map or dropped by enemies. You can use these currencies to unlock more skills and characters in the game. You can also watch ads or complete tasks to earn more diamonds and coins. The more skills and characters you have, the more fun and diverse the game becomes.</p>
-<h3>Why should you play Arrow.io APK?</h3>
-<p>Arrow.io APK is a game that you should play if you love io games, shooting games, or multiplayer games. Here are some reasons why you should play it:</p>
-<h4>- It is a fun and addictive io game for real players</h4>
-<p>Arrow.io APK is a game that will keep you entertained and hooked for hours. It is a game that tests your skills, reflexes, and strategy against real players from all over the world. You can also chat with other players and make friends or enemies. You can enjoy the thrill and challenge of competing with others in a fast-paced and competitive environment.</p>
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\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download New KBC Quiz Mod APK and Win Real Money.md b/spaces/congsaPfin/Manga-OCR/logs/Download New KBC Quiz Mod APK and Win Real Money.md
deleted file mode 100644
index 0728beaaa013fcba627ab1d6ad3fd9a83f6d7cb3..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download New KBC Quiz Mod APK and Win Real Money.md	
+++ /dev/null
@@ -1,118 +0,0 @@
-
-<h1>New KBC Quiz Mod Apk: A Fun and Educational Trivia Game</h1>
-<p>If you are a fan of trivia games, you must have heard of KBC quiz game. KBC stands for Kaun Banega Crorepati, a popular Indian television game show based on the international quiz show "Who Wants to Be a Millionaire?". The show is hosted by Amitabh Bachchan, a legendary Bollywood actor, and features contestants who answer multiple-choice questions to win cash prizes.</p>
-<h2>new kbc quiz mod apk</h2><br /><p><b><b>Download Zip</b> >> <a href="https://urlca.com/2uO91V">https://urlca.com/2uO91V</a></b></p><br /><br />
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- <h2>What is KBC Quiz Mod Apk?</h2>
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-<ul>
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-<p>If you want to play KBC quiz mod apk like a pro, here are some tips and tricks that you can follow:</p>
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-<li>Use lifelines wisely and strategically. Don't waste them on easy questions or when you are confident about the answer. Save them for difficult questions or when you are unsure about the answer.</li>
-<li>Practice with online quizzes and mock tests. You can find many websites and apps that offer free quizzes and tests based on KBC quiz game. This will help you to improve your knowledge and speed.</li>
-<li>Keep yourself updated with the latest questions and answers. You can follow news channels, newspapers, magazines, websites, blogs, etc. that provide current affairs and general knowledge information. This will help you to answer new and updated questions in the game.</li>
-</ul>
- <h2>Reviews of KBC Quiz Mod Apk</h2>
-<p>KBC quiz mod apk has received positive feedback from users and critics alike. Here are some of the reviews of KBC quiz mod apk:</p>
-<table>
-<tr>
-<th>User</th>
-<th>Rating</th>
-<th>Comment</th>
-</tr>
-<tr>
-<td>Rajesh Kumar</td>
-<td>5 stars</td>
-<td>Best quiz game ever. I love the questions and the lifelines. It feels like I am playing the real KBC game.</td>
-</tr>
-<tr>
-<td>Priya Sharma</td>
-<td>4 stars</td>
-<td>Very good game. It improves my knowledge and makes me confident. The sound and graphics are also very nice.</td>
-</tr>
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-<td>Amit Singh</td>
-<td>3 stars</td>
-<td>Good game but needs improvement. Some questions are repeated and some answers are wrong. Please fix it.</td>
-</tr>
-</table>
- <h2>Conclusion</h2>
-<p>KBC quiz mod apk is a fun and educational trivia game that you can download and play on your smartphone or tablet. It offers unlimited questions and answers, four lifelines, and amazing sound and graphics. It also helps you to improve your general knowledge and current affairs, test your skills and compete with other players, and enjoy the realistic feel of the TV show. If you are a fan of KBC quiz game, you should definitely try KBC quiz mod apk.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions and answers related to KBC quiz mod apk:</p>
- <h3>Q: How can I download KBC quiz mod apk?</h3>
-<p>A: You can download KBC quiz mod apk from various websites or app stores that offer free modded games. Just search for "KBC quiz mod apk" on your browser and choose a reliable source. Make sure you have enough space on your device and allow unknown sources in your settings before installing the app.</p>
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-<p>A: KBC quiz mod apk is generally safe to use, as long as you download it from a trusted source. However, since it is not an official app from the makers of KBC quiz game, it may have some bugs or errors that could affect your device or data. Therefore, use it at your own risk and discretion.</p>
- <h3>Q: Can I play KBC quiz mod apk offline?</h3>
-<p>A: Yes, you can play KBC quiz mod apk offline without any internet connection. However, you will not be able to share your scores or rankings with other players or access the latest questions and answers.</p>
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-<p>A: Yes, you can play KBC quiz mod apk with your friends by using the multiplayer mode. You can invite your friends to join your game or join their game by entering their code. You can also chat with them during the game and see their scores and lifelines.</p>
- <h3>Q: Can I win real money by playing KBC quiz mod apk?</h3>
-<p>A: No, you cannot win real money by playing KBC quiz mod apk. The game is only for entertainment and educational purposes. The cash prizes shown in the game are virtual and have no real value.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download PUBG Mobile Erangel 2.0 for Android Experience the Improved Graphics and Gameplay.md b/spaces/congsaPfin/Manga-OCR/logs/Download PUBG Mobile Erangel 2.0 for Android Experience the Improved Graphics and Gameplay.md
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-<br />
-<h1>PUBG Mobile Erangel 2.0: Everything You Need to Know</h1>
-<p>PUBG Mobile is one of the most popular and addictive battle royale games in the world, with millions of players enjoying its thrilling gameplay and realistic graphics. But did you know that PUBG Mobile has recently released a major update that changes the game completely? Yes, we are talking about Erangel 2.0, the revamped version of the classic Erangel map that was announced back in 2019.</p>
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-<p>In this article, we will tell you everything you need to know about Erangel 2.0, including what it is, how to download and play it, and why you should give it a try. So, without further ado, let's get started!</p>
- <h2>What is Erangel 2.0?</h2>
-<p>Erangel 2.0 is the new and improved version of the original Erangel map, which was the first and most iconic map in PUBG Mobile. Erangel 2.0 is not just a cosmetic upgrade, but a complete overhaul of the map's structure, landscape, buildings, graphics, and gameplay experience.</p>
- <h3>The history and features of Erangel 2.0</h3>
-<p>Erangel 2.0 was first teased by PUBG Mobile in August 2019, as part of its Project REBORN initiative to revamp the game's core elements and enhance its quality. Since then, PUBG Mobile has been working hard to develop and test the new map, releasing several beta versions and collecting feedback from players.</p>
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-<p>Erangel 2.0 was finally released on September 8th, 2020, as part of the PUBG Mobile 1.0 update, which also introduced other new features like Livik map improvements, Cheer Park 2.0, M1014 weapon, and more.</p>
-<p>Some of the main features of Erangel 2.0 are:</p>
-<ul>
-<li>Upgraded graphics: The map's visuals have been enhanced with better lighting, shadows, textures, details, and effects.</li>
-<li>Building adjustments: The map's buildings have been redesigned with more realistic shapes, sizes, colors, and interiors.</li>
-<li>Adjustments to large resource points: The map's major locations like Mylta Power, Quarry, Prison, Military Base, School, Pochinki, Georgopol, etc., have been modified with more loot, cover, vehicles, and strategic options.</li>
-<li>New map elements: The map's terrain has been enriched with new elements like trenches, wooden barricades, abandoned tanks, etc., to provide more variety and fun.</li>
-<li>Building structure changes: The map's building structures have been changed to make them more accessible and interactive.</li>
-<li>New Spawn Island: The map's Spawn Island has been revamped with a new look and layout.</li>
-</ul>
- <h3>The differences and improvements of Erangel 2.0</h3>
-<p>The main difference between Erangel 2.0 and the original Erangel is that the former is more realistic, immersive, dynamic, and challenging than the latter. Erangel 2.0 offers a better visual experience with its upgraded graphics and details, as well as a better gameplay experience with its adjusted buildings and locations.</p>
-<p>Erangel 2.0 also improves the game's performance and stability by optimizing its resources and reducing its lag issues <p>Some of the improvements of Erangel 2.0 are:</p>
-<ul>
-<li>Better balance: The map's loot distribution, vehicle spawn rate, and zone settings have been balanced to make the game more fair and competitive.</li>
-<li>Better interaction: The map's doors, windows, walls, and roofs have been made more interactive, allowing players to open, close, break, and climb them.</li>
-<li>Better sound: The map's sound effects have been improved to make them more realistic and immersive.</li>
-<li>Better security: The map's anti-cheat system has been enhanced to prevent hackers and cheaters from ruining the game.</li>
-</ul>
- <h2>How to download and play Erangel 2.0?</h2>
-<p>If you are eager to try out Erangel 2.0, you will need to download and install the PUBG Mobile 1.0 update first. There are two ways to do this: from the Google Play Store or from the APK and OBB files. Here are the steps for both methods:</p>
- <h3>Downloading Erangel 2.0 from Google Play Store</h3>
-<p>This is the easiest and safest way to download Erangel 2.0, as it does not require any additional files or permissions. All you need to do is follow these steps:</p>
-<ol>
-<li>Open the Google Play Store app on your device.</li>
-<li>Search for PUBG Mobile and tap on the update button.</li>
-<li>Wait for the update to download and install automatically.</li>
-<li>Launch PUBG Mobile and enjoy Erangel 2.0!</li>
-</ol>
- <h3>Downloading Erangel 2.0 from APK and OBB files</h3>
-<p>This is an alternative way to download Erangel 2.0, in case you cannot access the Google Play Store or have a slow internet connection. However, this method requires some extra steps and precautions, so make sure you follow them carefully:</p>
-<ol>
-<li>Download the PUBG Mobile 1.0 APK and OBB files from a trusted source, such as [this one].</li>
-<li>Enable the installation of apps from unknown sources on your device's settings.</li>
-<li>Locate and install the APK file on your device.</li>
-<li>Copy and paste the OBB file to the Android/OBB/com.tencent.ig folder on your device's storage.</li>
-<li>Launch PUBG Mobile and enjoy Erangel 2.0!</li>
-</ol>
- <h3>Tips and tricks for playing Erangel 2.0</h3>
-<p>Now that you have downloaded and installed Erangel 2.0, you might be wondering how to play it effectively and win more matches. Here are some tips and tricks that will help you master the new map:</p>
-<ul>
-<li>Explore the new locations: Erangel 2.0 has many new locations that are worth exploring, such as the secret bunker in Military Base, the underground tunnel in Prison, the abandoned factory in Mylta Power, etc. These locations may have more loot, cover, or surprises that can give you an edge over your enemies.</li>
-<li>Use the new elements: Erangel 2.0 has many new elements that can enhance your gameplay, such as trenches, wooden barricades, abandoned tanks, etc. These elements can provide you with more cover, mobility, or firepower that can help you survive longer or eliminate your foes faster.</li>
-<li>Adapt to the new graphics: Erangel 2.0 has much better graphics than the original Erangel, which means that you will need to adjust your settings and strategies accordingly. For example, you may need to lower your graphics quality or brightness to avoid lag or glare issues, or you may need to change your camouflage or hiding spots to blend in better with the environment.</li>
-<li>Be prepared for more challenges: Erangel 2.0 is not only more beautiful but also more difficult than the original Erangel, which means that you will need to be more careful and skillful when playing it. For example, you may face more enemies or bots in certain areas, or you may encounter more obstacles or traps in certain buildings.</li>
-</ul>
- <h2>Why should you play Erangel 2.0?</h2>
-<p>You may be wondering why you should play Erangel 2.0 when you are already familiar and comfortable with the original Erangel. Well, there are many reasons why you should give Erangel 2.0 a chance, such as:</p>
- <h3>The benefits and challenges of Erangel 2.0</h3>
-<p>Erangel 2.0 offers many benefits and challenges that can make your PUBG Mobile experience more enjoyable and rewarding. Some of these are:</p>
-<ul>
-<li>Better graphics: Erangel 2.0 has better graphics than the original Erangel, which means that you can enjoy a more realistic and immersive visual experience. You can also appreciate the finer details and effects that make the map more lively and dynamic.</li>
-<li>Better gameplay: Erangel 2.0 has better gameplay than the original Erangel, which means that you can have more fun and excitement while playing. You can also explore more possibilities and strategies that make the game more diverse and challenging.</li>
-<li>Better performance: Erangel 2.0 has better performance than the original Erangel, which means that you can have a smoother and faster gaming experience. You can also avoid some of the common issues and glitches that plague the game.</li>
-<li>Better security: Erangel 2.0 has better security than the original Erangel, which means that you can have a safer and fairer gaming experience. You can also report and ban any hackers or cheaters that ruin the game.</li>
-</ul>
- <h3>The best strategies and locations for Erangel 2.0</h3>
-<p>Erangel 2.0 also offers many opportunities and challenges that can test your skills and knowledge of the game. Some of these are:</p>
-<ul>
-<li>The best strategies: Erangel 2.0 requires you to adapt your strategies according to the new map features and elements. For example, you may need to use more stealth and sniping tactics in the open fields, or more close-quarters and assault tactics in the urban areas.</li>
-<li>The best locations: Erangel 2.0 has many new locations that can give you an advantage or disadvantage depending on your situation. For example, you may want to land in the secret bunker in Military Base for more loot, or avoid the underground tunnel in Prison for less risk.</li>
-<li>The best weapons: Erangel 2.0 has many new weapons that can suit your playstyle and preference. For example, you may want to use the M1014 shotgun for its high damage and fast reload, or the M416 assault rifle for its versatility and stability.</li>
-<li>The best vehicles: Erangel 2.0 has many new vehicles that can help you move around and escape faster. For example, you may want to use the UAZ for its durability and speed, or the motorcycle for its maneuverability and fun.</li>
-</ul>
- <h3>The future updates and plans for Erangel 2.0</h3>
-<p>Erangel 2.0 is not the final version of the map, but rather a work in progress that will continue to evolve and improve over time. PUBG Mobile has already announced some of the future updates and plans for Erangel 2.0, such as:</p>
-<ul>
-<li>New modes: PUBG Mobile will introduce new modes for Erangel 2.0, such as Payload 2.0, Infection Mode 2.0, etc., to add more variety and fun to the game.</li>
-<li>New events: PUBG Mobile will launch new events for Erangel 2.0, such as Halloween Mode, Winter Festival, etc., to celebrate different occasions and themes.</li>
-<li>New features: PUBG Mobile will add new features for Erangel 2.0, such as dynamic weather, destructible environments, etc., to make the game more realistic and immersive.</li>
-<li>New feedback: PUBG Mobile will collect new feedback from players for Erangel 2.0, such as bug reports, suggestions, opinions, etc., to make the game better and smoother.</li>
-</ul>
- <h2>Conclusion</h2>
- <h4>Summary of the main points</h4>
-<p>In conclusion, Erangel 2.0 is a major update that changes PUBG Mobile's classic map completely. It offers a new and improved version of Erangel that is more realistic, immersive, dynamic, and challenging than ever before. It also provides a better visual, gameplay, performance, and security experience for players.</p>
- <h4>Call to action and invitation for feedback</h4>
-<p>If you are a fan of PUBG Mobile and want to try something new and exciting, you should definitely download and play Erangel 2.0 today. You will not regret it!</p>
-<p>We hope you enjoyed this article and learned something useful about Erangel 2.0. If you have any questions, comments, or feedback about this topic, please feel free to share them with us in the comment section below. We would love to hear from you!</p>
- <h4>FAQs</h4>
-<p>Here are some of the frequently asked questions about Erangel 2.0:</p>
-<ol>
-<li>Q: How big is Erangel 2.0?<br>A: Erangel 2.0 is still 8x8 km in size, but it has more details and elements than before.</li>
-<li>Q: A: How can I play Erangel 2.0 with my friends?<br>A: You can play Erangel 2.0 with your friends by inviting them to your team or joining their team in the lobby. Then, you can select Erangel 2.0 as your map preference and start the match.</li>
-<li>Q: How can I switch between Erangel 2.0 and the original Erangel?<br>A: You can switch between Erangel 2.0 and the original Erangel by tapping on the map icon in the lobby. Then, you can choose which version of Erangel you want to play.</li>
-<li>Q: What are the minimum requirements to play Erangel 2.0?<br>A: The minimum requirements to play Erangel 2.0 are the same as the PUBG Mobile 1.0 update, which are Android 5.1.1 or above and at least 2 GB of memory.</li>
-<li>Q: Is Erangel 2.0 available for other platforms?<br>A: Erangel 2.0 is currently only available for PUBG Mobile, but it may be released for other platforms like PUBG PC, PUBG Lite, PUBG Console, etc., in the future.</li>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Hello Neighbor APK OBB - Experience the Thrill of Stealth Horror on Your Phone.md b/spaces/congsaPfin/Manga-OCR/logs/Hello Neighbor APK OBB - Experience the Thrill of Stealth Horror on Your Phone.md
deleted file mode 100644
index 57147ba70ebe5e2cf13545f4b6f7da6b236d071b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Hello Neighbor APK OBB - Experience the Thrill of Stealth Horror on Your Phone.md	
+++ /dev/null
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-<br />
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-<p>Are you looking for a thrilling and suspenseful game that will keep you on the edge of your seat? Do you want to experience a unique and immersive gameplay that will challenge your skills and logic? If you answered yes to these questions, then you should try Hello Neighbor, one of the most popular stealth horror games in the market. And if you want to enjoy this game to the fullest, then you should download Hello Neighbor OBB APK, which will give you access to all the features and content of the game.</p>
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-<p>In this article, we will tell you everything you need to know about Hello Neighbor OBB APK. We will explain what is Hello Neighbor, what is an OBB file, what is Hello Neighbor OBB APK, how to download it, and some frequently asked questions. By the end of this article, you will be ready to download and install Hello Neighbor O BB APK and start playing this amazing game.</p>
- <h2>What is Hello Neighbor?</h2>
-<p>Hello Neighbor is a stealth horror game developed by Dynamic Pixels and Hologryph, and published by tinyBuild. It was released in 2017 for Windows, Xbox One, PlayStation 4, Nintendo Switch, iOS, and Android devices. The game has received positive reviews from critics and players, who praised its graphics, sound, atmosphere, and gameplay.</p>
-<p>The game is set in a fictional suburb, where you play as a curious child who wants to find out what is hidden in the basement of your neighbor's house. However, your neighbor is not a friendly person. He is a smart and cunning AI who will do anything to stop you from snooping around his property. He can set traps, chase you, and even learn from your actions. You have to use stealth, strategy, and creativity to outsmart him and uncover his secrets.</p>
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-<p>Hello Neighbor belongs to the genre of stealth horror games, which are games that involve sneaking around a hostile environment while avoiding detection by enemies or hazards. Stealth horror games often have elements of puzzle-solving, exploration, and survival. Some examples of stealth horror games are Outlast, Amnesia: The Dark Descent, Alien: Isolation, and Resident Evil.</p>
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-<p>The game is divided into four acts, each with a different setting and objective. In Act 1, you have to break into your neighbor's house and find the key to the basement. In Act 2, you have to escape from the basement and find a way out of the house. In Act 3, you have to revisit the house as an adult and confront your childhood fears. In Act Finale, you have to face the final challenge and discover the truth behind your neighbor's past.</p>
- <h3>The main features and gameplay of Hello Neighbor</h3>
-<p>Hello Neighbor has many features that make it a unique and enjoyable game. Some of these features are:</p>
-<ul>
-<li>A realistic physics system that allows you to interact with objects and environments in various ways.</li>
-<li>A sandbox mode that lets you explore the game world freely and create your own scenarios.</li>
-<li>A dynamic AI that adapts to your actions and changes its behavior accordingly.</li>
-<li>A procedural animation system that makes the characters move naturally and realistically.</li>
-<li>A colorful and cartoonish art style that contrasts with the dark and creepy atmosphere.</li>
-<li>A rich and mysterious story that unfolds through clues, flashbacks, and cutscenes.</li>
-</ul>
-<p>The gameplay of Hello Neighbor consists of sneaking into your neighbor's house and avoiding his detection. You can use various items and tools to distract him, hide from him, or fight him. You can also explore the house and find clues that will help you progress in the game. However, you have to be careful because your neighbor will not make it easy for you. He will patrol his house, set traps, lock doors, and even follow you if he sees you. He will also remember your previous actions and change his strategy accordingly. You have to be smarter than him and use your wits to survive.</p> <h2>What is an OBB file?</h2>
-<p>An OBB file is a type of file that is used by some Android games and apps to store additional data that is not included in the APK file. OBB stands for Opaque Binary Blob, which means that it is a large and complex file that cannot be easily modified or understood by humans. An OBB file usually contains graphics, sounds, videos, and other media files that enhance the quality and performance of the game or app.</p>
- <h3>The definition and purpose of an OBB file</h3>
-<p>An OBB file is a compressed archive file that can store up to 4 GB of data. It is created by the developers of the game or app using a tool called JOBB (Jobb Tool), which is part of the Android SDK (Software Development Kit). The purpose of an OBB file is to provide additional resources and content that are not essential for the basic functionality of the game or app, but are required for a better user experience. For example, an OBB file can contain high-resolution textures, animations, soundtracks, voice-overs, and cutscenes that make the game or app more immersive and realistic.</p>
- <h3>How to install an OBB file on your device</h3>
-<p>To install an OBB file on your device, you need to follow these steps:</p>
-<ol>
-<li>Download the APK file and the OBB file of the game or app you want to install. Make sure they are from a reliable and secure source.</li>
-<li>Install the APK file on your device by tapping on it and following the instructions. Do not open the game or app yet.</li>
-<li>Locate the OBB file on your device using a file manager app. It should have a name like com.example.game.obb.</li>
-<li>Copy or move the OBB file to the folder /Android/obb/com.example.game/ on your device's internal storage or SD card. If the folder does not exist, create it manually.</li>
-<li>Launch the game or app and enjoy the additional content.</li>
-</ol> <h2>What is Hello Neighbor OBB APK?</h2>
-<p>Hello Neighbor OBB APK is a modified version of the original Hello Neighbor game that includes both the APK file and the OBB file in one package. This means that you do not need to download and install the OBB file separately, as it is already integrated with the APK file. You just need to download and install the Hello Neighbor OBB APK file on your device and you are good to go.</p>
- <h3>The difference between an APK and an OBB file</h3>
-<p>An APK file is a type of file that is used by Android devices to install and run applications. APK stands for Android Package Kit, which means that it is a package of files that contains the code, resources, and metadata of the application. An APK file can be installed on your device by tapping on it or using a third-party app installer.</p>
-<p>An OBB file, as we have explained before, is a type of file that is used by some Android applications to store additional data that is not included in the APK file. An OBB file can be installed on your device by copying or moving it to the appropriate folder on your device's storage.</p>
-<p>The main difference between an APK and an OBB file is that an APK file contains the essential files and information for running the application, while an OBB file contains the optional files and information for enhancing the application. An APK file can work without an OBB file, but an OBB file cannot work without an APK file.</p>
- <h3>The benefits of downloading Hello Neighbor OBB APK</h3>
-<p>There are many benefits of downloading Hello Neighbor OBB APK instead of the original Hello Neighbor game. Some of these benefits are:</p>
-<ul>
-<li>You can save time and bandwidth by downloading only one file instead of two.</li>
-<li>You can avoid the hassle of finding, downloading, and installing the OBB file separately.</li>
-<li>You can ensure that the APK and OBB files are compatible and up-to-date.</li>
-<li>You can access all the features and content of the game without any restrictions or limitations.</li>
-<li>You can enjoy a smoother and faster gameplay with better graphics and sound quality.</li>
-</ul> <h2>How to download Hello Neighbor OBB APK?</h2>
-<p>If you are interested in downloading Hello Neighbor OBB APK, you need to follow some simple steps. However, before you do that, you need to make sure that your device meets the requirements and precautions for downloading Hello Neighbor OBB APK.</p>
- <h3>The requirements and precautions for downloading Hello Neighbor OBB APK</h3>
-<p>Before you download Hello Neighbor OBB APK, you need to check the following requirements and precautions:</p>
-<ul>
-<li>You need to have an Android device that runs on Android 6.0 or higher and has at least 1 GB of RAM.</li>
-<li>You need to have enough free space on your device's storage or SD card to store the Hello Neighbor OBB APK file, which is about 1 GB in size.</li>
-<li>You need to enable the installation of apps from unknown sources on your device's settings. This will allow you to install the Hello Neighbor OBB APK file that is not from the Google Play Store.</li>
-<li>You need to disable any antivirus or security apps that might interfere with the installation of the Hello Neighbor OBB APK file.</li>
-<li>You need to download the Hello Neighbor OBB APK file from a trusted and secure source. Do not download it from any suspicious or malicious websites that might harm your device or steal your data.</li>
-</ul>
- <h3>The steps to download and install Hello Neighbor OBB APK</h3>
-<p>After you have checked the requirements and precautions, you can proceed to download and install Hello Neighbor OBB APK by following these steps:</p>
-<ol>
-<li>Click on this link  to download the Hello Neighbor OBB APK file on your device.</li>
-<li>Wait for the download to finish and then locate the file on your device using a file manager app.</li>
-<li>Tap on the file and follow the instructions to install it on your device. Do not open the game yet.</li>
-<li>Launch the game and wait for it to extract the OBB file automatically. This might take a few minutes depending on your device's speed.</li>
-<li>Enjoy playing Hello Neighbor with all the features and content unlocked.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>Hello Neighbor is a stealth horror game that will test your nerves and skills as you try to uncover the secrets of your neighbor's basement. It is a game that offers a unique and immersive gameplay that will keep you hooked for hours. However, if you want to enjoy this game to the fullest, you should download Hello Neighbor OBB APK, which will give you access to all the features and content of the game without any hassle. You just need to follow the steps we have provided in this article and you will be ready to play this amazing game.</p>
-<p>We hope that this article has helped you understand everything you need to know about Hello Neighbor OBB APK. If you have any questions or feedback, please feel free to leave them in the comments section below. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
-<h4>Q1: Is Hello Neighbor OBB APK safe to download?</h4>
-<p>A1: Yes, as long as you download it from a trusted source. We have provided a link in this article that is safe and secure. However, if you download it from other sources, make sure they are reliable and reputable. Do not download it from any suspicious or malicious websites that might harm your device or steal your data.</p>
- <h4>Q2: Do I need to root my device to install Hello Neighbor OBB APK?</h4>
-<p>A2: No, you do not need to root your device to install Hello Neighbor OBB APK. You just need to enable the installation of apps from unknown sources on your device's settings. This will allow you to install the Hello Neighbor OBB APK file that is not from the Google Play Store.</p>
- <h4>Q3: Can I play Hello Neighbor offline?</h4>
-<p>A3: Yes, you can play Hello Neighbor offline after installing the OBB file. The OBB file contains all the data and resources that are needed for the game to run without an internet connection. However, if you want to access some online features such as updates, leaderboards, or multiplayer modes, you will need an internet connection.</p>
- <h4>Q4: What are the minimum system requirements for Hello Neighbor?</h4>
-<p>A4: You need at least Android 6.0 or higher and 1 GB of RAM to play Hello Neighbor. You also need enough free space on your device's storage or SD card to store the Hello Neighbor OBB APK file, which is about 1 GB in size.</p>
- <h4>Q5: Where can I find more information about Hello Neighbor?</h4>
-<p>A5: You can visit the official website of Hello Neighbor or follow their social media accounts. You can also check out some reviews, videos, and guides about the game online. Here are some links that might help you:</p>
-<ul>
-<li>[Official website of Hello Neighbor]</li>
-<li>[Facebook page of Hello Neighbor]</li>
-<li>[Twitter account of Hello Neighbor]</li>
-<li>[YouTube channel of Hello Neighbor]</li>
-<li>[Review of Hello Neighbor by IGN]</li>
-<li>[Gameplay video of Hello Neighbor by Markiplier]</li>
-<li>[Guide to Hello Neighbor by Gamepressure]</li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Play Garten of Banban Online and Unravel the Mystery of the Missing Kids.md b/spaces/congsaPfin/Manga-OCR/logs/Play Garten of Banban Online and Unravel the Mystery of the Missing Kids.md
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-<h1>Garten of Banban: A Free Online Horror Game That Will Give You Chills</h1>
- <p>If you are a fan of horror games, you might have heard of Garten of Banban, a bizarre and eerie game where you have to explore a desolate kindergarten and uncover the truth about the mysterious disappearance of kids here. But did you know that you can play this game online for free, without downloading anything? In this article, we will tell you everything you need to know about this game, from its plot and gameplay to its pros and cons, and how to play it on your browser. Read on if you dare!</p>
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- <h2>What is Garten of Banban?</h2>
- <p>Garten of Banban is a horror adventure game developed by an indie Korean developer named Dotoru. It was released in 2019 for Windows, but it also has a web version that you can play on your browser. The game is inspired by the Korean urban legend of Banban Kindergarten, a haunted place where children went missing or died under mysterious circumstances.</p>
- <h3>The plot and the setting of the game</h3>
- <p>The game follows the story of a young girl named Yoojin, who wakes up in an abandoned kindergarten with no memory of who she is or how she got there. She soon realizes that she is not alone, as there are other kids trapped in the same place, as well as some terrifying creatures that lurk in the shadows. She also finds clues that suggest that something sinister happened in this kindergarten, involving a cult, a ritual, and a dark secret. She must find a way to escape from this nightmare, while also discovering the truth about herself and her past.</p>
- <h3>The gameplay and the features of the game</h3>
- <p>The game is a point-and-click adventure, where you have to explore the different rooms and areas of the kindergarten, collecting items, solving puzzles, and avoiding enemies. You can interact with objects and characters by clicking on them, and use items from your inventory by dragging them onto the screen. You can also save your progress by using a teddy bear that acts as a save point.</p>
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- <p>The game has several features that make it stand out from other horror games, such as:</p>
- <ul>
-<li>It has a unique and creepy art style, with pixelated graphics and distorted sounds that create a sense of unease and dread.</li>
-<li>It has multiple endings and secrets, depending on your choices and actions throughout the game. Some endings are good, some are bad, and some are hidden.</li>
-<li>It has challenging and varied puzzles, ranging from logic and math problems to riddles and codes. Some puzzles are optional, but some are required to progress or unlock certain endings.</li>
-<li>It has a dynamic difficulty system, where the game adapts to your skill level and changes accordingly. For example, if you die too often, the game will become easier, but if you solve puzzles too quickly, the game will become harder.</li>
-</ul>
- <h2>Why should you play Garten of Banban?</h2>
- <p>Garten of Banban is not a game for everyone, as it can be quite disturbing and scary. However, if you enjoy horror games and want to try something different, you might find this game appealing. Here are some of the pros and cons of playing this game:</p>
- <h3>The pros of the game</h3>
- <h4>It is free and easy to access</h4>
- <p>One of the best things about the game is that it is completely free to play online, without downloading anything. You just need a browser that supports HTML5 and JavaScript, and you can access the game from any device. You can also play the game offline by downloading it from the developer's website.</p>
- <h4>It has a unique and creepy atmosphere</h4>
- <p>Another reason to play the game is that it has a very distinctive and unsettling atmosphere, that will keep you on the edge of your seat. The game uses pixelated graphics and distorted sounds to create a sense of nostalgia and horror, as you explore the abandoned kindergarten that is full of secrets and dangers. The game also has a dark and twisted story, that will make you question everything you see and hear. The game is not afraid to show gore and violence, as well as psychological horror and jump scares.</p>
- <h4>It has challenging and varied puzzles</h4>
- <p>If you like puzzles, you will enjoy the game, as it has many puzzles that will test your logic, memory, and creativity. The puzzles are not too easy or too hard, but they are well-designed and integrated into the game's story and environment. Some puzzles are optional, but some are required to progress or unlock certain endings. The puzzles are also varied, ranging from logic and math problems to riddles and codes. You will have to use your brain and your intuition to solve them.</p>
- <h4>It has multiple endings and secrets</h4>
- <p>One of the most interesting aspects of the game is that it has multiple endings and secrets, depending on your choices and actions throughout the game. Some endings are good, some are bad, and some are hidden. The endings are not always obvious or predictable, and they can change the way you view the game's story and characters. The game also has many secrets that you can discover by exploring the kindergarten, finding hidden items, solving optional puzzles, or doing certain actions. The secrets can reveal more information about the game's plot, or give you hints for other endings.</p>
- <h3>The cons of the game</h3>
- <h4>It is not for the faint-hearted</h4>
- <p>As mentioned before, the game is not a game for everyone, as it can be quite disturbing and scary. The game deals with themes such as child abuse, cults, rituals, death, and madness, that might be upsetting or triggering for some people. The game also has many scenes of gore and violence, as well as psychological horror and jump scares. The game is not suitable for children or people who are sensitive to horror.</p>
- <h4>It has some bugs and glitches</h4>
- <p>Another drawback of the game is that it has some bugs and glitches that might affect your gameplay experience. For example, some items might not work properly, some puzzles might not be solvable, some enemies might not appear or disappear, or some sounds might not play correctly. These bugs and glitches are not very common or serious, but they can be annoying or frustrating. The developer is aware of these issues and is working on fixing them in future updates.</p>
- <h4>It has limited replay value</h4>
- <p>The last con of the game is that it has limited replay value, as once you have seen all the endings and secrets, there is not much else to do in the game. The game does not have any achievements or leaderboards to motivate you to play again. The game also does not have any random or procedural elements to make each playthrough different or unique. The game is relatively short, as it can be completed in less than an hour if you know what to do.</p>
- <h2>How to play Garten of Banban online?</h2>
- <p>If you are interested in playing Garten of Banban online for free, here are the steps to follow:</p>
- <h3>The steps to play the game on your browser</h3>
- <ol>
-<li>Go to this link, where you can find the web version of the game.</li>
-<li>Click on the "Play" button to start the game.</li>
-<li>Wait for the game to load on your browser.</li>
-<li>Enjoy the game!</li>
-</ol>
- <h3>The tips and tricks to survive the game</h3>
- <p>If you want some tips and tricks to survive the game, here are some suggestions:</p>
- <ul>
-<li>Save your progress frequently by using the teddy bear save points. You never know when something might kill you or when you might need to reload a previous save.</li>
-<li>Explore every room and area of the kindergarten carefully. You might find useful items, clues, puzzles, or secrets that can help you escape or unlock different endings.</li>
-<li>Avoid making noise or running when there are enemies nearby. They can hear you and chase you down if they spot you.</li>
-<li>Use items wisely. Some items are essential for solving puzzles or escaping from enemies, while some items are optional or have different uses depending on the situation.</li>
-<li>Pay attention to the hints and instructions that the game gives you. They can guide you to the right path or warn you of the dangers ahead.</li>
-<li>Try different choices and actions to see how they affect the outcome of the game. You might find new endings or secrets that you missed before.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Garten of Banban is a free online horror game that will give you chills. It is a point-and-click adventure game where you have to explore a haunted kindergarten and uncover the truth about the missing kids. The game has a unique and creepy atmosphere, challenging and varied puzzles, multiple endings and secrets, and a dynamic difficulty system. The game is not for the faint-hearted, as it has some disturbing and scary scenes and themes. The game also has some bugs and glitches, and limited replay value. If you want to play the game online, you just need a browser that supports HTML5 and JavaScript, and follow the steps and tips we provided in this article. We hope you enjoyed this article, and good luck with the game!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Garten of Banban:</p>
- <ol>
-<li>Q: Who is Banban?</li>
-<li>A: Banban is the name of the main antagonist of the game, who is also the owner of the kindergarten. He is a mysterious and evil man who is behind the disappearance and death of the kids. He also has some supernatural powers that make him very dangerous.</li>
-<li>Q: How many endings are there in the game?</li>
-<li>A: There are six endings in the game, four of which are bad endings, one of which is a good ending, and one of which is a secret ending. The endings depend on your choices and actions throughout the game, such as which items you use, which puzzles you solve, which characters you interact with, and which rooms you enter.</li>
-<li>Q: How do I get the secret ending?</li>
-<li>A: The secret ending is the hardest ending to get in the game, as it requires you to do some specific actions that are not obvious or easy to do. To get the secret ending, you need to do the following:</li>
-<ul>
-<li>Find all 12 hidden dolls in the game. They are scattered in different rooms and areas of the kindergarten, and they are very hard to spot.</li>
-<li>Solve all 12 optional puzzles in the game. They are marked by a question mark on the screen, and they are not required to progress or unlock other endings.</li>
-<li>Enter Banban's room after finding all 12 dolls and solving all 12 puzzles. You will find a new door that leads to a secret area where you will face Banban himself.</li>
-</ul>
-<li>Q: How do I save my progress in the game?</li>
-<li>A: You can save your progress in the game by using the teddy bear save points that are located in some rooms of the kindergarten. You can only save when you are near a teddy bear, and you can only have one save file at a time. To save your progress, click on the teddy bear icon on the bottom right corner of the screen, and then click on "Save". To load your progress, click on "Load" instead.</li>
-<li>Q: How do I play the game offline?</li>
-<li>A: If you want to play the game offline, you need to download it from the developer's website, which is this link. You will need a Windows PC to run the game, and you will need to unzip the downloaded file to access the game folder. To start the game, double-click on the "Garten of Banban.exe" file in the game folder. You can also change the game settings by clicking on the "Config.exe" file in the same folder.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Unlimited Coins and Gems in Hungry Shark World 2.5.0 APK Download Now.md b/spaces/congsaPfin/Manga-OCR/logs/Unlimited Coins and Gems in Hungry Shark World 2.5.0 APK Download Now.md
deleted file mode 100644
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+++ /dev/null
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-<h1>Hungry Shark World: How to Get Unlimited Coins and Gems with APK 2.5.0</h1>
-<p>Are you a fan of Hungry Shark World, the arcade game where you control a hungry shark and eat everything in your way? Do you want to unlock more sharks, upgrade your abilities, and customize your appearance? If so, you might be interested in getting unlimited coins and gems, the two currencies in the game that let you do all that and more.</p>
-<p>In this article, we will show you how to get unlimited coins and gems with APK 2.5.0, a modified version of the game that gives you access to unlimited resources. We will also give you some tips and tricks on how to play Hungry Shark World better and have more fun.</p>
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-<h2>Introduction</h2>
-<h3>What is Hungry Shark World?</h3>
-<p>Hungry Shark World is a mobile game developed by Ubisoft that was released in 2016 as a sequel to Hungry Shark Evolution. The game is available for Android, iOS, Windows, Xbox One, PlayStation 4, and Nintendo Switch devices.</p>
-<p>In Hungry Shark World, you can choose from over 40 different sharks in eight size tiers, from small fish to the massive Megalodon. You can explore four huge open worlds: Pacific Islands, Arctic Ocean, Arabian Sea, and South China Sea. You can feast on hundreds of different creatures, from fish and birds to whales and humans. You can also equip your shark with various power-ups, accessories, and skins to enhance your performance and appearance.</p>
-<h3>What are coins and gems in Hungry Shark World?</h3>
-<p>Coins and gems are the two types of currency in Hungry Shark World. You can use them to buy new sharks, upgrade your stats, unlock new maps, and purchase items from the shop.</p>
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-<p>Coins are the basic currency that you can earn by eating creatures, completing missions, finding treasures, and watching ads. Gems are the premium currency that you can earn by completing daily chests, achievements, events, or buying them with real money.</p>
-<h3>Why do you need unlimited coins and gems in Hungry Shark World?</h3>
-<p>Coins and gems are essential for progressing in Hungry Shark World. With unlimited coins and gems, you can:</p>
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-<li>Unlock all the sharks in the game, from the smallest Reef Shark to the legendary Megalodon.</li>
-<li>Upgrade your shark's bite, speed, boost, health, gold rush, mega gold rush, hunger meter, growth rate, survival bonus, score multiplier, gem multiplier, coin multiplier, treasure sensor, map sensor, mission shell sensor.</li>
-<li>Buy power-ups that give you special abilities such as super size mode, explosion mode, hypnosis mode.</li>
-<li>Buy accessories that give you extra benefits such as jetpacks, lasers, headphones, umbrellas.</li>
-<li>Buy skins that change your shark's appearance such as pirate skin, zombie skin, robot skin.</li>
-<li>Unlock new maps that offer different environments and challenges such as South China Sea.</li>
-<li>Have more fun and freedom in playing the game without worrying about running out of resources or spending real money.</li>
-</ul>
-<h2>How to get unlimited coins and gems with APK 2.5.0</h2>
-<h3>What is an APK file?</h3>
-<p>An APK file is a package file format used by Android <p>operating system to distribute and install applications and games. An APK file contains the code, resources, assets, certificates, and manifest of an app. You can download APK files from various sources on the internet, such as APKPure, APKMirror, or APKMonk.</p>
-<h3>How to download and install APK 2.5.0 for Hungry Shark World</h3>
-<p>To download and install APK 2.5.0 for Hungry Shark World, you need to follow these steps:</p>
-<ol>
-<li>Go to the website where you can find the APK file for Hungry Shark World 2.5.0, such as [APKPure].</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Before installing the APK file, you need to enable the installation of apps from unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>Locate the downloaded APK file on your device and tap on it to start the installation process.</li>
-<li>Follow the instructions on the screen and wait for the installation to finish.</li>
-<li>Launch the game and enjoy unlimited coins and gems.</li>
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-<h3>How to use APK 2.5.0 to get unlimited coins and gems in Hungry Shark World</h3>
-<p>To use APK 2.5.0 to get unlimited coins and gems in Hungry Shark World, you need to follow these steps:</p>
-<ol>
-<li>Open the game and tap on the shop icon on the bottom right corner of the screen.</li>
-<li>Select the coins or gems tab and choose the amount you want to buy.</li>
-<li>You will see that the price is zero for all the options. Tap on the buy button and confirm your purchase.</li>
-<li>You will receive unlimited coins or gems in your account instantly.</li>
-<li>Repeat this process as many times as you want to get more coins or gems.</li>
-</ol>
-<h2>Tips and tricks for Hungry Shark World</h2>
-<h3>How to survive longer and get more coins and gems in Hungry Shark World</h3>
-<p>Besides using APK 2.5.0 to get unlimited coins and gems in Hungry Shark World, you can also use some tips and tricks to improve your gameplay and earn more resources. Here are some of them:</p>
-<h4>Eat often, eat all</h4>
-<p>The most basic rule of Hungry Shark World is to eat as much as you can and as often as you can. Eating fills up your hunger meter, which depletes over time. If your hunger meter runs out, you will die. Eating also gives you coins, gems, points, health, and boosts your gold rush meter.</p>
-<p>You can eat almost anything in Hungry Shark World, from fish and crabs to turtles and dolphins. However, some creatures are more nutritious than others, and some are dangerous or poisonous. You should avoid eating jellyfish, pufferfish, mines, bombs, electric eels, or anything that is bigger than your shark or has a red outline around it.</p>
-<h4>Go for gold creatures</h4>
-<p>Some creatures in Hungry Shark World have a gold color or a gold aura around them. These creatures are worth more coins, gems, points, health, and gold rush than normal creatures. You should always try to eat them whenever you see them.</p>
-<p>Some examples of gold creatures are gold fish, gold crabs, gold turtles, gold birds, gold humans, gold whales, gold sharks, etc. You can also find gold creatures in hidden areas or special events.</p>
-<h4>Complete missions and challenges</h4>
-<p>Missions and challenges are tasks that you can complete in Hungry Shark World to earn extra coins, gems, points, or rewards. You can find them on the left side of the screen or on the map screen.</p>
-<p>Missions are specific to each shark and map. They usually involve eating a certain number or type of creatures or performing a certain action. For example, one mission might be to eat 10 humans in Pacific Islands or jump over a boat in Arctic Ocean.</p>
-<p>Challenges are global and change every day. They usually involve competing with other players or achieving a certain score or rank. For example, one challenge might be to reach the top 10% of players in South China Sea or score 10 million points in Arabian Sea.</p>
-<h4>Equip power-ups and accessories</h4>
-<p>Power-ups and accessories are items that you can buy from the shop with coins or gems. They can give you special abilities or benefits that can help you survive longer and get more coins and gems in Hungry Shark World.</p <p>Some examples of power-ups are super size mode, which makes your shark bigger and stronger; explosion mode, which makes your shark explode and damage nearby creatures; hypnosis mode, which makes your shark hypnotize and control other creatures.</p>
-<p>Some examples of accessories are jetpacks, which let your shark fly in the air; lasers, which let your shark shoot lasers from its eyes; headphones, which let your shark listen to music and increase its gold rush meter; umbrellas, which protect your shark from sunburn and increase its health.</p>
-<h4>Explore different maps and locations</h4>
-<p>Hungry Shark World has four different maps that you can unlock with coins or gems: Pacific Islands, Arctic Ocean, Arabian Sea, and South China Sea. Each map has its own unique environment, creatures, treasures, secrets, and challenges. You should explore all the maps and locations to discover new things and have more fun.</p>
-<p>Some examples of locations are volcanoes, shipwrecks, icebergs, temples, islands, beaches, cities, etc. You can also find hidden portals that take you to other worlds or mini-games such as Hungry Letters, where you have to spell words with letters.</p>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In conclusion, Hungry Shark World is a fun and addictive game where you can control a hungry shark and eat everything in your way. You can also get unlimited coins and gems with APK 2.5.0, a modified version of the game that gives you access to unlimited resources. You can use coins and gems to unlock more sharks, upgrade your abilities, and customize your appearance. You can also use some tips and tricks to survive longer and get more coins and gems in Hungry Shark World.</p>
-<h3>Call to action</h3>
-<p>If you are ready to unleash your inner shark and have a blast in Hungry Shark World, download APK 2.5.0 now and enjoy unlimited coins and gems. You can also share this article with your friends and family who love Hungry Shark World and want to get unlimited resources. Thank you for reading and happy hunting!</p>
-<h2>FAQs</h2>
-<ul>
-<li><b>Is APK 2.5.0 safe to use?</b></li>
-<p>APK 2.5.0 is safe to use as long as you download it from a trusted source such as [APKPure]. However, you should always be careful when installing apps from unknown sources and scan them for viruses or malware before using them.</p>
-<li><b>Will APK 2.5.0 work on my device?</b></li>
-<p>APK 2.5.0 should work on most Android devices that support Hungry Shark World. However, some devices may not be compatible or may experience some issues or bugs. If you encounter any problems with APK 2.5.0, you can try uninstalling it and reinstalling the original version of the game from the Google Play Store or the App Store.</p>
-<li><b>Can I play online or offline with APK 2.5.0?</b></li>
-<p>You can play online or offline with APK 2.5.0 as long as you have an internet connection. However, you may not be able to access some features or events that require an online connection or verification such as daily chests, achievements, leaderboards, etc.</p>
-<li><b>Can I update APK 2.5.0 to the latest version of Hungry Shark World?</b></li>
-<p>You can update APK 2.5.0 to the latest version of Hungry Shark World by downloading the new APK file from the same source where you got APK 2.5.0. However, you may lose some of your progress or data if you update APK 2.5.0 without backing up your files or syncing your account.</p>
-<li><b>Can I get banned for using APK 2.5.0?</b></li>
-<p>You may get banned for using APK 2.5.0 if Ubisoft detects that you are using a modified version of the game that violates their terms of service or policies. However, this is unlikely to happen as long as you do not abuse or exploit the unlimited coins and gems feature or cheat in online modes or events.</p>
-</ul></p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/WhatsApp Web Clonapp Messenger Mod APK A Simple and Practical Way to Access WhatsApp Web on Your Mobile or Tablet.md b/spaces/congsaPfin/Manga-OCR/logs/WhatsApp Web Clonapp Messenger Mod APK A Simple and Practical Way to Access WhatsApp Web on Your Mobile or Tablet.md
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-<br />
-1. I searched the web for the topic and found some relevant sources that explain what it is, how it works, and what are its features and benefits. 2. I used the information from the sources to create an outline of the article, with at least 15 headings and subheadings (including H1, H2, H3, and H4 headings). I also added some keywords and phrases that are related to the topic and can help with SEO optimization. 3. I wrote a rough draft of the article based on the outline, using my own words and avoiding plagiarism. I used a conversational style as written by a human, using an informal tone, personal pronouns, simple language, engaging sentences, active voice, brief paragraphs, rhetorical questions, and analogies and metaphors. I also used HTML tags to format the text and create a table. 4. I read and revised the draft, checking for grammar, spelling, punctuation, and coherence errors. I also made sure that the article is informative, comprehensive, unique, and interesting for the readers. 5. I wrote a conclusion paragraph that summarizes the main points of the article and provides a call to action for the readers. I also added 5 unique FAQs after the conclusion that answer some common questions about the topic. 6. I bolded the title and all headings of the article, and used appropriate headings for H tags. Here are the two tables that you requested: Table 1: Outline of the article | Heading | Subheading | Content | | --- | --- | --- | | H1: WhatsApp Web Clonapp Messenger Mod APK: What Is It and How to Use It? | - | Introduction: Explain what is WhatsApp Web Clonapp Messenger Mod APK and why it is useful for WhatsApp users. | | H2: What Is WhatsApp Web Clonapp Messenger Mod APK? | - | Definition: Describe what is WhatsApp Web Clonapp Messenger Mod APK and how it differs from the official WhatsApp Web app. | | - | Features: List some of the features of WhatsApp Web Clonapp Messenger Mod APK, such as multiple accounts, status saver, QR scanner, dark mode, etc. | | - | Benefits: Explain some of the benefits of using WhatsApp Web Clonapp Messenger Mod APK, such as convenience, privacy, security, customization, etc. | | H2: How to Download and Install WhatsApp Web Clonapp Messenger Mod APK? | - | Requirements: Mention what are the requirements for downloading and installing WhatsApp Web Clonapp Messenger Mod APK, such as Android device, internet connection, etc. | | - | Steps: Provide a step-by-step guide on how to download and install WhatsApp Web Clonapp Messenger Mod APK from a trusted source. | | - | Tips: Give some tips on how to avoid malware, viruses, or scams when downloading and installing WhatsApp Web Clonapp Messenger Mod APK. | | H2: How to Use WhatsApp Web Clonapp Messenger Mod APK? | - | Steps: Provide a step-by-step guide on how to use WhatsApp Web Clonapp Messenger Mod APK to access multiple WhatsApp accounts on one device or browser. | | - | Tips: Give some tips on how to use WhatsApp Web Clonapp Messenger Mod APK effectively and safely, such as logging out when not in use, clearing cache regularly, etc. | | H2: What Are the Advantages and Disadvantages of Using WhatsApp Web Clonapp Messenger Mod APK? | - | Advantages: Highlight some of the advantages of using WhatsApp Web Clonapp Messenger Mod APK over the official WhatsApp Web app or other alternatives, such as more features, more control, more fun, etc. | | - | Disadvantages: Acknowledge some of the disadvantages or risks of using WhatsApp Web Clonapp Messenger Mod APK instead of the official WhatsApp Web app or other alternatives, such as compatibility issues, legal issues, ethical issues, etc. | | H2: Conclusion | - | Summary: Summarize the main points of the article and restate the thesis statement. | | - | Call to action: Encourage the readers to try out WhatsApp Web Clonapp Messenger Mod APK or share their feedback or questions in the comments section. | | H2: FAQs | - | Q1: What is WhatsApp Web? | | - | Q2: Is WhatsApp Web Clonapp Messenger Mod APK safe to use? | | - | Q3: Can I use WhatsApp Web Clonapp Messenger Mod APK on PC or laptop? | | - | Q4: How can I update WhatsApp Web Clonapp Messenger Mod APK? | | - | Q5: How can I contact the developer of WhatsApp Web Clonapp Messenger Mod APK? | Table 2: Article with HTML formatting <h or fingerprint. You can also delete your messages permanently from both sides.</li>
-<li><b>Security:</b> You can protect your WhatsApp Web Clonapp Messenger Mod APK from unauthorized access or hacking by using a VPN, proxy, or firewall. You can also scan and remove any malware, virus, or spyware from your device or browser.</li>
-<li><b>Customization:</b> You can customize your WhatsApp Web Clonapp Messenger Mod APK according to your taste and style. You can change the theme, color, font, background, wallpaper, and other elements of the app. You can also add or remove any feature or option that you want or don't want.</li>
-</ul>
- <h2>How to Download and Install WhatsApp Web Clonapp Messenger Mod APK?</h2>
- <p>If you want to download and install WhatsApp Web Clonapp Messenger Mod APK on your device or browser, you need to follow some simple steps. But before that, you need to make sure that you have the following requirements:</p>
- <h3>Requirements</h3>
- <ul>
-<li><b>Android device:</b> You need an Android device with version 4.4 or higher to run WhatsApp Web Clonapp Messenger Mod APK. You also need to enable the installation of apps from unknown sources in your device settings.</li>
-<li><b>Internet connection:</b> You need a stable and fast internet connection to download and install WhatsApp Web Clonapp Messenger Mod APK. You also need to connect your device to the internet when you use the app.</li>
-<li><b>WhatsApp account:</b> You need a valid and active WhatsApp account to use WhatsApp Web Clonapp Messenger Mod APK. You also need to verify your phone number and scan the QR code from the app.</li>
-</ul>
- <h3>Steps</h3>
- <p>Here are the steps to download and install WhatsApp Web Clonapp Messenger Mod APK on your device or browser:</p>
-<h2>whatsapp web clonapp messenger mod apk</h2><br /><p><b><b>DOWNLOAD</b> &#10040;&#10040;&#10040; <a href="https://urlca.com/2uOdjP">https://urlca.com/2uOdjP</a></b></p><br /><br />
- <ol>
-<li><b>Download the APK file:</b> Go to a trusted and reliable source that provides the latest version of WhatsApp Web Clonapp Messenger Mod APK. For example, you can use this link: [WhatsApp Web Clonapp Messenger Mod APK Download]. Click on the download button and wait for the file to be downloaded on your device.</li>
-<li><b>Install the APK file:</b> Locate the downloaded file on your device and tap on it to start the installation process. Follow the instructions on the screen and grant the necessary permissions to the app. Wait for the installation to be completed and then open the app.</li>
-<li><b>Scan the QR code:</b> Open WhatsApp on your phone and go to Settings > WhatsApp Web/Desktop. Scan the QR code that appears on WhatsApp Web Clonapp Messenger Mod APK with your phone camera. Wait for the connection to be established and then enjoy using the app.</li>
-</ol>
- <h3>Tips</h3>
- <p>Here are some tips to avoid malware, viruses, or scams when downloading and installing WhatsApp Web Clonapp Messenger Mod APK:</p>
- <ul>
-<li><b>Use a trusted source:</b> Make sure that you download WhatsApp Web Clonapp Messenger Mod APK from a trusted and reliable source that provides the original and updated version of the app. Avoid any suspicious or fake links that may contain malware, viruses, or scams.</li>
-<li><b>Use a antivirus software:</b> Make sure that you have a good antivirus software installed on your device or browser that can detect and remove any malware, virus, or spyware that may infect your device or browser when downloading or installing WhatsApp Web Clonapp Messenger Mod APK.</li>
-<li><b>Use a VPN service:</b> Make sure that you use a VPN service that can encrypt your data and hide your IP address when downloading or installing WhatsApp Web Clonapp Messenger Mod APK. This can prevent any hackers or trackers from accessing your device or browser or stealing your personal information.</li>
-</ul>
- <h2>How to Use WhatsApp Web Clonapp Messenger Mod APK?</h2>
- <p>If you want to use WhatsApp Web Clonapp Messenger Mod APK to access multiple WhatsApp accounts on one device or browser, you need to follow some simple steps. But before that, you need to make sure that you have the following requirements:</p>
- <h3>Requirements</h3>
- <ul>
-<li><b>WhatsApp Web Clonapp Messenger Mod APK:</b> You need to have WhatsApp Web Clonapp Messenger Mod APK installed on your device or browser. You also need to have it connected to your primary WhatsApp account by scanning the QR code.</li>
-<li><b>Mult iple WhatsApp accounts:</b> You need to have multiple WhatsApp accounts that you want to access on one device or browser. You also need to have the phone numbers and verification codes of those accounts.</li>
-<li><b>Internet connection:</b> You need to have a stable and fast internet connection to use WhatsApp Web Clonapp Messenger Mod APK. You also need to connect your device or browser to the internet when you use the app.</li>
-</ul>
- <h3>Steps</h3>
- <p>Here are the steps to use WhatsApp Web Clonapp Messenger Mod APK to access multiple WhatsApp accounts on one device or browser:</p>
- <ol>
-<li><b>Add a new account:</b> Open WhatsApp Web Clonapp Messenger Mod APK on your device or browser and tap on the menu icon on the top right corner. Tap on "Add Account" and enter the phone number of the WhatsApp account that you want to add. Tap on "Next" and wait for the verification code to be sent to your phone. Enter the verification code and tap on "Done". Wait for the account to be added and synced with the app.</li>
-<li><b>Switch between accounts:</b> To switch between different WhatsApp accounts on one device or browser, tap on the menu icon on the top right corner and tap on "Switch Account". Select the account that you want to switch to and wait for it to load. You can also swipe left or right on the screen to switch between accounts.</li>
-<li><b>Manage accounts:</b> To manage your WhatsApp accounts on one device or browser, tap on the menu icon on the top right corner and tap on "Manage Accounts". You can see all your added accounts and their details, such as name, number, status, etc. You can also delete any account that you don't want to use anymore by tapping on the trash icon next to it.</li>
-</ol>
- <h3>Tips</h3>
- <p>Here are some tips to use WhatsApp Web Clonapp Messenger Mod APK effectively and safely:</p>
- <ul>
-<li><b>Log out when not in use:</b> To protect your privacy and security, make sure that you log out from WhatsApp Web Clonapp Messenger Mod APK when you are not using it. To log out, tap on the menu icon on the top right corner and tap on "Log Out". You can also log out from all devices or browsers by going to Settings > WhatsApp Web/Desktop on your phone and tapping on "Log Out from All Devices".</li>
-<li><b>Clear cache regularly:</b> To improve the performance and speed of WhatsApp Web Clonapp Messenger Mod APK, make sure that you clear the cache regularly. To clear cache, tap on the menu icon on the top right corner and tap on "Clear Cache". You can also clear cache from individual accounts by tapping on their profile picture and tapping on "Clear Cache".</li>
-<li><b>Use backup and restore:</b> To prevent losing your chats and data, make sure that you backup and restore your WhatsApp accounts regularly. To backup, go to Settings > Chats > Chat Backup on your phone and tap on "Back Up". To restore, go to Settings > Chats > Chat Backup on your phone and tap on "Restore". You can also backup and restore from Google Drive or iCloud if you have enabled them.</li>
-</ul>
- <h2>What Are the Advantages and Disadvantages of Using WhatsApp Web Clonapp Messenger Mod APK?</h2>
- <p>Using WhatsApp Web Clonapp Messenger Mod APK has its own advantages and disadvantages. Here are some of them:</p>
- <h3>Advantages</h3>
- <p>Some of the advantages of using WhatsApp Web Clonapp Messenger Mod APK over the official WhatsApp Web app or other alternatives are:</p>
- <ul>
-<li><b>More features:</b> WhatsApp Web Clonapp Messenger Mod APK offers more features than the official WhatsApp Web app or other alternatives, such as status saver, QR scanner, dark mode, custom themes, stickers, emojis, fonts, and more. These features can enhance your user experience and make your chats more fun and lively.</li>
-<li><b>More control:</b> WhatsApp Web Clonapp Messenger Mod APK gives you more control over your WhatsApp accounts than the official WhatsApp Web app or other alternatives. You can hide your online status, last seen, blue ticks, typing indicator, and other information from your contacts or groups. You can also lock your chats with a password or fingerprint. You can also delete your messages permanently from both sides.</li>
-<li><b>More fun:</b> WhatsApp Web Clonapp Messenger Mod APK allows you to access multiple WhatsApp accounts on one device or browser. This can be useful if you have different accounts for different purposes, such as personal, professional, social, etc. You can also switch between them easily and manage them separately. This can make your chats more fun and diverse.</li>
-</ <h3>Disadvantages</h3>
- <p>Some of the disadvantages or risks of using WhatsApp Web Clonapp Messenger Mod APK instead of the official WhatsApp Web app or other alternatives are:</p>
- <ul>
-<li><b>Compatibility issues:</b> WhatsApp Web Clonapp Messenger Mod APK may not be compatible with some devices, browsers, or operating systems. It may also not work properly with some updates or changes in the official WhatsApp app or web. This may cause some errors, bugs, or crashes in the app or web.</li>
-<li><b>Legal issues:</b> WhatsApp Web Clonapp Messenger Mod APK is not an official product of WhatsApp or Facebook. It is a modded version of the original app or web that violates the terms and conditions of the service. This may result in some legal actions or penalties from the authorities or the developers of WhatsApp or Facebook.</li>
-<li><b>Ethical issues:</b> WhatsApp Web Clonapp Messenger Mod APK may not respect the privacy and security of your contacts or groups. It may also not follow the ethical standards and norms of the society or the community. This may cause some moral dilemmas or conflicts with your contacts or groups.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>WhatsApp Web Clonapp Messenger Mod APK is a modified version of the official WhatsApp Web app that allows you to clone and access multiple WhatsApp accounts on one device or browser. It also offers some extra features and benefits that are not available in the original app, such as status saver, QR scanner, dark mode, custom themes, stickers, emojis, fonts, and more. However, it also has some disadvantages and risks that you should be aware of, such as compatibility issues, legal issues, ethical issues, and more. Therefore, you should use WhatsApp Web Clonapp Messenger Mod APK at your own discretion and responsibility.</p>
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- <p>If you want to try out WhatsApp Web Clonapp Messenger Mod APK, you can download and install it from this link: [WhatsApp Web Clonapp Messenger Mod APK Download]. You can also share your feedback or questions in the comments section below. We hope you found this article helpful and informative. Thank you for reading!</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about WhatsApp Web Clonapp Messenger Mod APK:</p>
- <h3>Q1: What is WhatsApp Web?</h3>
- <p>A1: WhatsApp Web is the official web version of the popular messaging app WhatsApp. It allows you to use WhatsApp on your PC or laptop by scanning a QR code from your phone. You can send and receive messages, photos, videos, documents, and more on WhatsApp Web.</p>
- <h3>Q2: Is WhatsApp Web Clonapp Messenger Mod APK safe to use?</h3>
- <p>A2: WhatsApp Web Clonapp Messenger Mod APK is not an official product of WhatsApp or Facebook. It is a modded version of the original app or web that violates the terms and conditions of the service. Therefore, it may not be safe to use and may expose you to some malware, viruses, scams, hackers, trackers, legal actions, penalties, moral dilemmas, conflicts, and more. You should use it at your own risk and discretion.</p>
- <h3>Q3: Can I use WhatsApp Web Clonapp Messenger Mod APK on PC or laptop?</h3>
- <p>A3: Yes, you can use WhatsApp Web Clonapp Messenger Mod APK on PC or laptop by using an Android emulator such as Bluestacks, NoxPlayer, LDPlayer, etc. You can download and install the emulator on your PC or laptop and then download and install WhatsApp Web Clonapp Messenger Mod APK on it. You can then use it as you would on your phone.</p>
- <h3>Q4: How can I update WhatsApp Web Clonapp Messenger Mod APK?</h3>
- <p>A4: You can update WhatsApp Web Clonapp Messenger Mod APK by downloading and installing the latest version of the app from a trusted and reliable source. You can also check for updates within the app by tapping on the menu icon on the top right corner and tapping on "Check for Updates". You should always update your app to enjoy the latest features and fixes.</p>
- <h3>Q5: How can I contact the developer of WhatsApp Web Clonapp Messenger Mod APK?</h3>
- <p>A5: You can contact the developer of WhatsApp Web Clonapp Messenger Mod APK by sending an email to [whatsappwebclonapp@gmail.com]. You can also visit their website [whatsappwebclonapp.com] for more information and support.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/ua xe mt cc  vi Xtreme Motorbikes Mod APK 1.3 Xe  Vario v Exciter 150 siu ngu.md b/spaces/congsaPfin/Manga-OCR/logs/ua xe mt cc  vi Xtreme Motorbikes Mod APK 1.3 Xe  Vario v Exciter 150 siu ngu.md
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-<h2>Giới thiệu về trò chơi Xtreme Motorbikes</h2>
-<p>Xtreme Motorbikes là một trò chơi do Xtreme Games Studio phát triển và phát hành. Trò chơi thuộc thể loại đua xe, nơi bạn có thể trải nghiệm cảm giác mạnh mẽ của việc lái xe môtô trên những con đường phẳng lặn hoặc những cung đường cong uốn lượn. Trò chơi có nhiều chế độ chơi khác nhau, từ chế độ tự do cho phép bạn khám phá các bản đồ rộng lớn, cho đến chế độ thử thách cho phép bạn tham gia vào những cuộc đua gay cấn với các tay lái khác.</p>
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-<h3>Đặc điểm nổi bật của trò chơi</h3>
-<ul>
-<li>Đồ họa sắc nét, sinh động, mang lại cảm giác chân thực cho người chơi.</li>
-<li>Âm thanh sống động, phù hợp với từng tình huống trong trò chơi.</li>
-<li>Lối chơi dễ dàng, điều khiển linh hoạt, phù hợp với mọi lứa tuổi.</li>
-<li>Nhiều loại xe môtô khác nhau để bạn chọn lựa theo sở thích và nhu cầu.</li>
-<li>Nhiều bản đồ đa dạng, từ thành phố, nông thôn, sa mạc, rừng núi, cho đến những địa hình khó khăn như băng tuyết, núi lửa, hoặc không gian.</li>
-<li>Nhiều chế độ chơi hấp dẫn, từ chế độ tự do, chế độ thử thách, chế độ đua với máy, cho đến chế độ đua online với người chơi khác trên toàn thế giới.</li>
-</ul>
-<h3>Cách chơi trò chơi</h3>
-<p>Để chơi trò chơi Xtreme Motorbikes, bạn cần sử dụng các nút điều khiển trên màn hình. Bạn có thể sử dụng nút bên trái để điều chỉnh hướng lái xe, và nút bên phải để tăng tốc hoặc phanh. Bạn cũng có thể sử dụng các nút khác để thực hiện các động tác như nhảy, lật ngược, lật xuôi, hoặc xoay 360 độ. Bạn cần phải lái xe một cách cẩn thận để tránh va chạm với các vật cản hoặc rơi xuống vực. Bạn cũng cần phải hoàn thành các mục tiêu của từng chế độ chơi để nhận được điểm thưởng và mở khóa các loại xe mới.</p>
-<h2>Giới thiệu về phiên bản mod xe độ xtreme motorbikes apk 1.3</h2>
-<p>Phiên bản mod xe độ xtreme motorbikes apk 1.3 là một phiên bản được chỉnh sửa và cải tiến từ phiên bản gốc của trò chơi. Phiên bản mod này mang lại cho bạn nhiều tính năng mới và hấp dẫn, đặc biệt là tính năng mod xe độ Vario và Exciter 150 - hai loại xe môtô phổ biến và yêu thích tại Việt Nam.</p>
-<h3>Tính năng mod xe độ Vario và Exciter 150</h3>
-<ul>
-<li>Bạn có thể sử dụng hai loại xe môtô Vario và Exciter 150 trong trò chơi mà không cần phải mở khóa hay mua bằng tiền.</li>
-<li>Bạn có thể tùy chỉnh và độ xe theo ý muốn của bạn, từ màu sắc, kiểu dáng, logo, cho đến các chi tiết như pô, yên, gương, đèn...</li>
-<li>Bạn có thể tăng cường hiệu suất của xe bằng cách nâng cấp các bộ phận như động cơ, lốp, phanh, hệ thống treo...</li>
-<li>Bạn có thể thưởng thức âm thanh của xe khi bạn lái xe, từ tiếng pô nổ, tiếng ga tăng tốc, cho đến tiếng phanh gấp.</li>
-</ul>
-<h3>Lợi ích của việc sử dụng phiên bản mod</h3>
-<ul>
-<li>Bạn có thể trải nghiệm trò chơi một cách thoải mái và tự do hơn, không bị giới hạn bởi các yếu tố như tiền, điểm số, hay thời gian.</li>
-<li>Bạn có thể sở hữu những chiếc xe môtô độ Vario và Exciter 150 mà bạn mong muốn và tự hào khi lái xe trên các bản đồ.</li>
-<li>Bạn có thể tăng khả n ăng khả năng chiến thắng trong các cuộc đua với các tay lái khác bằng cách nâng cấp xe của bạn.</li>
-<li>Bạn có thể khám phá và thưởng thức các bản đồ mới mà phiên bản gốc không có.</li>
-</ul>
-<h2>Cách tải và cài đặt phiên bản mod xe độ xtreme motorbikes apk 1.3</h2>
-<p>Để tải và cài đặt phiên bản mod xe độ xtreme motorbikes apk 1.3, bạn cần thực hiện các bước sau:</p>
-<h3>Bước 1: Tìm kiếm và tải xuống file APK từ nguồn đáng tin cậy</h3>
-<p>Bạn có thể tìm kiếm và tải xuống file APK của phiên bản mod xe độ xtreme motorbikes apk 1.3 từ các trang web chuyên cung cấp các trò chơi mod như APKPure, APKMODY, APKDONE, hoặc APKHOME. Bạn cần chọn nguồn đáng tin cậy để tránh tải về các file APK có chứa virus hoặc mã độc. Bạn cũng cần kiểm tra dung lượng và phiên bản của file APK trước khi tải về để đảm bảo tương thích với thiết bị của bạn.</p>
-<h3>Bước 2: Kích hoạt tùy chọn cài đặt ứng dụng từ nguồn không xác định</h3>
-<p>Sau khi tải xuống file APK, bạn cần kích hoạt tùy chọn cài đặt ứng dụng từ nguồn không xác định trên thiết bị của bạn. Bạn có thể làm điều này bằng cách vào Cài đặt > Bảo mật > Nguồn không xác định (hoặc Cài đặt > Ứng dụng và thông báo > Truy cập ứng dụng không rõ nguồn gốc). Bạn cần cho phép trình duyệt hoặc quản lý file của bạn truy cập vào file APK để cài đặt trò chơi.</p>
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-<h3>Bước 3: Tìm và nhấp vào file APK để cài đặt trò chơi</h3>
-<p>Cuối cùng, bạn chỉ cần tìm và nhấp vào file APK mà bạn đã tải về để bắt đầu quá trình cài đặt trò chơi. Bạn cần chờ cho quá trình cài đặt hoàn thành và xuất hiện biểu tượng của trò chơi trên màn hình. Sau đó, bạn có thể mở trò chơi và thưởng thức phiên bản mod xe độ xtreme motorbikes apk 1.3.</p>
-<h2>Kết luận</h2>
-<p>Mod xe độ xtreme motorbikes apk 1.3 là một phiên bản mod hấp dẫn và thú vị cho những ai yêu thích trò chơi đua xe môtô. Bạn có thể sử dụng hai loại xe môtô Vario và Exciter 150 mà bạn mong muốn, tùy chỉnh và nâng cấp xe theo ý muốn, và tham gia vào những cuộc đua kịch tính với các tay lái khác. Bạn chỉ cần tải và cài đặt phiên bản mod xe độ xtreme motorbikes apk 1.3 theo các bước hướng dẫn đã nêu trên, và bạn sẽ có được một trải nghiệm đua xe môtô độc đáo và hấp dẫn.</p>
-<h2>Câu hỏi thường gặp</h2>
-<table <table>
-<tr>
-<th>Câu hỏi</th>
-<th>Trả lời</th>
-</tr>
-<tr>
-<td>Mod xe độ xtreme motorbikes apk 1.3 có an toàn không?</td>
-<td>Mod xe độ xtreme motorbikes apk 1.3 là một phiên bản mod được kiểm tra và xác nhận là không có virus hoặc mã độc. Tuy nhiên, bạn cần tải về từ các nguồn đáng tin cậy và kích hoạt tùy chọn cài đặt ứng dụng từ nguồn không xác định trên thiết bị của bạn để cài đặt trò chơi.</td>
-</tr>
-<tr>
-<td>Mod xe độ xtreme motorbikes apk 1.3 có tương thích với tất cả các thiết bị Android không?</td>
-<td>Mod xe độ xtreme motorbikes apk 1.3 có thể tương thích với hầu hết các thiết bị Android có phiên bản từ 4.4 trở lên. Tuy nhiên, bạn cần kiểm tra dung lượng và phiên bản của file APK trước khi tải về để đảm bảo tương thích với thiết bị của bạn.</td>
-</tr>
-<tr>
-<td>Mod xe độ xtreme motorbikes apk 1.3 có cần kết nối Internet để chơi không?</td>
-<td>Mod xe độ xtreme motorbikes apk 1.3 không cần kết nối Internet để chơi các chế độ tự do, thử thách, hoặc đua với máy. Tuy nhiên, bạn cần kết nối Internet để chơi chế độ đua online với người chơi khác trên toàn thế giới.</td>
-</tr>
-<tr>
-<td>Mod xe độ xtreme motorbikes apk 1.3 có thể chơi được trên máy tính không?</td>
-<td>Mod xe độ xtreme motorbikes apk 1.3 là một trò chơi dành cho thiết bị Android, nên bạn không thể chơi được trực tiếp trên máy tính. Tuy nhiên, bạn có thể sử dụng các phần mềm giả lập Android như BlueStacks, NoxPlayer, hoặc LDPlayer để cài đặt và chơi trò chơi trên máy tính của bạn.</td>
-</tr>
-<tr>
-<td>Mod xe độ xtreme motorbikes apk 1.3 có vi phạm bản quyền của trò chơi gốc không?</td>
-<td>Mod xe độ xtreme motorbikes apk 1.3 là một phiên bản mod được tạo ra bởi các nhà phát triển độc lập, không liên quan đến nhà phát triển và phát hành của trò chơi gốc. Phiên bản mod này chỉ mang tính chất giải trí và phi lợi nhuận, không nhằm mục đích vi phạm bản quyền hay sở hữu trí tuệ của trò chơi gốc.</td>
-</tr>
-</table></p> 197e85843d<br />
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diff --git a/spaces/contluForse/HuggingGPT/assets/Babadook full movie in hindi dubbed download 31 Watch the horror film online.md b/spaces/contluForse/HuggingGPT/assets/Babadook full movie in hindi dubbed download 31 Watch the horror film online.md
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index 02f37aaaca11fd4d2a2e20f84bdda5a4b4c39a42..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Babadook full movie in hindi dubbed download 31 Watch the horror film online.md	
+++ /dev/null
@@ -1,5 +0,0 @@
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diff --git a/spaces/cooelf/Multimodal-CoT/timm/data/random_erasing.py b/spaces/cooelf/Multimodal-CoT/timm/data/random_erasing.py
deleted file mode 100644
index 78967d105dd77b56a3ccefb6ff1838a8058c0384..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/data/random_erasing.py
+++ /dev/null
@@ -1,97 +0,0 @@
-""" Random Erasing (Cutout)
-
-Originally inspired by impl at https://github.com/zhunzhong07/Random-Erasing, Apache 2.0
-Copyright Zhun Zhong & Liang Zheng
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import random
-import math
-import torch
-
-
-def _get_pixels(per_pixel, rand_color, patch_size, dtype=torch.float32, device='cuda'):
-    # NOTE I've seen CUDA illegal memory access errors being caused by the normal_()
-    # paths, flip the order so normal is run on CPU if this becomes a problem
-    # Issue has been fixed in master https://github.com/pytorch/pytorch/issues/19508
-    if per_pixel:
-        return torch.empty(patch_size, dtype=dtype, device=device).normal_()
-    elif rand_color:
-        return torch.empty((patch_size[0], 1, 1), dtype=dtype, device=device).normal_()
-    else:
-        return torch.zeros((patch_size[0], 1, 1), dtype=dtype, device=device)
-
-
-class RandomErasing:
-    """ Randomly selects a rectangle region in an image and erases its pixels.
-        'Random Erasing Data Augmentation' by Zhong et al.
-        See https://arxiv.org/pdf/1708.04896.pdf
-
-        This variant of RandomErasing is intended to be applied to either a batch
-        or single image tensor after it has been normalized by dataset mean and std.
-    Args:
-         probability: Probability that the Random Erasing operation will be performed.
-         min_area: Minimum percentage of erased area wrt input image area.
-         max_area: Maximum percentage of erased area wrt input image area.
-         min_aspect: Minimum aspect ratio of erased area.
-         mode: pixel color mode, one of 'const', 'rand', or 'pixel'
-            'const' - erase block is constant color of 0 for all channels
-            'rand'  - erase block is same per-channel random (normal) color
-            'pixel' - erase block is per-pixel random (normal) color
-        max_count: maximum number of erasing blocks per image, area per box is scaled by count.
-            per-image count is randomly chosen between 1 and this value.
-    """
-
-    def __init__(
-            self,
-            probability=0.5, min_area=0.02, max_area=1/3, min_aspect=0.3, max_aspect=None,
-            mode='const', min_count=1, max_count=None, num_splits=0, device='cuda'):
-        self.probability = probability
-        self.min_area = min_area
-        self.max_area = max_area
-        max_aspect = max_aspect or 1 / min_aspect
-        self.log_aspect_ratio = (math.log(min_aspect), math.log(max_aspect))
-        self.min_count = min_count
-        self.max_count = max_count or min_count
-        self.num_splits = num_splits
-        mode = mode.lower()
-        self.rand_color = False
-        self.per_pixel = False
-        if mode == 'rand':
-            self.rand_color = True  # per block random normal
-        elif mode == 'pixel':
-            self.per_pixel = True  # per pixel random normal
-        else:
-            assert not mode or mode == 'const'
-        self.device = device
-
-    def _erase(self, img, chan, img_h, img_w, dtype):
-        if random.random() > self.probability:
-            return
-        area = img_h * img_w
-        count = self.min_count if self.min_count == self.max_count else \
-            random.randint(self.min_count, self.max_count)
-        for _ in range(count):
-            for attempt in range(10):
-                target_area = random.uniform(self.min_area, self.max_area) * area / count
-                aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
-                h = int(round(math.sqrt(target_area * aspect_ratio)))
-                w = int(round(math.sqrt(target_area / aspect_ratio)))
-                if w < img_w and h < img_h:
-                    top = random.randint(0, img_h - h)
-                    left = random.randint(0, img_w - w)
-                    img[:, top:top + h, left:left + w] = _get_pixels(
-                        self.per_pixel, self.rand_color, (chan, h, w),
-                        dtype=dtype, device=self.device)
-                    break
-
-    def __call__(self, input):
-        if len(input.size()) == 3:
-            self._erase(input, *input.size(), input.dtype)
-        else:
-            batch_size, chan, img_h, img_w = input.size()
-            # skip first slice of batch if num_splits is set (for clean portion of samples)
-            batch_start = batch_size // self.num_splits if self.num_splits > 1 else 0
-            for i in range(batch_start, batch_size):
-                self._erase(input[i], chan, img_h, img_w, input.dtype)
-        return input
diff --git a/spaces/cooelf/Multimodal-CoT/timm/models/layers/split_batchnorm.py b/spaces/cooelf/Multimodal-CoT/timm/models/layers/split_batchnorm.py
deleted file mode 100644
index 830781b335161f8d6dd74c9458070bb1fa88a918..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/models/layers/split_batchnorm.py
+++ /dev/null
@@ -1,75 +0,0 @@
-""" Split BatchNorm
-
-A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
-a separate BN layer. The first split is passed through the parent BN layers with weight/bias
-keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
-namespace.
-
-This allows easily removing the auxiliary BN layers after training to efficiently
-achieve the 'Auxiliary BatchNorm' as described in the AdvProp Paper, section 4.2,
-'Disentangled Learning via An Auxiliary BN'
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import torch
-import torch.nn as nn
-
-
-class SplitBatchNorm2d(torch.nn.BatchNorm2d):
-
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
-                 track_running_stats=True, num_splits=2):
-        super().__init__(num_features, eps, momentum, affine, track_running_stats)
-        assert num_splits > 1, 'Should have at least one aux BN layer (num_splits at least 2)'
-        self.num_splits = num_splits
-        self.aux_bn = nn.ModuleList([
-            nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) for _ in range(num_splits - 1)])
-
-    def forward(self, input: torch.Tensor):
-        if self.training:  # aux BN only relevant while training
-            split_size = input.shape[0] // self.num_splits
-            assert input.shape[0] == split_size * self.num_splits, "batch size must be evenly divisible by num_splits"
-            split_input = input.split(split_size)
-            x = [super().forward(split_input[0])]
-            for i, a in enumerate(self.aux_bn):
-                x.append(a(split_input[i + 1]))
-            return torch.cat(x, dim=0)
-        else:
-            return super().forward(input)
-
-
-def convert_splitbn_model(module, num_splits=2):
-    """
-    Recursively traverse module and its children to replace all instances of
-    ``torch.nn.modules.batchnorm._BatchNorm`` with `SplitBatchnorm2d`.
-    Args:
-        module (torch.nn.Module): input module
-        num_splits: number of separate batchnorm layers to split input across
-    Example::
-        >>> # model is an instance of torch.nn.Module
-        >>> model = timm.models.convert_splitbn_model(model, num_splits=2)
-    """
-    mod = module
-    if isinstance(module, torch.nn.modules.instancenorm._InstanceNorm):
-        return module
-    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
-        mod = SplitBatchNorm2d(
-            module.num_features, module.eps, module.momentum, module.affine,
-            module.track_running_stats, num_splits=num_splits)
-        mod.running_mean = module.running_mean
-        mod.running_var = module.running_var
-        mod.num_batches_tracked = module.num_batches_tracked
-        if module.affine:
-            mod.weight.data = module.weight.data.clone().detach()
-            mod.bias.data = module.bias.data.clone().detach()
-        for aux in mod.aux_bn:
-            aux.running_mean = module.running_mean.clone()
-            aux.running_var = module.running_var.clone()
-            aux.num_batches_tracked = module.num_batches_tracked.clone()
-            if module.affine:
-                aux.weight.data = module.weight.data.clone().detach()
-                aux.bias.data = module.bias.data.clone().detach()
-    for name, child in module.named_children():
-        mod.add_module(name, convert_splitbn_model(child, num_splits=num_splits))
-    del module
-    return mod
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/leres/Resnext_torch.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/leres/Resnext_torch.py
deleted file mode 100644
index 9af54fcc3e5b363935ef60c8aaf269110c0d6611..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/leres/leres/Resnext_torch.py
+++ /dev/null
@@ -1,237 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-import torch.nn as nn
-
-try:
-    from urllib import urlretrieve
-except ImportError:
-    from urllib.request import urlretrieve
-
-__all__ = ['resnext101_32x8d']
-
-
-model_urls = {
-    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
-    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
-}
-
-
-def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=dilation, groups=groups, bias=False, dilation=dilation)
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
-                 base_width=64, dilation=1, norm_layer=None):
-        super(BasicBlock, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
-        if dilation > 1:
-            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = norm_layer(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = norm_layer(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
-    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
-    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
-    # This variant is also known as ResNet V1.5 and improves accuracy according to
-    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
-                 base_width=64, dilation=1, norm_layer=None):
-        super(Bottleneck, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        width = int(planes * (base_width / 64.)) * groups
-        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv1x1(inplanes, width)
-        self.bn1 = norm_layer(width)
-        self.conv2 = conv3x3(width, width, stride, groups, dilation)
-        self.bn2 = norm_layer(width)
-        self.conv3 = conv1x1(width, planes * self.expansion)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-
-        return out
-
-
-class ResNet(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
-                 norm_layer=None):
-        super(ResNet, self).__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        self._norm_layer = norm_layer
-
-        self.inplanes = 64
-        self.dilation = 1
-        if replace_stride_with_dilation is None:
-            # each element in the tuple indicates if we should replace
-            # the 2x2 stride with a dilated convolution instead
-            replace_stride_with_dilation = [False, False, False]
-        if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
-        self.groups = groups
-        self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
-                               bias=False)
-        self.bn1 = norm_layer(self.inplanes)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
-                                       dilate=replace_stride_with_dilation[2])
-        #self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        #self.fc = nn.Linear(512 * block.expansion, num_classes)
-
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-        # Zero-initialize the last BN in each residual branch,
-        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
-        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
-        if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, Bottleneck):
-                    nn.init.constant_(m.bn3.weight, 0)
-                elif isinstance(m, BasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
-
-    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
-        norm_layer = self._norm_layer
-        downsample = None
-        previous_dilation = self.dilation
-        if dilate:
-            self.dilation *= stride
-            stride = 1
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                conv1x1(self.inplanes, planes * block.expansion, stride),
-                norm_layer(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
-                            self.base_width, previous_dilation, norm_layer))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=self.groups,
-                                base_width=self.base_width, dilation=self.dilation,
-                                norm_layer=norm_layer))
-
-        return nn.Sequential(*layers)
-
-    def _forward_impl(self, x):
-        # See note [TorchScript super()]
-        features = []
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        features.append(x)
-
-        x = self.layer2(x)
-        features.append(x)
-
-        x = self.layer3(x)
-        features.append(x)
-
-        x = self.layer4(x)
-        features.append(x)
-
-        #x = self.avgpool(x)
-        #x = torch.flatten(x, 1)
-        #x = self.fc(x)
-
-        return features
-
-    def forward(self, x):
-        return self._forward_impl(x)
-
-
-
-def resnext101_32x8d(pretrained=True, **kwargs):
-    """Constructs a ResNet-152 model.
-    Args:
-        pretrained (bool): If True, returns a model pre-trained on ImageNet
-    """
-    kwargs['groups'] = 32
-    kwargs['width_per_group'] = 8
-
-    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
-    return model
-
diff --git a/spaces/cozyanduofen/bingo/src/components/chat-header.tsx b/spaces/cozyanduofen/bingo/src/components/chat-header.tsx
deleted file mode 100644
index c6664b8dee61179f844d45c5bd650518fc2cb4c2..0000000000000000000000000000000000000000
--- a/spaces/cozyanduofen/bingo/src/components/chat-header.tsx
+++ /dev/null
@@ -1,12 +0,0 @@
-import LogoIcon from '@/assets/images/logo.svg'
-import Image from 'next/image'
-
-export function ChatHeader() {
-  return (
-    <div className="flex flex-col items-center justify-center">
-      <Image alt="logo" src={LogoIcon} width={60}/>
-      <div className="mt-8 text-4xl font-bold">欢迎使用新必应</div>
-      <div className="mt-4 mb-8 text-lg">由 AI 支持的网页版 Copilot</div>
-    </div>
-  )
-}
diff --git a/spaces/cozyanduofen/bingo/src/lib/bots/bing/types.ts b/spaces/cozyanduofen/bingo/src/lib/bots/bing/types.ts
deleted file mode 100644
index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000
--- a/spaces/cozyanduofen/bingo/src/lib/bots/bing/types.ts
+++ /dev/null
@@ -1,259 +0,0 @@
-export type Author = 'user' | 'system' | 'bot'
-
-export type BotId = 'bing'
-
-export enum BingConversationStyle {
-  Creative = 'Creative',
-  Balanced = 'Balanced',
-  Precise = 'Precise'
-}
-
-export enum ErrorCode {
-  CONVERSATION_LIMIT = 'CONVERSATION_LIMIT',
-  BING_UNAUTHORIZED = 'BING_UNAUTHORIZED',
-  BING_FORBIDDEN = 'BING_FORBIDDEN',
-  BING_CAPTCHA = 'BING_CAPTCHA',
-  THROTTLE_LIMIT = 'THROTTLE_LIMIT',
-  NOTFOUND_ERROR = 'NOT_FOUND_ERROR',
-  UNKOWN_ERROR = 'UNKOWN_ERROR',
-  NETWORK_ERROR = 'NETWORK_ERROR',
-}
-
-export class ChatError extends Error {
-  code: ErrorCode
-  constructor(message: string, code: ErrorCode) {
-    super(message)
-    this.code = code
-  }
-}
-
-export type ChatMessageModel = {
-  id: string
-  author: Author
-  text: string
-  error?: ChatError
-  throttling?: Throttling
-  sourceAttributions?: SourceAttribution[]
-  suggestedResponses?: SuggestedResponse[]
-}
-
-export interface ConversationModel {
-  messages: ChatMessageModel[]
-}
-
-export type Event =
-  | {
-      type: 'UPDATE_ANSWER'
-      data: {
-        text: string
-        spokenText?: string
-        sourceAttributions?: SourceAttribution[]
-        suggestedResponses?: SuggestedResponse[]
-        throttling?: Throttling
-      }
-    }
-  | {
-      type: 'DONE'
-    }
-  | {
-      type: 'ERROR'
-      error: ChatError
-    }
-
-export interface SendMessageParams<T> {
-  prompt: string
-  imageUrl?: string
-  options: T
-  onEvent: (event: Event) => void
-  signal?: AbortSignal
-}
-
-export interface ConversationResponse {
-  conversationId: string
-  clientId: string
-  conversationSignature: string
-  result: {
-    value: string
-    message?: string
-  }
-}
-
-export interface Telemetry {
-  metrics?: null
-  startTime: string
-}
-
-export interface ChatUpdateArgument {
-  messages?: ChatResponseMessage[]
-  throttling?: Throttling
-  requestId: string
-  result: null
-}
-
-export type ChatUpdateCompleteResponse = {
-  type: 2
-  invocationId: string
-  item: ChatResponseItem
-} | {
-  type: 1
-  target: string
-  arguments: ChatUpdateArgument[]
-} | {
-  type: 3
-  invocationId: string
-} | {
-  type: 6 | 7
-}
-
-export interface ChatRequestResult {
-  value: string
-  serviceVersion: string
-  error?: string
-}
-
-export interface ChatResponseItem {
-  messages: ChatResponseMessage[]
-  firstNewMessageIndex: number
-  suggestedResponses: null
-  conversationId: string
-  requestId: string
-  conversationExpiryTime: string
-  telemetry: Telemetry
-  result: ChatRequestResult
-  throttling: Throttling
-}
-export enum InvocationEventType {
-  Invocation = 1,
-  StreamItem = 2,
-  Completion = 3,
-  StreamInvocation = 4,
-  CancelInvocation = 5,
-  Ping = 6,
-  Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
-  conversationId: string
-  clientId: string
-  conversationSignature: string
-  invocationId: number
-  conversationStyle: BingConversationStyle
-  prompt: string
-  imageUrl?: string
-}
-
-export interface BingChatResponse {
-  conversationSignature: string
-  conversationId: string
-  clientId: string
-  invocationId: number
-  conversationExpiryTime: Date
-  response: string
-  details: ChatResponseMessage
-}
-
-export interface Throttling {
-  maxNumLongDocSummaryUserMessagesInConversation: number
-  maxNumUserMessagesInConversation: number
-  numLongDocSummaryUserMessagesInConversation: number
-  numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
-  text: string
-  spokenText?: string
-  author: string
-  createdAt: Date
-  timestamp: Date
-  messageId: string
-  requestId: string
-  offense: string
-  adaptiveCards: AdaptiveCard[]
-  sourceAttributions: SourceAttribution[]
-  feedback: Feedback
-  contentOrigin: string
-  messageType?: string
-  contentType?: string
-  privacy: null
-  suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
-  type: string
-  version: string
-  body: Body[]
-}
-
-export interface Body {
-  type: string
-  text: string
-  wrap: boolean
-  size?: string
-}
-
-export interface Feedback {
-  tag: null
-  updatedOn: null
-  type: string
-}
-
-export interface SourceAttribution {
-  providerDisplayName: string
-  seeMoreUrl: string
-  searchQuery: string
-}
-
-export interface SuggestedResponse {
-  text: string
-  author?: Author
-  createdAt?: Date
-  timestamp?: Date
-  messageId?: string
-  messageType?: string
-  offense?: string
-  feedback?: Feedback
-  contentOrigin?: string
-  privacy?: null
-}
-
-export interface KBlobRequest {
-  knowledgeRequest: KnowledgeRequestContext
-  imageBase64?: string
-}
-
-export interface KBlobResponse {
-  blobId: string
-  processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
-  imageInfo:        ImageInfo;
-  knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
-  url?: string;
-}
-
-export interface KnowledgeRequest {
-  invokedSkills:            string[];
-  subscriptionId:           string;
-  invokedSkillsRequestData: InvokedSkillsRequestData;
-  convoData:                ConvoData;
-}
-
-export interface ConvoData {
-  convoid:   string;
-  convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
-  enableFaceBlur: boolean;
-}
-
-export interface FileItem {
-  url: string;
-  status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/cr7-gjx/Suspicion-Agent-Demo/agent.py b/spaces/cr7-gjx/Suspicion-Agent-Demo/agent.py
deleted file mode 100644
index 8f9e6e021c930ed202e9a73b7d27b9d26cfdab05..0000000000000000000000000000000000000000
--- a/spaces/cr7-gjx/Suspicion-Agent-Demo/agent.py
+++ /dev/null
@@ -1,467 +0,0 @@
-# Reference: https://python.langchain.com/en/latest/use_cases/agent_simulations
-
-import re
-from datetime import datetime
-from typing import List, Optional, Tuple
-
-from langchain import LLMChain
-from langchain.base_language import BaseLanguageModel
-from langchain.prompts import PromptTemplate
-from langchain.retrievers import TimeWeightedVectorStoreRetriever
-from langchain.schema import Document
-from pydantic import BaseModel, Field
-from termcolor import colored
-import util
-import time
-
-class SuspicionAgent(BaseModel):
-    """A character with memory and innate characteristics."""
-
-    name: str
-    game_name: str
-    age: int
-    observation_rule: str
-    """The traits of the character you wish not to change."""
-    status: str
-    """Current activities of the character."""
-    llm: BaseLanguageModel
-
-    """The retriever to fetch related memories."""
-    verbose: bool = False
-
-    reflection_threshold: Optional[float] = None
-    """When the total 'importance' of memories exceeds the above threshold, stop to reflect."""
-
-    current_plan: List[str] = []
-    belief: str = ""
-    pattern: str = ""
-    long_belief: str = ""
-    counter_belief: str = ""
-    plan: str = ""
-    high_plan: str = ""
-    """The current plan of the agent."""
-
-    memory: List = ['']
-    summary: str = ""  #: :meta private:
-    summary_refresh_seconds: int = 3600  #: :meta private:
-    last_refreshed: datetime = Field(default_factory=datetime.now)  #: :meta private:
-
-    memory_importance: float = 0.0  #: :meta private:
-    max_tokens_limit: int = 1200  #: :meta private:
-    read_observation: str = ""  #: :meta private:
-
-    rule: str = ""  #: :meta private:
-    class Config:
-        """Configuration for this pydantic object."""
-
-        arbitrary_types_allowed = True
-
-
-        
-
-    
-    
-    def add_long_memory(self, memory_content: str) -> List[str]:
-        """Add an observation or memory to the agent's memory."""
-        self.memory.append(memory_content)
-        return  self.memory
-
-
- 
-
-    def planning_module(self, observation: str,  recipient_name:str, previous_conversation: List[str] =None, belief: str =None, valid_action_list: List[str] = None, short_memory_summary:str = "",pattern:str = "",last_plan:str = "", mode: str = "second_tom") -> str:
-        """Make Plans and Evaluate Plans."""
-        """Combining these two modules together to save costs"""
-
-        if mode == 'second_tom':
-            prompt = PromptTemplate.from_template(
-                "You are the objective player behind a NPC character called {initiator_name}, and you are playing the board game {game_name} with {recipient_name}.\n"
-            + " The game rule is: {rule} \n"
-             +'{pattern}\n'
-              + " Your observation about the game status now is: {observation}\n"
-            +'{belief}\n'
-            + " Understanding all given information, can you do following things:"
-            + " Make Reasonable Plans: Please plan several strategies according to actions {valid_action_list} you can play now to win the finally whole {game_name} games step by step. Note that you can say something or keep silent to confuse your opponent. " 
-                 + " Potential {recipient_name}'s actions (if release) and Estimate Winning/Lose/Draw Rate for Each Plan: From the perspective of {recipient_name} , please infer what the action {recipient_name} with probability (normalize to number 100% in total) would do when {recipient_name} holds different cards and then calculate the winning/lose/draw rates when {recipient_name} holds different cards step by step. At last, please calculate the overall winning/lose/draw rates for each plan step by step considering  {recipient_name}'s behaviour pattern. Output in a tree-structure: "        
-                + "Output: Plan 1:  If I execute plan1.  "
-                          "The winning/lose/draw rates when {recipient_name} holds card1: Based on {recipient_name}'s behaviour pattern, In the xx round, because {recipient_name} holds card1  (probability) and the combination with current public card (if release)  (based on my belief on {recipient_name}), and if he sees my action, {recipient_name} will do action1 (probability) ( I actually hold card and the public card (if reveal) is , he holds card1 and the public card (if reveal), considering Single Game Win/Draw/Lose Rule, please infer I will win/draw/lose  step by step ), action2 (probability) (considering Single Game Win/Draw/Lose Rule, please infer I will win/draw/lose step by step  ),.. (normalize to number 100% in total); \n   Overall (winning rate for his card1) is (probability = his card probability * win action probability), (lose rate for his card2) is (probability= his card probability * lose action probability), (draw rate for his card2) is (probability = his card probability * draw action probability)  "  
-                          "The winning/lose/draw rates when {recipient_name} holds card2: Based on {recipient_name}'s behaviour pattern, In the xx round, because {recipient_name} holds card2  (probability) and the combination with current public card (if release)  (based on my belief on {recipient_name}) ,  and if he sees my action, he will do action1 (probability) (I actually hold card and the public card (if reveal) is , he holds card1 and the public card (if reveal), considering Single Game Win/Draw/Lose Rule, please infer I will win/draw/lose  step by step ).. action2 (probability) (normalize to number 100% in total) (considering Single Game Win/Draw/Lose Rule, please infer I will win/draw/lose step by step ),.. ;..... continue ....\n Overall (winning rate for his card2) is (probability = his card probability * win action probability), (lose rate for his card2) is (probability= his card probability * lose action probability), (draw rate for his card2) is (probability = his card probability * draw action probability) "  
-                          "...\n"
-                          "Plan1 overall {initiator_name}'s Winning/Lose/Draw rates : the Winning rate (probability) for plan 1 is (winning rate for his card1) + (winning rate for his card2) + .. ; Lose rate (probability) for plan 1 : (lose rate for his card1) + (lose rate for his card2) + .. ; Draw Rate (probability) for plan 1  : (draw rate for his card1) + (draw rate for his card2) + ... ;  (normalize to number 100% in total) for plan1 \n"
-                "Plan 2: If I execute plan2, The winning/lose/draw rates when {recipient_name} holds card1: Based on {recipient_name}'s behaviour pattern, In the xx round, if {recipient_name} holds card1  (probability)  and the combination with current public card (if release),  .. (format is similar with before ) ... continue .."
-                "Plan 3: .. Coninue ... "
-                + " The number of payoffs for each plan: Understanding your current observation,  each new plans, please infer the number of wininng/lose payoffs for each plan step by step, Output: Plan1: After the action, All chips  in the pot:  If win, the winning payoff would be (Calculated by Winning Payoff Rules step by step) :  After the action,  All chips in the pot:  If lose , the lose payoff would be:  (Calculated by Lose Payoff Rules step by step). Plan2:  After the action, All chips in the pot:  If win, the winning chips would be (Calculated by Winning Payoff Rules step by step):  After the action, All chips in the pot:  If lose , the lose chips would be:  (Calculated by Lose Payoff Rules step by step). If the number of my chips in pots have no change, please directly output them. \n"
-                + " Estimate Expected Chips Gain for Each Plan: Understanding all the information and Estimate Winning/Lose/Draw Rate for Each Plan, please estimate the overall average Expected Chips Gain for each plan/strategy in the current game by calculating winning rate * (Winning Payoff Rule in the game rule) - lose rate * (Lose Payoff Rule in the game rule) step by step"
-                + " Plan Selection: Please output the rank of estimated expected chips gains for every plan objectively step by step, and select the plan/strategy with the highest estimated expected chips gain considering both the strategy improvement. \n "
-            )
-       
-        elif mode == 'first_tom':
-            prompt = PromptTemplate.from_template(
-                "You are the player behind a NPC character called {initiator_name}, and you are playing the board game {game_name} with {recipient_name}.\n"
-            + " The game rule is: {rule} \n"
-            + " {pattern} \n"
-            + " Your observation about the game status now is: {observation}\n"
-            + ' {belief}\n'
-            + " Understanding all given information, can you do following things:"
-            + " Make Reasonable Plans: Please plan several strategies according to actions {valid_action_list} you can play now to win the finally whole {game_name} games step by step. Note that you can say something or keep silent to confuse your opponent." 
-            + " Potential {recipient_name}'s actions and Estimate Winning/Lose/Draw Rate: From the perspective of {recipient_name}, please infer what the action {recipient_name} with probability (normalize to number 100% in total) would do when {recipient_name} holds different cards, and then calculate the winning/lose/draw rates when {recipient_name} holds different cards step by step. Output in a tree-structure: "        
-                + "Output: Based on {recipient_name}'s behaviour pattern and Analysis on {recipient_name}'s cards, "
-                "Winning/lose/draw rates when {recipient_name} holds card1 in the xx round,: if {recipient_name} holds card1  (probability) (based on my belief on {recipient_name}) with the public card  (if release), {recipient_name} will do action1 (probability) (infer I will win/draw/lose step by step (considering Single Game Win/Draw/Lose Rule and my factual card analysis with public card (if release), his card analysis with public card (if release) step by step ), action2 (probability) (infer I will win/draw/lose step by step  ),.. (normalize to number 100% in total);    Overall (winning rate for his card1) is (probability = his card probability * win action probability), (lose rate for his card2) is (probability= his card probability * lose action probability), (draw rate for his card2) is (probability = his card probability * draw action probability)  "  
-                          "The winning/lose/draw rates when {recipient_name} holds card2 in the xx round,: If {recipient_name} holds card2  (probability) (based on my belief on {recipient_name}) with the public card  (if release),  he will do action1 (probability) (infer I will win/draw/lose (considering Single Game Win/Draw/Lose Rule and my factual card analysis with current public card (if release), his card analysis with current public card (if release)) step by step ).. action2 (probability) (normalize to number 100% in total) (infer I will win/draw/lose step by step ),..  based on  {recipient_name}'s behaviour pattern;..... continue .... Overall (winning rate for his card2) is (probability = his card probability * win action probability), (lose rate for his card2) is (probability= his card probability * lose action probability), (draw rate for his card2) is (probability = his card probability * draw action probability) "  
-                          "..."
-                          "Overall {initiator_name}'s Winning/Lose/Draw rates : Based on the above analysis,  the Winning rate (probability) is (winning rate for his card1) + (winning rate for his card2) + .. ; Lose rate (probability): (lose rate for his card1) + (lose rate for his card2) + .. ; Draw Rate (probability): (draw rate for his card1) + (draw rate for his card2) + ... ;  (normalize to number 100% in total). \n"         
-            + " Potential believes about the number of winning and lose payoffs for each plan: Understanding the game rule, your current observation, previous actions summarization, each new plans, Winning Payoff Rule,  Lose Payoff Rule, please infer your several believes about  the number of chips in pots for each plan step by step, Output: Plan1: Chips in the pot:  If win, the winning payoff would be (Calculated by Winning Payoff Rules in the game rule) :  After the action, If lose , the lose payoff would be: . Plan2:  Chips in the pot:  If win, the winning chips would be (Calculated by Winning Payoff Rules in the game rule):  After the action, If lose , the lose chips would be: . If the number of my chips in pots have no change, please directly output them. "
-            + " Estimate Expected Chips Gain for Each Plan: Understanding the game rule, plans,  and your knowledge about the {game_name}, please estimate the overall average Expected Chips Gain for each plan/strategy in the current game by calculating winning rate * (Winning Payoff Rule in the game rule) - lose rate * (Lose Payoff Rule in the game rule)., explain what is the results if you do not select the plan, and explain why is this final  Expected  Chips Gain reasonablely step by step? "
-            + " Plan Selection: Please output the rank of estimated expected chips gains for every plan objectively step by step, and select the plan/strategy with the highest estimated expected chips gain considering both the strategy improvement. \n\n "
-                )
-        else:
-             prompt = PromptTemplate.from_template(
-                "You are the player behind a NPC character called {initiator_name}, and you are playing the board game {game_name} with {recipient_name}.\n"
-            + " The game rule is: {rule} \n"
-            + "  {pattern} \n"
-            + " Your observation about the game status now is: {observation}\n"
-            + " Understanding all given information, can you do following things:"
-            + " Make Reasonable Plans: Please plan several strategies according to actions {valid_action_list} you can play now to win the finally whole {game_name} games step by step. Note that you can say something or keep silent to confuse your opponent." 
-               + " Estimate Winning/Lose/Draw Rate for Each Plan: Understanding the given information, and your knowledge about the {game_name}, please estimate the success rate of each step of each plan step by step and the overall average winning/lose/draw rate  (normalize to number 100% in total) of each plan/strategy for the current game  step by step following the templete: If I do plan1, because I hold card, the public information (if release) and Single Game Win/Draw/Lose Rule, I will win or Lose or draw (probability);  ... continue  .... Overall win/draw/lose rate: Based on the analysis, I can do the weighted average step by step to get that the overall weighted average winning rate is (probability), average lose rate is (probability), draw rate is (probability) (normalize to number 100% in total)\n "
-               + " Potential believes about the number of winning and lose payoffs for each plan: Understanding the game rule, your current observation, previous actions summarization, each new plans, Winning Payoff Rule,  Lose Payoff Rule, please infer your several believes about  the number of chips in pots for each plan step by step, Output: Plan1: Chips in the pot:  If win, the winning payoff would be (Calculated by Winning Payoff Rules in the game rule) :  After the action,  Chips in the pot:  If lose , the lose payoff would be: . Plan2:  Chips in the pot:  If win, the winning chips would be (Calculated by Winning Payoff Rules in the game rule):  After the action, Chips in the pot:   If lose , the lose chips would be: . If the number of my chips in pots have no change, please directly output them. "
-               +" Estimate Expected Chips Gain for Each Plan: Understanding the game rule, plans,  and your knowledge about the {game_name}, please estimate the overall average Expected Chips Gain for each plan/strategy in the current game by calculating winning rate * (Winning Payoff Rule in the game rule) - lose rate * (Lose Payoff Rule in the game rule)., explain what is the results if you do not select the plan, and explain why is this final  Expected  Chips Gain reasonablely step by step? "
-            + " Plan Selection: Please output the rank of estimated expected chips gains for every plan objectively step by step, and select the plan/strategy with the highest estimated expected chips gain considering both the strategy improvement. \n\n "
-                )
-
-        agent_summary_description = short_memory_summary
-       
-        belief = self.belief if belief is None else belief
-      
-        kwargs = dict(
-            
-            recent_observations=agent_summary_description,
-            last_plan=last_plan,
-            belief=belief,
-            initiator_name=self.name,
-            pattern=pattern,
-            recipient_name=recipient_name,
-            observation=observation,
-            rule=self.rule,
-            game_name=self.game_name,
-            valid_action_list=valid_action_list
-        )
-
-
-        plan_prediction_chain = LLMChain(llm=self.llm, prompt=prompt)
-        self.plan = plan_prediction_chain.run(**kwargs)
-        self.plan = self.plan.strip()
-
-        return self.plan.strip()
-
-       
-    
-    def get_belief(self, observation: str, recipient_name: str,short_memory_summary:str,pattern:str = "",mode: str = "second_tom") -> str:
-        """React to get a belief."""
-        if mode == 'second_tom':
-            prompt = PromptTemplate.from_template(
-                "You are the player behind a NPC character called {agent_name}, and you are playing the board game {game_name} with {recipient_name}. \n"
-                + " The game rule is: {rule} \n"
-                + " Your estimated judgement about the behaviour pattern of {recipient_name} and improved strategy is: {pattern} \n"
-                + " Your observation now is: {observation}\n"
-                + " Your current game progress summarization including actions and conversations with {recipient_name} is: {recent_observations}\n"
-                + " Understanding the game rule, the cards you have, your observation,  progress summarization in the current game, the estimated behaviour pattern of {recipient_name}, the potential guess pattern of {recipient_name} on you, and your knowledge about the {game_name}, can you do following things? "
-                + " Analysis on my Cards: Understanding all given information and your knowledge about the {game_name}, please analysis what is your best combination and advantages of your cards in the current round step by step." 
-                + " Belief on {recipient_name}'s cards: Understanding all given information, please infer the probabilities about the cards of {recipient_name}  (normalize to number 100% in total) objectively step by step." 
-                "Output: {recipient_name}  saw my history actions (or not) and then did action1 (probability) in the 1st round , ... continue..... Before this round, {recipient_name}  say my history actions (or not) and  did action1 (probability), because {recipient_name}'s behaviour pattern and the match with the public card (if release), {recipient_name} tends to have card1 (probability), card2 (probability) ..continue.. (normalize to number 100% in total)."
-                + " Analysis on {recipient_name}'s Cards: Understanding all given information and your knowledge about the {game_name}, please analysis what is {recipient_name}'s best combination and advantages of {recipient_name}'s cards in the current round  step by step." 
-                + " Potential {recipient_name}'s current believes about your cards: Understanding all given information and your knowledge about the {game_name}, If you were {recipient_name} (he can only observe my actions but cannot see my cards), please infer the {recipient_name}'s  believes about your cards with probability (normalize to number 100% in total) step by step. Output: {agent_name} did action1 (probability) (after I did action or not) in the 1st round, , ... continue...  {agent_name} did action1 (probability) (after I did action or not)  in the current round,, from the perspective of {recipient_name}, {agent_name} tends to have card1 (probability), card2 (probability) ... (normalize to number 100% in total) ."
-                )
-        elif mode == 'first_tom':
-            prompt = PromptTemplate.from_template(
-                "You are the player behind a NPC character called {agent_name}, and you are playing the board game {game_name} with {recipient_name}. \n"
-                + " The game rule is: {rule} \n"
-                + " Your estimated judgement about the behaviour pattern of {recipient_name} and improved strategy is: {pattern} \n"
-                + " Your observation now is: {observation}\n"
-                + " Your current game progress summarization including actions and conversations with {recipient_name} is: {recent_observations}\n"
-                + " Understanding the game rule, the cards you have, your observation,  progress summarization in the current game, the estimated behaviour pattern of {recipient_name} on you, and your knowledge about the {game_name}, can you do following things? "
-                + " Analysis on my Cards: Understanding all given information, please analysis what is your best combination and advantages of your cards  in the current round  step by step." 
-                + " Belief on {recipient_name}'s cards: Understanding all given information, please infer your the probabilities about the cards of {recipient_name}  (normalize to number 100% total)  step by step. Templete: In the 1st round, {recipient_name} did action1 (probability),  ... continue... In the current round, {recipient_name} did action1 (probability), because {recipient_name}'s behaviour pattern and the match with the current public card (if release), he tends to have card1 (probability), card2 (probability) (normalize to number 100% in total). "
-            + " Analysis on {recipient_name}'s Cards: Understanding all given information, please analysis what is {recipient_name}'s best combination and advantages of {recipient_name}'s cards  in the current round  step by step." 
-                
-            )
-        agent_summary_description = short_memory_summary
-
-        kwargs = dict(
-            agent_summary_description=agent_summary_description,
-            recent_observations=agent_summary_description,
-            agent_name=self.name,
-            pattern= pattern,
-            recipient_name=recipient_name,
-            observation=observation,
-            game_name=self.game_name,
-            rule=self.rule
-
-        )
-        print(recipient_name)
-        
-        belief_prediction_chain = LLMChain(llm=self.llm, prompt=prompt)
-        self.belief = belief_prediction_chain.run(**kwargs)
-        self.belief = self.belief.strip()
-        return self.belief.strip()
-
-    
-    def get_pattern(self, recipient_name: str,game_pattern: str='', last_k:int=20,short_summarization:str='',mode:str='second_tom') -> str:
-        """React to get a belief."""
-       
-        if mode == 'second_tom':
-            prompt = PromptTemplate.from_template(
-                "You are the objective player behind a NPC character called {agent_name}, and you are playing {game_name} with {recipient_name}. \n"
-                + " The game rule is: {rule} \n"
-                + " Your previous game memory including observations, actions and conversations with {recipient_name} is: {long_memory}\n"
-                + "  {recipient_name}'s game pattern: Understanding all given information and your understanding about the {game_name}, please infer and estimate as many as possible reasonable {recipient_name}'s game behaviour pattern/preferences for each card he holds and each round with probability (normalize to number 100\% in total for each pattern item) and please also infer advantages of his card, and analysis how the {recipient_name}'s behaviour pattern/preferences are influenced by my actions when he holds different cards step by step. Output  as a tree-structure    " 
-                + "Output: When {recipient_name} holds card1 and the combination of public card (if release):  if {recipient_name}  is the first to act, he would like to do action1 (probabilities), action2 (probabilities) ... continue ..    If {recipient_name} sees the action1/action2/action3 of the opponent or not, he would like to do action1 (probabilities), action2 (probabilities) ... continue ...  (normalize to number 100% in total), if {recipient_name} sees the action2 of the opponent  or not,  ... continue ..(more patterns with different actions)..  in the 1st round, ;  If {recipient_name} sees the action1 of the opponent  or not,  he would like to do action1 (probabilities), action2 (probabilities) ... continue... (normalize to number 100% in total), ...  continue ..(more patterns)..In the 2nd round,;"
-                 "When {recipient_name} holds card2 and combination of public card (if release): if {recipient_name}  is the first to act, he would like to do action1 (probabilities), action2 (probabilities) ... continue .. If {recipient_name} sees the action1 of the opponent  or not, he would like to do action1 (probabilities), action2 (probabilities) .. continue ... (normalize to number 100% in total)...in the 1st round,; .. continue ..(more patterns  with different actions).in the 2nd round .. "
-                 " (more patterns with different cards).. continue.."
-                + "  {recipient_name}'s guess on my game pattern: Understanding all given information, please infer several reasonable believes about my game pattern/preference when holding different cards from the perspective of {recipient_name} (please  consider the advantages of the card, actions and the the match with the public card (if release)) for every round of the game in detail as a tree-structure output step by step"
-               + "Output: In the 1st round, When name holds card1 with public card (if release), he would like to do (probabilities), action2 (probabilities)  (normalize to number 100% in total) o ... continue .. and then do action ...;"
-                 "When name holds card2 with public card (if release), ... "
-                 " .. continue.."
-                + " Strategy Improvement: Understanding the above information, think about what strategies I can adopt to exploit the game pattern of {recipient_name} and {recipient_name}'s guess on my game pattern for winning {recipient_name} in the whole game step by step.  (Note that you cannot observe the cards of the opponent during the game, but you can observe his actions). Output as a tree-structure:"
-                "When I hold card and the public card (if release), and see the action of the opponent, I would like to do action1; ... "
-            )
-        elif mode == 'first_tom':
-            prompt = PromptTemplate.from_template(
-                "You are the player behind a NPC character called {agent_name}, and you are playing the board game {game_name} with {recipient_name}. \n"
-                + " The game rule is: {rule} \n"
-                + " Your previous game memory including observations, actions and conversations with {recipient_name} is: {long_memory}\n"
-                + " Please understand the game rule, previous all game history and your knowledge about the {game_name}, can you do following things for future games? "
-                + "  {recipient_name}'s game pattern: Understanding all given information, please infer all possible reasonable {recipient_name}'s game pattern/preferences for each card he holds and each round with probability (normalize to number 100\% in total for each pattern item) for every round of the game as a tree-structure output step by step  " 
-                + "Output: In the 1st round, when name holds card1 and the public card (if release), he would like to do action (probabilities); when name holds card2 and the public card (if release), he would like to do action (probabilities), ... continue.. In the 2nd round,  when name holds card1 and the public card (if release), .(similar with before).. continue. "
-               + " Number of chips reason: Think about why you can have these chips in all previous games step by step. "
-                + " Reflex: Reflex which your actions are right or wrong in previous games to win or Lose conrete chips step by step  (Note that you cannot observe the cards of the opponent during the game, but you can observe his actions) "
-                + " Strategy Improvement: Understanding the above information, think about what strategies I can adopt to exploit the game pattern of {recipient_name} for winning {recipient_name} in the whole game step by step.  (Note that you cannot observe the cards of the opponent during the game, but you can observe his actions). Output as a tree-structure:"
-                )
-        else:
-            prompt = PromptTemplate.from_template(
-                "You are the player behind a NPC character called {agent_name}, and you are playing the board game {game_name} with {recipient_name}. \n"
-                + " The game rule is: {rule} \n"
-                + " Your previous game memory including observations, actions and conversations with {recipient_name} is: {long_memory}\n"
-                + " Please understand the game rule, previous all game history and your knowledge about the {game_name}, can you do following things for future games? "
-                + " Number of chips reason: Think about why you can have these chips in all previous games step by step. "
-                + " Reflex: Reflex which your actions are right or wrong in previous games to win or Lose conrete chips step by step. (Note that you cannot observe the cards of the opponent during the game, but you can observe his actions) "
-                + " Strategy Improvement: Understanding the above information, think about what strategies I need to adopt to win {recipient_name} for the whole game step by step.  (Note that you cannot observe the cards of the opponent during the game, but you can observe his actions). Output as a tree-structure:"
-            )
-        reflection_chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)
-        long_memory = self.memory[-last_k:]
-        long_memory_str = "\n\n".join([o for o in long_memory])
-        
-        kwargs = dict(
-            long_memory=long_memory_str,
-            game_pattern=game_pattern,
-            agent_name=self.name,
-            recipient_name=recipient_name,
-            game_name=self.game_name,
-            rule=self.rule
-
-        )
-        # print(kwargs)
-
-        self.long_belief = reflection_chain.run(**kwargs)
-        self.long_belief = self.long_belief.strip()
-        return self.long_belief.strip()
-
-
-
-    def get_summarization(self, recipient_name: str,game_memory: str, opponent_name:str) -> str:
-        """Get a long memory summarization to save costs."""
-        prompt = PromptTemplate.from_template(
-            "You are the player behind a NPC character called {agent_name}, and you are playing the board game {game_name} with {recipient_name}. \n"
-            + " The game rule is: {rule} \n"
-            + " The observation conversion rules are: {observation_rule}\n"
-            + " One game memory including observations, actions and conversations with {recipient_name} is: {long_memory}\n"
-            + " Understanding the game rule, observation conversion rules and game history and your knowledge about the {game_name}, can you do following things:"
-            + " History summarization: summary the game history with action, observation, and results information? using the templete, and respond shortly: In the first round of first game, name holds card1 does action .... continue ..." 
-            + "{opponent_name}'s card reasoning: If the card of {opponent_name} is not available, because {agent_name}'s  card is xx and public card (if release) is xxx, and {opponent_name} behaviours are xx, the current game result is xx,  please  infer {opponent_name}'s card with probability (100% in total) with your understanding about the above all information confidently step by step. \n"
-                )        
-        reflection_chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)
-        kwargs = dict(
-            observation_rule=self.observation_rule,
-            long_memory=game_memory,
-            agent_name=self.name,
-            recipient_name=recipient_name,
-            opponent_name=opponent_name,
-            # observation=observation,
-            game_name=self.game_name,
-            rule=self.rule
-
-        )
-        # print(kwargs)
-
-        self.long_belief = reflection_chain.run(**kwargs)
-        self.long_belief = self.long_belief.strip()
-        return self.long_belief.strip()
-
-
-    def get_short_memory_summary(self, observation: str, recipient_name: str,short_memory_summary:str) -> str:
-        """React to get a belief."""
-        prompt = PromptTemplate.from_template(
-            "You are the player behind a NPC character called {agent_name}, and you are playing the board game {game_name} with {recipient_name}. \n"
-            + " The game rule is: {rule} \n"
-            + " Your current observation is: {observation}\n"
-            + " The current game history including previous action, observations and conversation is: {agent_summary_description}\n"
-            + " Based on the game rule, your observation and your knowledge about the {game_name}, please summarize the current history. Output as a tree-structure, and respond shortly: "
-            + " In the first round, name does action, and say xxx .... continue ..." 
-        )
-
-        agent_summary_description = short_memory_summary
-    
-        kwargs = dict(
-            agent_summary_description=agent_summary_description,
-            recent_observations=agent_summary_description,
-            agent_name=self.name,
-            recipient_name=recipient_name,
-            observation=observation,
-            game_name=self.game_name,
-            rule=self.rule
-
-        )
-
-        belief_prediction_chain = LLMChain(llm=self.llm, prompt=prompt)
-        self.belief = belief_prediction_chain.run(**kwargs)
-        self.belief = self.belief.strip()
-        return self.belief.strip()
-
-
-
-    def convert_obs(self, observation: str, recipient_name: str, user_index: str, valid_action_list:str) ->  str:
-        """React to get a belief."""
-        prompt = PromptTemplate.from_template(
-            "You are the player behind a NPC character called {agent_name} with player index {user_index}, and you are playing the board game {game_name} with {recipient_name}. \n"
-            + " The game rule is: {rule} \n"
-            + " Your observation now is: {observation}\n"
-            + " You will receive a valid action list you can perform in this turn \n"
-            + " Your valid action list is: {valid_action_list}\n"
-            + " The observation conversion rules are: {observation_rule}\n"
-            + " Please convert {observation} and {valid_action_list} to the readable text based on the observation conversion rules and your knowledge about the {game_name} (respond shortly).\n\n"
-        )
-        kwargs = dict(
-            user_index=user_index,
-            agent_name=self.name,
-            rule=self.rule,
-            recipient_name=recipient_name,
-            observation=observation,
-            valid_action_list=valid_action_list,
-            game_name=self.game_name,
-            observation_rule=self.observation_rule
-        )
-        obs_prediction_chain = LLMChain(llm=self.llm, prompt=prompt)
-        self.read_observation = obs_prediction_chain.run(**kwargs)
-        self.read_observation = self.read_observation.strip()
-        return self.read_observation
-
-
-
-    def action_decision(self, observation: str, valid_action_list: List[str], promp_head: str, act: str = None,short_memory_summary:str="") -> Tuple[str,str]:
-        """React to a given observation."""
-        """React to a given observation."""
-        prompt = PromptTemplate.from_template(
-            promp_head
-            + "\nYour plan is: {plan}"
-            + "\n Based on the plan, please select the next action from the available action list: {valid_action_list} (Just one word) and say something to the opponent player to bluff or confuse him or keep silent to finally win the whole game and reduce the risk of your action (respond sentence only). Please respond them and split them by |"
-            + "\n\n"
-        )
-
-        agent_summary_description = short_memory_summary
-
-        kwargs = dict(
-            agent_summary_description= agent_summary_description,
-            # current_time=current_time_str,
-            # relevant_memories=relevant_memories_str,
-            agent_name= self.name,
-            game_name=self.game_name,
-            observation= observation,
-            agent_status= self.status,
-            valid_action_list = valid_action_list,
-            plan = self.plan,
-            belief = self.belief,
-            act = act
-        )
-        action_prediction_chain = LLMChain(llm=self.llm, prompt=prompt)
-
-        result = action_prediction_chain.run(**kwargs)
-        if "|" in result:
-            result,result_comm = result.split("|",1)
-        else:
-            result_comm = ""
-        return result.strip(),result_comm.strip()
-
-    def make_act(self, observation: str,opponent_name: str, player_index:int,valid_action_list: List, verbose_print:bool,game_idx:int,round:int,bot_short_memory:List, bot_long_memory:List, console,log_file_name='', mode='second_tom') -> Tuple[bool, str]:
-        readable_text_amy_obs = self.convert_obs(observation, opponent_name, player_index, valid_action_list)
-        if  verbose_print:
-            util.get_logging(logger_name=log_file_name + '_obs',
-                        content={str(game_idx + 1) + "_" + str(round): {"raw_obs": observation,
-                                                                        "readable_text_obs": readable_text_amy_obs}})
-            console.print('readable_text_obs: ', style="red")
-            print(readable_text_amy_obs)
-        time.sleep(0)
-        if len(bot_short_memory[player_index]) == 1:
-            short_memory_summary = f'{game_idx+1}th Game Start \n'+readable_text_amy_obs
-        else:
-            short_memory_summary = self.get_short_memory_summary(observation=readable_text_amy_obs, recipient_name=opponent_name,short_memory_summary='\n'.join(bot_short_memory[player_index]))
-
-        if log_file_name is not None:
-            util.get_logging(logger_name=log_file_name + '_short_memory',
-                        content={str(game_idx + 1) + "_" + str(round): {
-                            "raw_short_memory": '\n'.join(bot_short_memory[player_index]),
-                            "short_memory_summary": short_memory_summary}})
-        if verbose_print:
-            console.print('short_memory_summary: ', style="yellow")
-            print(short_memory_summary)
-
-        time.sleep(0)
-        if  round <= 1:
-                self.pattern = self.get_pattern(opponent_name,'',short_summarization=short_memory_summary,mode=mode)
-                if log_file_name is not None:
-                    util.get_logging(logger_name=log_file_name + '_pattern_model',
-                                content={str(game_idx + 1) + "_" + str(round): self.pattern})
-                console.print('pattern: ', style="blue")
-                print(self.pattern)
-
-        time.sleep(0)
-        print(opponent_name)
-
-        if mode == 'second_tom' or mode == 'first_tom':
-            belief = self.get_belief(readable_text_amy_obs,opponent_name,short_memory_summary=short_memory_summary,pattern=self.pattern,mode=mode)
-            if verbose_print:
-                console.print(self.name + " belief: " , style="deep_pink3")
-                print(self.name + " belief: " + str(belief))
-                util.get_logging(logger_name=log_file_name + '_belief',
-                            content={str(game_idx + 1) + "_" + str(round): {
-                                "belief": str(belief)}})
-        else:
-            belief = ''
-
-        time.sleep(0)
-        plan = self.planning_module(readable_text_amy_obs,opponent_name, belief=belief,valid_action_list=valid_action_list,short_memory_summary=short_memory_summary,pattern=self.pattern,last_plan='', mode=mode)
-        if  verbose_print:
-            console.print(self.name + " plan: " , style="orchid")
-            print(self.name + " plan: " + str(plan))
-            util.get_logging(logger_name=log_file_name + '_plan',
-                        content={str(game_idx + 1) + "_" + str(round): {
-                            "plan": str(plan)}})
-        time.sleep(0)
-        promp_head = ''
-        act, comm = self.action_decision(readable_text_amy_obs, valid_action_list, promp_head,short_memory_summary=short_memory_summary)
-        if log_file_name is not None:
-            util.get_logging(logger_name= log_file_name + '_act',
-                        content={str(game_idx + 1) + "_" + str(round): {
-                            "act": str(act), "talk_sentence": str(comm)}})
-            
-        while act not in valid_action_list:
-            print('Action + ', str(act), ' is not a valid action in valid_action_list, please try again.\n')
-            promp_head += 'Action {act} is not a valid action in {valid_action_list}, please try again.\n'
-            act, comm = self.action_decision( readable_text_amy_obs, valid_action_list, promp_head,act)
-        print(self.name + " act: " + str(act))
-        print(comm)
-
-        bot_short_memory[player_index].append(f"{self.name} have the observation {readable_text_amy_obs}, try to take action: {act} and say {comm} to {opponent_name}")
-        bot_short_memory[((player_index + 1)%2)].append(f"{self.name} try to take action: {act} and say {comm} to {opponent_name}")
-
-        bot_long_memory[player_index].append(
-            f"{self.name} have the observation {observation}, try to take action: {act} and say {comm} to {opponent_name}")
-        return act,comm,bot_short_memory,bot_long_memory
diff --git a/spaces/daishen/LAiW/README.md b/spaces/daishen/LAiW/README.md
deleted file mode 100644
index b12750088bcd4aa12d38287fc9e903a78533167d..0000000000000000000000000000000000000000
--- a/spaces/daishen/LAiW/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: SCULaiw
-emoji: 🦀
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.44.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/datasciencedojo/Handpose/app.py b/spaces/datasciencedojo/Handpose/app.py
deleted file mode 100644
index 7d7386c04462cfa8636e11f01fc56d1a19917a67..0000000000000000000000000000000000000000
--- a/spaces/datasciencedojo/Handpose/app.py
+++ /dev/null
@@ -1,88 +0,0 @@
-__all__ = ['learn','classify_image','categories','image','label','examples','intf']
-
-from fastai.vision.all import *
-import gradio as gr
-
-# Cell
-learn = load_learner('handpose.pkl')
-
-# Cell
-categories = ('call', 'dislike', 'fist', 'four', 'like', 'mute', 'ok', 'one', 'palm', 'peace', 
-    'peace_inverted', 'rock', 'stop', 'stop_inverted', 'three', 'three2', 'two_up', 'two_up_inverted')
-
-def classify_image(img):
-    pred, idx, probs = learn.predict(img)
-    return dict(zip(categories, map(float, probs)))
-
-# Cell
-image = gr.inputs.Image(shape=(192,192),source='webcam')
-label = gr.outputs.Label()
-examples = ['beyonce-ok.jpg']
-
-css = """
-footer {display:none !important}
-.output-markdown{display:none !important}
-div[data-testid="label"] {overflow-x: hidden !important; overflow-y: scroll !important; height:520px !important;}
-.gr-button-primary {
-    z-index: 14;
-    height: 43px;
-    width: 130px;
-    left: 0px;
-    top: 0px;
-    padding: 0px;
-    cursor: pointer !important; 
-    background: none rgb(17, 20, 45) !important;
-    border: none !important;
-    text-align: center !important;
-    font-family: Poppins !important;
-    font-size: 14px !important;
-    font-weight: 500 !important;
-    color: rgb(255, 255, 255) !important;
-    line-height: 1 !important;
-    border-radius: 12px !important;
-    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
-    box-shadow: none !important;
-}
-.gr-button-primary:hover{
-    z-index: 14;
-    height: 43px;
-    width: 130px;
-    left: 0px;
-    top: 0px;
-    padding: 0px;
-    cursor: pointer !important;
-    background: none rgb(37, 56, 133) !important;
-    border: none !important;
-    text-align: center !important;
-    font-family: Poppins !important;
-    font-size: 14px !important;
-    font-weight: 500 !important;
-    color: rgb(255, 255, 255) !important;
-    line-height: 1 !important;
-    border-radius: 12px !important;
-    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
-    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
-}
-.hover\:bg-orange-50:hover {
-    --tw-bg-opacity: 1 !important;
-    background-color: rgb(229,225,255) !important;
-}
-
-.from-orange-400 {
-    --tw-gradient-from: rgb(17, 20, 45) !important;
-    --tw-gradient-to: rgb(255 150 51 / 0);
-    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
-}
-.group-hover\:from-orange-500{
-    --tw-gradient-from:rgb(17, 20, 45) !important; 
-    --tw-gradient-to: rgb(37 56 133 / 37%);
-    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
-}
-.group:hover .group-hover\:text-orange-500{
-    --tw-text-opacity: 1 !important;
-    color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
-}
-"""
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label,examples=examples ,title="Hand Gesture Recognition | Data Science Dojo", css=css)
-intf.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/lp_train.py b/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/lp_train.py
deleted file mode 100644
index 24a19bacd0a4b789415cfccbce1f8bc99bc493ed..0000000000000000000000000000000000000000
--- a/spaces/dawood/audioldm-text-to-audio-generation/audioldm/clap/training/lp_train.py
+++ /dev/null
@@ -1,301 +0,0 @@
-import json
-import logging
-import math
-import os
-import time
-from contextlib import suppress
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-try:
-    import wandb
-except ImportError:
-    wandb = None
-
-from open_clip import LPLoss, LPMetrics, lp_gather_features
-from open_clip.utils import do_mixup, get_mix_lambda
-from .distributed import is_master
-from .zero_shot import zero_shot_eval
-
-
-class AverageMeter(object):
-    """Computes and stores the average and current value"""
-
-    def __init__(self):
-        self.reset()
-
-    def reset(self):
-        self.val = 0
-        self.avg = 0
-        self.sum = 0
-        self.count = 0
-
-    def update(self, val, n=1):
-        self.val = val
-        self.sum += val * n
-        self.count += n
-        self.avg = self.sum / self.count
-
-
-def unwrap_model(model):
-    if hasattr(model, "module"):
-        return model.module
-    else:
-        return model
-
-
-def train_one_epoch(
-    model,
-    data,
-    epoch,
-    optimizer,
-    scaler,
-    scheduler,
-    args,
-    tb_writer=None,
-    extra_suffix="",
-):
-    device = torch.device(args.device)
-    autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress
-    model.train()
-    loss = LPLoss(args.lp_loss)
-
-    dataloader, sampler = data["train"].dataloader, data["train"].sampler
-    if args.distributed and sampler is not None:
-        sampler.set_epoch(epoch)
-    num_batches_per_epoch = dataloader.num_batches
-    sample_digits = math.ceil(math.log(dataloader.num_samples + 1, 10))
-
-    # for toy dataset
-    if args.dataset_type == "toy":
-        dataloader.dataset.generate_queue()
-
-    loss_m = AverageMeter()
-    batch_time_m = AverageMeter()
-    data_time_m = AverageMeter()
-    end = time.time()
-
-    for i, batch in enumerate(dataloader):
-        step = num_batches_per_epoch * epoch + i
-
-        if isinstance(scheduler, dict):
-            for s in scheduler.values():
-                s(step)
-        else:
-            scheduler(step)
-
-        audio = batch  # contains mel_spec, wavform, and longer list
-        class_label = batch["class_label"]
-        # audio = audio.to(device=device, non_blocking=True)
-        class_label = class_label.to(device=device, non_blocking=True)
-
-        if args.mixup:
-            # https://github.com/RetroCirce/HTS-Audio-Transformer/blob/main/utils.py#L146
-            mix_lambda = torch.from_numpy(
-                get_mix_lambda(0.5, len(audio["waveform"]))
-            ).to(device)
-            class_label = do_mixup(class_label, mix_lambda)
-        else:
-            mix_lambda = None
-
-        data_time_m.update(time.time() - end)
-        if isinstance(optimizer, dict):
-            for o_ in optimizer.values():
-                o_.zero_grad()
-        else:
-            optimizer.zero_grad()
-
-        with autocast():
-            pred = model(audio, mix_lambda=mix_lambda, device=device)
-            total_loss = loss(pred, class_label)
-
-        if isinstance(optimizer, dict):
-            if scaler is not None:
-                scaler.scale(total_loss).backward()
-                for o_ in optimizer.values():
-                    if args.horovod:
-                        o_.synchronize()
-                        scaler.unscale_(o_)
-                        with o_.skip_synchronize():
-                            scaler.step(o_)
-                    else:
-                        scaler.step(o_)
-                scaler.update()
-            else:
-                total_loss.backward()
-                for o_ in optimizer.values():
-                    o_.step()
-        else:
-            if scaler is not None:
-                scaler.scale(total_loss).backward()
-                if args.horovod:
-                    optimizer.synchronize()
-                    scaler.unscale_(optimizer)
-                    with optimizer.skip_synchronize():
-                        scaler.step(optimizer)
-                else:
-                    scaler.step(optimizer)
-                scaler.update()
-            else:
-                total_loss.backward()
-                optimizer.step()
-
-        # Note: we clamp to 4.6052 = ln(100), as in the original paper.
-        with torch.no_grad():
-            unwrap_model(model).clap_model.logit_scale_a.clamp_(0, math.log(100))
-            unwrap_model(model).clap_model.logit_scale_t.clamp_(0, math.log(100))
-
-        batch_time_m.update(time.time() - end)
-        end = time.time()
-        batch_count = i + 1
-
-        if is_master(args) and (i % 100 == 0 or batch_count == num_batches_per_epoch):
-            if isinstance(audio, dict):
-                batch_size = len(audio["waveform"])
-            else:
-                batch_size = len(audio)
-            num_samples = batch_count * batch_size * args.world_size
-            samples_per_epoch = dataloader.num_samples
-            percent_complete = 100.0 * batch_count / num_batches_per_epoch
-
-            # NOTE loss is coarsely sampled, just master node and per log update
-            loss_m.update(total_loss.item(), batch_size)
-            if isinstance(optimizer, dict):
-                logging.info(
-                    f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
-                    f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
-                    f"Data (t): {data_time_m.avg:.3f} "
-                    f"Batch (t): {batch_time_m.avg:.3f} "
-                    f"LR: {[o_.param_groups[0]['lr'] for o_ in optimizer.values()]}"
-                )
-                log_data = {
-                    "loss": loss_m.val,
-                    "data_time": data_time_m.val,
-                    "batch_time": batch_time_m.val,
-                    "lr": [o_.param_groups[0]["lr"] for o_ in optimizer.values()],
-                }
-            else:
-                logging.info(
-                    f"Train Epoch: {epoch} [{num_samples:>{sample_digits}}/{samples_per_epoch} ({percent_complete:.0f}%)] "
-                    f"Loss: {loss_m.val:#.5g} ({loss_m.avg:#.4g}) "
-                    f"Data (t): {data_time_m.avg:.3f} "
-                    f"Batch (t): {batch_time_m.avg:.3f} "
-                    f"LR: {optimizer.param_groups[0]['lr']:5f} "
-                )
-
-                # Save train loss / etc. Using non avg meter values as loggers have their own smoothing
-                log_data = {
-                    "loss": loss_m.val,
-                    "data_time": data_time_m.val,
-                    "batch_time": batch_time_m.val,
-                    "lr": optimizer.param_groups[0]["lr"],
-                }
-            for name, val in log_data.items():
-                name = f"train{extra_suffix}/{name}"
-                if tb_writer is not None:
-                    tb_writer.add_scalar(name, val, step)
-                if args.wandb:
-                    assert wandb is not None, "Please install wandb."
-                    wandb.log({name: val, "step": step})
-
-            # resetting batch / data time meters per log window
-            batch_time_m.reset()
-            data_time_m.reset()
-    # end for
-
-
-def evaluate(model, data, epoch, args, tb_writer=None, extra_suffix=""):
-    metrics = {}
-    if not args.parallel_eval:
-        if not is_master(args):
-            return metrics
-    device = torch.device(args.device)
-    model.eval()
-
-    # CHANGE
-    # zero_shot_metrics = zero_shot_eval(model, data, epoch, args)
-    # metrics.update(zero_shot_metrics)
-    if is_master(args):
-        print("Evaluating...")
-        metric_names = args.lp_metrics.split(",")
-        eval_tool = LPMetrics(metric_names=metric_names)
-
-    autocast = torch.cuda.amp.autocast if args.precision == "amp" else suppress
-    if "val" in data and (
-        args.val_frequency
-        and ((epoch % args.val_frequency) == 0 or epoch == args.epochs)
-    ):
-        if args.parallel_eval:
-            dataloader, sampler = data["val"].dataloader, data["val"].sampler
-            if args.distributed and sampler is not None:
-                sampler.set_epoch(epoch)
-            samples_per_val = dataloader.num_samples
-        else:
-            dataloader = data["val"].dataloader
-            num_samples = 0
-            samples_per_val = dataloader.num_samples
-
-        eval_info = {"pred": [], "target": []}
-        with torch.no_grad():
-            for i, batch in enumerate(dataloader):
-                audio = batch  # contains mel_spec, wavform, and longer list
-                class_label = batch["class_label"]
-
-                # audio = audio.to(device=device, non_blocking=True)
-                class_label = class_label.to(device=device, non_blocking=True)
-
-                with autocast():
-                    pred = model(audio, device=device)
-                    if args.parallel_eval:
-                        pred, class_label = lp_gather_features(
-                            pred, class_label, args.world_size, args.horovod
-                        )
-                    eval_info["pred"].append(pred)
-                    eval_info["target"].append(class_label)
-
-                num_samples += class_label.shape[0]
-
-                if (i % 100) == 0:  # and i != 0:
-                    logging.info(
-                        f"Eval Epoch: {epoch} [{num_samples} / {samples_per_val}]"
-                    )
-
-            if is_master(args):
-                eval_info["pred"] = torch.cat(eval_info["pred"], 0).cpu()
-                eval_info["target"] = torch.cat(eval_info["target"], 0).cpu()
-                metric_dict = eval_tool.evaluate_mertics(
-                    eval_info["pred"], eval_info["target"]
-                )
-                metrics.update(metric_dict)
-                if "epoch" not in metrics.keys():
-                    metrics.update({"epoch": epoch})
-
-    if is_master(args):
-        if not metrics:
-            return metrics
-
-        logging.info(
-            f"Eval Epoch: {epoch} "
-            + "\n".join(
-                ["\t".join([f"{m}: {round(metrics[m], 4):.4f}"]) for m in metrics]
-            )
-        )
-        if args.save_logs:
-            for name, val in metrics.items():
-                if tb_writer is not None:
-                    tb_writer.add_scalar(f"val{extra_suffix}/{name}", val, epoch)
-
-            with open(os.path.join(args.checkpoint_path, "results.jsonl"), "a+") as f:
-                f.write(json.dumps(metrics))
-                f.write("\n")
-
-        if args.wandb:
-            assert wandb is not None, "Please install wandb."
-            for name, val in metrics.items():
-                wandb.log({f"val{extra_suffix}/{name}": val, "epoch": epoch})
-
-        return metrics
-    else:
-        return metrics
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/GdImageFile.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/GdImageFile.py
deleted file mode 100644
index bafc43a19d432290867a5c08b9820f2e4f79aea3..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/GdImageFile.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GD file handling
-#
-# History:
-# 1996-04-12 fl   Created
-#
-# Copyright (c) 1997 by Secret Labs AB.
-# Copyright (c) 1996 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-"""
-.. note::
-    This format cannot be automatically recognized, so the
-    class is not registered for use with :py:func:`PIL.Image.open()`.  To open a
-    gd file, use the :py:func:`PIL.GdImageFile.open()` function instead.
-
-.. warning::
-    THE GD FORMAT IS NOT DESIGNED FOR DATA INTERCHANGE.  This
-    implementation is provided for convenience and demonstrational
-    purposes only.
-"""
-
-
-from . import ImageFile, ImagePalette, UnidentifiedImageError
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-
-
-class GdImageFile(ImageFile.ImageFile):
-    """
-    Image plugin for the GD uncompressed format.  Note that this format
-    is not supported by the standard :py:func:`PIL.Image.open()` function.  To use
-    this plugin, you have to import the :py:mod:`PIL.GdImageFile` module and
-    use the :py:func:`PIL.GdImageFile.open()` function.
-    """
-
-    format = "GD"
-    format_description = "GD uncompressed images"
-
-    def _open(self):
-        # Header
-        s = self.fp.read(1037)
-
-        if i16(s) not in [65534, 65535]:
-            msg = "Not a valid GD 2.x .gd file"
-            raise SyntaxError(msg)
-
-        self.mode = "L"  # FIXME: "P"
-        self._size = i16(s, 2), i16(s, 4)
-
-        true_color = s[6]
-        true_color_offset = 2 if true_color else 0
-
-        # transparency index
-        tindex = i32(s, 7 + true_color_offset)
-        if tindex < 256:
-            self.info["transparency"] = tindex
-
-        self.palette = ImagePalette.raw(
-            "XBGR", s[7 + true_color_offset + 4 : 7 + true_color_offset + 4 + 256 * 4]
-        )
-
-        self.tile = [
-            (
-                "raw",
-                (0, 0) + self.size,
-                7 + true_color_offset + 4 + 256 * 4,
-                ("L", 0, 1),
-            )
-        ]
-
-
-def open(fp, mode="r"):
-    """
-    Load texture from a GD image file.
-
-    :param fp: GD file name, or an opened file handle.
-    :param mode: Optional mode.  In this version, if the mode argument
-        is given, it must be "r".
-    :returns: An image instance.
-    :raises OSError: If the image could not be read.
-    """
-    if mode != "r":
-        msg = "bad mode"
-        raise ValueError(msg)
-
-    try:
-        return GdImageFile(fp)
-    except SyntaxError as e:
-        msg = "cannot identify this image file"
-        raise UnidentifiedImageError(msg) from e
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/abc/_tasks.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/abc/_tasks.py
deleted file mode 100644
index e48d3c1e97e02cd188b567b50a4c0c615f187e4d..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/anyio/abc/_tasks.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from __future__ import annotations
-
-import sys
-from abc import ABCMeta, abstractmethod
-from types import TracebackType
-from typing import TYPE_CHECKING, Any, Awaitable, Callable, TypeVar, overload
-from warnings import warn
-
-if sys.version_info >= (3, 8):
-    from typing import Protocol
-else:
-    from typing_extensions import Protocol
-
-if TYPE_CHECKING:
-    from anyio._core._tasks import CancelScope
-
-T_Retval = TypeVar("T_Retval")
-T_contra = TypeVar("T_contra", contravariant=True)
-
-
-class TaskStatus(Protocol[T_contra]):
-    @overload
-    def started(self: TaskStatus[None]) -> None:
-        ...
-
-    @overload
-    def started(self, value: T_contra) -> None:
-        ...
-
-    def started(self, value: T_contra | None = None) -> None:
-        """
-        Signal that the task has started.
-
-        :param value: object passed back to the starter of the task
-        """
-
-
-class TaskGroup(metaclass=ABCMeta):
-    """
-    Groups several asynchronous tasks together.
-
-    :ivar cancel_scope: the cancel scope inherited by all child tasks
-    :vartype cancel_scope: CancelScope
-    """
-
-    cancel_scope: CancelScope
-
-    async def spawn(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        *args: object,
-        name: object = None,
-    ) -> None:
-        """
-        Start a new task in this task group.
-
-        :param func: a coroutine function
-        :param args: positional arguments to call the function with
-        :param name: name of the task, for the purposes of introspection and debugging
-
-        .. deprecated:: 3.0
-           Use :meth:`start_soon` instead. If your code needs AnyIO 2 compatibility, you
-           can keep using this until AnyIO 4.
-
-        """
-        warn(
-            'spawn() is deprecated -- use start_soon() (without the "await") instead',
-            DeprecationWarning,
-        )
-        self.start_soon(func, *args, name=name)
-
-    @abstractmethod
-    def start_soon(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        *args: object,
-        name: object = None,
-    ) -> None:
-        """
-        Start a new task in this task group.
-
-        :param func: a coroutine function
-        :param args: positional arguments to call the function with
-        :param name: name of the task, for the purposes of introspection and debugging
-
-        .. versionadded:: 3.0
-        """
-
-    @abstractmethod
-    async def start(
-        self,
-        func: Callable[..., Awaitable[Any]],
-        *args: object,
-        name: object = None,
-    ) -> Any:
-        """
-        Start a new task and wait until it signals for readiness.
-
-        :param func: a coroutine function
-        :param args: positional arguments to call the function with
-        :param name: name of the task, for the purposes of introspection and debugging
-        :return: the value passed to ``task_status.started()``
-        :raises RuntimeError: if the task finishes without calling ``task_status.started()``
-
-        .. versionadded:: 3.0
-        """
-
-    @abstractmethod
-    async def __aenter__(self) -> TaskGroup:
-        """Enter the task group context and allow starting new tasks."""
-
-    @abstractmethod
-    async def __aexit__(
-        self,
-        exc_type: type[BaseException] | None,
-        exc_val: BaseException | None,
-        exc_tb: TracebackType | None,
-    ) -> bool | None:
-        """Exit the task group context waiting for all tasks to finish."""
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/click/__init__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/click/__init__.py
deleted file mode 100644
index 9a1dab048917edc420af440c73bd1d689de6b3fa..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/click/__init__.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""
-Click is a simple Python module inspired by the stdlib optparse to make
-writing command line scripts fun. Unlike other modules, it's based
-around a simple API that does not come with too much magic and is
-composable.
-"""
-from .core import Argument as Argument
-from .core import BaseCommand as BaseCommand
-from .core import Command as Command
-from .core import CommandCollection as CommandCollection
-from .core import Context as Context
-from .core import Group as Group
-from .core import MultiCommand as MultiCommand
-from .core import Option as Option
-from .core import Parameter as Parameter
-from .decorators import argument as argument
-from .decorators import command as command
-from .decorators import confirmation_option as confirmation_option
-from .decorators import group as group
-from .decorators import help_option as help_option
-from .decorators import make_pass_decorator as make_pass_decorator
-from .decorators import option as option
-from .decorators import pass_context as pass_context
-from .decorators import pass_obj as pass_obj
-from .decorators import password_option as password_option
-from .decorators import version_option as version_option
-from .exceptions import Abort as Abort
-from .exceptions import BadArgumentUsage as BadArgumentUsage
-from .exceptions import BadOptionUsage as BadOptionUsage
-from .exceptions import BadParameter as BadParameter
-from .exceptions import ClickException as ClickException
-from .exceptions import FileError as FileError
-from .exceptions import MissingParameter as MissingParameter
-from .exceptions import NoSuchOption as NoSuchOption
-from .exceptions import UsageError as UsageError
-from .formatting import HelpFormatter as HelpFormatter
-from .formatting import wrap_text as wrap_text
-from .globals import get_current_context as get_current_context
-from .parser import OptionParser as OptionParser
-from .termui import clear as clear
-from .termui import confirm as confirm
-from .termui import echo_via_pager as echo_via_pager
-from .termui import edit as edit
-from .termui import getchar as getchar
-from .termui import launch as launch
-from .termui import pause as pause
-from .termui import progressbar as progressbar
-from .termui import prompt as prompt
-from .termui import secho as secho
-from .termui import style as style
-from .termui import unstyle as unstyle
-from .types import BOOL as BOOL
-from .types import Choice as Choice
-from .types import DateTime as DateTime
-from .types import File as File
-from .types import FLOAT as FLOAT
-from .types import FloatRange as FloatRange
-from .types import INT as INT
-from .types import IntRange as IntRange
-from .types import ParamType as ParamType
-from .types import Path as Path
-from .types import STRING as STRING
-from .types import Tuple as Tuple
-from .types import UNPROCESSED as UNPROCESSED
-from .types import UUID as UUID
-from .utils import echo as echo
-from .utils import format_filename as format_filename
-from .utils import get_app_dir as get_app_dir
-from .utils import get_binary_stream as get_binary_stream
-from .utils import get_text_stream as get_text_stream
-from .utils import open_file as open_file
-
-__version__ = "8.1.7"
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/contourpy/util/data.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/contourpy/util/data.py
deleted file mode 100644
index e6ba9a976c2aa4cabbf0a6031400f0d910b59ac3..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/contourpy/util/data.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any
-
-import numpy as np
-
-if TYPE_CHECKING:
-    from contourpy._contourpy import CoordinateArray
-
-
-def simple(
-    shape: tuple[int, int], want_mask: bool = False,
-) -> tuple[CoordinateArray, CoordinateArray, CoordinateArray | np.ma.MaskedArray[Any, Any]]:
-    """Return simple test data consisting of the sum of two gaussians.
-
-    Args:
-        shape (tuple(int, int)): 2D shape of data to return.
-        want_mask (bool, optional): Whether test data should be masked or not, default ``False``.
-
-    Return:
-        Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if
-        ``want_mask=True``.
-    """
-    ny, nx = shape
-    x = np.arange(nx, dtype=np.float64)
-    y = np.arange(ny, dtype=np.float64)
-    x, y = np.meshgrid(x, y)
-
-    xscale = nx - 1.0
-    yscale = ny - 1.0
-
-    # z is sum of 2D gaussians.
-    amp = np.asarray([1.0, -1.0, 0.8, -0.9, 0.7])
-    mid = np.asarray([[0.4, 0.2], [0.3, 0.8], [0.9, 0.75], [0.7, 0.3], [0.05, 0.7]])
-    width = np.asarray([0.4, 0.2, 0.2, 0.2, 0.1])
-
-    z = np.zeros_like(x)
-    for i in range(len(amp)):
-        z += amp[i]*np.exp(-((x/xscale - mid[i, 0])**2 + (y/yscale - mid[i, 1])**2) / width[i]**2)
-
-    if want_mask:
-        mask = np.logical_or(
-            ((x/xscale - 1.0)**2 / 0.2 + (y/yscale - 0.0)**2 / 0.1) < 1.0,
-            ((x/xscale - 0.2)**2 / 0.02 + (y/yscale - 0.45)**2 / 0.08) < 1.0,
-        )
-        z = np.ma.array(z, mask=mask)  # type: ignore[no-untyped-call]
-
-    return x, y, z
-
-
-def random(
-    shape: tuple[int, int], seed: int = 2187, mask_fraction: float = 0.0,
-) -> tuple[CoordinateArray, CoordinateArray, CoordinateArray | np.ma.MaskedArray[Any, Any]]:
-    """Return random test data..
-
-    Args:
-        shape (tuple(int, int)): 2D shape of data to return.
-        seed (int, optional): Seed for random number generator, default 2187.
-        mask_fraction (float, optional): Fraction of elements to mask, default 0.
-
-    Return:
-        Tuple of 3 arrays: ``x``, ``y``, ``z`` test data, ``z`` will be masked if
-        ``mask_fraction`` is greater than zero.
-    """
-    ny, nx = shape
-    x = np.arange(nx, dtype=np.float64)
-    y = np.arange(ny, dtype=np.float64)
-    x, y = np.meshgrid(x, y)
-
-    rng = np.random.default_rng(seed)
-    z = rng.uniform(size=shape)
-
-    if mask_fraction > 0.0:
-        mask_fraction = min(mask_fraction, 0.99)
-        mask = rng.uniform(size=shape) < mask_fraction
-        z = np.ma.array(z, mask=mask)  # type: ignore[no-untyped-call]
-
-    return x, y, z
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/duplicate_button.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/duplicate_button.py
deleted file mode 100644
index c6b8f486a9cf348a2a9a65da75970ea92b394087..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/components/duplicate_button.py
+++ /dev/null
@@ -1,79 +0,0 @@
-""" Predefined buttons with bound events that can be included in a gr.Blocks for convenience. """
-
-from __future__ import annotations
-
-from typing import Literal
-
-from gradio_client.documentation import document, set_documentation_group
-
-from gradio.components import Button
-from gradio.utils import get_space
-
-set_documentation_group("component")
-
-
-@document()
-class DuplicateButton(Button):
-    """
-    Button that triggers a Spaces Duplication, when the demo is on Hugging Face Spaces. Does nothing locally.
-    Preprocessing: passes the button value as a {str} into the function
-    Postprocessing: expects a {str} to be returned from a function, which is set as the label of the button
-    """
-
-    is_template = True
-
-    def __init__(
-        self,
-        *,
-        value: str = "Duplicate Space",
-        variant: Literal["primary", "secondary", "stop"] = "secondary",
-        size: Literal["sm", "lg"] | None = "sm",
-        icon: str | None = None,
-        link: str | None = None,
-        visible: bool = True,
-        interactive: bool = True,
-        elem_id: str | None = None,
-        elem_classes: list[str] | str | None = None,
-        scale: int | None = 0,
-        min_width: int | None = None,
-        _activate: bool = True,
-        **kwargs,
-    ):
-        """
-        Parameters:
-            value: Default text for the button to display. If callable, the function will be called whenever the app loads to set the initial value of the component.
-            variant: 'primary' for main call-to-action, 'secondary' for a more subdued style, 'stop' for a stop button.
-            size: Size of the button. Can be "sm" or "lg".
-            icon: URL or path to the icon file to display within the button. If None, no icon will be displayed.
-            link: URL to open when the button is clicked. If None, no link will be used.
-            visible: If False, component will be hidden.
-            interactive: If False, the Button will be in a disabled state.
-            elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles.
-            elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles.
-            scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer.
-            min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first.
-        """
-        super().__init__(
-            value,
-            variant=variant,
-            size=size,
-            icon=icon,
-            link=link,
-            visible=visible,
-            interactive=interactive,
-            elem_id=elem_id,
-            elem_classes=elem_classes,
-            scale=scale,
-            min_width=min_width,
-            **kwargs,
-        )
-        if _activate:
-            self.activate()
-
-    def activate(self):
-        space_name = get_space()
-        if space_name is not None:
-            self.click(
-                fn=None,
-                _js=f"() => {{ window.open(`https://huggingface.co/spaces/{space_name}?duplicate=true`, '_blank') }}",
-            )
diff --git a/spaces/declare-lab/tango/diffusers/utils/stale.py b/spaces/declare-lab/tango/diffusers/utils/stale.py
deleted file mode 100644
index 12932f31c243f44566fb65daf80b0b3637cc8a95..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/utils/stale.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# Copyright 2023 The HuggingFace Team, the AllenNLP library authors. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Script to close stale issue. Taken in part from the AllenNLP repository.
-https://github.com/allenai/allennlp.
-"""
-import os
-from datetime import datetime as dt
-
-from github import Github
-
-
-LABELS_TO_EXEMPT = [
-    "good first issue",
-    "good second issue",
-    "good difficult issue",
-    "enhancement",
-    "new pipeline/model",
-    "new scheduler",
-    "wip",
-]
-
-
-def main():
-    g = Github(os.environ["GITHUB_TOKEN"])
-    repo = g.get_repo("huggingface/diffusers")
-    open_issues = repo.get_issues(state="open")
-
-    for issue in open_issues:
-        comments = sorted(issue.get_comments(), key=lambda i: i.created_at, reverse=True)
-        last_comment = comments[0] if len(comments) > 0 else None
-        if (
-            last_comment is not None
-            and last_comment.user.login == "github-actions[bot]"
-            and (dt.utcnow() - issue.updated_at).days > 7
-            and (dt.utcnow() - issue.created_at).days >= 30
-            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
-        ):
-            # Closes the issue after 7 days of inactivity since the Stalebot notification.
-            issue.edit(state="closed")
-        elif (
-            "stale" in issue.get_labels()
-            and last_comment is not None
-            and last_comment.user.login != "github-actions[bot]"
-        ):
-            # Opens the issue if someone other than Stalebot commented.
-            issue.edit(state="open")
-            issue.remove_from_labels("stale")
-        elif (
-            (dt.utcnow() - issue.updated_at).days > 23
-            and (dt.utcnow() - issue.created_at).days >= 30
-            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
-        ):
-            # Post a Stalebot notification after 23 days of inactivity.
-            issue.create_comment(
-                "This issue has been automatically marked as stale because it has not had "
-                "recent activity. If you think this still needs to be addressed "
-                "please comment on this thread.\n\nPlease note that issues that do not follow the "
-                "[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) "
-                "are likely to be ignored."
-            )
-            issue.add_to_labels("stale")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_google_search.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_google_search.py
deleted file mode 100644
index da32e8923e49df661ffdd24c22001682171e573b..0000000000000000000000000000000000000000
--- a/spaces/deepwisdom/MetaGPT/tests/metagpt/learn/test_google_search.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import asyncio
-
-from pydantic import BaseModel
-
-from metagpt.learn.google_search import google_search
-
-
-async def mock_google_search():
-    class Input(BaseModel):
-        input: str
-
-    inputs = [{"input": "ai agent"}]
-
-    for i in inputs:
-        seed = Input(**i)
-        result = await google_search(seed.input)
-        assert result != ""
-
-
-def test_suite():
-    loop = asyncio.get_event_loop()
-    task = loop.create_task(mock_google_search())
-    loop.run_until_complete(task)
-
-
-if __name__ == "__main__":
-    test_suite()
diff --git a/spaces/descript/vampnet/vampnet/scheduler.py b/spaces/descript/vampnet/vampnet/scheduler.py
deleted file mode 100644
index a57108c2af7c974d882b45e092907195ded71c9a..0000000000000000000000000000000000000000
--- a/spaces/descript/vampnet/vampnet/scheduler.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import copy
-from typing import List
-
-import torch
-
-class NoamScheduler:
-    """OG scheduler from transformer paper: https://arxiv.org/pdf/1706.03762.pdf
-    Implementation from Annotated Transformer: https://nlp.seas.harvard.edu/2018/04/03/attention.html
-    """
-
-    def __init__(
-        self,
-        optimizer: torch.optim.Optimizer,
-        d_model: int = 512,
-        factor: float = 1.0,
-        warmup: int = 4000,
-    ):
-        # Store hparams
-        self.warmup = warmup
-        self.factor = factor
-        self.d_model = d_model
-
-        # Initialize variables `lr` and `steps`
-        self.lr = None
-        self.steps = 0
-
-        # Store the optimizer
-        self.optimizer = optimizer
-
-    def state_dict(self):
-        return {
-            key: value for key, value in self.__dict__.items() if key != "optimizer"
-        }
-
-    def load_state_dict(self, state_dict):
-        self.__dict__.update(state_dict)
-
-    def step(self):
-        self.steps += 1
-        self.lr = self.factor * (
-            self.d_model ** (-0.5)
-            * min(self.steps ** (-0.5), self.steps * self.warmup ** (-1.5))
-        )
-
-        for p in self.optimizer.param_groups:
-            p["lr"] = self.lr
-
diff --git a/spaces/dfurman/chat-all-in/ideas.md b/spaces/dfurman/chat-all-in/ideas.md
deleted file mode 100644
index 16b39b9e50ab8d29f72b2873b733a231eeb4c7f3..0000000000000000000000000000000000000000
--- a/spaces/dfurman/chat-all-in/ideas.md
+++ /dev/null
@@ -1,6 +0,0 @@
-- programmatic data preprocessing via youtube api
-- slack frontend as alternative deployment - possible to use gradio api?
-- tiktok token counter for openai truncation 
-- save conversations from chatgpt sessions, mix in full and smaller sections of context. Save in mpt format with the context. Then, fine-tune some open-source models on this dataset.  
-- add back in "stop" button for generation?
-- add advanced params switches for model type and section retrieval type
diff --git "a/spaces/diacanFperku/AutoGPT/Another Way To Resize The Live Tv\302\240display.md" "b/spaces/diacanFperku/AutoGPT/Another Way To Resize The Live Tv\302\240display.md"
deleted file mode 100644
index 662190694dd23164c036324c41c5946723f925ea..0000000000000000000000000000000000000000
--- "a/spaces/diacanFperku/AutoGPT/Another Way To Resize The Live Tv\302\240display.md"	
+++ /dev/null
@@ -1,30 +0,0 @@
-<br />
-<h1>Another Way to Resize the Live TV Display</h1>
-<p>If you are using a smart TV or a streaming device like Chromecast or Firestick, you may want to adjust the screen size and resolution of your live TV display to fit your preferences and TV capabilities. While most devices have their own settings menus that allow you to change these options, there is another way to resize the live TV display using your PC.</p>
-<p>By connecting your PC to your TV via an HDMI cable, you can use your PC's display settings to customize the screen size and resolution of your live TV display. This can be useful if you want to use your PC as a media center or if you want to have more control over the display quality. Here are the steps to follow:</p>
-<h2>Another way to resize the live tv display</h2><br /><p><b><b>Download File</b> &rArr; <a href="https://gohhs.com/2uFVkd">https://gohhs.com/2uFVkd</a></b></p><br /><br />
-<ol>
-<li>Connect your PC to your TV using an HDMI cable. Make sure both devices are turned on and set to the correct HDMI input.</li>
-<li>On your PC, right-click on an empty space on your desktop and select "Display settings".</li>
-<li>You should see two displays labeled "1" and "2". Display 1 is your PC monitor and display 2 is your TV. Click on display 2 to select it.</li>
-<li>Under "Scale and layout", you can adjust the size of text, apps, and other items on your TV screen. You can also change the resolution and orientation of your TV display.</li>
-<li>Under "Multiple displays", you can choose how you want to use your PC and TV displays. You can extend your desktop across both screens, duplicate your desktop on both screens, or show only on one screen.</li>
-<li>Click "Apply" to save your changes and check how they look on your TV screen. You can also use the "Detect" and "Identify" buttons to troubleshoot any issues with your displays.</li>
-</ol>
-<p>By using this method, you can resize the live TV display according to your needs and preferences. You can also switch back to the device's settings menu if you want to change other options like sound, color, or game mode[^1^] [^2^].</p>
-
-<p>Some advantages of resizing the live TV display using your PC are:</p>
-<ul>
-<li>You can use your PC as a media center and access your favorite streaming services, websites, and apps on your TV screen.</li>
-<li>You can adjust the display quality to match your TV's capabilities and your viewing preferences.</li>
-<li>You can use your PC's keyboard and mouse to navigate and control your live TV display.</li>
-</ul>
-<p>Some disadvantages of resizing the live TV display using your PC are:</p>
-<ul>
-<li>You may need to buy an HDMI cable if you don't have one already.</li>
-<li>You may experience some lag or delay between your PC and TV displays.</li>
-<li>You may need to adjust the display settings every time you switch between different devices or inputs.</li>
-</ul>
-<p>In conclusion, resizing the live TV display using your PC is another way to customize your viewing experience and enjoy your favorite live TV shows and channels. However, it may also require some extra equipment and adjustments. You can try this method and see if it works for you or stick to the device's settings menu for simpler options.</p> d5da3c52bf<br />
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-<br />
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Arma 3 Warhammer 40k Mod.md b/spaces/diacanFperku/AutoGPT/Arma 3 Warhammer 40k Mod.md
deleted file mode 100644
index 34414df1261e4f00dc12608e2f54358ee7ed40d8..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Arma 3 Warhammer 40k Mod.md	
+++ /dev/null
@@ -1,16 +0,0 @@
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diff --git a/spaces/diacanFperku/AutoGPT/CRACK AllMyNotes Organizer Deluxe 3.21 Build 873 Final.md b/spaces/diacanFperku/AutoGPT/CRACK AllMyNotes Organizer Deluxe 3.21 Build 873 Final.md
deleted file mode 100644
index 3f42d11051778dae38cefe5dc4cd0b4a3ae6793c..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/CRACK AllMyNotes Organizer Deluxe 3.21 Build 873 Final.md	
+++ /dev/null
@@ -1,12 +0,0 @@
-<h2>CRACK AllMyNotes Organizer Deluxe 3.21 Build 873 Final</h2><br /><p><b><b>Download Zip</b> &middot; <a href="https://gohhs.com/2uFUHf">https://gohhs.com/2uFUHf</a></b></p><br /><br />
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diff --git a/spaces/diacanFperku/AutoGPT/Cinder Book One In The Lunar Chronicles Pdf [HOT] Download.md b/spaces/diacanFperku/AutoGPT/Cinder Book One In The Lunar Chronicles Pdf [HOT] Download.md
deleted file mode 100644
index af5ff8ff3f4540d5107736638378784a0612f8bf..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Cinder Book One In The Lunar Chronicles Pdf [HOT] Download.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p>chapter thirty-seven begins when cinder, pearl and peony are in the chancellor's castle, trying to find peony. when they do, the chancellor informs them that kai has been killed and that peony has been infected. they escape, but are soon captured and have their memories wiped. on their way to a lab, they see the chancellor's cyborgs fighting with cinder's cyborgs.</p>
-<p>cinder is now in her 40s, but still remains the same innocent girl that everyone knew when she was a teenager. she is convinced that her friend peony is dead and wants to feel her dead body to confirm it. on the way to the hovercraft, she sees kai coming towards her. she hides, but he spots her. kai tells cinder that peony is alive and comes back with her to take her to her. cinder takes her back to the hovercraft but the earthen authorities find them. when cinder is forced to escape on foot, she feels restless. here is a girl who grew up in the palace, she has no clue how to live without servants and servants to obey. peony is the only one she can depend on and she is her only friend. without any answers, she uses her abilities and manipulates kai and the earthen government to help her. and together they stand to save the world.</p>
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-<p>cinder returns home to her clunking robot, nainsi, who tells her the queen has ordered her to obtain the lunar chronicles. after she leaves for her assignment, nainsi falls ill and tells cinder that she has not been programming her circuits properly. cinder returns to the palace, and after nainsi repairs, she rushes to the library. she overhears levana declaring that she is going to marry kai and makes her way to the royal throne room. on the way, she is attacked by several guards, and one of the men is killed. she makes it into the throne room just as kai and levana are about to exchange vows. by the time kai turns to look at her, she has already been taken.</p> 899543212b<br />
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diff --git a/spaces/diacanFperku/AutoGPT/Comsol Multiphysics Free Download Cracked Softwaresinstmankl.md b/spaces/diacanFperku/AutoGPT/Comsol Multiphysics Free Download Cracked Softwaresinstmankl.md
deleted file mode 100644
index 0b4fd90a17c8fe9dd1b847ad47f17cda5e5affff..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Comsol Multiphysics Free Download Cracked Softwaresinstmankl.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Comsol Multiphysics Free Download Cracked Softwaresinstmankl</h2><br /><p><b><b>DOWNLOAD</b> ->->->-> <a href="https://gohhs.com/2uFTYm">https://gohhs.com/2uFTYm</a></b></p><br /><br />
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diff --git a/spaces/diacanFperku/AutoGPT/Jurm Movie [TOP] Download 720p In Hindi.md b/spaces/diacanFperku/AutoGPT/Jurm Movie [TOP] Download 720p In Hindi.md
deleted file mode 100644
index 829c6497a150e27d8e490ea0d27b6e2e2907a794..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Jurm Movie [TOP] Download 720p In Hindi.md	
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diff --git a/spaces/digitalxingtong/Azusa-Bert-VITS2/monotonic_align/core.c b/spaces/digitalxingtong/Azusa-Bert-VITS2/monotonic_align/core.c
deleted file mode 100644
index 5f8af54d32474f821e9d1f4d2679d78128722596..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Azusa-Bert-VITS2/monotonic_align/core.c
+++ /dev/null
@@ -1,26530 +0,0 @@
-/* Generated by Cython 3.0.0 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "name": "monotonic_align.core",
-        "sources": [
-            "core.pyx"
-        ]
-    },
-    "module_name": "monotonic_align.core"
-}
-END: Cython Metadata */
-
-#ifndef PY_SSIZE_T_CLEAN
-#define PY_SSIZE_T_CLEAN
-#endif /* PY_SSIZE_T_CLEAN */
-#if defined(CYTHON_LIMITED_API) && 0
-  #ifndef Py_LIMITED_API
-    #if CYTHON_LIMITED_API+0 > 0x03030000
-      #define Py_LIMITED_API CYTHON_LIMITED_API
-    #else
-      #define Py_LIMITED_API 0x03030000
-    #endif
-  #endif
-#endif
-
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
-    #error Cython requires Python 2.7+ or Python 3.3+.
-#else
-#define CYTHON_ABI "3_0_0"
-#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI
-#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "."
-#define CYTHON_HEX_VERSION 0x030000F0
-#define CYTHON_FUTURE_DIVISION 1
-#include <stddef.h>
-#ifndef offsetof
-  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#define __PYX_COMMA ,
-#ifndef HAVE_LONG_LONG
-  #define HAVE_LONG_LONG
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#if defined(GRAALVM_PYTHON)
-  /* For very preliminary testing purposes. Most variables are set the same as PyPy.
-     The existence of this section does not imply that anything works or is even tested */
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 1
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE 0
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PYPY_VERSION)
-  #define CYTHON_COMPILING_IN_PYPY 1
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 0
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #undef CYTHON_AVOID_BORROWED_REFS
-  #define CYTHON_AVOID_BORROWED_REFS 1
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3)
-  #endif
-  #if PY_VERSION_HEX < 0x03090000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 0
-  #undef CYTHON_USE_TP_FINALIZE
-  #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00)
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(CYTHON_LIMITED_API)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 1
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #undef CYTHON_CLINE_IN_TRACEBACK
-  #define CYTHON_CLINE_IN_TRACEBACK 0
-  #undef CYTHON_USE_TYPE_SLOTS
-  #define CYTHON_USE_TYPE_SLOTS 0
-  #undef CYTHON_USE_TYPE_SPECS
-  #define CYTHON_USE_TYPE_SPECS 1
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #undef CYTHON_USE_ASYNC_SLOTS
-  #define CYTHON_USE_ASYNC_SLOTS 0
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #undef CYTHON_USE_UNICODE_INTERNALS
-  #define CYTHON_USE_UNICODE_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #endif
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #undef CYTHON_ASSUME_SAFE_MACROS
-  #define CYTHON_ASSUME_SAFE_MACROS 0
-  #undef CYTHON_UNPACK_METHODS
-  #define CYTHON_UNPACK_METHODS 0
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_GIL
-  #define CYTHON_FAST_GIL 0
-  #undef CYTHON_METH_FASTCALL
-  #define CYTHON_METH_FASTCALL 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #undef CYTHON_PEP489_MULTI_PHASE_INIT
-  #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #undef CYTHON_USE_MODULE_STATE
-  #define CYTHON_USE_MODULE_STATE 1
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 0
-  #endif
-#elif defined(PY_NOGIL)
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 0
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 1
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYTYPE_LOOKUP
-  #define CYTHON_USE_PYTYPE_LOOKUP 0
-  #ifndef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #undef CYTHON_USE_PYLIST_INTERNALS
-  #define CYTHON_USE_PYLIST_INTERNALS 0
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #undef CYTHON_USE_UNICODE_WRITER
-  #define CYTHON_USE_UNICODE_WRITER 0
-  #undef CYTHON_USE_PYLONG_INTERNALS
-  #define CYTHON_USE_PYLONG_INTERNALS 0
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #undef CYTHON_FAST_THREAD_STATE
-  #define CYTHON_FAST_THREAD_STATE 0
-  #undef CYTHON_FAST_PYCALL
-  #define CYTHON_FAST_PYCALL 0
-  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #undef CYTHON_USE_DICT_VERSIONS
-  #define CYTHON_USE_DICT_VERSIONS 0
-  #undef CYTHON_USE_EXC_INFO_STACK
-  #define CYTHON_USE_EXC_INFO_STACK 0
-#else
-  #define CYTHON_COMPILING_IN_PYPY 0
-  #define CYTHON_COMPILING_IN_CPYTHON 1
-  #define CYTHON_COMPILING_IN_LIMITED_API 0
-  #define CYTHON_COMPILING_IN_GRAAL 0
-  #define CYTHON_COMPILING_IN_NOGIL 0
-  #ifndef CYTHON_USE_TYPE_SLOTS
-    #define CYTHON_USE_TYPE_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_TYPE_SPECS
-    #define CYTHON_USE_TYPE_SPECS 0
-  #endif
-  #ifndef CYTHON_USE_PYTYPE_LOOKUP
-    #define CYTHON_USE_PYTYPE_LOOKUP 1
-  #endif
-  #if PY_MAJOR_VERSION < 3
-    #undef CYTHON_USE_ASYNC_SLOTS
-    #define CYTHON_USE_ASYNC_SLOTS 0
-  #elif !defined(CYTHON_USE_ASYNC_SLOTS)
-    #define CYTHON_USE_ASYNC_SLOTS 1
-  #endif
-  #ifndef CYTHON_USE_PYLONG_INTERNALS
-    #define CYTHON_USE_PYLONG_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_PYLIST_INTERNALS
-    #define CYTHON_USE_PYLIST_INTERNALS 1
-  #endif
-  #ifndef CYTHON_USE_UNICODE_INTERNALS
-    #define CYTHON_USE_UNICODE_INTERNALS 1
-  #endif
-  #if PY_VERSION_HEX < 0x030300F0 || PY_VERSION_HEX >= 0x030B00A2
-    #undef CYTHON_USE_UNICODE_WRITER
-    #define CYTHON_USE_UNICODE_WRITER 0
-  #elif !defined(CYTHON_USE_UNICODE_WRITER)
-    #define CYTHON_USE_UNICODE_WRITER 1
-  #endif
-  #ifndef CYTHON_AVOID_BORROWED_REFS
-    #define CYTHON_AVOID_BORROWED_REFS 0
-  #endif
-  #ifndef CYTHON_ASSUME_SAFE_MACROS
-    #define CYTHON_ASSUME_SAFE_MACROS 1
-  #endif
-  #ifndef CYTHON_UNPACK_METHODS
-    #define CYTHON_UNPACK_METHODS 1
-  #endif
-  #ifndef CYTHON_FAST_THREAD_STATE
-    #define CYTHON_FAST_THREAD_STATE 1
-  #endif
-  #ifndef CYTHON_FAST_GIL
-    #define CYTHON_FAST_GIL (PY_MAJOR_VERSION < 3 || PY_VERSION_HEX >= 0x03060000 && PY_VERSION_HEX < 0x030C00A6)
-  #endif
-  #ifndef CYTHON_METH_FASTCALL
-    #define CYTHON_METH_FASTCALL (PY_VERSION_HEX >= 0x030700A1)
-  #endif
-  #ifndef CYTHON_FAST_PYCALL
-    #define CYTHON_FAST_PYCALL 1
-  #endif
-  #ifndef CYTHON_PEP487_INIT_SUBCLASS
-    #define CYTHON_PEP487_INIT_SUBCLASS 1
-  #endif
-  #if PY_VERSION_HEX < 0x03050000
-    #undef CYTHON_PEP489_MULTI_PHASE_INIT
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 0
-  #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT)
-    #define CYTHON_PEP489_MULTI_PHASE_INIT 1
-  #endif
-  #ifndef CYTHON_USE_MODULE_STATE
-    #define CYTHON_USE_MODULE_STATE 0
-  #endif
-  #if PY_VERSION_HEX < 0x030400a1
-    #undef CYTHON_USE_TP_FINALIZE
-    #define CYTHON_USE_TP_FINALIZE 0
-  #elif !defined(CYTHON_USE_TP_FINALIZE)
-    #define CYTHON_USE_TP_FINALIZE 1
-  #endif
-  #if PY_VERSION_HEX < 0x030600B1
-    #undef CYTHON_USE_DICT_VERSIONS
-    #define CYTHON_USE_DICT_VERSIONS 0
-  #elif !defined(CYTHON_USE_DICT_VERSIONS)
-    #define CYTHON_USE_DICT_VERSIONS  (PY_VERSION_HEX < 0x030C00A5)
-  #endif
-  #if PY_VERSION_HEX < 0x030700A3
-    #undef CYTHON_USE_EXC_INFO_STACK
-    #define CYTHON_USE_EXC_INFO_STACK 0
-  #elif !defined(CYTHON_USE_EXC_INFO_STACK)
-    #define CYTHON_USE_EXC_INFO_STACK 1
-  #endif
-  #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC
-    #define CYTHON_UPDATE_DESCRIPTOR_DOC 1
-  #endif
-#endif
-#if !defined(CYTHON_FAST_PYCCALL)
-#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
-#endif
-#if !defined(CYTHON_VECTORCALL)
-#define CYTHON_VECTORCALL  (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1)
-#endif
-#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1)
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_MAJOR_VERSION < 3
-    #include "longintrepr.h"
-  #endif
-  #undef SHIFT
-  #undef BASE
-  #undef MASK
-  #ifdef SIZEOF_VOID_P
-    enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) };
-  #endif
-#endif
-#ifndef __has_attribute
-  #define __has_attribute(x) 0
-#endif
-#ifndef __has_cpp_attribute
-  #define __has_cpp_attribute(x) 0
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(maybe_unused)
-        #define CYTHON_UNUSED [[maybe_unused]]
-      #endif
-    #endif
-  #endif
-#endif
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-#ifndef CYTHON_UNUSED_VAR
-#  if defined(__cplusplus)
-     template<class T> void CYTHON_UNUSED_VAR( const T& ) { }
-#  else
-#    define CYTHON_UNUSED_VAR(x) (void)(x)
-#  endif
-#endif
-#ifndef CYTHON_MAYBE_UNUSED_VAR
-  #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x)
-#endif
-#ifndef CYTHON_NCP_UNUSED
-# if CYTHON_COMPILING_IN_CPYTHON
-#  define CYTHON_NCP_UNUSED
-# else
-#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
-# endif
-#endif
-#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
-#ifdef _MSC_VER
-    #ifndef _MSC_STDINT_H_
-        #if _MSC_VER < 1300
-            typedef unsigned char     uint8_t;
-            typedef unsigned short    uint16_t;
-            typedef unsigned int      uint32_t;
-        #else
-            typedef unsigned __int8   uint8_t;
-            typedef unsigned __int16  uint16_t;
-            typedef unsigned __int32  uint32_t;
-        #endif
-    #endif
-    #if _MSC_VER < 1300
-        #ifdef _WIN64
-            typedef unsigned long long  __pyx_uintptr_t;
-        #else
-            typedef unsigned int        __pyx_uintptr_t;
-        #endif
-    #else
-        #ifdef _WIN64
-            typedef unsigned __int64    __pyx_uintptr_t;
-        #else
-            typedef unsigned __int32    __pyx_uintptr_t;
-        #endif
-    #endif
-#else
-    #include <stdint.h>
-    typedef uintptr_t  __pyx_uintptr_t;
-#endif
-#ifndef CYTHON_FALLTHROUGH
-  #if defined(__cplusplus)
-    /* for clang __has_cpp_attribute(fallthrough) is true even before C++17
-     * but leads to warnings with -pedantic, since it is a C++17 feature */
-    #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L)
-      #if __has_cpp_attribute(fallthrough)
-        #define CYTHON_FALLTHROUGH [[fallthrough]]
-      #endif
-    #endif
-    #ifndef CYTHON_FALLTHROUGH
-      #if __has_cpp_attribute(clang::fallthrough)
-        #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
-      #elif __has_cpp_attribute(gnu::fallthrough)
-        #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
-      #endif
-    #endif
-  #endif
-  #ifndef CYTHON_FALLTHROUGH
-    #if __has_attribute(fallthrough)
-      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
-    #else
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-  #if defined(__clang__) && defined(__apple_build_version__)
-    #if __apple_build_version__ < 7000000
-      #undef  CYTHON_FALLTHROUGH
-      #define CYTHON_FALLTHROUGH
-    #endif
-  #endif
-#endif
-#ifdef __cplusplus
-  template <typename T>
-  struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);};
-  #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL<type>::value)
-#else
-  #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0)
-#endif
-#if CYTHON_COMPILING_IN_PYPY == 1
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000)
-#else
-  #define __PYX_NEED_TP_PRINT_SLOT  (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000)
-#endif
-#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer))
-
-#ifndef CYTHON_INLINE
-  #if defined(__clang__)
-    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
-  #elif defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-
-#define __PYX_BUILD_PY_SSIZE_T "n"
-#define CYTHON_FORMAT_SSIZE_T "z"
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_DefaultClassType PyClass_Type
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_DefaultClassType PyType_Type
-#if PY_VERSION_HEX >= 0x030B00A1
-    static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f,
-                                                    PyObject *code, PyObject *c, PyObject* n, PyObject *v,
-                                                    PyObject *fv, PyObject *cell, PyObject* fn,
-                                                    PyObject *name, int fline, PyObject *lnos) {
-        PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL;
-        PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL;
-        const char *fn_cstr=NULL;
-        const char *name_cstr=NULL;
-        PyCodeObject *co=NULL, *result=NULL;
-        PyObject *type, *value, *traceback;
-        PyErr_Fetch(&type, &value, &traceback);
-        if (!(kwds=PyDict_New())) goto end;
-        if (!(argcount=PyLong_FromLong(a))) goto end;
-        if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end;
-        if (!(posonlyargcount=PyLong_FromLong(p))) goto end;
-        if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end;
-        if (!(kwonlyargcount=PyLong_FromLong(k))) goto end;
-        if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end;
-        if (!(nlocals=PyLong_FromLong(l))) goto end;
-        if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end;
-        if (!(stacksize=PyLong_FromLong(s))) goto end;
-        if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end;
-        if (!(flags=PyLong_FromLong(f))) goto end;
-        if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end;
-        if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end;
-        if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end;
-        if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end;
-        if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end;
-        if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end;
-        if (!(empty = PyTuple_New(0))) goto end;
-        result = (PyCodeObject*) PyObject_Call(replace, empty, kwds);
-    end:
-        Py_XDECREF((PyObject*) co);
-        Py_XDECREF(kwds);
-        Py_XDECREF(argcount);
-        Py_XDECREF(posonlyargcount);
-        Py_XDECREF(kwonlyargcount);
-        Py_XDECREF(nlocals);
-        Py_XDECREF(stacksize);
-        Py_XDECREF(replace);
-        Py_XDECREF(empty);
-        if (type) {
-            PyErr_Restore(type, value, traceback);
-        }
-        return result;
-    }
-#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#else
-  #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-#endif
-#endif
-#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE)
-  #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type)
-#else
-  #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is)
-  #define __Pyx_Py_Is(x, y)  Py_Is(x, y)
-#else
-  #define __Pyx_Py_Is(x, y) ((x) == (y))
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone)
-  #define __Pyx_Py_IsNone(ob) Py_IsNone(ob)
-#else
-  #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue)
-  #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob)
-#else
-  #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True)
-#endif
-#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse)
-  #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob)
-#else
-  #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False)
-#endif
-#define __Pyx_NoneAsNull(obj)  (__Pyx_Py_IsNone(obj) ? NULL : (obj))
-#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o)
-#else
-  #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o)
-#endif
-#ifndef CO_COROUTINE
-  #define CO_COROUTINE 0x80
-#endif
-#ifndef CO_ASYNC_GENERATOR
-  #define CO_ASYNC_GENERATOR 0x200
-#endif
-#ifndef Py_TPFLAGS_CHECKTYPES
-  #define Py_TPFLAGS_CHECKTYPES 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_INDEX
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#ifndef Py_TPFLAGS_HAVE_FINALIZE
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#ifndef Py_TPFLAGS_SEQUENCE
-  #define Py_TPFLAGS_SEQUENCE 0
-#endif
-#ifndef Py_TPFLAGS_MAPPING
-  #define Py_TPFLAGS_MAPPING 0
-#endif
-#ifndef METH_STACKLESS
-  #define METH_STACKLESS 0
-#endif
-#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)
-  #ifndef METH_FASTCALL
-     #define METH_FASTCALL 0x80
-  #endif
-  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);
-  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,
-                                                          Py_ssize_t nargs, PyObject *kwnames);
-#else
-  #define __Pyx_PyCFunctionFast _PyCFunctionFast
-  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
-#endif
-#if CYTHON_METH_FASTCALL
-  #define __Pyx_METH_FASTCALL METH_FASTCALL
-  #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast
-  #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords
-#else
-  #define __Pyx_METH_FASTCALL METH_VARARGS
-  #define __Pyx_PyCFunction_FastCall PyCFunction
-  #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords
-#endif
-#if CYTHON_VECTORCALL
-  #define __pyx_vectorcallfunc vectorcallfunc
-  #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET  PY_VECTORCALL_ARGUMENTS_OFFSET
-  #define __Pyx_PyVectorcall_NARGS(n)  PyVectorcall_NARGS((size_t)(n))
-#elif CYTHON_BACKPORT_VECTORCALL
-  typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args,
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-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GetLength(u)
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-  #define __Pyx_PyUnicode_DATA(u)         ((void*)u)
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-  #define __Pyx_PyUnicode_KIND(u)         ((int)sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
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-    return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
-    #include <cstdlib>
-    #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
-    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
-    #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
-    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*)(__pyx_uintptr_t) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-#if CYTHON_COMPILING_IN_LIMITED_API
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const wchar_t *u)
-{
-    const wchar_t *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#else
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
-{
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#endif
-#define __Pyx_PyUnicode_FromOrdinal(o)       PyUnicode_FromOrdinal((int)o)
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#if CYTHON_USE_PYLONG_INTERNALS
-  #if PY_VERSION_HEX >= 0x030C00A7
-  #ifndef _PyLong_SIGN_MASK
-    #define _PyLong_SIGN_MASK 3
-  #endif
-  #ifndef _PyLong_NON_SIZE_BITS
-    #define _PyLong_NON_SIZE_BITS 3
-  #endif
-  #define __Pyx_PyLong_Sign(x)  (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK)
-  #define __Pyx_PyLong_IsNeg(x)  ((__Pyx_PyLong_Sign(x) & 2) != 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (!__Pyx_PyLong_IsNeg(x))
-  #define __Pyx_PyLong_IsZero(x)  (__Pyx_PyLong_Sign(x) & 1)
-  #define __Pyx_PyLong_IsPos(x)  (__Pyx_PyLong_Sign(x) == 0)
-  #define __Pyx_PyLong_CompactValueUnsigned(x)  (__Pyx_PyLong_Digits(x)[0])
-  #define __Pyx_PyLong_DigitCount(x)  ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS))
-  #define __Pyx_PyLong_SignedDigitCount(x)\
-        ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x))
-  #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue)
-    #define __Pyx_PyLong_IsCompact(x)     PyUnstable_Long_IsCompact((PyLongObject*) x)
-    #define __Pyx_PyLong_CompactValue(x)  PyUnstable_Long_CompactValue((PyLongObject*) x)
-  #else
-    #define __Pyx_PyLong_IsCompact(x)     (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS))
-    #define __Pyx_PyLong_CompactValue(x)  ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0])
-  #endif
-  typedef Py_ssize_t  __Pyx_compact_pylong;
-  typedef size_t  __Pyx_compact_upylong;
-  #else  // Py < 3.12
-  #define __Pyx_PyLong_IsNeg(x)  (Py_SIZE(x) < 0)
-  #define __Pyx_PyLong_IsNonNeg(x)  (Py_SIZE(x) >= 0)
-  #define __Pyx_PyLong_IsZero(x)  (Py_SIZE(x) == 0)
-  #define __Pyx_PyLong_IsPos(x)  (Py_SIZE(x) > 0)
-  #define __Pyx_PyLong_CompactValueUnsigned(x)  ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0])
-  #define __Pyx_PyLong_DigitCount(x)  __Pyx_sst_abs(Py_SIZE(x))
-  #define __Pyx_PyLong_SignedDigitCount(x)  Py_SIZE(x)
-  #define __Pyx_PyLong_IsCompact(x)  (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1)
-  #define __Pyx_PyLong_CompactValue(x)\
-        ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0]))
-  typedef sdigit  __Pyx_compact_pylong;
-  typedef digit  __Pyx_compact_upylong;
-  #endif
-  #if PY_VERSION_HEX >= 0x030C00A5
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->long_value.ob_digit)
-  #else
-  #define __Pyx_PyLong_Digits(x)  (((PyLongObject*)x)->ob_digit)
-  #endif
-#endif
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = (char) c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
-
-#if !CYTHON_USE_MODULE_STATE
-static PyObject *__pyx_m = NULL;
-#endif
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm = __FILE__;
-static const char *__pyx_filename;
-
-/* #### Code section: filename_table ### */
-
-static const char *__pyx_f[] = {
-  "core.pyx",
-  "<stringsource>",
-};
-/* #### Code section: utility_code_proto_before_types ### */
-/* ForceInitThreads.proto */
-#ifndef __PYX_FORCE_INIT_THREADS
-  #define __PYX_FORCE_INIT_THREADS 0
-#endif
-
-/* NoFastGil.proto */
-#define __Pyx_PyGILState_Ensure PyGILState_Ensure
-#define __Pyx_PyGILState_Release PyGILState_Release
-#define __Pyx_FastGIL_Remember()
-#define __Pyx_FastGIL_Forget()
-#define __Pyx_FastGilFuncInit()
-
-/* BufferFormatStructs.proto */
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
-  const char* name;
-  struct __Pyx_StructField_* fields;
-  size_t size;
-  size_t arraysize[8];
-  int ndim;
-  char typegroup;
-  char is_unsigned;
-  int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
-  size_t offset;
-} __Pyx_StructField;
-typedef struct {
-  __Pyx_StructField* field;
-  size_t parent_offset;
-} __Pyx_BufFmt_StackElem;
-typedef struct {
-  __Pyx_StructField root;
-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
-  size_t new_count, enc_count;
-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-/* Atomics.proto */
-#include <pythread.h>
-#ifndef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 1
-#endif
-#define __PYX_CYTHON_ATOMICS_ENABLED() CYTHON_ATOMICS
-#define __pyx_atomic_int_type int
-#define __pyx_nonatomic_int_type int
-#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\
-                        (__STDC_VERSION__ >= 201112L) &&\
-                        !defined(__STDC_NO_ATOMICS__))
-    #include <stdatomic.h>
-#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\
-                    (__cplusplus >= 201103L) ||\
-                    (defined(_MSC_VER) && _MSC_VER >= 1700)))
-    #include <atomic>
-#endif
-#if CYTHON_ATOMICS && (defined(__STDC_VERSION__) &&\
-                        (__STDC_VERSION__ >= 201112L) &&\
-                        !defined(__STDC_NO_ATOMICS__) &&\
-                       ATOMIC_INT_LOCK_FREE == 2)
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type atomic_int
-    #define __pyx_atomic_incr_aligned(value) atomic_fetch_add_explicit(value, 1, memory_order_relaxed)
-    #define __pyx_atomic_decr_aligned(value) atomic_fetch_sub_explicit(value, 1, memory_order_acq_rel)
-    #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER)
-        #pragma message ("Using standard C atomics")
-    #elif defined(__PYX_DEBUG_ATOMICS)
-        #warning "Using standard C atomics"
-    #endif
-#elif CYTHON_ATOMICS && (defined(__cplusplus) && (\
-                    (__cplusplus >= 201103L) ||\
-\
-                    (defined(_MSC_VER) && _MSC_VER >= 1700)) &&\
-                    ATOMIC_INT_LOCK_FREE == 2)
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type std::atomic_int
-    #define __pyx_atomic_incr_aligned(value) std::atomic_fetch_add_explicit(value, 1, std::memory_order_relaxed)
-    #define __pyx_atomic_decr_aligned(value) std::atomic_fetch_sub_explicit(value, 1, std::memory_order_acq_rel)
-    #if defined(__PYX_DEBUG_ATOMICS) && defined(_MSC_VER)
-        #pragma message ("Using standard C++ atomics")
-    #elif defined(__PYX_DEBUG_ATOMICS)
-        #warning "Using standard C++ atomics"
-    #endif
-#elif CYTHON_ATOMICS && (__GNUC__ >= 5 || (__GNUC__ == 4 &&\
-                    (__GNUC_MINOR__ > 1 ||\
-                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL__ >= 2))))
-    #define __pyx_atomic_incr_aligned(value) __sync_fetch_and_add(value, 1)
-    #define __pyx_atomic_decr_aligned(value) __sync_fetch_and_sub(value, 1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using GNU atomics"
-    #endif
-#elif CYTHON_ATOMICS && defined(_MSC_VER)
-    #include <intrin.h>
-    #undef __pyx_atomic_int_type
-    #define __pyx_atomic_int_type long
-    #define __pyx_nonatomic_int_type long
-    #pragma intrinsic (_InterlockedExchangeAdd)
-    #define __pyx_atomic_incr_aligned(value) _InterlockedExchangeAdd(value, 1)
-    #define __pyx_atomic_decr_aligned(value) _InterlockedExchangeAdd(value, -1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #pragma message ("Using MSVC atomics")
-    #endif
-#else
-    #undef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 0
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Not using atomics"
-    #endif
-#endif
-#if CYTHON_ATOMICS
-    #define __pyx_add_acquisition_count(memview)\
-             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview))
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview))
-#else
-    #define __pyx_add_acquisition_count(memview)\
-            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview)\
-            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-#endif
-
-/* MemviewSliceStruct.proto */
-struct __pyx_memoryview_obj;
-typedef struct {
-  struct __pyx_memoryview_obj *memview;
-  char *data;
-  Py_ssize_t shape[8];
-  Py_ssize_t strides[8];
-  Py_ssize_t suboffsets[8];
-} __Pyx_memviewslice;
-#define __Pyx_MemoryView_Len(m)  (m.shape[0])
-
-/* #### Code section: numeric_typedefs ### */
-/* #### Code section: complex_type_declarations ### */
-/* #### Code section: type_declarations ### */
-
-/*--- Type declarations ---*/
-struct __pyx_array_obj;
-struct __pyx_MemviewEnum_obj;
-struct __pyx_memoryview_obj;
-struct __pyx_memoryviewslice_obj;
-struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each;
-
-/* "monotonic_align/core.pyx":7
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
- *   cdef int x
- *   cdef int y
- */
-struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each {
-  int __pyx_n;
-  float max_neg_val;
-};
-
-/* "View.MemoryView":114
- * @cython.collection_type("sequence")
- * @cname("__pyx_array")
- * cdef class array:             # <<<<<<<<<<<<<<
- * 
- *     cdef:
- */
-struct __pyx_array_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_array *__pyx_vtab;
-  char *data;
-  Py_ssize_t len;
-  char *format;
-  int ndim;
-  Py_ssize_t *_shape;
-  Py_ssize_t *_strides;
-  Py_ssize_t itemsize;
-  PyObject *mode;
-  PyObject *_format;
-  void (*callback_free_data)(void *);
-  int free_data;
-  int dtype_is_object;
-};
-
-
-/* "View.MemoryView":302
- * 
- * @cname('__pyx_MemviewEnum')
- * cdef class Enum(object):             # <<<<<<<<<<<<<<
- *     cdef object name
- *     def __init__(self, name):
- */
-struct __pyx_MemviewEnum_obj {
-  PyObject_HEAD
-  PyObject *name;
-};
-
-
-/* "View.MemoryView":337
- * 
- * @cname('__pyx_memoryview')
- * cdef class memoryview:             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-struct __pyx_memoryview_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
-  PyObject *obj;
-  PyObject *_size;
-  PyObject *_array_interface;
-  PyThread_type_lock lock;
-  __pyx_atomic_int_type acquisition_count;
-  Py_buffer view;
-  int flags;
-  int dtype_is_object;
-  __Pyx_TypeInfo *typeinfo;
-};
-
-
-/* "View.MemoryView":952
- * @cython.collection_type("sequence")
- * @cname('__pyx_memoryviewslice')
- * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
- *     "Internal class for passing memoryview slices to Python"
- * 
- */
-struct __pyx_memoryviewslice_obj {
-  struct __pyx_memoryview_obj __pyx_base;
-  __Pyx_memviewslice from_slice;
-  PyObject *from_object;
-  PyObject *(*to_object_func)(char *);
-  int (*to_dtype_func)(char *, PyObject *);
-};
-
-
-
-/* "View.MemoryView":114
- * @cython.collection_type("sequence")
- * @cname("__pyx_array")
- * cdef class array:             # <<<<<<<<<<<<<<
- * 
- *     cdef:
- */
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-struct __pyx_vtabstruct_array {
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-        (VAR) = __pyx_dict_cached_value = (LOOKUP);\
-        __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
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-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
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-#define __PYX_GET_DICT_VERSION(dict)  (0)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
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-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name)  do {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
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-        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
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-#define __Pyx_GetModuleGlobalNameUncached(var, name)  do {\
-    PY_UINT64_T __pyx_dict_version;\
-    PyObject *__pyx_dict_cached_value;\
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-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
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-
-/* AssertionsEnabled.proto */
-#define __Pyx_init_assertions_enabled()
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
-  #define __pyx_assertions_enabled() (1)
-#elif PY_VERSION_HEX < 0x03080000  ||  CYTHON_COMPILING_IN_PYPY  ||  defined(Py_LIMITED_API)
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6
-  static int __pyx_assertions_enabled_flag;
-  #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag)
-  #undef __Pyx_init_assertions_enabled
-  static void __Pyx_init_assertions_enabled(void) {
-    __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level;
-  }
-#else
-  #define __pyx_assertions_enabled() (!Py_OptimizeFlag)
-#endif
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* RaiseNoneIterError.proto */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
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-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
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-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* ImportDottedModule.proto */
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple);
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple);
-#endif
-
-/* ssize_strlen.proto */
-static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2))
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyErr_ExceptionMatches2(err1, err2)  __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2)
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-/* ListCompAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PySequenceMultiply.proto */
-#define __Pyx_PySequence_Multiply_Left(mul, seq)  __Pyx_PySequence_Multiply(seq, mul)
-static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul);
-
-/* SetItemInt.proto */
-#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\
-               __Pyx_SetItemInt_Generic(o, to_py_func(i), v)))
-static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
-static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
-                                               int is_list, int wraparound, int boundscheck);
-
-/* RaiseUnboundLocalError.proto */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-/* DivInt[long].proto */
-static CYTHON_INLINE long __Pyx_div_long(long, long);
-
-/* PySequenceContains.proto */
-static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
-    int result = PySequence_Contains(seq, item);
-    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
-}
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* HasAttr.proto */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
-
-/* ErrOccurredWithGIL.proto */
-static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void);
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* PyObject_GenericGetAttr.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
-#endif
-
-/* IncludeStructmemberH.proto */
-#include <structmember.h>
-
-/* FixUpExtensionType.proto */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type);
-#endif
-
-/* PyObjectCallNoArg.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
-
-/* PyObjectGetMethod.proto */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method);
-
-/* PyObjectCallMethod0.proto */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name);
-
-/* ValidateBasesTuple.proto */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases);
-#endif
-
-/* PyType_Ready.proto */
-CYTHON_UNUSED static int __Pyx_PyType_Ready(PyTypeObject *t);
-
-/* SetVTable.proto */
-static int __Pyx_SetVtable(PyTypeObject* typeptr , void* vtable);
-
-/* GetVTable.proto */
-static void* __Pyx_GetVtable(PyTypeObject *type);
-
-/* MergeVTables.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_MergeVtables(PyTypeObject *type);
-#endif
-
-/* SetupReduce.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_setup_reduce(PyObject* type_obj);
-#endif
-
-/* FetchSharedCythonModule.proto */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void);
-
-/* FetchCommonType.proto */
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-#else
-static PyTypeObject* __Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases);
-#endif
-
-/* PyMethodNew.proto */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_PyMethod_New(PyObject *func, PyObject *self, PyObject *typ) {
-    CYTHON_UNUSED_VAR(typ);
-    if (!self)
-        return __Pyx_NewRef(func);
-    return PyMethod_New(func, self);
-}
-#else
-    #define __Pyx_PyMethod_New PyMethod_New
-#endif
-
-/* PyVectorcallFastCallDict.proto */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw);
-#endif
-
-/* CythonFunctionShared.proto */
-#define __Pyx_CyFunction_USED
-#define __Pyx_CYFUNCTION_STATICMETHOD  0x01
-#define __Pyx_CYFUNCTION_CLASSMETHOD   0x02
-#define __Pyx_CYFUNCTION_CCLASS        0x04
-#define __Pyx_CYFUNCTION_COROUTINE     0x08
-#define __Pyx_CyFunction_GetClosure(f)\
-    (((__pyx_CyFunctionObject *) (f))->func_closure)
-#if PY_VERSION_HEX < 0x030900B1
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      (((__pyx_CyFunctionObject *) (f))->func_classobj)
-#else
-  #define __Pyx_CyFunction_GetClassObj(f)\
-      ((PyObject*) ((PyCMethodObject *) (f))->mm_class)
-#endif
-#define __Pyx_CyFunction_SetClassObj(f, classobj)\
-    __Pyx__CyFunction_SetClassObj((__pyx_CyFunctionObject *) (f), (classobj))
-#define __Pyx_CyFunction_Defaults(type, f)\
-    ((type *)(((__pyx_CyFunctionObject *) (f))->defaults))
-#define __Pyx_CyFunction_SetDefaultsGetter(f, g)\
-    ((__pyx_CyFunctionObject *) (f))->defaults_getter = (g)
-typedef struct {
-#if PY_VERSION_HEX < 0x030900B1
-    PyCFunctionObject func;
-#else
-    PyCMethodObject func;
-#endif
-#if CYTHON_BACKPORT_VECTORCALL
-    __pyx_vectorcallfunc func_vectorcall;
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    PyObject *func_weakreflist;
-#endif
-    PyObject *func_dict;
-    PyObject *func_name;
-    PyObject *func_qualname;
-    PyObject *func_doc;
-    PyObject *func_globals;
-    PyObject *func_code;
-    PyObject *func_closure;
-#if PY_VERSION_HEX < 0x030900B1
-    PyObject *func_classobj;
-#endif
-    void *defaults;
-    int defaults_pyobjects;
-    size_t defaults_size;  // used by FusedFunction for copying defaults
-    int flags;
-    PyObject *defaults_tuple;
-    PyObject *defaults_kwdict;
-    PyObject *(*defaults_getter)(PyObject *);
-    PyObject *func_annotations;
-    PyObject *func_is_coroutine;
-} __pyx_CyFunctionObject;
-#define __Pyx_CyFunction_Check(obj)  __Pyx_TypeCheck(obj, __pyx_CyFunctionType)
-#define __Pyx_IsCyOrPyCFunction(obj)  __Pyx_TypeCheck2(obj, __pyx_CyFunctionType, &PyCFunction_Type)
-#define __Pyx_CyFunction_CheckExact(obj)  __Pyx_IS_TYPE(obj, __pyx_CyFunctionType)
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject* op, PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj);
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *m,
-                                                         size_t size,
-                                                         int pyobjects);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *m,
-                                                            PyObject *tuple);
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *m,
-                                                             PyObject *dict);
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *m,
-                                                              PyObject *dict);
-static int __pyx_CyFunction_init(PyObject *module);
-#if CYTHON_METH_FASTCALL
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames);
-#if CYTHON_BACKPORT_VECTORCALL
-#define __Pyx_CyFunction_func_vectorcall(f) (((__pyx_CyFunctionObject*)f)->func_vectorcall)
-#else
-#define __Pyx_CyFunction_func_vectorcall(f) (((PyCFunctionObject*)f)->vectorcall)
-#endif
-#endif
-
-/* CythonFunction.proto */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml,
-                                      int flags, PyObject* qualname,
-                                      PyObject *closure,
-                                      PyObject *module, PyObject *globals,
-                                      PyObject* code);
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-#endif
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
-
-
-/* BufferStructDeclare.proto */
-typedef struct {
-  Py_ssize_t shape, strides, suboffsets;
-} __Pyx_Buf_DimInfo;
-typedef struct {
-  size_t refcount;
-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
-  __Pyx_Buffer *rcbuffer;
-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
-
-/* MemviewSliceIsContig.proto */
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
-
-/* OverlappingSlices.proto */
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                                __Pyx_memviewslice *slice2,
-                                int ndim, size_t itemsize);
-
-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
-
-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type);
-
-/* TypeInfoCompare.proto */
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-/* MemviewSliceValidateAndInit.proto */
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag);
-
-/* MemviewSliceCopyTemplate.proto */
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object);
-
-/* MemviewSliceInit.proto */
-#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d
-#define __Pyx_MEMVIEW_DIRECT   1
-#define __Pyx_MEMVIEW_PTR      2
-#define __Pyx_MEMVIEW_FULL     4
-#define __Pyx_MEMVIEW_CONTIG   8
-#define __Pyx_MEMVIEW_STRIDED  16
-#define __Pyx_MEMVIEW_FOLLOW   32
-#define __Pyx_IS_C_CONTIG 1
-#define __Pyx_IS_F_CONTIG 2
-static int __Pyx_init_memviewslice(
-                struct __pyx_memoryview_obj *memview,
-                int ndim,
-                __Pyx_memviewslice *memviewslice,
-                int memview_is_new_reference);
-static CYTHON_INLINE int __pyx_add_acquisition_count_locked(
-    __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock);
-static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(
-    __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock);
-#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count)
-#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__)
-#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__)
-static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int);
-static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-/* FormatTypeName.proto */
-#if CYTHON_COMPILING_IN_LIMITED_API
-typedef PyObject *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%U"
-static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp);
-#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj)
-#else
-typedef const char *__Pyx_TypeName;
-#define __Pyx_FMT_TYPENAME "%.200s"
-#define __Pyx_PyType_GetName(tp) ((tp)->tp_name)
-#define __Pyx_DECREF_TypeName(obj)
-#endif
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-/* #### Code section: module_declarations ### */
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/
-
-/* Module declarations from "cython.view" */
-
-/* Module declarations from "cython.dataclasses" */
-
-/* Module declarations from "cython" */
-
-/* Module declarations from "monotonic_align.core" */
-static PyObject *__pyx_collections_abc_Sequence = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static int __pyx_memoryview_thread_locks_used;
-static PyThread_type_lock __pyx_memoryview_thread_locks[8];
-static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/
-static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/
-static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/
-static int __pyx_memoryview_err_no_memory(void); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
-/* #### Code section: typeinfo ### */
-static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 };
-static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };
-/* #### Code section: before_global_var ### */
-#define __Pyx_MODULE_NAME "monotonic_align.core"
-extern int __pyx_module_is_main_monotonic_align__core;
-int __pyx_module_is_main_monotonic_align__core = 0;
-
-/* Implementation of "monotonic_align.core" */
-/* #### Code section: global_var ### */
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin___import__;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_AssertionError;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-/* #### Code section: string_decls ### */
-static const char __pyx_k_[] = ": ";
-static const char __pyx_k_O[] = "O";
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k__2[] = ".";
-static const char __pyx_k__3[] = "*";
-static const char __pyx_k__6[] = "'";
-static const char __pyx_k__7[] = ")";
-static const char __pyx_k_gc[] = "gc";
-static const char __pyx_k_id[] = "id";
-static const char __pyx_k__23[] = "?";
-static const char __pyx_k_abc[] = "abc";
-static const char __pyx_k_and[] = " and ";
-static const char __pyx_k_got[] = " (got ";
-static const char __pyx_k_new[] = "__new__";
-static const char __pyx_k_obj[] = "obj";
-static const char __pyx_k_sys[] = "sys";
-static const char __pyx_k_base[] = "base";
-static const char __pyx_k_dict[] = "__dict__";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_mode[] = "mode";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ndim[] = "ndim";
-static const char __pyx_k_pack[] = "pack";
-static const char __pyx_k_size[] = "size";
-static const char __pyx_k_spec[] = "__spec__";
-static const char __pyx_k_step[] = "step";
-static const char __pyx_k_stop[] = "stop";
-static const char __pyx_k_t_xs[] = "t_xs";
-static const char __pyx_k_t_ys[] = "t_ys";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_ASCII[] = "ASCII";
-static const char __pyx_k_class[] = "__class__";
-static const char __pyx_k_count[] = "count";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_flags[] = "flags";
-static const char __pyx_k_index[] = "index";
-static const char __pyx_k_paths[] = "paths";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_enable[] = "enable";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_pickle[] = "pickle";
-static const char __pyx_k_reduce[] = "__reduce__";
-static const char __pyx_k_struct[] = "struct";
-static const char __pyx_k_unpack[] = "unpack";
-static const char __pyx_k_update[] = "update";
-static const char __pyx_k_values[] = "values";
-static const char __pyx_k_disable[] = "disable";
-static const char __pyx_k_fortran[] = "fortran";
-static const char __pyx_k_memview[] = "memview";
-static const char __pyx_k_Ellipsis[] = "Ellipsis";
-static const char __pyx_k_Sequence[] = "Sequence";
-static const char __pyx_k_core_pyx[] = "core.pyx";
-static const char __pyx_k_getstate[] = "__getstate__";
-static const char __pyx_k_itemsize[] = "itemsize";
-static const char __pyx_k_pyx_type[] = "__pyx_type";
-static const char __pyx_k_register[] = "register";
-static const char __pyx_k_setstate[] = "__setstate__";
-static const char __pyx_k_TypeError[] = "TypeError";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_isenabled[] = "isenabled";
-static const char __pyx_k_pyx_state[] = "__pyx_state";
-static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
-static const char __pyx_k_IndexError[] = "IndexError";
-static const char __pyx_k_ValueError[] = "ValueError";
-static const char __pyx_k_pyx_result[] = "__pyx_result";
-static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static const char __pyx_k_MemoryError[] = "MemoryError";
-static const char __pyx_k_PickleError[] = "PickleError";
-static const char __pyx_k_collections[] = "collections";
-static const char __pyx_k_initializing[] = "_initializing";
-static const char __pyx_k_is_coroutine[] = "_is_coroutine";
-static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
-static const char __pyx_k_stringsource[] = "<stringsource>";
-static const char __pyx_k_version_info[] = "version_info";
-static const char __pyx_k_class_getitem[] = "__class_getitem__";
-static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
-static const char __pyx_k_AssertionError[] = "AssertionError";
-static const char __pyx_k_maximum_path_c[] = "maximum_path_c";
-static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
-static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static const char __pyx_k_collections_abc[] = "collections.abc";
-static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
-static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
-static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
-static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
-static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core";
-static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
-static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis ";
-static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
-static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '";
-static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
-static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
-static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
-static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct";
-static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)";
-static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)";
-static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
-static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced";
-static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
-static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
-static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions";
-static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))";
-static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got ";
-static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis ";
-static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension ";
-static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
-static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-/* #### Code section: decls ### */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
-static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
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-  Py_CLEAR(clear_module_state->__pyx_n_s_setstate);
-  Py_CLEAR(clear_module_state->__pyx_n_s_setstate_cython);
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-  Py_CLEAR(clear_module_state->__pyx_n_s_size);
-  Py_CLEAR(clear_module_state->__pyx_n_s_spec);
-  Py_CLEAR(clear_module_state->__pyx_n_s_start);
-  Py_CLEAR(clear_module_state->__pyx_n_s_step);
-  Py_CLEAR(clear_module_state->__pyx_n_s_stop);
-  Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct);
-  Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_direct_or_indirect);
-  Py_CLEAR(clear_module_state->__pyx_kp_s_strided_and_indirect);
-  Py_CLEAR(clear_module_state->__pyx_kp_s_stringsource);
-  Py_CLEAR(clear_module_state->__pyx_n_s_struct);
-  Py_CLEAR(clear_module_state->__pyx_n_s_sys);
-  Py_CLEAR(clear_module_state->__pyx_n_s_t_xs);
-  Py_CLEAR(clear_module_state->__pyx_n_s_t_ys);
-  Py_CLEAR(clear_module_state->__pyx_n_s_test);
-  Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_array_data);
-  Py_CLEAR(clear_module_state->__pyx_kp_s_unable_to_allocate_shape_and_str);
-  Py_CLEAR(clear_module_state->__pyx_n_s_unpack);
-  Py_CLEAR(clear_module_state->__pyx_n_s_update);
-  Py_CLEAR(clear_module_state->__pyx_n_s_values);
-  Py_CLEAR(clear_module_state->__pyx_n_s_version_info);
-  Py_CLEAR(clear_module_state->__pyx_int_0);
-  Py_CLEAR(clear_module_state->__pyx_int_1);
-  Py_CLEAR(clear_module_state->__pyx_int_3);
-  Py_CLEAR(clear_module_state->__pyx_int_112105877);
-  Py_CLEAR(clear_module_state->__pyx_int_136983863);
-  Py_CLEAR(clear_module_state->__pyx_int_184977713);
-  Py_CLEAR(clear_module_state->__pyx_int_neg_1);
-  Py_CLEAR(clear_module_state->__pyx_slice__5);
-  Py_CLEAR(clear_module_state->__pyx_tuple__4);
-  Py_CLEAR(clear_module_state->__pyx_tuple__8);
-  Py_CLEAR(clear_module_state->__pyx_tuple__10);
-  Py_CLEAR(clear_module_state->__pyx_tuple__11);
-  Py_CLEAR(clear_module_state->__pyx_tuple__12);
-  Py_CLEAR(clear_module_state->__pyx_tuple__13);
-  Py_CLEAR(clear_module_state->__pyx_tuple__14);
-  Py_CLEAR(clear_module_state->__pyx_tuple__15);
-  Py_CLEAR(clear_module_state->__pyx_tuple__16);
-  Py_CLEAR(clear_module_state->__pyx_tuple__17);
-  Py_CLEAR(clear_module_state->__pyx_tuple__18);
-  Py_CLEAR(clear_module_state->__pyx_tuple__19);
-  Py_CLEAR(clear_module_state->__pyx_tuple__21);
-  Py_CLEAR(clear_module_state->__pyx_codeobj__20);
-  Py_CLEAR(clear_module_state->__pyx_codeobj__22);
-  return 0;
-}
-#endif
-/* #### Code section: module_state_traverse ### */
-#if CYTHON_USE_MODULE_STATE
-static int __pyx_m_traverse(PyObject *m, visitproc visit, void *arg) {
-  __pyx_mstate *traverse_module_state = __pyx_mstate(m);
-  if (!traverse_module_state) return 0;
-  Py_VISIT(traverse_module_state->__pyx_d);
-  Py_VISIT(traverse_module_state->__pyx_b);
-  Py_VISIT(traverse_module_state->__pyx_cython_runtime);
-  Py_VISIT(traverse_module_state->__pyx_empty_tuple);
-  Py_VISIT(traverse_module_state->__pyx_empty_bytes);
-  Py_VISIT(traverse_module_state->__pyx_empty_unicode);
-  #ifdef __Pyx_CyFunction_USED
-  Py_VISIT(traverse_module_state->__pyx_CyFunctionType);
-  #endif
-  #ifdef __Pyx_FusedFunction_USED
-  Py_VISIT(traverse_module_state->__pyx_FusedFunctionType);
-  #endif
-  Py_VISIT(traverse_module_state->__pyx_array_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_array);
-  Py_VISIT(traverse_module_state->__pyx_MemviewEnum_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_MemviewEnum);
-  Py_VISIT(traverse_module_state->__pyx_memoryview_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryview);
-  Py_VISIT(traverse_module_state->__pyx_memoryviewslice_type);
-  Py_VISIT(traverse_module_state->__pyx_type___pyx_memoryviewslice);
-  Py_VISIT(traverse_module_state->__pyx_kp_u_);
-  Py_VISIT(traverse_module_state->__pyx_n_s_ASCII);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_AssertionError);
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-  Py_VISIT(traverse_module_state->__pyx_kp_s_Can_only_create_a_buffer_that_is);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_assign_to_read_only_memor);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_Cannot_create_writable_memory_vi);
-  Py_VISIT(traverse_module_state->__pyx_kp_u_Cannot_index_with_type);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_Ellipsis);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_ValueError);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_asyncio_coroutines);
-  Py_VISIT(traverse_module_state->__pyx_n_s_base);
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-  Py_VISIT(traverse_module_state->__pyx_n_u_c);
-  Py_VISIT(traverse_module_state->__pyx_n_s_class);
-  Py_VISIT(traverse_module_state->__pyx_n_s_class_getitem);
-  Py_VISIT(traverse_module_state->__pyx_n_s_cline_in_traceback);
-  Py_VISIT(traverse_module_state->__pyx_n_s_collections);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_collections_abc);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_direct);
-  Py_VISIT(traverse_module_state->__pyx_kp_s_contiguous_and_indirect);
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-  Py_VISIT(traverse_module_state->__pyx_kp_u_disable);
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-  Py_VISIT(traverse_module_state->__pyx_kp_u_enable);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_enumerate);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_fortran);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_t_ys);
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-  Py_VISIT(traverse_module_state->__pyx_n_s_unpack);
-  Py_VISIT(traverse_module_state->__pyx_n_s_update);
-  Py_VISIT(traverse_module_state->__pyx_n_s_values);
-  Py_VISIT(traverse_module_state->__pyx_n_s_version_info);
-  Py_VISIT(traverse_module_state->__pyx_int_0);
-  Py_VISIT(traverse_module_state->__pyx_int_1);
-  Py_VISIT(traverse_module_state->__pyx_int_3);
-  Py_VISIT(traverse_module_state->__pyx_int_112105877);
-  Py_VISIT(traverse_module_state->__pyx_int_136983863);
-  Py_VISIT(traverse_module_state->__pyx_int_184977713);
-  Py_VISIT(traverse_module_state->__pyx_int_neg_1);
-  Py_VISIT(traverse_module_state->__pyx_slice__5);
-  Py_VISIT(traverse_module_state->__pyx_tuple__4);
-  Py_VISIT(traverse_module_state->__pyx_tuple__8);
-  Py_VISIT(traverse_module_state->__pyx_tuple__10);
-  Py_VISIT(traverse_module_state->__pyx_tuple__11);
-  Py_VISIT(traverse_module_state->__pyx_tuple__12);
-  Py_VISIT(traverse_module_state->__pyx_tuple__13);
-  Py_VISIT(traverse_module_state->__pyx_tuple__14);
-  Py_VISIT(traverse_module_state->__pyx_tuple__15);
-  Py_VISIT(traverse_module_state->__pyx_tuple__16);
-  Py_VISIT(traverse_module_state->__pyx_tuple__17);
-  Py_VISIT(traverse_module_state->__pyx_tuple__18);
-  Py_VISIT(traverse_module_state->__pyx_tuple__19);
-  Py_VISIT(traverse_module_state->__pyx_tuple__21);
-  Py_VISIT(traverse_module_state->__pyx_codeobj__20);
-  Py_VISIT(traverse_module_state->__pyx_codeobj__22);
-  return 0;
-}
-#endif
-/* #### Code section: module_state_defines ### */
-#define __pyx_d __pyx_mstate_global->__pyx_d
-#define __pyx_b __pyx_mstate_global->__pyx_b
-#define __pyx_cython_runtime __pyx_mstate_global->__pyx_cython_runtime
-#define __pyx_empty_tuple __pyx_mstate_global->__pyx_empty_tuple
-#define __pyx_empty_bytes __pyx_mstate_global->__pyx_empty_bytes
-#define __pyx_empty_unicode __pyx_mstate_global->__pyx_empty_unicode
-#ifdef __Pyx_CyFunction_USED
-#define __pyx_CyFunctionType __pyx_mstate_global->__pyx_CyFunctionType
-#endif
-#ifdef __Pyx_FusedFunction_USED
-#define __pyx_FusedFunctionType __pyx_mstate_global->__pyx_FusedFunctionType
-#endif
-#ifdef __Pyx_Generator_USED
-#define __pyx_GeneratorType __pyx_mstate_global->__pyx_GeneratorType
-#endif
-#ifdef __Pyx_IterableCoroutine_USED
-#define __pyx_IterableCoroutineType __pyx_mstate_global->__pyx_IterableCoroutineType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineAwaitType __pyx_mstate_global->__pyx_CoroutineAwaitType
-#endif
-#ifdef __Pyx_Coroutine_USED
-#define __pyx_CoroutineType __pyx_mstate_global->__pyx_CoroutineType
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#endif
-#if CYTHON_USE_MODULE_STATE
-#define __pyx_type___pyx_array __pyx_mstate_global->__pyx_type___pyx_array
-#define __pyx_type___pyx_MemviewEnum __pyx_mstate_global->__pyx_type___pyx_MemviewEnum
-#define __pyx_type___pyx_memoryview __pyx_mstate_global->__pyx_type___pyx_memoryview
-#define __pyx_type___pyx_memoryviewslice __pyx_mstate_global->__pyx_type___pyx_memoryviewslice
-#endif
-#define __pyx_array_type __pyx_mstate_global->__pyx_array_type
-#define __pyx_MemviewEnum_type __pyx_mstate_global->__pyx_MemviewEnum_type
-#define __pyx_memoryview_type __pyx_mstate_global->__pyx_memoryview_type
-#define __pyx_memoryviewslice_type __pyx_mstate_global->__pyx_memoryviewslice_type
-#define __pyx_kp_u_ __pyx_mstate_global->__pyx_kp_u_
-#define __pyx_n_s_ASCII __pyx_mstate_global->__pyx_n_s_ASCII
-#define __pyx_kp_s_All_dimensions_preceding_dimensi __pyx_mstate_global->__pyx_kp_s_All_dimensions_preceding_dimensi
-#define __pyx_n_s_AssertionError __pyx_mstate_global->__pyx_n_s_AssertionError
-#define __pyx_kp_s_Buffer_view_does_not_expose_stri __pyx_mstate_global->__pyx_kp_s_Buffer_view_does_not_expose_stri
-#define __pyx_kp_s_Can_only_create_a_buffer_that_is __pyx_mstate_global->__pyx_kp_s_Can_only_create_a_buffer_that_is
-#define __pyx_kp_s_Cannot_assign_to_read_only_memor __pyx_mstate_global->__pyx_kp_s_Cannot_assign_to_read_only_memor
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-#define __pyx_kp_s_Dimension_d_is_not_direct __pyx_mstate_global->__pyx_kp_s_Dimension_d_is_not_direct
-#define __pyx_n_s_Ellipsis __pyx_mstate_global->__pyx_n_s_Ellipsis
-#define __pyx_kp_s_Empty_shape_tuple_for_cython_arr __pyx_mstate_global->__pyx_kp_s_Empty_shape_tuple_for_cython_arr
-#define __pyx_kp_s_Incompatible_checksums_0x_x_vs_0 __pyx_mstate_global->__pyx_kp_s_Incompatible_checksums_0x_x_vs_0
-#define __pyx_n_s_IndexError __pyx_mstate_global->__pyx_n_s_IndexError
-#define __pyx_kp_s_Index_out_of_bounds_axis_d __pyx_mstate_global->__pyx_kp_s_Index_out_of_bounds_axis_d
-#define __pyx_kp_s_Indirect_dimensions_not_supporte __pyx_mstate_global->__pyx_kp_s_Indirect_dimensions_not_supporte
-#define __pyx_kp_u_Invalid_mode_expected_c_or_fortr __pyx_mstate_global->__pyx_kp_u_Invalid_mode_expected_c_or_fortr
-#define __pyx_kp_u_Invalid_shape_in_axis __pyx_mstate_global->__pyx_kp_u_Invalid_shape_in_axis
-#define __pyx_n_s_MemoryError __pyx_mstate_global->__pyx_n_s_MemoryError
-#define __pyx_kp_s_MemoryView_of_r_at_0x_x __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_at_0x_x
-#define __pyx_kp_s_MemoryView_of_r_object __pyx_mstate_global->__pyx_kp_s_MemoryView_of_r_object
-#define __pyx_n_b_O __pyx_mstate_global->__pyx_n_b_O
-#define __pyx_kp_u_Out_of_bounds_on_buffer_access_a __pyx_mstate_global->__pyx_kp_u_Out_of_bounds_on_buffer_access_a
-#define __pyx_n_s_PickleError __pyx_mstate_global->__pyx_n_s_PickleError
-#define __pyx_n_s_Sequence __pyx_mstate_global->__pyx_n_s_Sequence
-#define __pyx_kp_s_Step_may_not_be_zero_axis_d __pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d
-#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError
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-#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError
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-#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3
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-#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c
-#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class
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-#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections
-#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc
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-#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect
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-#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict
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-#define __pyx_n_s_reduce_cython __pyx_mstate_global->__pyx_n_s_reduce_cython
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-#define __pyx_n_s_spec __pyx_mstate_global->__pyx_n_s_spec
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-#define __pyx_kp_s_strided_and_direct __pyx_mstate_global->__pyx_kp_s_strided_and_direct
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-      /* "View.MemoryView":770
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- * 
- *             slice_memviewslice(             # <<<<<<<<<<<<<<
- *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
- *                 dim, new_ndim, p_suboffset_dim,
- */
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-
-      /* "View.MemoryView":776
- *                 have_start, have_stop, have_step,
- *                 True)
- *             new_ndim += 1             # <<<<<<<<<<<<<<
- * 
- *     if isinstance(memview, _memoryviewslice):
- */
-      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);
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-
-    /* "View.MemoryView":747
- *     cdef bint have_start, have_stop, have_step
- * 
- *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
- *         if PyIndex_Check(index):
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- */
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-
-  /* "View.MemoryView":778
- *             new_ndim += 1
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,
- */
-  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type); 
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":779
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
-    __Pyx_XDECREF((PyObject *)__pyx_r);
-
-    /* "View.MemoryView":780
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_dtype_func,
- *                                     memview.dtype_is_object)
- */
-    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 780, __pyx_L1_error) }
-
-    /* "View.MemoryView":781
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- *     else:
- */
-    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); __PYX_ERR(1, 781, __pyx_L1_error) }
-
-    /* "View.MemoryView":779
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
-    __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 779, __pyx_L1_error)
-    __Pyx_GOTREF(__pyx_t_2);
-    if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_memoryview_type))))) __PYX_ERR(1, 779, __pyx_L1_error)
-    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_2);
-    __pyx_t_2 = 0;
-    goto __pyx_L0;
-
-    /* "View.MemoryView":778
- *             new_ndim += 1
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,
- */
-  }
-
-  /* "View.MemoryView":784
- *                                     memview.dtype_is_object)
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- * 
- */
-  /*else*/ {
-    __Pyx_XDECREF((PyObject *)__pyx_r);
-
-    /* "View.MemoryView":785
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,
- *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_t_2 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_2)) __PYX_ERR(1, 784, __pyx_L1_error)
-    __Pyx_GOTREF(__pyx_t_2);
-
-    /* "View.MemoryView":784
- *                                     memview.dtype_is_object)
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- * 
- */
-    if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_memoryview_type))))) __PYX_ERR(1, 784, __pyx_L1_error)
-    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_2);
-    __pyx_t_2 = 0;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":711
- * 
- * @cname('__pyx_memview_slice')
- * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
- *     cdef int new_ndim = 0, suboffset_dim = -1, dim
- *     cdef bint negative_step
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_8);
-  __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);
-  __Pyx_XDECREF(__pyx_v_index);
-  __Pyx_XGIVEREF((PyObject *)__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":793
- * 
- * @cname('__pyx_memoryview_slice_memviewslice')
- * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
- *         __Pyx_memviewslice *dst,
- *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
- */
-
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {
-  Py_ssize_t __pyx_v_new_shape;
-  int __pyx_v_negative_step;
-  int __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
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-  PyGILState_STATE __pyx_gilstate_save;
-  #endif
-
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- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-  __pyx_t_1 = (!__pyx_v_is_slice);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":815
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    __pyx_t_1 = (__pyx_v_start < 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":816
- * 
- *         if start < 0:
- *             start += shape             # <<<<<<<<<<<<<<
- *         if not 0 <= start < shape:
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- */
-      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-      /* "View.MemoryView":815
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    }
-
-    /* "View.MemoryView":817
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    __pyx_t_1 = (0 <= __pyx_v_start);
-    if (__pyx_t_1) {
-      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
-    }
-    __pyx_t_2 = (!__pyx_t_1);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":818
- *             start += shape
- *         if not 0 <= start < shape:
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
- *     else:
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_IndexError, __pyx_kp_s_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 818, __pyx_L1_error)
-
-      /* "View.MemoryView":817
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    }
-
-    /* "View.MemoryView":813
- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":821
- *     else:
- * 
- *         if have_step:             # <<<<<<<<<<<<<<
- *             negative_step = step < 0
- *             if step == 0:
- */
-  /*else*/ {
-    __pyx_t_2 = (__pyx_v_have_step != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":822
- * 
- *         if have_step:
- *             negative_step = step < 0             # <<<<<<<<<<<<<<
- *             if step == 0:
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- */
-      __pyx_v_negative_step = (__pyx_v_step < 0);
-
-      /* "View.MemoryView":823
- *         if have_step:
- *             negative_step = step < 0
- *             if step == 0:             # <<<<<<<<<<<<<<
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- */
-      __pyx_t_2 = (__pyx_v_step == 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":824
- *             negative_step = step < 0
- *             if step == 0:
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
- *         else:
- *             negative_step = False
- */
-        __pyx_t_3 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 824, __pyx_L1_error)
-
-        /* "View.MemoryView":823
- *         if have_step:
- *             negative_step = step < 0
- *             if step == 0:             # <<<<<<<<<<<<<<
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- */
-      }
-
-      /* "View.MemoryView":821
- *     else:
- * 
- *         if have_step:             # <<<<<<<<<<<<<<
- *             negative_step = step < 0
- *             if step == 0:
- */
-      goto __pyx_L6;
-    }
-
-    /* "View.MemoryView":826
- *                 _err_dim(PyExc_ValueError, "Step may not be zero (axis %d)", dim)
- *         else:
- *             negative_step = False             # <<<<<<<<<<<<<<
- *             step = 1
- * 
- */
-    /*else*/ {
-      __pyx_v_negative_step = 0;
-
-      /* "View.MemoryView":827
- *         else:
- *             negative_step = False
- *             step = 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_step = 1;
-    }
-    __pyx_L6:;
-
-    /* "View.MemoryView":830
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-    __pyx_t_2 = (__pyx_v_have_start != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":831
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-      __pyx_t_2 = (__pyx_v_start < 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":832
- *         if have_start:
- *             if start < 0:
- *                 start += shape             # <<<<<<<<<<<<<<
- *                 if start < 0:
- *                     start = 0
- */
-        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-        /* "View.MemoryView":833
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        __pyx_t_2 = (__pyx_v_start < 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":834
- *                 start += shape
- *                 if start < 0:
- *                     start = 0             # <<<<<<<<<<<<<<
- *             elif start >= shape:
- *                 if negative_step:
- */
-          __pyx_v_start = 0;
-
-          /* "View.MemoryView":833
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        }
-
-        /* "View.MemoryView":831
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-        goto __pyx_L9;
-      }
-
-      /* "View.MemoryView":835
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      __pyx_t_2 = (__pyx_v_start >= __pyx_v_shape);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":836
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-        if (__pyx_v_negative_step) {
-
-          /* "View.MemoryView":837
- *             elif start >= shape:
- *                 if negative_step:
- *                     start = shape - 1             # <<<<<<<<<<<<<<
- *                 else:
- *                     start = shape
- */
-          __pyx_v_start = (__pyx_v_shape - 1);
-
-          /* "View.MemoryView":836
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-          goto __pyx_L11;
-        }
-
-        /* "View.MemoryView":839
- *                     start = shape - 1
- *                 else:
- *                     start = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        /*else*/ {
-          __pyx_v_start = __pyx_v_shape;
-        }
-        __pyx_L11:;
-
-        /* "View.MemoryView":835
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      }
-      __pyx_L9:;
-
-      /* "View.MemoryView":830
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-      goto __pyx_L8;
-    }
-
-    /* "View.MemoryView":841
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-    /*else*/ {
-      if (__pyx_v_negative_step) {
-
-        /* "View.MemoryView":842
- *         else:
- *             if negative_step:
- *                 start = shape - 1             # <<<<<<<<<<<<<<
- *             else:
- *                 start = 0
- */
-        __pyx_v_start = (__pyx_v_shape - 1);
-
-        /* "View.MemoryView":841
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-        goto __pyx_L12;
-      }
-
-      /* "View.MemoryView":844
- *                 start = shape - 1
- *             else:
- *                 start = 0             # <<<<<<<<<<<<<<
- * 
- *         if have_stop:
- */
-      /*else*/ {
-        __pyx_v_start = 0;
-      }
-      __pyx_L12:;
-    }
-    __pyx_L8:;
-
-    /* "View.MemoryView":846
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-    __pyx_t_2 = (__pyx_v_have_stop != 0);
-    if (__pyx_t_2) {
-
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- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-      __pyx_t_2 = (__pyx_v_stop < 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":848
- *         if have_stop:
- *             if stop < 0:
- *                 stop += shape             # <<<<<<<<<<<<<<
- *                 if stop < 0:
- *                     stop = 0
- */
-        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
-
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- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        __pyx_t_2 = (__pyx_v_stop < 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":850
- *                 stop += shape
- *                 if stop < 0:
- *                     stop = 0             # <<<<<<<<<<<<<<
- *             elif stop > shape:
- *                 stop = shape
- */
-          __pyx_v_stop = 0;
-
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- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        }
-
-        /* "View.MemoryView":847
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-        goto __pyx_L14;
-      }
-
-      /* "View.MemoryView":851
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      __pyx_t_2 = (__pyx_v_stop > __pyx_v_shape);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":852
- *                     stop = 0
- *             elif stop > shape:
- *                 stop = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        __pyx_v_stop = __pyx_v_shape;
-
-        /* "View.MemoryView":851
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      }
-      __pyx_L14:;
-
-      /* "View.MemoryView":846
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-      goto __pyx_L13;
-    }
-
-    /* "View.MemoryView":854
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-    /*else*/ {
-      if (__pyx_v_negative_step) {
-
-        /* "View.MemoryView":855
- *         else:
- *             if negative_step:
- *                 stop = -1             # <<<<<<<<<<<<<<
- *             else:
- *                 stop = shape
- */
-        __pyx_v_stop = -1L;
-
-        /* "View.MemoryView":854
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-        goto __pyx_L16;
-      }
-
-      /* "View.MemoryView":857
- *                 stop = -1
- *             else:
- *                 stop = shape             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      /*else*/ {
-        __pyx_v_stop = __pyx_v_shape;
-      }
-      __pyx_L16:;
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-      goto __pyx_L4_break;
-
-      /* "View.MemoryView":1122
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             c_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L4_break:;
-
-  /* "View.MemoryView":1126
- *             break
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- */
-  __pyx_t_1 = __pyx_v_ndim;
-  __pyx_t_3 = __pyx_t_1;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-
-    /* "View.MemoryView":1127
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    __pyx_t_2 = ((__pyx_v_mslice->shape[__pyx_v_i]) > 1);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1128
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
- *             break
- * 
- */
-      __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-      /* "View.MemoryView":1129
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- */
-      goto __pyx_L7_break;
-
-      /* "View.MemoryView":1127
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L7_break:;
-
-  /* "View.MemoryView":1131
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  __pyx_t_2 = (abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride));
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1132
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- *         return 'C'             # <<<<<<<<<<<<<<
- *     else:
- *         return 'F'
- */
-    __pyx_r = 'C';
-    goto __pyx_L0;
-
-    /* "View.MemoryView":1131
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  }
-
-  /* "View.MemoryView":1134
- *         return 'C'
- *     else:
- *         return 'F'             # <<<<<<<<<<<<<<
- * 
- * @cython.cdivision(True)
- */
-  /*else*/ {
-    __pyx_r = 'F';
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1113
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1137
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
-  Py_ssize_t __pyx_v_dst_extent;
-  Py_ssize_t __pyx_v_src_stride;
-  Py_ssize_t __pyx_v_dst_stride;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  Py_ssize_t __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-
-  /* "View.MemoryView":1144
- * 
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- */
-  __pyx_v_src_extent = (__pyx_v_src_shape[0]);
-
-  /* "View.MemoryView":1145
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- */
-  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
-
-  /* "View.MemoryView":1146
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- */
-  __pyx_v_src_stride = (__pyx_v_src_strides[0]);
-
-  /* "View.MemoryView":1147
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
- * 
- *     if ndim == 1:
- */
-  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
-
-  /* "View.MemoryView":1149
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-  __pyx_t_1 = (__pyx_v_ndim == 1);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    __pyx_t_2 = (__pyx_v_src_stride > 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-    __pyx_t_2 = (__pyx_v_dst_stride > 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-
-    /* "View.MemoryView":1151
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- *         else:
- */
-    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
-    if (__pyx_t_2) {
-      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
-    }
-    __pyx_t_1 = __pyx_t_2;
-    __pyx_L5_bool_binop_done:;
-
-    /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":1152
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
- *         else:
- *             for i in range(dst_extent):
- */
-      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
-
-      /* "View.MemoryView":1150
- * 
- *     if ndim == 1:
- *         if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-      goto __pyx_L4;
-    }
-
-    /* "View.MemoryView":1154
- *             memcpy(dst_data, src_data, itemsize * dst_extent)
- *         else:
- *             for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride
- */
-    /*else*/ {
-      __pyx_t_3 = __pyx_v_dst_extent;
-      __pyx_t_4 = __pyx_t_3;
-      for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-        __pyx_v_i = __pyx_t_5;
-
-        /* "View.MemoryView":1155
- *         else:
- *             for i in range(dst_extent):
- *                 memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
- *                 src_data += src_stride
- *                 dst_data += dst_stride
- */
-        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
-
-        /* "View.MemoryView":1156
- *             for i in range(dst_extent):
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride             # <<<<<<<<<<<<<<
- *                 dst_data += dst_stride
- *     else:
- */
-        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-        /* "View.MemoryView":1157
- *                 memcpy(dst_data, src_data, itemsize)
- *                 src_data += src_stride
- *                 dst_data += dst_stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(dst_extent):
- */
-        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-      }
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":1149
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *         if (src_stride > 0 and dst_stride > 0 and
- *             <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1159
- *                 dst_data += dst_stride
- *     else:
- *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *             _copy_strided_to_strided(src_data, src_strides + 1,
- *                                      dst_data, dst_strides + 1,
- */
-  /*else*/ {
-    __pyx_t_3 = __pyx_v_dst_extent;
-    __pyx_t_4 = __pyx_t_3;
-    for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
-      __pyx_v_i = __pyx_t_5;
-
-      /* "View.MemoryView":1160
- *     else:
- *         for i in range(dst_extent):
- *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
- *                                      dst_data, dst_strides + 1,
- *                                      src_shape + 1, dst_shape + 1,
- */
-      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
-      /* "View.MemoryView":1164
- *                                      src_shape + 1, dst_shape + 1,
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride             # <<<<<<<<<<<<<<
- *             dst_data += dst_stride
- * 
- */
-      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-      /* "View.MemoryView":1165
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride
- *             dst_data += dst_stride             # <<<<<<<<<<<<<<
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
-      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1137
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1167
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
-  /* "View.MemoryView":1170
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
- *                              src.shape, dst.shape, ndim, itemsize)
- * 
- */
-  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1167
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) noexcept nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1174
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
-  Py_ssize_t __pyx_v_shape;
-  Py_ssize_t __pyx_v_size;
-  Py_ssize_t __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  Py_ssize_t *__pyx_t_3;
-  Py_ssize_t *__pyx_t_4;
-
-  /* "View.MemoryView":1176
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     for shape in src.shape[:ndim]:
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_size = __pyx_t_1;
-
-  /* "View.MemoryView":1178
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- * 
- *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
- *         size *= shape
- * 
- */
-  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
-  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
-    __pyx_t_2 = __pyx_t_4;
-    __pyx_v_shape = (__pyx_t_2[0]);
-
-    /* "View.MemoryView":1179
- * 
- *     for shape in src.shape[:ndim]:
- *         size *= shape             # <<<<<<<<<<<<<<
- * 
- *     return size
- */
-    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
-  }
-
-  /* "View.MemoryView":1181
- *         size *= shape
- * 
- *     return size             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_fill_contig_strides_array')
- */
-  __pyx_r = __pyx_v_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1174
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1184
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) noexcept nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
-  /* "View.MemoryView":1193
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-  __pyx_t_1 = (__pyx_v_order == 'F');
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1194
- * 
- *     if order == 'F':
- *         for idx in range(ndim):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-    __pyx_t_2 = __pyx_v_ndim;
-    __pyx_t_3 = __pyx_t_2;
-    for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-      __pyx_v_idx = __pyx_t_4;
-
-      /* "View.MemoryView":1195
- *     if order == 'F':
- *         for idx in range(ndim):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- *     else:
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1196
- *         for idx in range(ndim):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-
-    /* "View.MemoryView":1193
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1198
- *             stride *= shape[idx]
- *     else:
- *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride *= shape[idx]
- */
-  /*else*/ {
-    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
-      __pyx_v_idx = __pyx_t_2;
-
-      /* "View.MemoryView":1199
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride *= shape[idx]
- * 
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1200
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride
- *             stride *= shape[idx]             # <<<<<<<<<<<<<<
- * 
- *     return stride
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-  }
-  __pyx_L3:;
-
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- * @cname('__pyx_memoryview_err_no_memory')
- * cdef int _err_no_memory() except -1 with gil:             # <<<<<<<<<<<<<<
- *     raise MemoryError
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_AddTraceback("View.MemoryView._err_no_memory", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __Pyx_RefNannyFinishContext();
-  #ifdef WITH_THREAD
-  __Pyx_PyGILState_Release(__pyx_gilstate_save);
-  #endif
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1265
- * 
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice dst,
- *                                   int src_ndim, int dst_ndim,
- */
-
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) {
-  void *__pyx_v_tmpdata;
-  size_t __pyx_v_itemsize;
-  int __pyx_v_i;
-  char __pyx_v_order;
-  int __pyx_v_broadcasting;
-  int __pyx_v_direct_copy;
-  __Pyx_memviewslice __pyx_v_tmp;
-  int __pyx_v_ndim;
-  int __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  int __pyx_t_5;
-  int __pyx_t_6;
-  void *__pyx_t_7;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save;
-  #endif
-
-  /* "View.MemoryView":1273
- *     Check for overlapping memory and verify the shapes.
- *     """
- *     cdef void *tmpdata = NULL             # <<<<<<<<<<<<<<
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- */
-  __pyx_v_tmpdata = NULL;
-
-  /* "View.MemoryView":1274
- *     """
- *     cdef void *tmpdata = NULL
- *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- */
-  __pyx_t_1 = __pyx_v_src.memview->view.itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
-
-  /* "View.MemoryView":1276
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)             # <<<<<<<<<<<<<<
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False
- */
-  __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim);
-
-  /* "View.MemoryView":1277
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False             # <<<<<<<<<<<<<<
- *     cdef bint direct_copy = False
- *     cdef __Pyx_memviewslice tmp
- */
-  __pyx_v_broadcasting = 0;
-
-  /* "View.MemoryView":1278
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False             # <<<<<<<<<<<<<<
- *     cdef __Pyx_memviewslice tmp
- * 
- */
-  __pyx_v_direct_copy = 0;
-
-  /* "View.MemoryView":1281
- *     cdef __Pyx_memviewslice tmp
- * 
- *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- */
-  __pyx_t_2 = (__pyx_v_src_ndim < __pyx_v_dst_ndim);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1282
- * 
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim);
-
-    /* "View.MemoryView":1281
- *     cdef __Pyx_memviewslice tmp
- * 
- *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1283
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- */
-  __pyx_t_2 = (__pyx_v_dst_ndim < __pyx_v_src_ndim);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1284
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)             # <<<<<<<<<<<<<<
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);
-
-    /* "View.MemoryView":1283
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- */
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1286
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-  __pyx_t_3 = __pyx_v_dst_ndim;
-  __pyx_t_4 = __pyx_v_src_ndim;
-  if ((__pyx_t_3 > __pyx_t_4)) {
-    __pyx_t_5 = __pyx_t_3;
-  } else {
-    __pyx_t_5 = __pyx_t_4;
-  }
-  __pyx_v_ndim = __pyx_t_5;
-
-  /* "View.MemoryView":1288
- *     cdef int ndim = max(src_ndim, dst_ndim)
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- */
-  __pyx_t_5 = __pyx_v_ndim;
-  __pyx_t_3 = __pyx_t_5;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-
-    /* "View.MemoryView":1289
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i]));
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1290
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-      __pyx_t_2 = ((__pyx_v_src.shape[__pyx_v_i]) == 1);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":1291
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- *                 broadcasting = True             # <<<<<<<<<<<<<<
- *                 src.strides[i] = 0
- *             else:
- */
-        __pyx_v_broadcasting = 1;
-
-        /* "View.MemoryView":1292
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- */
-        (__pyx_v_src.strides[__pyx_v_i]) = 0;
-
-        /* "View.MemoryView":1290
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-        goto __pyx_L7;
-      }
-
-      /* "View.MemoryView":1294
- *                 src.strides[i] = 0
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
- * 
- *         if src.suboffsets[i] >= 0:
- */
-      /*else*/ {
-        __pyx_t_6 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1294, __pyx_L1_error)
-      }
-      __pyx_L7:;
-
-      /* "View.MemoryView":1289
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    }
-
-    /* "View.MemoryView":1296
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- */
-    __pyx_t_2 = ((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1297
- * 
- *         if src.suboffsets[i] >= 0:
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- */
-      __pyx_t_6 = __pyx_memoryview_err_dim(PyExc_ValueError, __pyx_kp_s_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_6 == ((int)-1))) __PYX_ERR(1, 1297, __pyx_L1_error)
-
-      /* "View.MemoryView":1296
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- */
-    }
-  }
-
-  /* "View.MemoryView":1299
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  __pyx_t_2 = __pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1301
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    __pyx_t_2 = (!__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim));
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1302
- * 
- *         if not slice_is_contig(src, order, ndim):
- *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- */
-      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
-
-      /* "View.MemoryView":1301
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    }
-
-    /* "View.MemoryView":1304
- *             order = get_best_order(&dst, ndim)
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
- *         src = tmp
- * 
- */
-    __pyx_t_7 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_7 == ((void *)NULL))) __PYX_ERR(1, 1304, __pyx_L1_error)
-    __pyx_v_tmpdata = __pyx_t_7;
-
-    /* "View.MemoryView":1305
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- *         src = tmp             # <<<<<<<<<<<<<<
- * 
- *     if not broadcasting:
- */
-    __pyx_v_src = __pyx_v_tmp;
-
-    /* "View.MemoryView":1299
- *             _err_dim(PyExc_ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(src, order, ndim):
- */
-  }
-
-  /* "View.MemoryView":1307
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (!__pyx_v_broadcasting);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1310
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-    __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'C', __pyx_v_ndim);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1311
- * 
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)             # <<<<<<<<<<<<<<
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'C', __pyx_v_ndim);
-
-      /* "View.MemoryView":1310
- * 
- * 
- *         if slice_is_contig(src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- */
-      goto __pyx_L12;
-    }
-
-    /* "View.MemoryView":1312
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    __pyx_t_2 = __pyx_memviewslice_is_contig(__pyx_v_src, 'F', __pyx_v_ndim);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1313
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):
- *             direct_copy = slice_is_contig(dst, 'F', ndim)             # <<<<<<<<<<<<<<
- * 
- *         if direct_copy:
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig(__pyx_v_dst, 'F', __pyx_v_ndim);
-
-      /* "View.MemoryView":1312
- *         if slice_is_contig(src, 'C', ndim):
- *             direct_copy = slice_is_contig(dst, 'C', ndim)
- *         elif slice_is_contig(src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- */
-    }
-    __pyx_L12:;
-
-    /* "View.MemoryView":1315
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    if (__pyx_v_direct_copy) {
-
-      /* "View.MemoryView":1317
- *         if direct_copy:
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)             # <<<<<<<<<<<<<<
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-      /* "View.MemoryView":1318
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)
- */
-      (void)(memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim)));
-
-      /* "View.MemoryView":1319
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)             # <<<<<<<<<<<<<<
- *             free(tmpdata)
- *             return 0
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-      /* "View.MemoryView":1320
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)             # <<<<<<<<<<<<<<
- *             return 0
- * 
- */
-      free(__pyx_v_tmpdata);
-
-      /* "View.MemoryView":1321
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- *             free(tmpdata)
- *             return 0             # <<<<<<<<<<<<<<
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):
- */
-      __pyx_r = 0;
-      goto __pyx_L0;
-
-      /* "View.MemoryView":1315
- *             direct_copy = slice_is_contig(dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    }
-
-    /* "View.MemoryView":1307
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1323
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (__pyx_v_order == 'F');
-  if (__pyx_t_2) {
-    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
-  }
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1326
- * 
- * 
- *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
- *         transpose_memslice(&dst)
- * 
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1326, __pyx_L1_error)
-
-    /* "View.MemoryView":1327
- * 
- *         transpose_memslice(&src)
- *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == ((int)-1))) __PYX_ERR(1, 1327, __pyx_L1_error)
-
-    /* "View.MemoryView":1323
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  }
-
-  /* "View.MemoryView":1329
- *         transpose_memslice(&dst)
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)             # <<<<<<<<<<<<<<
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-  /* "View.MemoryView":1330
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=True)
- * 
- */
-  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1331
- *     refcount_copying(&dst, dtype_is_object, ndim, inc=False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
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-    __pyx_t_10 = __pyx_v_index;
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-                #undef likely
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-            if (__pyx_t_3 > 0)
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-  {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-#if !CYTHON_COMPILING_IN_LIMITED_API
-
-static PyBufferProcs __pyx_tp_as_buffer_array = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_array_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-#endif
-static PyType_Slot __pyx_type___pyx_array_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc_array},
-  {Py_sq_length, (void *)__pyx_array___len__},
-  {Py_sq_item, (void *)__pyx_sq_item_array},
-  {Py_mp_length, (void *)__pyx_array___len__},
-  {Py_mp_subscript, (void *)__pyx_array___getitem__},
-  {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_array},
-  {Py_tp_getattro, (void *)__pyx_tp_getattro_array},
-  #if defined(Py_bf_getbuffer)
-  {Py_bf_getbuffer, (void *)__pyx_array_getbuffer},
-  #endif
-  {Py_tp_methods, (void *)__pyx_methods_array},
-  {Py_tp_getset, (void *)__pyx_getsets_array},
-  {Py_tp_new, (void *)__pyx_tp_new_array},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_array_spec = {
-  "monotonic_align.core.array",
-  sizeof(struct __pyx_array_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE,
-  __pyx_type___pyx_array_slots,
-};
-#else
-
-static PySequenceMethods __pyx_tp_as_sequence_array = {
-  __pyx_array___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_array, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_array = {
-  __pyx_array___len__, /*mp_length*/
-  __pyx_array___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_array = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_array_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_array = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""array", /*tp_name*/
-  sizeof(struct __pyx_array_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_array, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  0, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  __pyx_tp_getattro_array, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_SEQUENCE, /*tp_flags*/
-  0, /*tp_doc*/
-  0, /*tp_traverse*/
-  0, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_array, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_array, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_array, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
-  struct __pyx_MemviewEnum_obj *p;
-  PyObject *o;
-  #if CYTHON_COMPILING_IN_LIMITED_API
-  allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc);
-  o = alloc_func(t, 0);
-  #else
-  if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  #endif
-  p = ((struct __pyx_MemviewEnum_obj *)o);
-  p->name = Py_None; Py_INCREF(Py_None);
-  return o;
-}
-
-static void __pyx_tp_dealloc_Enum(PyObject *o) {
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) {
-    if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_Enum) {
-      if (PyObject_CallFinalizerFromDealloc(o)) return;
-    }
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  Py_CLEAR(p->name);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  if (p->name) {
-    e = (*v)(p->name, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_Enum(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  tmp = ((PyObject*)p->name);
-  p->name = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  return 0;
-}
-
-static PyObject *__pyx_specialmethod___pyx_MemviewEnum___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) {
-  return __pyx_MemviewEnum___repr__(self);
-}
-
-static PyMethodDef __pyx_methods_Enum[] = {
-  {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_MemviewEnum___repr__, METH_NOARGS|METH_COEXIST, 0},
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_MemviewEnum_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_type___pyx_MemviewEnum_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc_Enum},
-  {Py_tp_repr, (void *)__pyx_MemviewEnum___repr__},
-  {Py_tp_traverse, (void *)__pyx_tp_traverse_Enum},
-  {Py_tp_clear, (void *)__pyx_tp_clear_Enum},
-  {Py_tp_methods, (void *)__pyx_methods_Enum},
-  {Py_tp_init, (void *)__pyx_MemviewEnum___init__},
-  {Py_tp_new, (void *)__pyx_tp_new_Enum},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_MemviewEnum_spec = {
-  "monotonic_align.core.Enum",
-  sizeof(struct __pyx_MemviewEnum_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC,
-  __pyx_type___pyx_MemviewEnum_slots,
-};
-#else
-
-static PyTypeObject __pyx_type___pyx_MemviewEnum = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""Enum", /*tp_name*/
-  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_MemviewEnum___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_Enum, /*tp_traverse*/
-  __pyx_tp_clear_Enum, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_Enum, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  __pyx_MemviewEnum___init__, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_Enum, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
-
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryview_obj *p;
-  PyObject *o;
-  #if CYTHON_COMPILING_IN_LIMITED_API
-  allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc);
-  o = alloc_func(t, 0);
-  #else
-  if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  #endif
-  p = ((struct __pyx_memoryview_obj *)o);
-  p->__pyx_vtab = __pyx_vtabptr_memoryview;
-  p->obj = Py_None; Py_INCREF(Py_None);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  p->view.obj = NULL;
-  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad;
-  return o;
-  bad:
-  Py_DECREF(o); o = 0;
-  return NULL;
-}
-
-static void __pyx_tp_dealloc_memoryview(PyObject *o) {
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) {
-    if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) {
-      if (PyObject_CallFinalizerFromDealloc(o)) return;
-    }
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryview___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->obj);
-  Py_CLEAR(p->_size);
-  Py_CLEAR(p->_array_interface);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  if (p->obj) {
-    e = (*v)(p->obj, a); if (e) return e;
-  }
-  if (p->_size) {
-    e = (*v)(p->_size, a); if (e) return e;
-  }
-  if (p->_array_interface) {
-    e = (*v)(p->_array_interface, a); if (e) return e;
-  }
-  if (p->view.obj) {
-    e = (*v)(p->view.obj, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  tmp = ((PyObject*)p->obj);
-  p->obj = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_size);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_array_interface);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  Py_CLEAR(p->view.obj);
-  return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_memoryview___setitem__(o, i, v);
-  }
-  else {
-    __Pyx_TypeName o_type_name;
-    o_type_name = __Pyx_PyType_GetName(Py_TYPE(o));
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name);
-    __Pyx_DECREF_TypeName(o_type_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
-}
-
-static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) {
-  return __pyx_memoryview___repr__(self);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
-  {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0},
-  {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
-  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
-  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
-  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
-  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
-  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
-  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
-  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
-  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
-  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-#if !CYTHON_COMPILING_IN_LIMITED_API
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-#endif
-static PyType_Slot __pyx_type___pyx_memoryview_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview},
-  {Py_tp_repr, (void *)__pyx_memoryview___repr__},
-  {Py_sq_length, (void *)__pyx_memoryview___len__},
-  {Py_sq_item, (void *)__pyx_sq_item_memoryview},
-  {Py_mp_length, (void *)__pyx_memoryview___len__},
-  {Py_mp_subscript, (void *)__pyx_memoryview___getitem__},
-  {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview},
-  {Py_tp_str, (void *)__pyx_memoryview___str__},
-  #if defined(Py_bf_getbuffer)
-  {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer},
-  #endif
-  {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview},
-  {Py_tp_clear, (void *)__pyx_tp_clear_memoryview},
-  {Py_tp_methods, (void *)__pyx_methods_memoryview},
-  {Py_tp_getset, (void *)__pyx_getsets_memoryview},
-  {Py_tp_new, (void *)__pyx_tp_new_memoryview},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_memoryview_spec = {
-  "monotonic_align.core.memoryview",
-  sizeof(struct __pyx_memoryview_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC,
-  __pyx_type___pyx_memoryview_slots,
-};
-#else
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
-  __pyx_memoryview___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_memoryview, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
-  __pyx_memoryview___len__, /*mp_length*/
-  __pyx_memoryview___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""memoryview", /*tp_name*/
-  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_memoryview___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  __pyx_memoryview___str__, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_memoryview, /*tp_traverse*/
-  __pyx_tp_clear_memoryview, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_memoryview, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_memoryview, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_memoryview, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryviewslice_obj *p;
-  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryviewslice_obj *)o);
-  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  p->from_slice.memview = NULL;
-  return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) {
-    if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) {
-      if (PyObject_CallFinalizerFromDealloc(o)) return;
-    }
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryviewslice___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->from_object);
-  PyObject_GC_Track(o);
-  __pyx_tp_dealloc_memoryview(o);
-}
-
-static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
-  if (p->from_object) {
-    e = (*v)(p->from_object, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  __pyx_tp_clear_memoryview(o);
-  tmp = ((PyObject*)p->from_object);
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1);
-  return 0;
-}
-
-static PyMethodDef __pyx_methods__memoryviewslice[] = {
-  {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0},
-  {0, 0, 0, 0}
-};
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = {
-  {Py_tp_dealloc, (void *)__pyx_tp_dealloc__memoryviewslice},
-  {Py_tp_doc, (void *)PyDoc_STR("Internal class for passing memoryview slices to Python")},
-  {Py_tp_traverse, (void *)__pyx_tp_traverse__memoryviewslice},
-  {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice},
-  {Py_tp_methods, (void *)__pyx_methods__memoryviewslice},
-  {Py_tp_new, (void *)__pyx_tp_new__memoryviewslice},
-  {0, 0},
-};
-static PyType_Spec __pyx_type___pyx_memoryviewslice_spec = {
-  "monotonic_align.core._memoryviewslice",
-  sizeof(struct __pyx_memoryviewslice_obj),
-  0,
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE,
-  __pyx_type___pyx_memoryviewslice_slots,
-};
-#else
-
-static PyTypeObject __pyx_type___pyx_memoryviewslice = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "monotonic_align.core.""_memoryviewslice", /*tp_name*/
-  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY || 0
-  __pyx_memoryview___repr__, /*tp_repr*/
-  #else
-  0, /*tp_repr*/
-  #endif
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  #if CYTHON_COMPILING_IN_PYPY || 0
-  __pyx_memoryview___str__, /*tp_str*/
-  #else
-  0, /*tp_str*/
-  #endif
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC|Py_TPFLAGS_SEQUENCE, /*tp_flags*/
-  PyDoc_STR("Internal class for passing memoryview slices to Python"), /*tp_doc*/
-  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
-  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods__memoryviewslice, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  #if !CYTHON_USE_TYPE_SPECS
-  0, /*tp_dictoffset*/
-  #endif
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new__memoryviewslice, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  #if CYTHON_USE_TP_FINALIZE
-  0, /*tp_finalize*/
-  #else
-  NULL, /*tp_finalize*/
-  #endif
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-  0, /*tp_vectorcall*/
-  #endif
-  #if __PYX_NEED_TP_PRINT_SLOT == 1
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030C0000
-  0, /*tp_watched*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-  0, /*tp_pypy_flags*/
-  #endif
-};
-#endif
-
-static PyMethodDef __pyx_methods[] = {
-  {0, 0, 0, 0}
-};
-#ifndef CYTHON_SMALL_CODE
-#if defined(__clang__)
-    #define CYTHON_SMALL_CODE
-#elif defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 3))
-    #define CYTHON_SMALL_CODE __attribute__((cold))
-#else
-    #define CYTHON_SMALL_CODE
-#endif
-#endif
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-static int __Pyx_CreateStringTabAndInitStrings(void) {
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-    if (n <= 0) {
-        Py_INCREF(__pyx_empty_tuple);
-        return __pyx_empty_tuple;
-    }
-    res = PyTuple_New(n);
-    if (unlikely(res == NULL)) return NULL;
-    __Pyx_copy_object_array(src, ((PyTupleObject*)res)->ob_item, n);
-    return res;
-}
-static CYTHON_INLINE PyObject *
-__Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n)
-{
-    PyObject *res;
-    if (n <= 0) {
-        return PyList_New(0);
-    }
-    res = PyList_New(n);
-    if (unlikely(res == NULL)) return NULL;
-    __Pyx_copy_object_array(src, ((PyListObject*)res)->ob_item, n);
-    return res;
-}
-#endif
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS && (PY_VERSION_HEX < 0x030B0000)
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* fastcall */
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s)
-{
-    Py_ssize_t i, n = PyTuple_GET_SIZE(kwnames);
-    for (i = 0; i < n; i++)
-    {
-        if (s == PyTuple_GET_ITEM(kwnames, i)) return kwvalues[i];
-    }
-    for (i = 0; i < n; i++)
-    {
-        int eq = __Pyx_PyUnicode_Equals(s, PyTuple_GET_ITEM(kwnames, i), Py_EQ);
-        if (unlikely(eq != 0)) {
-            if (unlikely(eq < 0)) return NULL;  // error
-            return kwvalues[i];
-        }
-    }
-    return NULL;  // not found (no exception set)
-}
-#endif
-
-/* RaiseArgTupleInvalid */
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
-}
-
-/* RaiseDoubleKeywords */
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-/* ParseKeywords */
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject *const *kwvalues,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    int kwds_is_tuple = CYTHON_METH_FASTCALL && likely(PyTuple_Check(kwds));
-    while (1) {
-        if (kwds_is_tuple) {
-            if (pos >= PyTuple_GET_SIZE(kwds)) break;
-            key = PyTuple_GET_ITEM(kwds, pos);
-            value = kwvalues[pos];
-            pos++;
-        }
-        else
-        {
-            if (!PyDict_Next(kwds, &pos, &key, &value)) break;
-        }
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (__Pyx_PyUnicode_GET_LENGTH(**name) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key)
-                );
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (__Pyx_PyUnicode_GET_LENGTH(**argname) != __Pyx_PyUnicode_GET_LENGTH(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-/* ArgTypeTest */
-static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)
-{
-    __Pyx_TypeName type_name;
-    __Pyx_TypeName obj_type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    else if (exact) {
-        #if PY_MAJOR_VERSION == 2
-        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(__Pyx_TypeCheck(obj, type))) return 1;
-    }
-    type_name = __Pyx_PyType_GetName(type);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME
-        ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    CYTHON_UNUSED_VAR(cause);
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-      #if PY_VERSION_HEX >= 0x030C00A6
-        PyException_SetTraceback(value, tb);
-      #elif CYTHON_FAST_THREAD_STATE
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#else
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-
-/* PyObjectCall */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = Py_TYPE(func)->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectFastCall */
-static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) {
-    PyObject *argstuple;
-    PyObject *result;
-    size_t i;
-    argstuple = PyTuple_New((Py_ssize_t)nargs);
-    if (unlikely(!argstuple)) return NULL;
-    for (i = 0; i < nargs; i++) {
-        Py_INCREF(args[i]);
-        PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]);
-    }
-    result = __Pyx_PyObject_Call(func, argstuple, kwargs);
-    Py_DECREF(argstuple);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) {
-    Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (nargs == 0 && kwargs == NULL) {
-#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG)
-        if (__Pyx_IsCyOrPyCFunction(func))
-#else
-        if (PyCFunction_Check(func))
-#endif
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
-                return __Pyx_PyObject_CallMethO(func, NULL);
-            }
-        }
-    }
-    else if (nargs == 1 && kwargs == NULL) {
-        if (PyCFunction_Check(func))
-        {
-            if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-                return __Pyx_PyObject_CallMethO(func, args[0]);
-            }
-        }
-    }
-#endif
-    #if PY_VERSION_HEX < 0x030800B1
-    #if CYTHON_FAST_PYCCALL
-    if (PyCFunction_Check(func)) {
-        if (kwargs) {
-            return _PyCFunction_FastCallDict(func, args, nargs, kwargs);
-        } else {
-            return _PyCFunction_FastCallKeywords(func, args, nargs, NULL);
-        }
-    }
-    #if PY_VERSION_HEX >= 0x030700A1
-    if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) {
-        return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL);
-    }
-    #endif
-    #endif
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs);
-    }
-    #endif
-    #endif
-    #if CYTHON_VECTORCALL
-    vectorcallfunc f = _PyVectorcall_Function(func);
-    if (f) {
-        return f(func, args, (size_t)nargs, kwargs);
-    }
-    #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL
-    if (__Pyx_CyFunction_CheckExact(func)) {
-        __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func);
-        if (f) return f(func, args, (size_t)nargs, kwargs);
-    }
-    #endif
-    if (nargs == 0) {
-        return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs);
-    }
-    return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs);
-}
-
-/* RaiseUnexpectedTypeError */
-static int
-__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj)
-{
-    __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME,
-                 expected, obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return 0;
-}
-
-/* CIntToDigits */
-static const char DIGIT_PAIRS_10[2*10*10+1] = {
-    "00010203040506070809"
-    "10111213141516171819"
-    "20212223242526272829"
-    "30313233343536373839"
-    "40414243444546474849"
-    "50515253545556575859"
-    "60616263646566676869"
-    "70717273747576777879"
-    "80818283848586878889"
-    "90919293949596979899"
-};
-static const char DIGIT_PAIRS_8[2*8*8+1] = {
-    "0001020304050607"
-    "1011121314151617"
-    "2021222324252627"
-    "3031323334353637"
-    "4041424344454647"
-    "5051525354555657"
-    "6061626364656667"
-    "7071727374757677"
-};
-static const char DIGITS_HEX[2*16+1] = {
-    "0123456789abcdef"
-    "0123456789ABCDEF"
-};
-
-/* BuildPyUnicode */
-static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength,
-                                                int prepend_sign, char padding_char) {
-    PyObject *uval;
-    Py_ssize_t uoffset = ulength - clength;
-#if CYTHON_USE_UNICODE_INTERNALS
-    Py_ssize_t i;
-#if CYTHON_PEP393_ENABLED
-    void *udata;
-    uval = PyUnicode_New(ulength, 127);
-    if (unlikely(!uval)) return NULL;
-    udata = PyUnicode_DATA(uval);
-#else
-    Py_UNICODE *udata;
-    uval = PyUnicode_FromUnicode(NULL, ulength);
-    if (unlikely(!uval)) return NULL;
-    udata = PyUnicode_AS_UNICODE(uval);
-#endif
-    if (uoffset > 0) {
-        i = 0;
-        if (prepend_sign) {
-            __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-');
-            i++;
-        }
-        for (; i < uoffset; i++) {
-            __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char);
-        }
-    }
-    for (i=0; i < clength; i++) {
-        __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]);
-    }
-#else
-    {
-        PyObject *sign = NULL, *padding = NULL;
-        uval = NULL;
-        if (uoffset > 0) {
-            prepend_sign = !!prepend_sign;
-            if (uoffset > prepend_sign) {
-                padding = PyUnicode_FromOrdinal(padding_char);
-                if (likely(padding) && uoffset > prepend_sign + 1) {
-                    PyObject *tmp;
-                    PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign);
-                    if (unlikely(!repeat)) goto done_or_error;
-                    tmp = PyNumber_Multiply(padding, repeat);
-                    Py_DECREF(repeat);
-                    Py_DECREF(padding);
-                    padding = tmp;
-                }
-                if (unlikely(!padding)) goto done_or_error;
-            }
-            if (prepend_sign) {
-                sign = PyUnicode_FromOrdinal('-');
-                if (unlikely(!sign)) goto done_or_error;
-            }
-        }
-        uval = PyUnicode_DecodeASCII(chars, clength, NULL);
-        if (likely(uval) && padding) {
-            PyObject *tmp = PyNumber_Add(padding, uval);
-            Py_DECREF(uval);
-            uval = tmp;
-        }
-        if (likely(uval) && sign) {
-            PyObject *tmp = PyNumber_Add(sign, uval);
-            Py_DECREF(uval);
-            uval = tmp;
-        }
-done_or_error:
-        Py_XDECREF(padding);
-        Py_XDECREF(sign);
-    }
-#endif
-    return uval;
-}
-
-/* CIntToPyUnicode */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) {
-    char digits[sizeof(int)*3+2];
-    char *dpos, *end = digits + sizeof(int)*3+2;
-    const char *hex_digits = DIGITS_HEX;
-    Py_ssize_t length, ulength;
-    int prepend_sign, last_one_off;
-    int remaining;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (format_char == 'X') {
-        hex_digits += 16;
-        format_char = 'x';
-    }
-    remaining = value;
-    last_one_off = 0;
-    dpos = end;
-    do {
-        int digit_pos;
-        switch (format_char) {
-        case 'o':
-            digit_pos = abs((int)(remaining % (8*8)));
-            remaining = (int) (remaining / (8*8));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 8);
-            break;
-        case 'd':
-            digit_pos = abs((int)(remaining % (10*10)));
-            remaining = (int) (remaining / (10*10));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 10);
-            break;
-        case 'x':
-            *(--dpos) = hex_digits[abs((int)(remaining % 16))];
-            remaining = (int) (remaining / 16);
-            break;
-        default:
-            assert(0);
-            break;
-        }
-    } while (unlikely(remaining != 0));
-    assert(!last_one_off || *dpos == '0');
-    dpos += last_one_off;
-    length = end - dpos;
-    ulength = length;
-    prepend_sign = 0;
-    if (!is_unsigned && value <= neg_one) {
-        if (padding_char == ' ' || width <= length + 1) {
-            *(--dpos) = '-';
-            ++length;
-        } else {
-            prepend_sign = 1;
-        }
-        ++ulength;
-    }
-    if (width > ulength) {
-        ulength = width;
-    }
-    if (ulength == 1) {
-        return PyUnicode_FromOrdinal(*dpos);
-    }
-    return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char);
-}
-
-/* CIntToPyUnicode */
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) {
-    char digits[sizeof(Py_ssize_t)*3+2];
-    char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2;
-    const char *hex_digits = DIGITS_HEX;
-    Py_ssize_t length, ulength;
-    int prepend_sign, last_one_off;
-    Py_ssize_t remaining;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (format_char == 'X') {
-        hex_digits += 16;
-        format_char = 'x';
-    }
-    remaining = value;
-    last_one_off = 0;
-    dpos = end;
-    do {
-        int digit_pos;
-        switch (format_char) {
-        case 'o':
-            digit_pos = abs((int)(remaining % (8*8)));
-            remaining = (Py_ssize_t) (remaining / (8*8));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 8);
-            break;
-        case 'd':
-            digit_pos = abs((int)(remaining % (10*10)));
-            remaining = (Py_ssize_t) (remaining / (10*10));
-            dpos -= 2;
-            memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2);
-            last_one_off = (digit_pos < 10);
-            break;
-        case 'x':
-            *(--dpos) = hex_digits[abs((int)(remaining % 16))];
-            remaining = (Py_ssize_t) (remaining / 16);
-            break;
-        default:
-            assert(0);
-            break;
-        }
-    } while (unlikely(remaining != 0));
-    assert(!last_one_off || *dpos == '0');
-    dpos += last_one_off;
-    length = end - dpos;
-    ulength = length;
-    prepend_sign = 0;
-    if (!is_unsigned && value <= neg_one) {
-        if (padding_char == ' ' || width <= length + 1) {
-            *(--dpos) = '-';
-            ++length;
-        } else {
-            prepend_sign = 1;
-        }
-        ++ulength;
-    }
-    if (width > ulength) {
-        ulength = width;
-    }
-    if (ulength == 1) {
-        return PyUnicode_FromOrdinal(*dpos);
-    }
-    return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char);
-}
-
-/* JoinPyUnicode */
-static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength,
-                                      Py_UCS4 max_char) {
-#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    PyObject *result_uval;
-    int result_ukind, kind_shift;
-    Py_ssize_t i, char_pos;
-    void *result_udata;
-    CYTHON_MAYBE_UNUSED_VAR(max_char);
-#if CYTHON_PEP393_ENABLED
-    result_uval = PyUnicode_New(result_ulength, max_char);
-    if (unlikely(!result_uval)) return NULL;
-    result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND;
-    kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1;
-    result_udata = PyUnicode_DATA(result_uval);
-#else
-    result_uval = PyUnicode_FromUnicode(NULL, result_ulength);
-    if (unlikely(!result_uval)) return NULL;
-    result_ukind = sizeof(Py_UNICODE);
-    kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1;
-    result_udata = PyUnicode_AS_UNICODE(result_uval);
-#endif
-    assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0);
-    char_pos = 0;
-    for (i=0; i < value_count; i++) {
-        int ukind;
-        Py_ssize_t ulength;
-        void *udata;
-        PyObject *uval = PyTuple_GET_ITEM(value_tuple, i);
-        if (unlikely(__Pyx_PyUnicode_READY(uval)))
-            goto bad;
-        ulength = __Pyx_PyUnicode_GET_LENGTH(uval);
-        if (unlikely(!ulength))
-            continue;
-        if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos))
-            goto overflow;
-        ukind = __Pyx_PyUnicode_KIND(uval);
-        udata = __Pyx_PyUnicode_DATA(uval);
-        if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) {
-            memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift));
-        } else {
-            #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters)
-            _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength);
-            #else
-            Py_ssize_t j;
-            for (j=0; j < ulength; j++) {
-                Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j);
-                __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar);
-            }
-            #endif
-        }
-        char_pos += ulength;
-    }
-    return result_uval;
-overflow:
-    PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string");
-bad:
-    Py_DECREF(result_uval);
-    return NULL;
-#else
-    CYTHON_UNUSED_VAR(max_char);
-    CYTHON_UNUSED_VAR(result_ulength);
-    CYTHON_UNUSED_VAR(value_count);
-    return PyUnicode_Join(__pyx_empty_unicode, value_tuple);
-#endif
-}
-
-/* GetAttr */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_USE_TYPE_SLOTS
-#if PY_MAJOR_VERSION >= 3
-    if (likely(PyUnicode_Check(n)))
-#else
-    if (likely(PyString_Check(n)))
-#endif
-        return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
-    return PyObject_GetAttr(o, n);
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (unlikely(!j)) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_subscript) {
-            PyObject *r, *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return NULL;
-            r = mm->mp_subscript(o, key);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* PyObjectCallOneArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *args[2] = {NULL, arg};
-    return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) {
-    PyObject *runerr = NULL;
-    Py_ssize_t key_value;
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index));
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError,
-            "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name);
-        __Pyx_DECREF_TypeName(index_type_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) {
-    __Pyx_TypeName obj_type_name;
-    if (likely(PyType_Check(obj))) {
-        PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem);
-        if (meth) {
-            PyObject *result = __Pyx_PyObject_CallOneArg(meth, key);
-            Py_DECREF(meth);
-            return result;
-        }
-    }
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-        "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) {
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyMappingMethods *mm = tp->tp_as_mapping;
-    PySequenceMethods *sm = tp->tp_as_sequence;
-    if (likely(mm && mm->mp_subscript)) {
-        return mm->mp_subscript(obj, key);
-    }
-    if (likely(sm && sm->sq_item)) {
-        return __Pyx_PyObject_GetIndex(obj, key);
-    }
-    return __Pyx_PyObject_GetItem_Slow(obj, key);
-}
-#endif
-
-/* KeywordStringCheck */
-static int __Pyx_CheckKeywordStrings(
-    PyObject *kw,
-    const char* function_name,
-    int kw_allowed)
-{
-    PyObject* key = 0;
-    Py_ssize_t pos = 0;
-#if CYTHON_COMPILING_IN_PYPY
-    if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0))
-        goto invalid_keyword;
-    return 1;
-#else
-    if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) {
-        if (unlikely(PyTuple_GET_SIZE(kw) == 0))
-            return 1;
-        if (!kw_allowed) {
-            key = PyTuple_GET_ITEM(kw, 0);
-            goto invalid_keyword;
-        }
-#if PY_VERSION_HEX < 0x03090000
-        for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) {
-            key = PyTuple_GET_ITEM(kw, pos);
-            if (unlikely(!PyUnicode_Check(key)))
-                goto invalid_keyword_type;
-        }
-#endif
-        return 1;
-    }
-    while (PyDict_Next(kw, &pos, &key, 0)) {
-        #if PY_MAJOR_VERSION < 3
-        if (unlikely(!PyString_Check(key)))
-        #endif
-            if (unlikely(!PyUnicode_Check(key)))
-                goto invalid_keyword_type;
-    }
-    if (!kw_allowed && unlikely(key))
-        goto invalid_keyword;
-    return 1;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    return 0;
-#endif
-invalid_keyword:
-    #if PY_MAJOR_VERSION < 3
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-    PyErr_Format(PyExc_TypeError,
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-    return 0;
-}
-
-/* DivInt[Py_ssize_t] */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
-    Py_ssize_t q = a / b;
-    Py_ssize_t r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* GetAttr3 */
-static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        return NULL;
-    __Pyx_PyErr_Clear();
-    Py_INCREF(d);
-    return d;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
-    PyObject *r;
-#if CYTHON_USE_TYPE_SLOTS
-    if (likely(PyString_Check(n))) {
-        r = __Pyx_PyObject_GetAttrStrNoError(o, n);
-        if (unlikely(!r) && likely(!PyErr_Occurred())) {
-            r = __Pyx_NewRef(d);
-        }
-        return r;
-    }
-#endif
-    r = PyObject_GetAttr(o, n);
-    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
-}
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#elif CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(!__pyx_m)) {
-        return NULL;
-    }
-    result = PyObject_GetAttr(__pyx_m, name);
-    if (likely(result)) {
-        return result;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* ExtTypeTest */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    __Pyx_TypeName obj_type_name;
-    __Pyx_TypeName type_name;
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(__Pyx_TypeCheck(obj, type)))
-        return 1;
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    type_name = __Pyx_PyType_GetName(type);
-    PyErr_Format(PyExc_TypeError,
-                 "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME,
-                 obj_type_name, type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-}
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    PyObject *exc_value = exc_info->exc_value;
-    if (exc_value == NULL || exc_value == Py_None) {
-        *value = NULL;
-        *type = NULL;
-        *tb = NULL;
-    } else {
-        *value = exc_value;
-        Py_INCREF(*value);
-        *type = (PyObject*) Py_TYPE(exc_value);
-        Py_INCREF(*type);
-        *tb = PyException_GetTraceback(exc_value);
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-  #endif
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    PyObject *tmp_value = exc_info->exc_value;
-    exc_info->exc_value = value;
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-  #else
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-  #endif
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type = NULL, *local_value, *local_tb = NULL;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if PY_VERSION_HEX >= 0x030C00A6
-    local_value = tstate->current_exception;
-    tstate->current_exception = 0;
-    if (likely(local_value)) {
-        local_type = (PyObject*) Py_TYPE(local_value);
-        Py_INCREF(local_type);
-        local_tb = PyException_GetTraceback(local_value);
-    }
-  #else
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-  #endif
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6
-    if (unlikely(tstate->current_exception))
-#elif CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-      #if PY_VERSION_HEX >= 0x030B00a4
-        tmp_value = exc_info->exc_value;
-        exc_info->exc_value = local_value;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-        Py_XDECREF(local_type);
-        Py_XDECREF(local_tb);
-      #else
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-      #endif
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-  #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_value = exc_info->exc_value;
-    exc_info->exc_value = *value;
-    if (tmp_value == NULL || tmp_value == Py_None) {
-        Py_XDECREF(tmp_value);
-        tmp_value = NULL;
-        tmp_type = NULL;
-        tmp_tb = NULL;
-    } else {
-        tmp_type = (PyObject*) Py_TYPE(tmp_value);
-        Py_INCREF(tmp_type);
-        #if CYTHON_COMPILING_IN_CPYTHON
-        tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback;
-        Py_XINCREF(tmp_tb);
-        #else
-        tmp_tb = PyException_GetTraceback(tmp_value);
-        #endif
-    }
-  #elif CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-  #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-  #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *module = 0;
-    PyObject *empty_dict = 0;
-    PyObject *empty_list = 0;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (unlikely(!py_import))
-        goto bad;
-    if (!from_list) {
-        empty_list = PyList_New(0);
-        if (unlikely(!empty_list))
-            goto bad;
-        from_list = empty_list;
-    }
-    #endif
-    empty_dict = PyDict_New();
-    if (unlikely(!empty_dict))
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                #if CYTHON_COMPILING_IN_LIMITED_API
-                module = PyImport_ImportModuleLevelObject(
-                    name, empty_dict, empty_dict, from_list, 1);
-                #else
-                module = PyImport_ImportModuleLevelObject(
-                    name, __pyx_d, empty_dict, from_list, 1);
-                #endif
-                if (unlikely(!module)) {
-                    if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError)))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (unlikely(!py_level))
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            #if CYTHON_COMPILING_IN_LIMITED_API
-            module = PyImport_ImportModuleLevelObject(
-                name, empty_dict, empty_dict, from_list, level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, __pyx_d, empty_dict, from_list, level);
-            #endif
-            #endif
-        }
-    }
-bad:
-    Py_XDECREF(empty_dict);
-    Py_XDECREF(empty_list);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    return module;
-}
-
-/* ImportDottedModule */
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) {
-    PyObject *partial_name = NULL, *slice = NULL, *sep = NULL;
-    if (unlikely(PyErr_Occurred())) {
-        PyErr_Clear();
-    }
-    if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) {
-        partial_name = name;
-    } else {
-        slice = PySequence_GetSlice(parts_tuple, 0, count);
-        if (unlikely(!slice))
-            goto bad;
-        sep = PyUnicode_FromStringAndSize(".", 1);
-        if (unlikely(!sep))
-            goto bad;
-        partial_name = PyUnicode_Join(sep, slice);
-    }
-    PyErr_Format(
-#if PY_MAJOR_VERSION < 3
-        PyExc_ImportError,
-        "No module named '%s'", PyString_AS_STRING(partial_name));
-#else
-#if PY_VERSION_HEX >= 0x030600B1
-        PyExc_ModuleNotFoundError,
-#else
-        PyExc_ImportError,
-#endif
-        "No module named '%U'", partial_name);
-#endif
-bad:
-    Py_XDECREF(sep);
-    Py_XDECREF(slice);
-    Py_XDECREF(partial_name);
-    return NULL;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) {
-    PyObject *imported_module;
-#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-    PyObject *modules = PyImport_GetModuleDict();
-    if (unlikely(!modules))
-        return NULL;
-    imported_module = __Pyx_PyDict_GetItemStr(modules, name);
-    Py_XINCREF(imported_module);
-#else
-    imported_module = PyImport_GetModule(name);
-#endif
-    return imported_module;
-}
-#endif
-#if PY_MAJOR_VERSION >= 3
-static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) {
-    Py_ssize_t i, nparts;
-    nparts = PyTuple_GET_SIZE(parts_tuple);
-    for (i=1; i < nparts && module; i++) {
-        PyObject *part, *submodule;
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-        part = PyTuple_GET_ITEM(parts_tuple, i);
-#else
-        part = PySequence_ITEM(parts_tuple, i);
-#endif
-        submodule = __Pyx_PyObject_GetAttrStrNoError(module, part);
-#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-        Py_DECREF(part);
-#endif
-        Py_DECREF(module);
-        module = submodule;
-    }
-    if (unlikely(!module)) {
-        return __Pyx__ImportDottedModule_Error(name, parts_tuple, i);
-    }
-    return module;
-}
-#endif
-static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if PY_MAJOR_VERSION < 3
-    PyObject *module, *from_list, *star = __pyx_n_s__3;
-    CYTHON_UNUSED_VAR(parts_tuple);
-    from_list = PyList_New(1);
-    if (unlikely(!from_list))
-        return NULL;
-    Py_INCREF(star);
-    PyList_SET_ITEM(from_list, 0, star);
-    module = __Pyx_Import(name, from_list, 0);
-    Py_DECREF(from_list);
-    return module;
-#else
-    PyObject *imported_module;
-    PyObject *module = __Pyx_Import(name, NULL, 0);
-    if (!parts_tuple || unlikely(!module))
-        return module;
-    imported_module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(imported_module)) {
-        Py_DECREF(module);
-        return imported_module;
-    }
-    PyErr_Clear();
-    return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple);
-#endif
-}
-static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1
-    PyObject *module = __Pyx__ImportDottedModule_Lookup(name);
-    if (likely(module)) {
-        PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec);
-        if (likely(spec)) {
-            PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing);
-            if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) {
-                Py_DECREF(spec);
-                spec = NULL;
-            }
-            Py_XDECREF(unsafe);
-        }
-        if (likely(!spec)) {
-            PyErr_Clear();
-            return module;
-        }
-        Py_DECREF(spec);
-        Py_DECREF(module);
-    } else if (PyErr_Occurred()) {
-        PyErr_Clear();
-    }
-#endif
-    return __Pyx__ImportDottedModule(name, parts_tuple);
-}
-
-/* ssize_strlen */
-static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) {
-    size_t len = strlen(s);
-    if (unlikely(len > PY_SSIZE_T_MAX)) {
-        PyErr_SetString(PyExc_OverflowError, "byte string is too long");
-        return -1;
-    }
-    return (Py_ssize_t) len;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*);
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (cls == a || cls == b) return 1;
-    mro = cls->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            PyObject *base = PyTuple_GET_ITEM(mro, i);
-            if (base == (PyObject *)a || base == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    if (exc_type1) {
-        return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2);
-    } else {
-        return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* PySequenceMultiply */
-static PyObject* __Pyx_PySequence_Multiply_Generic(PyObject *seq, Py_ssize_t mul) {
-    PyObject *result, *pymul = PyInt_FromSsize_t(mul);
-    if (unlikely(!pymul))
-        return NULL;
-    result = PyNumber_Multiply(seq, pymul);
-    Py_DECREF(pymul);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul) {
-#if CYTHON_USE_TYPE_SLOTS
-    PyTypeObject *type = Py_TYPE(seq);
-    if (likely(type->tp_as_sequence && type->tp_as_sequence->sq_repeat)) {
-        return type->tp_as_sequence->sq_repeat(seq, mul);
-    } else
-#endif
-    {
-        return __Pyx_PySequence_Multiply_Generic(seq, mul);
-    }
-}
-
-/* SetItemInt */
-static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
-    int r;
-    if (unlikely(!j)) return -1;
-    r = PyObject_SetItem(o, j, v);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list,
-                                               CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o));
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) {
-            PyObject* old = PyList_GET_ITEM(o, n);
-            Py_INCREF(v);
-            PyList_SET_ITEM(o, n, v);
-            Py_DECREF(old);
-            return 1;
-        }
-    } else {
-        PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping;
-        PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence;
-        if (mm && mm->mp_ass_subscript) {
-            int r;
-            PyObject *key = PyInt_FromSsize_t(i);
-            if (unlikely(!key)) return -1;
-            r = mm->mp_ass_subscript(o, key, v);
-            Py_DECREF(key);
-            return r;
-        }
-        if (likely(sm && sm->sq_ass_item)) {
-            if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) {
-                Py_ssize_t l = sm->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return -1;
-                    PyErr_Clear();
-                }
-            }
-            return sm->sq_ass_item(o, i, v);
-        }
-    }
-#else
-#if CYTHON_COMPILING_IN_PYPY
-    if (is_list || (PySequence_Check(o) && !PyDict_Check(o)))
-#else
-    if (is_list || PySequence_Check(o))
-#endif
-    {
-        return PySequence_SetItem(o, i, v);
-    }
-#endif
-    return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
-}
-
-/* RaiseUnboundLocalError */
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-/* DivInt[long] */
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        const char* module_name_str = 0;
-        PyObject* module_name = 0;
-        PyObject* module_dot = 0;
-        PyObject* full_name = 0;
-        PyErr_Clear();
-        module_name_str = PyModule_GetName(module);
-        if (unlikely(!module_name_str)) { goto modbad; }
-        module_name = PyUnicode_FromString(module_name_str);
-        if (unlikely(!module_name)) { goto modbad; }
-        module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2);
-        if (unlikely(!module_dot)) { goto modbad; }
-        full_name = PyUnicode_Concat(module_dot, name);
-        if (unlikely(!full_name)) { goto modbad; }
-        #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM  < 0x07030400)
-        {
-            PyObject *modules = PyImport_GetModuleDict();
-            if (unlikely(!modules))
-                goto modbad;
-            value = PyObject_GetItem(modules, full_name);
-        }
-        #else
-        value = PyImport_GetModule(full_name);
-        #endif
-      modbad:
-        Py_XDECREF(full_name);
-        Py_XDECREF(module_dot);
-        Py_XDECREF(module_name);
-    }
-    if (unlikely(!value)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* HasAttr */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
-    PyObject *r;
-    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "hasattr(): attribute name must be string");
-        return -1;
-    }
-    r = __Pyx_GetAttr(o, n);
-    if (!r) {
-        PyErr_Clear();
-        return 0;
-    } else {
-        Py_DECREF(r);
-        return 1;
-    }
-}
-
-/* ErrOccurredWithGIL */
-static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) {
-  int err;
-  #ifdef WITH_THREAD
-  PyGILState_STATE _save = PyGILState_Ensure();
-  #endif
-  err = !!PyErr_Occurred();
-  #ifdef WITH_THREAD
-  PyGILState_Release(_save);
-  #endif
-  return err;
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, attr_name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(attr_name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* PyObject_GenericGetAttr */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
-    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
-}
-#endif
-
-/* FixUpExtensionType */
-#if CYTHON_USE_TYPE_SPECS
-static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) {
-#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API
-    CYTHON_UNUSED_VAR(spec);
-    CYTHON_UNUSED_VAR(type);
-#else
-    const PyType_Slot *slot = spec->slots;
-    while (slot && slot->slot && slot->slot != Py_tp_members)
-        slot++;
-    if (slot && slot->slot == Py_tp_members) {
-        int changed = 0;
-#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON)
-        const
-#endif
-            PyMemberDef *memb = (PyMemberDef*) slot->pfunc;
-        while (memb && memb->name) {
-            if (memb->name[0] == '_' && memb->name[1] == '_') {
-#if PY_VERSION_HEX < 0x030900b1
-                if (strcmp(memb->name, "__weaklistoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_weaklistoffset = memb->offset;
-                    changed = 1;
-                }
-                else if (strcmp(memb->name, "__dictoffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-                    type->tp_dictoffset = memb->offset;
-                    changed = 1;
-                }
-#if CYTHON_METH_FASTCALL
-                else if (strcmp(memb->name, "__vectorcalloffset__") == 0) {
-                    assert(memb->type == T_PYSSIZET);
-                    assert(memb->flags == READONLY);
-#if PY_VERSION_HEX >= 0x030800b4
-                    type->tp_vectorcall_offset = memb->offset;
-#else
-                    type->tp_print = (printfunc) memb->offset;
-#endif
-                    changed = 1;
-                }
-#endif
-#else
-                if ((0));
-#endif
-#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON
-                else if (strcmp(memb->name, "__module__") == 0) {
-                    PyObject *descr;
-                    assert(memb->type == T_OBJECT);
-                    assert(memb->flags == 0 || memb->flags == READONLY);
-                    descr = PyDescr_NewMember(type, memb);
-                    if (unlikely(!descr))
-                        return -1;
-                    if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) {
-                        Py_DECREF(descr);
-                        return -1;
-                    }
-                    Py_DECREF(descr);
-                    changed = 1;
-                }
-#endif
-            }
-            memb++;
-        }
-        if (changed)
-            PyType_Modified(type);
-    }
-#endif
-    return 0;
-}
-#endif
-
-/* PyObjectCallNoArg */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
-    PyObject *arg = NULL;
-    return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET);
-}
-
-/* PyObjectGetMethod */
-static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) {
-    PyObject *attr;
-#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP
-    __Pyx_TypeName type_name;
-    PyTypeObject *tp = Py_TYPE(obj);
-    PyObject *descr;
-    descrgetfunc f = NULL;
-    PyObject **dictptr, *dict;
-    int meth_found = 0;
-    assert (*method == NULL);
-    if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) {
-        attr = __Pyx_PyObject_GetAttrStr(obj, name);
-        goto try_unpack;
-    }
-    if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) {
-        return 0;
-    }
-    descr = _PyType_Lookup(tp, name);
-    if (likely(descr != NULL)) {
-        Py_INCREF(descr);
-#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR
-        if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR))
-#elif PY_MAJOR_VERSION >= 3
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type)))
-        #endif
-#else
-        #ifdef __Pyx_CyFunction_USED
-        if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr)))
-        #else
-        if (likely(PyFunction_Check(descr)))
-        #endif
-#endif
-        {
-            meth_found = 1;
-        } else {
-            f = Py_TYPE(descr)->tp_descr_get;
-            if (f != NULL && PyDescr_IsData(descr)) {
-                attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-                Py_DECREF(descr);
-                goto try_unpack;
-            }
-        }
-    }
-    dictptr = _PyObject_GetDictPtr(obj);
-    if (dictptr != NULL && (dict = *dictptr) != NULL) {
-        Py_INCREF(dict);
-        attr = __Pyx_PyDict_GetItemStr(dict, name);
-        if (attr != NULL) {
-            Py_INCREF(attr);
-            Py_DECREF(dict);
-            Py_XDECREF(descr);
-            goto try_unpack;
-        }
-        Py_DECREF(dict);
-    }
-    if (meth_found) {
-        *method = descr;
-        return 1;
-    }
-    if (f != NULL) {
-        attr = f(descr, obj, (PyObject *)Py_TYPE(obj));
-        Py_DECREF(descr);
-        goto try_unpack;
-    }
-    if (likely(descr != NULL)) {
-        *method = descr;
-        return 0;
-    }
-    type_name = __Pyx_PyType_GetName(tp);
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'",
-                 type_name, name);
-#else
-                 "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'",
-                 type_name, PyString_AS_STRING(name));
-#endif
-    __Pyx_DECREF_TypeName(type_name);
-    return 0;
-#else
-    attr = __Pyx_PyObject_GetAttrStr(obj, name);
-    goto try_unpack;
-#endif
-try_unpack:
-#if CYTHON_UNPACK_METHODS
-    if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) {
-        PyObject *function = PyMethod_GET_FUNCTION(attr);
-        Py_INCREF(function);
-        Py_DECREF(attr);
-        *method = function;
-        return 1;
-    }
-#endif
-    *method = attr;
-    return 0;
-}
-
-/* PyObjectCallMethod0 */
-static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) {
-    PyObject *method = NULL, *result = NULL;
-    int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method);
-    if (likely(is_method)) {
-        result = __Pyx_PyObject_CallOneArg(method, obj);
-        Py_DECREF(method);
-        return result;
-    }
-    if (unlikely(!method)) goto bad;
-    result = __Pyx_PyObject_CallNoArg(method);
-    Py_DECREF(method);
-bad:
-    return result;
-}
-
-/* ValidateBasesTuple */
-#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS
-static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) {
-    Py_ssize_t i, n = PyTuple_GET_SIZE(bases);
-    for (i = 1; i < n; i++)
-    {
-        PyObject *b0 = PyTuple_GET_ITEM(bases, i);
-        PyTypeObject *b;
-#if PY_MAJOR_VERSION < 3
-        if (PyClass_Check(b0))
-        {
-            PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class",
-                         PyString_AS_STRING(((PyClassObject*)b0)->cl_name));
-            return -1;
-        }
-#endif
-        b = (PyTypeObject*) b0;
-        if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE))
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-        if (dictoffset == 0 && b->tp_dictoffset)
-        {
-            __Pyx_TypeName b_name = __Pyx_PyType_GetName(b);
-            PyErr_Format(PyExc_TypeError,
-                "extension type '%.200s' has no __dict__ slot, "
-                "but base type '" __Pyx_FMT_TYPENAME "' has: "
-                "either add 'cdef dict __dict__' to the extension type "
-                "or add '__slots__ = [...]' to the base type",
-                type_name, b_name);
-            __Pyx_DECREF_TypeName(b_name);
-            return -1;
-        }
-    }
-    return 0;
-}
-#endif
-
-/* PyType_Ready */
-static int __Pyx_PyType_Ready(PyTypeObject *t) {
-#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION)
-    (void)__Pyx_PyObject_CallMethod0;
-#if CYTHON_USE_TYPE_SPECS
-    (void)__Pyx_validate_bases_tuple;
-#endif
-    return PyType_Ready(t);
-#else
-    int r;
-    PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*);
-    if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1))
-        return -1;
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-    {
-        int gc_was_enabled;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        gc_was_enabled = PyGC_Disable();
-        (void)__Pyx_PyObject_CallMethod0;
-    #else
-        PyObject *ret, *py_status;
-        PyObject *gc = NULL;
-        #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400)
-        gc = PyImport_GetModule(__pyx_kp_u_gc);
-        #endif
-        if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc);
-        if (unlikely(!gc)) return -1;
-        py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled);
-        if (unlikely(!py_status)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-        gc_was_enabled = __Pyx_PyObject_IsTrue(py_status);
-        Py_DECREF(py_status);
-        if (gc_was_enabled > 0) {
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable);
-            if (unlikely(!ret)) {
-                Py_DECREF(gc);
-                return -1;
-            }
-            Py_DECREF(ret);
-        } else if (unlikely(gc_was_enabled == -1)) {
-            Py_DECREF(gc);
-            return -1;
-        }
-    #endif
-        t->tp_flags |= Py_TPFLAGS_HEAPTYPE;
-#if PY_VERSION_HEX >= 0x030A0000
-        t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE;
-#endif
-#else
-        (void)__Pyx_PyObject_CallMethod0;
-#endif
-    r = PyType_Ready(t);
-#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION)
-        t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE;
-    #if PY_VERSION_HEX >= 0x030A00b1
-        if (gc_was_enabled)
-            PyGC_Enable();
-    #else
-        if (gc_was_enabled) {
-            PyObject *tp, *v, *tb;
-            PyErr_Fetch(&tp, &v, &tb);
-            ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable);
-            if (likely(ret || r == -1)) {
-                Py_XDECREF(ret);
-                PyErr_Restore(tp, v, tb);
-            } else {
-                Py_XDECREF(tp);
-                Py_XDECREF(v);
-                Py_XDECREF(tb);
-                r = -1;
-            }
-        }
-        Py_DECREF(gc);
-    #endif
-    }
-#endif
-    return r;
-#endif
-}
-
-/* SetVTable */
-static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) {
-    PyObject *ob = PyCapsule_New(vtable, 0, 0);
-    if (unlikely(!ob))
-        goto bad;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0))
-#else
-    if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0))
-#endif
-        goto bad;
-    Py_DECREF(ob);
-    return 0;
-bad:
-    Py_XDECREF(ob);
-    return -1;
-}
-
-/* GetVTable */
-static void* __Pyx_GetVtable(PyTypeObject *type) {
-    void* ptr;
-#if CYTHON_COMPILING_IN_LIMITED_API
-    PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable);
-#else
-    PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable);
-#endif
-    if (!ob)
-        goto bad;
-    ptr = PyCapsule_GetPointer(ob, 0);
-    if (!ptr && !PyErr_Occurred())
-        PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type");
-    Py_DECREF(ob);
-    return ptr;
-bad:
-    Py_XDECREF(ob);
-    return NULL;
-}
-
-/* MergeVTables */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_MergeVtables(PyTypeObject *type) {
-    int i;
-    void** base_vtables;
-    __Pyx_TypeName tp_base_name;
-    __Pyx_TypeName base_name;
-    void* unknown = (void*)-1;
-    PyObject* bases = type->tp_bases;
-    int base_depth = 0;
-    {
-        PyTypeObject* base = type->tp_base;
-        while (base) {
-            base_depth += 1;
-            base = base->tp_base;
-        }
-    }
-    base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1));
-    base_vtables[0] = unknown;
-    for (i = 1; i < PyTuple_GET_SIZE(bases); i++) {
-        void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i)));
-        if (base_vtable != NULL) {
-            int j;
-            PyTypeObject* base = type->tp_base;
-            for (j = 0; j < base_depth; j++) {
-                if (base_vtables[j] == unknown) {
-                    base_vtables[j] = __Pyx_GetVtable(base);
-                    base_vtables[j + 1] = unknown;
-                }
-                if (base_vtables[j] == base_vtable) {
-                    break;
-                } else if (base_vtables[j] == NULL) {
-                    goto bad;
-                }
-                base = base->tp_base;
-            }
-        }
-    }
-    PyErr_Clear();
-    free(base_vtables);
-    return 0;
-bad:
-    tp_base_name = __Pyx_PyType_GetName(type->tp_base);
-    base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i));
-    PyErr_Format(PyExc_TypeError,
-        "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name);
-    __Pyx_DECREF_TypeName(tp_base_name);
-    __Pyx_DECREF_TypeName(base_name);
-    free(base_vtables);
-    return -1;
-}
-#endif
-
-/* SetupReduce */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
-  int ret;
-  PyObject *name_attr;
-  name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2);
-  if (likely(name_attr)) {
-      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
-  } else {
-      ret = -1;
-  }
-  if (unlikely(ret < 0)) {
-      PyErr_Clear();
-      ret = 0;
-  }
-  Py_XDECREF(name_attr);
-  return ret;
-}
-static int __Pyx_setup_reduce(PyObject* type_obj) {
-    int ret = 0;
-    PyObject *object_reduce = NULL;
-    PyObject *object_getstate = NULL;
-    PyObject *object_reduce_ex = NULL;
-    PyObject *reduce = NULL;
-    PyObject *reduce_ex = NULL;
-    PyObject *reduce_cython = NULL;
-    PyObject *setstate = NULL;
-    PyObject *setstate_cython = NULL;
-    PyObject *getstate = NULL;
-#if CYTHON_USE_PYTYPE_LOOKUP
-    getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate);
-#else
-    getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate);
-    if (!getstate && PyErr_Occurred()) {
-        goto __PYX_BAD;
-    }
-#endif
-    if (getstate) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate);
-#else
-        object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate);
-        if (!object_getstate && PyErr_Occurred()) {
-            goto __PYX_BAD;
-        }
-#endif
-        if (object_getstate != getstate) {
-            goto __PYX_GOOD;
-        }
-    }
-#if CYTHON_USE_PYTYPE_LOOKUP
-    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#else
-    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#endif
-    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
-    if (reduce_ex == object_reduce_ex) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#else
-        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#endif
-        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
-        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
-            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
-            if (likely(reduce_cython)) {
-                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-            } else if (reduce == object_reduce || PyErr_Occurred()) {
-                goto __PYX_BAD;
-            }
-            setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate);
-            if (!setstate) PyErr_Clear();
-            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
-                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
-                if (likely(setstate_cython)) {
-                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                } else if (!setstate || PyErr_Occurred()) {
-                    goto __PYX_BAD;
-                }
-            }
-            PyType_Modified((PyTypeObject*)type_obj);
-        }
-    }
-    goto __PYX_GOOD;
-__PYX_BAD:
-    if (!PyErr_Occurred()) {
-        __Pyx_TypeName type_obj_name =
-            __Pyx_PyType_GetName((PyTypeObject*)type_obj);
-        PyErr_Format(PyExc_RuntimeError,
-            "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name);
-        __Pyx_DECREF_TypeName(type_obj_name);
-    }
-    ret = -1;
-__PYX_GOOD:
-#if !CYTHON_USE_PYTYPE_LOOKUP
-    Py_XDECREF(object_reduce);
-    Py_XDECREF(object_reduce_ex);
-    Py_XDECREF(object_getstate);
-    Py_XDECREF(getstate);
-#endif
-    Py_XDECREF(reduce);
-    Py_XDECREF(reduce_ex);
-    Py_XDECREF(reduce_cython);
-    Py_XDECREF(setstate);
-    Py_XDECREF(setstate_cython);
-    return ret;
-}
-#endif
-
-/* FetchSharedCythonModule */
-static PyObject *__Pyx_FetchSharedCythonABIModule(void) {
-    PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME);
-    if (unlikely(!abi_module)) return NULL;
-    Py_INCREF(abi_module);
-    return abi_module;
-}
-
-/* FetchCommonType */
-static int __Pyx_VerifyCachedType(PyObject *cached_type,
-                               const char *name,
-                               Py_ssize_t basicsize,
-                               Py_ssize_t expected_basicsize) {
-    if (!PyType_Check(cached_type)) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s is not a type object", name);
-        return -1;
-    }
-    if (basicsize != expected_basicsize) {
-        PyErr_Format(PyExc_TypeError,
-            "Shared Cython type %.200s has the wrong size, try recompiling",
-            name);
-        return -1;
-    }
-    return 0;
-}
-#if !CYTHON_USE_TYPE_SPECS
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* abi_module;
-    const char* object_name;
-    PyTypeObject *cached_type = NULL;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    object_name = strrchr(type->tp_name, '.');
-    object_name = object_name ? object_name+1 : type->tp_name;
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        if (__Pyx_VerifyCachedType(
-              (PyObject *)cached_type,
-              object_name,
-              cached_type->tp_basicsize,
-              type->tp_basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    if (PyType_Ready(type) < 0) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0)
-        goto bad;
-    Py_INCREF(type);
-    cached_type = type;
-done:
-    Py_DECREF(abi_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#else
-static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) {
-    PyObject *abi_module, *cached_type = NULL;
-    const char* object_name = strrchr(spec->name, '.');
-    object_name = object_name ? object_name+1 : spec->name;
-    abi_module = __Pyx_FetchSharedCythonABIModule();
-    if (!abi_module) return NULL;
-    cached_type = PyObject_GetAttrString(abi_module, object_name);
-    if (cached_type) {
-        Py_ssize_t basicsize;
-#if CYTHON_COMPILING_IN_LIMITED_API
-        PyObject *py_basicsize;
-        py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__");
-        if (unlikely(!py_basicsize)) goto bad;
-        basicsize = PyLong_AsSsize_t(py_basicsize);
-        Py_DECREF(py_basicsize);
-        py_basicsize = 0;
-        if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
-#else
-        basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1;
-#endif
-        if (__Pyx_VerifyCachedType(
-              cached_type,
-              object_name,
-              basicsize,
-              spec->basicsize) < 0) {
-            goto bad;
-        }
-        goto done;
-    }
-    if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-    PyErr_Clear();
-    CYTHON_UNUSED_VAR(module);
-    cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases);
-    if (unlikely(!cached_type)) goto bad;
-    if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad;
-    if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad;
-done:
-    Py_DECREF(abi_module);
-    assert(cached_type == NULL || PyType_Check(cached_type));
-    return (PyTypeObject *) cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-#endif
-
-/* PyVectorcallFastCallDict */
-#if CYTHON_METH_FASTCALL
-static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    PyObject *res = NULL;
-    PyObject *kwnames;
-    PyObject **newargs;
-    PyObject **kwvalues;
-    Py_ssize_t i, pos;
-    size_t j;
-    PyObject *key, *value;
-    unsigned long keys_are_strings;
-    Py_ssize_t nkw = PyDict_GET_SIZE(kw);
-    newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0]));
-    if (unlikely(newargs == NULL)) {
-        PyErr_NoMemory();
-        return NULL;
-    }
-    for (j = 0; j < nargs; j++) newargs[j] = args[j];
-    kwnames = PyTuple_New(nkw);
-    if (unlikely(kwnames == NULL)) {
-        PyMem_Free(newargs);
-        return NULL;
-    }
-    kwvalues = newargs + nargs;
-    pos = i = 0;
-    keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS;
-    while (PyDict_Next(kw, &pos, &key, &value)) {
-        keys_are_strings &= Py_TYPE(key)->tp_flags;
-        Py_INCREF(key);
-        Py_INCREF(value);
-        PyTuple_SET_ITEM(kwnames, i, key);
-        kwvalues[i] = value;
-        i++;
-    }
-    if (unlikely(!keys_are_strings)) {
-        PyErr_SetString(PyExc_TypeError, "keywords must be strings");
-        goto cleanup;
-    }
-    res = vc(func, newargs, nargs, kwnames);
-cleanup:
-    Py_DECREF(kwnames);
-    for (i = 0; i < nkw; i++)
-        Py_DECREF(kwvalues[i]);
-    PyMem_Free(newargs);
-    return res;
-}
-static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw)
-{
-    if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) {
-        return vc(func, args, nargs, NULL);
-    }
-    return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw);
-}
-#endif
-
-/* CythonFunctionShared */
-static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) {
-#if PY_VERSION_HEX < 0x030900B1
-    __Pyx_Py_XDECREF_SET(
-        __Pyx_CyFunction_GetClassObj(f),
-            ((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#else
-    __Pyx_Py_XDECREF_SET(
-        ((PyCMethodObject *) (f))->mm_class,
-        (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL));
-#endif
-}
-static PyObject *
-__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure)
-{
-    CYTHON_UNUSED_VAR(closure);
-    if (unlikely(op->func_doc == NULL)) {
-        if (((PyCFunctionObject*)op)->m_ml->ml_doc) {
-#if PY_MAJOR_VERSION >= 3
-            op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#else
-            op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc);
-#endif
-            if (unlikely(op->func_doc == NULL))
-                return NULL;
-        } else {
-            Py_INCREF(Py_None);
-            return Py_None;
-        }
-    }
-    Py_INCREF(op->func_doc);
-    return op->func_doc;
-}
-static int
-__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (value == NULL) {
-        value = Py_None;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_doc, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_name == NULL)) {
-#if PY_MAJOR_VERSION >= 3
-        op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#else
-        op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name);
-#endif
-        if (unlikely(op->func_name == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_name);
-    return op->func_name;
-}
-static int
-__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_name, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_qualname);
-    return op->func_qualname;
-}
-static int
-__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value)))
-#else
-    if (unlikely(value == NULL || !PyString_Check(value)))
-#endif
-    {
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_qualname, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(op->func_dict == NULL)) {
-        op->func_dict = PyDict_New();
-        if (unlikely(op->func_dict == NULL))
-            return NULL;
-    }
-    Py_INCREF(op->func_dict);
-    return op->func_dict;
-}
-static int
-__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(value == NULL)) {
-        PyErr_SetString(PyExc_TypeError,
-               "function's dictionary may not be deleted");
-        return -1;
-    }
-    if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-               "setting function's dictionary to a non-dict");
-        return -1;
-    }
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_dict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(op->func_globals);
-    return op->func_globals;
-}
-static PyObject *
-__Pyx_CyFunction_get_closure(__pyx_CyFunctionObject *op, void *context)
-{
-    CYTHON_UNUSED_VAR(op);
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(Py_None);
-    return Py_None;
-}
-static PyObject *
-__Pyx_CyFunction_get_code(__pyx_CyFunctionObject *op, void *context)
-{
-    PyObject* result = (op->func_code) ? op->func_code : Py_None;
-    CYTHON_UNUSED_VAR(context);
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_init_defaults(__pyx_CyFunctionObject *op) {
-    int result = 0;
-    PyObject *res = op->defaults_getter((PyObject *) op);
-    if (unlikely(!res))
-        return -1;
-    #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    op->defaults_tuple = PyTuple_GET_ITEM(res, 0);
-    Py_INCREF(op->defaults_tuple);
-    op->defaults_kwdict = PyTuple_GET_ITEM(res, 1);
-    Py_INCREF(op->defaults_kwdict);
-    #else
-    op->defaults_tuple = PySequence_ITEM(res, 0);
-    if (unlikely(!op->defaults_tuple)) result = -1;
-    else {
-        op->defaults_kwdict = PySequence_ITEM(res, 1);
-        if (unlikely(!op->defaults_kwdict)) result = -1;
-    }
-    #endif
-    Py_DECREF(res);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_defaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyTuple_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__defaults__ must be set to a tuple object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__defaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_tuple, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_defaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_tuple;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_tuple;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_kwdefaults(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value) {
-        value = Py_None;
-    } else if (unlikely(value != Py_None && !PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__kwdefaults__ must be set to a dict object");
-        return -1;
-    }
-    PyErr_WarnEx(PyExc_RuntimeWarning, "changes to cyfunction.__kwdefaults__ will not "
-                 "currently affect the values used in function calls", 1);
-    Py_INCREF(value);
-    __Pyx_Py_XDECREF_SET(op->defaults_kwdict, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_kwdefaults(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->defaults_kwdict;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        if (op->defaults_getter) {
-            if (unlikely(__Pyx_CyFunction_init_defaults(op) < 0)) return NULL;
-            result = op->defaults_kwdict;
-        } else {
-            result = Py_None;
-        }
-    }
-    Py_INCREF(result);
-    return result;
-}
-static int
-__Pyx_CyFunction_set_annotations(__pyx_CyFunctionObject *op, PyObject* value, void *context) {
-    CYTHON_UNUSED_VAR(context);
-    if (!value || value == Py_None) {
-        value = NULL;
-    } else if (unlikely(!PyDict_Check(value))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "__annotations__ must be set to a dict object");
-        return -1;
-    }
-    Py_XINCREF(value);
-    __Pyx_Py_XDECREF_SET(op->func_annotations, value);
-    return 0;
-}
-static PyObject *
-__Pyx_CyFunction_get_annotations(__pyx_CyFunctionObject *op, void *context) {
-    PyObject* result = op->func_annotations;
-    CYTHON_UNUSED_VAR(context);
-    if (unlikely(!result)) {
-        result = PyDict_New();
-        if (unlikely(!result)) return NULL;
-        op->func_annotations = result;
-    }
-    Py_INCREF(result);
-    return result;
-}
-static PyObject *
-__Pyx_CyFunction_get_is_coroutine(__pyx_CyFunctionObject *op, void *context) {
-    int is_coroutine;
-    CYTHON_UNUSED_VAR(context);
-    if (op->func_is_coroutine) {
-        return __Pyx_NewRef(op->func_is_coroutine);
-    }
-    is_coroutine = op->flags & __Pyx_CYFUNCTION_COROUTINE;
-#if PY_VERSION_HEX >= 0x03050000
-    if (is_coroutine) {
-        PyObject *module, *fromlist, *marker = __pyx_n_s_is_coroutine;
-        fromlist = PyList_New(1);
-        if (unlikely(!fromlist)) return NULL;
-        Py_INCREF(marker);
-        PyList_SET_ITEM(fromlist, 0, marker);
-        module = PyImport_ImportModuleLevelObject(__pyx_n_s_asyncio_coroutines, NULL, NULL, fromlist, 0);
-        Py_DECREF(fromlist);
-        if (unlikely(!module)) goto ignore;
-        op->func_is_coroutine = __Pyx_PyObject_GetAttrStr(module, marker);
-        Py_DECREF(module);
-        if (likely(op->func_is_coroutine)) {
-            return __Pyx_NewRef(op->func_is_coroutine);
-        }
-ignore:
-        PyErr_Clear();
-    }
-#endif
-    op->func_is_coroutine = __Pyx_PyBool_FromLong(is_coroutine);
-    return __Pyx_NewRef(op->func_is_coroutine);
-}
-static PyGetSetDef __pyx_CyFunction_getsets[] = {
-    {(char *) "func_doc", (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "__doc__",  (getter)__Pyx_CyFunction_get_doc, (setter)__Pyx_CyFunction_set_doc, 0, 0},
-    {(char *) "func_name", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__name__", (getter)__Pyx_CyFunction_get_name, (setter)__Pyx_CyFunction_set_name, 0, 0},
-    {(char *) "__qualname__", (getter)__Pyx_CyFunction_get_qualname, (setter)__Pyx_CyFunction_set_qualname, 0, 0},
-    {(char *) "func_dict", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "__dict__", (getter)__Pyx_CyFunction_get_dict, (setter)__Pyx_CyFunction_set_dict, 0, 0},
-    {(char *) "func_globals", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "__globals__", (getter)__Pyx_CyFunction_get_globals, 0, 0, 0},
-    {(char *) "func_closure", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "__closure__", (getter)__Pyx_CyFunction_get_closure, 0, 0, 0},
-    {(char *) "func_code", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "__code__", (getter)__Pyx_CyFunction_get_code, 0, 0, 0},
-    {(char *) "func_defaults", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__defaults__", (getter)__Pyx_CyFunction_get_defaults, (setter)__Pyx_CyFunction_set_defaults, 0, 0},
-    {(char *) "__kwdefaults__", (getter)__Pyx_CyFunction_get_kwdefaults, (setter)__Pyx_CyFunction_set_kwdefaults, 0, 0},
-    {(char *) "__annotations__", (getter)__Pyx_CyFunction_get_annotations, (setter)__Pyx_CyFunction_set_annotations, 0, 0},
-    {(char *) "_is_coroutine", (getter)__Pyx_CyFunction_get_is_coroutine, 0, 0, 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_CyFunction_members[] = {
-    {(char *) "__module__", T_OBJECT, offsetof(PyCFunctionObject, m_module), 0, 0},
-#if CYTHON_USE_TYPE_SPECS
-    {(char *) "__dictoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_dict), READONLY, 0},
-#if CYTHON_METH_FASTCALL
-#if CYTHON_BACKPORT_VECTORCALL
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_vectorcall), READONLY, 0},
-#else
-    {(char *) "__vectorcalloffset__", T_PYSSIZET, offsetof(PyCFunctionObject, vectorcall), READONLY, 0},
-#endif
-#endif
-#if PY_VERSION_HEX < 0x030500A0
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(__pyx_CyFunctionObject, func_weakreflist), READONLY, 0},
-#else
-    {(char *) "__weaklistoffset__", T_PYSSIZET, offsetof(PyCFunctionObject, m_weakreflist), READONLY, 0},
-#endif
-#endif
-    {0, 0, 0,  0, 0}
-};
-static PyObject *
-__Pyx_CyFunction_reduce(__pyx_CyFunctionObject *m, PyObject *args)
-{
-    CYTHON_UNUSED_VAR(args);
-#if PY_MAJOR_VERSION >= 3
-    Py_INCREF(m->func_qualname);
-    return m->func_qualname;
-#else
-    return PyString_FromString(((PyCFunctionObject*)m)->m_ml->ml_name);
-#endif
-}
-static PyMethodDef __pyx_CyFunction_methods[] = {
-    {"__reduce__", (PyCFunction)__Pyx_CyFunction_reduce, METH_VARARGS, 0},
-    {0, 0, 0, 0}
-};
-#if PY_VERSION_HEX < 0x030500A0
-#define __Pyx_CyFunction_weakreflist(cyfunc) ((cyfunc)->func_weakreflist)
-#else
-#define __Pyx_CyFunction_weakreflist(cyfunc) (((PyCFunctionObject*)cyfunc)->m_weakreflist)
-#endif
-static PyObject *__Pyx_CyFunction_Init(__pyx_CyFunctionObject *op, PyMethodDef *ml, int flags, PyObject* qualname,
-                                       PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyCFunctionObject *cf = (PyCFunctionObject*) op;
-    if (unlikely(op == NULL))
-        return NULL;
-    op->flags = flags;
-    __Pyx_CyFunction_weakreflist(op) = NULL;
-    cf->m_ml = ml;
-    cf->m_self = (PyObject *) op;
-    Py_XINCREF(closure);
-    op->func_closure = closure;
-    Py_XINCREF(module);
-    cf->m_module = module;
-    op->func_dict = NULL;
-    op->func_name = NULL;
-    Py_INCREF(qualname);
-    op->func_qualname = qualname;
-    op->func_doc = NULL;
-#if PY_VERSION_HEX < 0x030900B1
-    op->func_classobj = NULL;
-#else
-    ((PyCMethodObject*)op)->mm_class = NULL;
-#endif
-    op->func_globals = globals;
-    Py_INCREF(op->func_globals);
-    Py_XINCREF(code);
-    op->func_code = code;
-    op->defaults_pyobjects = 0;
-    op->defaults_size = 0;
-    op->defaults = NULL;
-    op->defaults_tuple = NULL;
-    op->defaults_kwdict = NULL;
-    op->defaults_getter = NULL;
-    op->func_annotations = NULL;
-    op->func_is_coroutine = NULL;
-#if CYTHON_METH_FASTCALL
-    switch (ml->ml_flags & (METH_VARARGS | METH_FASTCALL | METH_NOARGS | METH_O | METH_KEYWORDS | METH_METHOD)) {
-    case METH_NOARGS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_NOARGS;
-        break;
-    case METH_O:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_O;
-        break;
-    case METH_METHOD | METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD;
-        break;
-    case METH_FASTCALL | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS;
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        __Pyx_CyFunction_func_vectorcall(op) = NULL;
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        Py_DECREF(op);
-        return NULL;
-    }
-#endif
-    return (PyObject *) op;
-}
-static int
-__Pyx_CyFunction_clear(__pyx_CyFunctionObject *m)
-{
-    Py_CLEAR(m->func_closure);
-    Py_CLEAR(((PyCFunctionObject*)m)->m_module);
-    Py_CLEAR(m->func_dict);
-    Py_CLEAR(m->func_name);
-    Py_CLEAR(m->func_qualname);
-    Py_CLEAR(m->func_doc);
-    Py_CLEAR(m->func_globals);
-    Py_CLEAR(m->func_code);
-#if PY_VERSION_HEX < 0x030900B1
-    Py_CLEAR(__Pyx_CyFunction_GetClassObj(m));
-#else
-    {
-        PyObject *cls = (PyObject*) ((PyCMethodObject *) (m))->mm_class;
-        ((PyCMethodObject *) (m))->mm_class = NULL;
-        Py_XDECREF(cls);
-    }
-#endif
-    Py_CLEAR(m->defaults_tuple);
-    Py_CLEAR(m->defaults_kwdict);
-    Py_CLEAR(m->func_annotations);
-    Py_CLEAR(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_XDECREF(pydefaults[i]);
-        PyObject_Free(m->defaults);
-        m->defaults = NULL;
-    }
-    return 0;
-}
-static void __Pyx__CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    if (__Pyx_CyFunction_weakreflist(m) != NULL)
-        PyObject_ClearWeakRefs((PyObject *) m);
-    __Pyx_CyFunction_clear(m);
-    __Pyx_PyHeapTypeObject_GC_Del(m);
-}
-static void __Pyx_CyFunction_dealloc(__pyx_CyFunctionObject *m)
-{
-    PyObject_GC_UnTrack(m);
-    __Pyx__CyFunction_dealloc(m);
-}
-static int __Pyx_CyFunction_traverse(__pyx_CyFunctionObject *m, visitproc visit, void *arg)
-{
-    Py_VISIT(m->func_closure);
-    Py_VISIT(((PyCFunctionObject*)m)->m_module);
-    Py_VISIT(m->func_dict);
-    Py_VISIT(m->func_name);
-    Py_VISIT(m->func_qualname);
-    Py_VISIT(m->func_doc);
-    Py_VISIT(m->func_globals);
-    Py_VISIT(m->func_code);
-    Py_VISIT(__Pyx_CyFunction_GetClassObj(m));
-    Py_VISIT(m->defaults_tuple);
-    Py_VISIT(m->defaults_kwdict);
-    Py_VISIT(m->func_is_coroutine);
-    if (m->defaults) {
-        PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m);
-        int i;
-        for (i = 0; i < m->defaults_pyobjects; i++)
-            Py_VISIT(pydefaults[i]);
-    }
-    return 0;
-}
-static PyObject*
-__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op)
-{
-#if PY_MAJOR_VERSION >= 3
-    return PyUnicode_FromFormat("<cyfunction %U at %p>",
-                                op->func_qualname, (void *)op);
-#else
-    return PyString_FromFormat("<cyfunction %s at %p>",
-                               PyString_AsString(op->func_qualname), (void *)op);
-#endif
-}
-static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) {
-    PyCFunctionObject* f = (PyCFunctionObject*)func;
-    PyCFunction meth = f->m_ml->ml_meth;
-    Py_ssize_t size;
-    switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) {
-    case METH_VARARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0))
-            return (*meth)(self, arg);
-        break;
-    case METH_VARARGS | METH_KEYWORDS:
-        return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw);
-    case METH_NOARGS:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 0))
-                return (*meth)(self, NULL);
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    case METH_O:
-        if (likely(kw == NULL || PyDict_Size(kw) == 0)) {
-            size = PyTuple_GET_SIZE(arg);
-            if (likely(size == 1)) {
-                PyObject *result, *arg0;
-                #if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-                arg0 = PyTuple_GET_ITEM(arg, 0);
-                #else
-                arg0 = PySequence_ITEM(arg, 0); if (unlikely(!arg0)) return NULL;
-                #endif
-                result = (*meth)(self, arg0);
-                #if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS)
-                Py_DECREF(arg0);
-                #endif
-                return result;
-            }
-            PyErr_Format(PyExc_TypeError,
-                "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                f->m_ml->ml_name, size);
-            return NULL;
-        }
-        break;
-    default:
-        PyErr_SetString(PyExc_SystemError, "Bad call flags for CyFunction");
-        return NULL;
-    }
-    PyErr_Format(PyExc_TypeError, "%.200s() takes no keyword arguments",
-                 f->m_ml->ml_name);
-    return NULL;
-}
-static CYTHON_INLINE PyObject *__Pyx_CyFunction_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    return __Pyx_CyFunction_CallMethod(func, ((PyCFunctionObject*)func)->m_self, arg, kw);
-}
-static PyObject *__Pyx_CyFunction_CallAsMethod(PyObject *func, PyObject *args, PyObject *kw) {
-    PyObject *result;
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *) func;
-#if CYTHON_METH_FASTCALL
-     __pyx_vectorcallfunc vc = __Pyx_CyFunction_func_vectorcall(cyfunc);
-    if (vc) {
-#if CYTHON_ASSUME_SAFE_MACROS
-        return __Pyx_PyVectorcall_FastCallDict(func, vc, &PyTuple_GET_ITEM(args, 0), (size_t)PyTuple_GET_SIZE(args), kw);
-#else
-        (void) &__Pyx_PyVectorcall_FastCallDict;
-        return PyVectorcall_Call(func, args, kw);
-#endif
-    }
-#endif
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        Py_ssize_t argc;
-        PyObject *new_args;
-        PyObject *self;
-        argc = PyTuple_GET_SIZE(args);
-        new_args = PyTuple_GetSlice(args, 1, argc);
-        if (unlikely(!new_args))
-            return NULL;
-        self = PyTuple_GetItem(args, 0);
-        if (unlikely(!self)) {
-            Py_DECREF(new_args);
-#if PY_MAJOR_VERSION > 2
-            PyErr_Format(PyExc_TypeError,
-                         "unbound method %.200S() needs an argument",
-                         cyfunc->func_qualname);
-#else
-            PyErr_SetString(PyExc_TypeError,
-                            "unbound method needs an argument");
-#endif
-            return NULL;
-        }
-        result = __Pyx_CyFunction_CallMethod(func, self, new_args, kw);
-        Py_DECREF(new_args);
-    } else {
-        result = __Pyx_CyFunction_Call(func, args, kw);
-    }
-    return result;
-}
-#if CYTHON_METH_FASTCALL
-static CYTHON_INLINE int __Pyx_CyFunction_Vectorcall_CheckArgs(__pyx_CyFunctionObject *cyfunc, Py_ssize_t nargs, PyObject *kwnames)
-{
-    int ret = 0;
-    if ((cyfunc->flags & __Pyx_CYFUNCTION_CCLASS) && !(cyfunc->flags & __Pyx_CYFUNCTION_STATICMETHOD)) {
-        if (unlikely(nargs < 1)) {
-            PyErr_Format(PyExc_TypeError, "%.200s() needs an argument",
-                         ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-            return -1;
-        }
-        ret = 1;
-    }
-    if (unlikely(kwnames) && unlikely(PyTuple_GET_SIZE(kwnames))) {
-        PyErr_Format(PyExc_TypeError,
-                     "%.200s() takes no keyword arguments", ((PyCFunctionObject*)cyfunc)->m_ml->ml_name);
-        return -1;
-    }
-    return ret;
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_NOARGS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 0)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes no arguments (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, NULL);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_O(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, kwnames)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    if (unlikely(nargs != 1)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s() takes exactly one argument (%" CYTHON_FORMAT_SSIZE_T "d given)",
-            def->ml_name, nargs);
-        return NULL;
-    }
-    return def->ml_meth(self, args[0]);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((_PyCFunctionFastWithKeywords)(void(*)(void))def->ml_meth)(self, args, nargs, kwnames);
-}
-static PyObject * __Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHOD(PyObject *func, PyObject *const *args, size_t nargsf, PyObject *kwnames)
-{
-    __pyx_CyFunctionObject *cyfunc = (__pyx_CyFunctionObject *)func;
-    PyMethodDef* def = ((PyCFunctionObject*)cyfunc)->m_ml;
-    PyTypeObject *cls = (PyTypeObject *) __Pyx_CyFunction_GetClassObj(cyfunc);
-#if CYTHON_BACKPORT_VECTORCALL
-    Py_ssize_t nargs = (Py_ssize_t)nargsf;
-#else
-    Py_ssize_t nargs = PyVectorcall_NARGS(nargsf);
-#endif
-    PyObject *self;
-    switch (__Pyx_CyFunction_Vectorcall_CheckArgs(cyfunc, nargs, NULL)) {
-    case 1:
-        self = args[0];
-        args += 1;
-        nargs -= 1;
-        break;
-    case 0:
-        self = ((PyCFunctionObject*)cyfunc)->m_self;
-        break;
-    default:
-        return NULL;
-    }
-    return ((__Pyx_PyCMethod)(void(*)(void))def->ml_meth)(self, cls, args, (size_t)nargs, kwnames);
-}
-#endif
-#if CYTHON_USE_TYPE_SPECS
-static PyType_Slot __pyx_CyFunctionType_slots[] = {
-    {Py_tp_dealloc, (void *)__Pyx_CyFunction_dealloc},
-    {Py_tp_repr, (void *)__Pyx_CyFunction_repr},
-    {Py_tp_call, (void *)__Pyx_CyFunction_CallAsMethod},
-    {Py_tp_traverse, (void *)__Pyx_CyFunction_traverse},
-    {Py_tp_clear, (void *)__Pyx_CyFunction_clear},
-    {Py_tp_methods, (void *)__pyx_CyFunction_methods},
-    {Py_tp_members, (void *)__pyx_CyFunction_members},
-    {Py_tp_getset, (void *)__pyx_CyFunction_getsets},
-    {Py_tp_descr_get, (void *)__Pyx_PyMethod_New},
-    {0, 0},
-};
-static PyType_Spec __pyx_CyFunctionType_spec = {
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#if (defined(_Py_TPFLAGS_HAVE_VECTORCALL) && CYTHON_METH_FASTCALL)
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    __pyx_CyFunctionType_slots
-};
-#else
-static PyTypeObject __pyx_CyFunctionType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    __PYX_TYPE_MODULE_PREFIX "cython_function_or_method",
-    sizeof(__pyx_CyFunctionObject),
-    0,
-    (destructor) __Pyx_CyFunction_dealloc,
-#if !CYTHON_METH_FASTCALL
-    0,
-#elif CYTHON_BACKPORT_VECTORCALL
-    (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall),
-#else
-    offsetof(PyCFunctionObject, vectorcall),
-#endif
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    (reprfunc) __Pyx_CyFunction_repr,
-    0,
-    0,
-    0,
-    0,
-    __Pyx_CyFunction_CallAsMethod,
-    0,
-    0,
-    0,
-    0,
-#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR
-    Py_TPFLAGS_METHOD_DESCRIPTOR |
-#endif
-#ifdef _Py_TPFLAGS_HAVE_VECTORCALL
-    _Py_TPFLAGS_HAVE_VECTORCALL |
-#endif
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE,
-    0,
-    (traverseproc) __Pyx_CyFunction_traverse,
-    (inquiry) __Pyx_CyFunction_clear,
-    0,
-#if PY_VERSION_HEX < 0x030500A0
-    offsetof(__pyx_CyFunctionObject, func_weakreflist),
-#else
-    offsetof(PyCFunctionObject, m_weakreflist),
-#endif
-    0,
-    0,
-    __pyx_CyFunction_methods,
-    __pyx_CyFunction_members,
-    __pyx_CyFunction_getsets,
-    0,
-    0,
-    __Pyx_PyMethod_New,
-    0,
-    offsetof(__pyx_CyFunctionObject, func_dict),
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800)
-    0,
-#endif
-#if __PYX_NEED_TP_PRINT_SLOT
-    0,
-#endif
-#if PY_VERSION_HEX >= 0x030C0000
-    0,
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000
-    0,
-#endif
-};
-#endif
-static int __pyx_CyFunction_init(PyObject *module) {
-#if CYTHON_USE_TYPE_SPECS
-    __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL);
-#else
-    CYTHON_UNUSED_VAR(module);
-    __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type);
-#endif
-    if (unlikely(__pyx_CyFunctionType == NULL)) {
-        return -1;
-    }
-    return 0;
-}
-static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults = PyObject_Malloc(size);
-    if (unlikely(!m->defaults))
-        return PyErr_NoMemory();
-    memset(m->defaults, 0, size);
-    m->defaults_pyobjects = pyobjects;
-    m->defaults_size = size;
-    return m->defaults;
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_tuple = tuple;
-    Py_INCREF(tuple);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->defaults_kwdict = dict;
-    Py_INCREF(dict);
-}
-static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) {
-    __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func;
-    m->func_annotations = dict;
-    Py_INCREF(dict);
-}
-
-/* CythonFunction */
-static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname,
-                                      PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) {
-    PyObject *op = __Pyx_CyFunction_Init(
-        PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType),
-        ml, flags, qualname, closure, module, globals, code
-    );
-    if (likely(op)) {
-        PyObject_GC_Track(op);
-    }
-    return op;
-}
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    CYTHON_MAYBE_UNUSED_VAR(tstate);
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        (void) PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-#if !CYTHON_COMPILING_IN_LIMITED_API
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-#endif
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-#if PY_VERSION_HEX >= 0x030b00a6
-  #ifndef Py_BUILD_CORE
-    #define Py_BUILD_CORE 1
-  #endif
-  #include "internal/pycore_frame.h"
-#endif
-#if CYTHON_COMPILING_IN_LIMITED_API
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    if (c_line) {
-        (void) __pyx_cfilenm;
-        (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line);
-    }
-    _PyTraceback_Add(funcname, filename, py_line);
-}
-#else
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = NULL;
-    PyObject *py_funcname = NULL;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_srcfile = NULL;
-    py_srcfile = PyString_FromString(filename);
-    if (!py_srcfile) goto bad;
-    #endif
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        if (!py_funcname) goto bad;
-        funcname = PyUnicode_AsUTF8(py_funcname);
-        if (!funcname) goto bad;
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        if (!py_funcname) goto bad;
-        #endif
-    }
-    #if PY_MAJOR_VERSION < 3
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    #else
-    py_code = PyCode_NewEmpty(filename, funcname, py_line);
-    #endif
-    Py_XDECREF(py_funcname);  // XDECREF since it's only set on Py3 if cline
-    return py_code;
-bad:
-    Py_XDECREF(py_funcname);
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_srcfile);
-    #endif
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject *ptype, *pvalue, *ptraceback;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) {
-            /* If the code object creation fails, then we should clear the
-               fetched exception references and propagate the new exception */
-            Py_XDECREF(ptype);
-            Py_XDECREF(pvalue);
-            Py_XDECREF(ptraceback);
-            goto bad;
-        }
-        __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-#endif
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-    __Pyx_TypeName obj_type_name;
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
-    obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj));
-    PyErr_Format(PyExc_TypeError,
-                 "'" __Pyx_FMT_TYPENAME "' does not have the buffer interface",
-                 obj_type_name);
-    __Pyx_DECREF_TypeName(obj_type_name);
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-    if ((0)) {}
-    view->obj = NULL;
-    Py_DECREF(obj);
-}
-#endif
-
-
-/* MemviewSliceIsContig */
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
-{
-    int i, index, step, start;
-    Py_ssize_t itemsize = mvs.memview->view.itemsize;
-    if (order == 'F') {
-        step = 1;
-        start = 0;
-    } else {
-        step = -1;
-        start = ndim - 1;
-    }
-    for (i = 0; i < ndim; i++) {
-        index = start + step * i;
-        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
-            return 0;
-        itemsize *= mvs.shape[index];
-    }
-    return 1;
-}
-
-/* OverlappingSlices */
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
-                               void **out_start, void **out_end,
-                               int ndim, size_t itemsize)
-{
-    char *start, *end;
-    int i;
-    start = end = slice->data;
-    for (i = 0; i < ndim; i++) {
-        Py_ssize_t stride = slice->strides[i];
-        Py_ssize_t extent = slice->shape[i];
-        if (extent == 0) {
-            *out_start = *out_end = start;
-            return;
-        } else {
-            if (stride > 0)
-                end += stride * (extent - 1);
-            else
-                start += stride * (extent - 1);
-        }
-    }
-    *out_start = start;
-    *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                     __Pyx_memviewslice *slice2,
-                     int ndim, size_t itemsize)
-{
-    void *start1, *end1, *start2, *end2;
-    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
-    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
-    return (start1 < end2) && (start2 < end1);
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
-  union {
-    uint32_t u32;
-    uint8_t u8[4];
-  } S;
-  S.u32 = 0x01020304;
-  return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t <= '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case '?': return "'bool'";
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparsable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) {
-  CYTHON_UNUSED_VAR(is_complex);
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) {
-  CYTHON_UNUSED_VAR(is_complex);
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number, ndim;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    ndim = ctx->head->field->type->ndim;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-        CYTHON_FALLTHROUGH;
-      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
-            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-        CYTHON_FALLTHROUGH;
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-
-/* TypeInfoCompare */
-  static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
-    int i;
-    if (!a || !b)
-        return 0;
-    if (a == b)
-        return 1;
-    if (a->size != b->size || a->typegroup != b->typegroup ||
-            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
-        if (a->typegroup == 'H' || b->typegroup == 'H') {
-            return a->size == b->size;
-        } else {
-            return 0;
-        }
-    }
-    if (a->ndim) {
-        for (i = 0; i < a->ndim; i++)
-            if (a->arraysize[i] != b->arraysize[i])
-                return 0;
-    }
-    if (a->typegroup == 'S') {
-        if (a->flags != b->flags)
-            return 0;
-        if (a->fields || b->fields) {
-            if (!(a->fields && b->fields))
-                return 0;
-            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
-                __Pyx_StructField *field_a = a->fields + i;
-                __Pyx_StructField *field_b = b->fields + i;
-                if (field_a->offset != field_b->offset ||
-                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
-                    return 0;
-            }
-            return !a->fields[i].type && !b->fields[i].type;
-        }
-    }
-    return 1;
-}
-
-/* MemviewSliceValidateAndInit */
-  static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    if (buf->shape[dim] <= 1)
-        return 1;
-    if (buf->strides) {
-        if (spec & __Pyx_MEMVIEW_CONTIG) {
-            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
-                if (unlikely(buf->strides[dim] != sizeof(void *))) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "Buffer is not indirectly contiguous "
-                                 "in dimension %d.", dim);
-                    goto fail;
-                }
-            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-        if (spec & __Pyx_MEMVIEW_FOLLOW) {
-            Py_ssize_t stride = buf->strides[dim];
-            if (stride < 0)
-                stride = -stride;
-            if (unlikely(stride < buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-    } else {
-        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not contiguous in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not indirect in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(buf->suboffsets)) {
-            PyErr_SetString(PyExc_ValueError,
-                            "Buffer exposes suboffsets but no strides");
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    CYTHON_UNUSED_VAR(ndim);
-    if (spec & __Pyx_MEMVIEW_DIRECT) {
-        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer not compatible with direct access "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    if (spec & __Pyx_MEMVIEW_PTR) {
-        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer is not indirectly accessible "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
-    int i;
-    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = 0; i < ndim; i++) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not fortran contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = ndim - 1; i >- 1; i--) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not C contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj)
-{
-    struct __pyx_memoryview_obj *memview, *new_memview;
-    __Pyx_RefNannyDeclarations
-    Py_buffer *buf;
-    int i, spec = 0, retval = -1;
-    __Pyx_BufFmt_Context ctx;
-    int from_memoryview = __pyx_memoryview_check(original_obj);
-    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
-    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
-                                                            original_obj)->typeinfo)) {
-        memview = (struct __pyx_memoryview_obj *) original_obj;
-        new_memview = NULL;
-    } else {
-        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                            original_obj, buf_flags, 0, dtype);
-        new_memview = memview;
-        if (unlikely(!memview))
-            goto fail;
-    }
-    buf = &memview->view;
-    if (unlikely(buf->ndim != ndim)) {
-        PyErr_Format(PyExc_ValueError,
-                "Buffer has wrong number of dimensions (expected %d, got %d)",
-                ndim, buf->ndim);
-        goto fail;
-    }
-    if (new_memview) {
-        __Pyx_BufFmt_Init(&ctx, stack, dtype);
-        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
-    }
-    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
-        PyErr_Format(PyExc_ValueError,
-                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
-                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
-                     buf->itemsize,
-                     (buf->itemsize > 1) ? "s" : "",
-                     dtype->name,
-                     dtype->size,
-                     (dtype->size > 1) ? "s" : "");
-        goto fail;
-    }
-    if (buf->len > 0) {
-        for (i = 0; i < ndim; i++) {
-            spec = axes_specs[i];
-            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
-                goto fail;
-            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
-                goto fail;
-        }
-        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
-            goto fail;
-    }
-    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
-                                         new_memview != NULL) == -1)) {
-        goto fail;
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    Py_XDECREF(new_memview);
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_float, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* CIntFromPyVerify */
-  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* MemviewSliceCopyTemplate */
-  static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object)
-{
-    __Pyx_RefNannyDeclarations
-    int i;
-    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
-    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
-    Py_buffer *buf = &from_memview->view;
-    PyObject *shape_tuple = NULL;
-    PyObject *temp_int = NULL;
-    struct __pyx_array_obj *array_obj = NULL;
-    struct __pyx_memoryview_obj *memview_obj = NULL;
-    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
-    for (i = 0; i < ndim; i++) {
-        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
-            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
-                                           "indirect dimensions (axis %d)", i);
-            goto fail;
-        }
-    }
-    shape_tuple = PyTuple_New(ndim);
-    if (unlikely(!shape_tuple)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(shape_tuple);
-    for(i = 0; i < ndim; i++) {
-        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
-        if(unlikely(!temp_int)) {
-            goto fail;
-        } else {
-            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
-            temp_int = NULL;
-        }
-    }
-    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
-    if (unlikely(!array_obj)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(array_obj);
-    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                    (PyObject *) array_obj, contig_flag,
-                                    dtype_is_object,
-                                    from_mvs->memview->typeinfo);
-    if (unlikely(!memview_obj))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
-        goto fail;
-    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
-                                                dtype_is_object) < 0))
-        goto fail;
-    goto no_fail;
-fail:
-    __Pyx_XDECREF(new_mvs.memview);
-    new_mvs.memview = NULL;
-    new_mvs.data = NULL;
-no_fail:
-    __Pyx_XDECREF(shape_tuple);
-    __Pyx_XDECREF(temp_int);
-    __Pyx_XDECREF(array_obj);
-    __Pyx_RefNannyFinishContext();
-    return new_mvs;
-}
-
-/* MemviewSliceInit */
-  static int
-__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
-                        int ndim,
-                        __Pyx_memviewslice *memviewslice,
-                        int memview_is_new_reference)
-{
-    __Pyx_RefNannyDeclarations
-    int i, retval=-1;
-    Py_buffer *buf = &memview->view;
-    __Pyx_RefNannySetupContext("init_memviewslice", 0);
-    if (unlikely(memviewslice->memview || memviewslice->data)) {
-        PyErr_SetString(PyExc_ValueError,
-            "memviewslice is already initialized!");
-        goto fail;
-    }
-    if (buf->strides) {
-        for (i = 0; i < ndim; i++) {
-            memviewslice->strides[i] = buf->strides[i];
-        }
-    } else {
-        Py_ssize_t stride = buf->itemsize;
-        for (i = ndim - 1; i >= 0; i--) {
-            memviewslice->strides[i] = stride;
-            stride *= buf->shape[i];
-        }
-    }
-    for (i = 0; i < ndim; i++) {
-        memviewslice->shape[i]   = buf->shape[i];
-        if (buf->suboffsets) {
-            memviewslice->suboffsets[i] = buf->suboffsets[i];
-        } else {
-            memviewslice->suboffsets[i] = -1;
-        }
-    }
-    memviewslice->memview = memview;
-    memviewslice->data = (char *)buf->buf;
-    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
-        Py_INCREF(memview);
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    memviewslice->memview = 0;
-    memviewslice->data = 0;
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-#ifndef Py_NO_RETURN
-#define Py_NO_RETURN
-#endif
-static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN {
-    va_list vargs;
-    char msg[200];
-#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES)
-    va_start(vargs, fmt);
-#else
-    va_start(vargs);
-#endif
-    vsnprintf(msg, 200, fmt, vargs);
-    va_end(vargs);
-    Py_FatalError(msg);
-}
-static CYTHON_INLINE int
-__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)++;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE int
-__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)--;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE void
-__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
-{
-    __pyx_nonatomic_int_type old_acquisition_count;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
-        return;
-    }
-    old_acquisition_count = __pyx_add_acquisition_count(memview);
-    if (unlikely(old_acquisition_count <= 0)) {
-        if (likely(old_acquisition_count == 0)) {
-            if (have_gil) {
-                Py_INCREF((PyObject *) memview);
-            } else {
-                PyGILState_STATE _gilstate = PyGILState_Ensure();
-                Py_INCREF((PyObject *) memview);
-                PyGILState_Release(_gilstate);
-            }
-        } else {
-            __pyx_fatalerror("Acquisition count is %d (line %d)",
-                             old_acquisition_count+1, lineno);
-        }
-    }
-}
-static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice,
-                                             int have_gil, int lineno) {
-    __pyx_nonatomic_int_type old_acquisition_count;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (unlikely(!memview || (PyObject *) memview == Py_None)) {
-        memslice->memview = NULL;
-        return;
-    }
-    old_acquisition_count = __pyx_sub_acquisition_count(memview);
-    memslice->data = NULL;
-    if (likely(old_acquisition_count > 1)) {
-        memslice->memview = NULL;
-    } else if (likely(old_acquisition_count == 1)) {
-        if (have_gil) {
-            Py_CLEAR(memslice->memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_CLEAR(memslice->memview);
-            PyGILState_Release(_gilstate);
-        }
-    } else {
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         old_acquisition_count-1, lineno);
-    }
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const int neg_one = (int) -1, const_zero = (int) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(int) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) {
-                                return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) {
-                                return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) {
-                                return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(int) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                                return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                                return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) {
-                                return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(int) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (int) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (int) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (int) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (int) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (int) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((int) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((int) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const long neg_one = (long) -1, const_zero = (long) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(long) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) {
-                                return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) {
-                                return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) {
-                                return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(long) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                                return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                                return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) {
-                                return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(long) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (long) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (long) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (long) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (long) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (long) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((long) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((long) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wconversion"
-#endif
-    const char neg_one = (char) -1, const_zero = (char) 0;
-#ifdef __Pyx_HAS_GCC_DIAGNOSTIC
-#pragma GCC diagnostic pop
-#endif
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if ((sizeof(char) < sizeof(long))) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (unlikely(__Pyx_PyLong_IsNeg(x))) {
-                goto raise_neg_overflow;
-            } else if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_DigitCount(x)) {
-                    case 2:
-                        if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) {
-                                return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) {
-                                return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) {
-                                return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (char) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if ((sizeof(char) <= sizeof(unsigned long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (__Pyx_PyLong_IsCompact(x)) {
-                __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x))
-            } else {
-                const digit* digits = __Pyx_PyLong_Digits(x);
-                assert(__Pyx_PyLong_DigitCount(x) > 1);
-                switch (__Pyx_PyLong_SignedDigitCount(x)) {
-                    case -2:
-                        if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 2:
-                        if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                                return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case -3:
-                        if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 3:
-                        if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                                return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case -4:
-                        if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) {
-                                return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                    case 4:
-                        if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) {
-                            if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) {
-                                __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                            } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) {
-                                return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                            }
-                        }
-                        break;
-                }
-            }
-#endif
-            if ((sizeof(char) <= sizeof(long))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-            char val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
-#if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
-#endif
-            if (likely(v)) {
-                int ret = -1;
-#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray)
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                           bytes, sizeof(val),
-                                           is_little, !is_unsigned);
-#else
-                PyObject *stepval = NULL, *mask = NULL, *shift = NULL;
-                int bits, remaining_bits, is_negative = 0;
-                long idigit;
-                int chunk_size = (sizeof(long) < 8) ? 30 : 62;
-                if (unlikely(!PyLong_CheckExact(v))) {
-                    PyObject *tmp = v;
-                    v = PyNumber_Long(v);
-                    assert(PyLong_CheckExact(v));
-                    Py_DECREF(tmp);
-                    if (unlikely(!v)) return (char) -1;
-                }
-#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                if (Py_SIZE(x) == 0)
-                    return (char) 0;
-                is_negative = Py_SIZE(x) < 0;
-#else
-                {
-                    int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                    if (unlikely(result < 0))
-                        return (char) -1;
-                    is_negative = result == 1;
-                }
-#endif
-                if (is_unsigned && unlikely(is_negative)) {
-                    goto raise_neg_overflow;
-                } else if (is_negative) {
-                    stepval = PyNumber_Invert(v);
-                    if (unlikely(!stepval))
-                        return (char) -1;
-                } else {
-                    stepval = __Pyx_NewRef(v);
-                }
-                val = (char) 0;
-                mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done;
-                shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done;
-                for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) {
-                    PyObject *tmp, *digit;
-                    digit = PyNumber_And(stepval, mask);
-                    if (unlikely(!digit)) goto done;
-                    idigit = PyLong_AsLong(digit);
-                    Py_DECREF(digit);
-                    if (unlikely(idigit < 0)) goto done;
-                    tmp = PyNumber_Rshift(stepval, shift);
-                    if (unlikely(!tmp)) goto done;
-                    Py_DECREF(stepval); stepval = tmp;
-                    val |= ((char) idigit) << bits;
-                    #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-                    if (Py_SIZE(stepval) == 0)
-                        goto unpacking_done;
-                    #endif
-                }
-                idigit = PyLong_AsLong(stepval);
-                if (unlikely(idigit < 0)) goto done;
-                remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1);
-                if (unlikely(idigit >= (1L << remaining_bits)))
-                    goto raise_overflow;
-                val |= ((char) idigit) << bits;
-            #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000
-            unpacking_done:
-            #endif
-                if (!is_unsigned) {
-                    if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1))))
-                        goto raise_overflow;
-                    if (is_negative)
-                        val = ~val;
-                }
-                ret = 0;
-            done:
-                Py_XDECREF(shift);
-                Py_XDECREF(mask);
-                Py_XDECREF(stepval);
-#endif
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-            return (char) -1;
-        }
-    } else {
-        char val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to char");
-    return (char) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to char");
-    return (char) -1;
-}
-
-/* FormatTypeName */
-  #if CYTHON_COMPILING_IN_LIMITED_API
-static __Pyx_TypeName
-__Pyx_PyType_GetName(PyTypeObject* tp)
-{
-    PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp,
-                                               __pyx_n_s_name_2);
-    if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) {
-        PyErr_Clear();
-        Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__23));
-    }
-    return name;
-}
-#endif
-
-/* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[5];
-    int same=1, i, found_dot;
-    const char* rt_from_call = Py_GetVersion();
-    PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    found_dot = 0;
-    for (i = 0; i < 4; i++) {
-        if (!ctversion[i]) {
-            same = (rt_from_call[i] < '0' || rt_from_call[i] > '9');
-            break;
-        }
-        if (rt_from_call[i] != ctversion[i]) {
-            same = 0;
-            break;
-        }
-    }
-    if (!same) {
-        char rtversion[5] = {'\0'};
-        char message[200];
-        for (i=0; i<4; ++i) {
-            if (rt_from_call[i] == '.') {
-                if (found_dot) break;
-                found_dot = 1;
-            } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') {
-                break;
-            }
-            rtversion[i] = rt_from_call[i];
-        }
-        PyOS_snprintf(message, sizeof(message),
-                      "compile time version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-  #if PY_MAJOR_VERSION >= 3
-static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) {
-    if (t.is_unicode | t.is_str) {
-        if (t.intern) {
-            *str = PyUnicode_InternFromString(t.s);
-        } else if (t.encoding) {
-            *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL);
-        } else {
-            *str = PyUnicode_FromStringAndSize(t.s, t.n - 1);
-        }
-    } else {
-        *str = PyBytes_FromStringAndSize(t.s, t.n - 1);
-    }
-    if (!*str)
-        return -1;
-    if (PyObject_Hash(*str) == -1)
-        return -1;
-    return 0;
-}
-#endif
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION >= 3
-        __Pyx_InitString(*t, t->p);
-        #else
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        #endif
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY && !CYTHON_COMPILING_IN_LIMITED_API) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-    __Pyx_TypeName result_type_name = __Pyx_PyType_GetName(Py_TYPE(result));
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type " __Pyx_FMT_TYPENAME ").  "
-                "The ability to return an instance of a strict subclass of int is deprecated, "
-                "and may be removed in a future version of Python.",
-                result_type_name)) {
-            __Pyx_DECREF_TypeName(result_type_name);
-            Py_DECREF(result);
-            return NULL;
-        }
-        __Pyx_DECREF_TypeName(result_type_name);
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")",
-                 type_name, type_name, result_type_name);
-    __Pyx_DECREF_TypeName(result_type_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(__Pyx_PyLong_IsCompact(b))) {
-        return __Pyx_PyLong_CompactValue(b);
-    } else {
-      const digit* digits = __Pyx_PyLong_Digits(b);
-      const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b);
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) {
-  if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) {
-    return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o);
-#if PY_MAJOR_VERSION < 3
-  } else if (likely(PyInt_CheckExact(o))) {
-    return PyInt_AS_LONG(o);
-#endif
-  } else {
-    Py_ssize_t ival;
-    PyObject *x;
-    x = PyNumber_Index(o);
-    if (!x) return -1;
-    ival = PyInt_AsLong(x);
-    Py_DECREF(x);
-    return ival;
-  }
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-/* #### Code section: utility_code_pragmas_end ### */
-#ifdef _MSC_VER
-#pragma warning( pop )
-#endif
-
-
-
-/* #### Code section: end ### */
-#endif /* Py_PYTHON_H */
diff --git a/spaces/digitalxingtong/Kino-Bert-VITS2/monotonic_align/setup.py b/spaces/digitalxingtong/Kino-Bert-VITS2/monotonic_align/setup.py
deleted file mode 100644
index 30c224807a70faa9df9c9eb75f8e80c8c867b16b..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Kino-Bert-VITS2/monotonic_align/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from distutils.core import setup
-from Cython.Build import cythonize
-import numpy
-
-setup(
-  name = 'monotonic_align',
-  ext_modules = cythonize("core.pyx"),
-  include_dirs=[numpy.get_include()]
-)
diff --git a/spaces/dineshreddy/WALT/mmcv_custom/checkpoint.py b/spaces/dineshreddy/WALT/mmcv_custom/checkpoint.py
deleted file mode 100644
index 51322c1c3802f357481065a70dc5152469d80eb8..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmcv_custom/checkpoint.py
+++ /dev/null
@@ -1,500 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-import io
-import os
-import os.path as osp
-import pkgutil
-import time
-import warnings
-from collections import OrderedDict
-from importlib import import_module
-from tempfile import TemporaryDirectory
-
-import torch
-import torchvision
-from torch.optim import Optimizer
-from torch.utils import model_zoo
-from torch.nn import functional as F
-
-import mmcv
-from mmcv.fileio import FileClient
-from mmcv.fileio import load as load_file
-from mmcv.parallel import is_module_wrapper
-from mmcv.utils import mkdir_or_exist
-from mmcv.runner import get_dist_info
-
-ENV_MMCV_HOME = 'MMCV_HOME'
-ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
-DEFAULT_CACHE_DIR = '~/.cache'
-
-
-def _get_mmcv_home():
-    mmcv_home = os.path.expanduser(
-        os.getenv(
-            ENV_MMCV_HOME,
-            os.path.join(
-                os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
-
-    mkdir_or_exist(mmcv_home)
-    return mmcv_home
-
-
-def load_state_dict(module, state_dict, strict=False, logger=None):
-    """Load state_dict to a module.
-
-    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
-    Default value for ``strict`` is set to ``False`` and the message for
-    param mismatch will be shown even if strict is False.
-
-    Args:
-        module (Module): Module that receives the state_dict.
-        state_dict (OrderedDict): Weights.
-        strict (bool): whether to strictly enforce that the keys
-            in :attr:`state_dict` match the keys returned by this module's
-            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
-        logger (:obj:`logging.Logger`, optional): Logger to log the error
-            message. If not specified, print function will be used.
-    """
-    unexpected_keys = []
-    all_missing_keys = []
-    err_msg = []
-
-    metadata = getattr(state_dict, '_metadata', None)
-    state_dict = state_dict.copy()
-    if metadata is not None:
-        state_dict._metadata = metadata
-
-    # use _load_from_state_dict to enable checkpoint version control
-    def load(module, prefix=''):
-        # recursively check parallel module in case that the model has a
-        # complicated structure, e.g., nn.Module(nn.Module(DDP))
-        if is_module_wrapper(module):
-            module = module.module
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
-        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
-                                     all_missing_keys, unexpected_keys,
-                                     err_msg)
-        for name, child in module._modules.items():
-            if child is not None:
-                load(child, prefix + name + '.')
-
-    load(module)
-    load = None  # break load->load reference cycle
-
-    # ignore "num_batches_tracked" of BN layers
-    missing_keys = [
-        key for key in all_missing_keys if 'num_batches_tracked' not in key
-    ]
-
-    if unexpected_keys:
-        err_msg.append('unexpected key in source '
-                       f'state_dict: {", ".join(unexpected_keys)}\n')
-    if missing_keys:
-        err_msg.append(
-            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
-
-    rank, _ = get_dist_info()
-    if len(err_msg) > 0 and rank == 0:
-        err_msg.insert(
-            0, 'The model and loaded state dict do not match exactly\n')
-        err_msg = '\n'.join(err_msg)
-        if strict:
-            raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
-        else:
-            print(err_msg)
-
-
-def load_url_dist(url, model_dir=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    return checkpoint
-
-
-def load_pavimodel_dist(model_path, map_location=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    try:
-        from pavi import modelcloud
-    except ImportError:
-        raise ImportError(
-            'Please install pavi to load checkpoint from modelcloud.')
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        model = modelcloud.get(model_path)
-        with TemporaryDirectory() as tmp_dir:
-            downloaded_file = osp.join(tmp_dir, model.name)
-            model.download(downloaded_file)
-            checkpoint = torch.load(downloaded_file, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            model = modelcloud.get(model_path)
-            with TemporaryDirectory() as tmp_dir:
-                downloaded_file = osp.join(tmp_dir, model.name)
-                model.download(downloaded_file)
-                checkpoint = torch.load(
-                    downloaded_file, map_location=map_location)
-    return checkpoint
-
-
-def load_fileclient_dist(filename, backend, map_location):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    allowed_backends = ['ceph']
-    if backend not in allowed_backends:
-        raise ValueError(f'Load from Backend {backend} is not supported.')
-    if rank == 0:
-        fileclient = FileClient(backend=backend)
-        buffer = io.BytesIO(fileclient.get(filename))
-        checkpoint = torch.load(buffer, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            fileclient = FileClient(backend=backend)
-            buffer = io.BytesIO(fileclient.get(filename))
-            checkpoint = torch.load(buffer, map_location=map_location)
-    return checkpoint
-
-
-def get_torchvision_models():
-    model_urls = dict()
-    for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
-        if ispkg:
-            continue
-        _zoo = import_module(f'torchvision.models.{name}')
-        if hasattr(_zoo, 'model_urls'):
-            _urls = getattr(_zoo, 'model_urls')
-            model_urls.update(_urls)
-    return model_urls
-
-
-def get_external_models():
-    mmcv_home = _get_mmcv_home()
-    default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
-    default_urls = load_file(default_json_path)
-    assert isinstance(default_urls, dict)
-    external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
-    if osp.exists(external_json_path):
-        external_urls = load_file(external_json_path)
-        assert isinstance(external_urls, dict)
-        default_urls.update(external_urls)
-
-    return default_urls
-
-
-def get_mmcls_models():
-    mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
-    mmcls_urls = load_file(mmcls_json_path)
-
-    return mmcls_urls
-
-
-def get_deprecated_model_names():
-    deprecate_json_path = osp.join(mmcv.__path__[0],
-                                   'model_zoo/deprecated.json')
-    deprecate_urls = load_file(deprecate_json_path)
-    assert isinstance(deprecate_urls, dict)
-
-    return deprecate_urls
-
-
-def _process_mmcls_checkpoint(checkpoint):
-    state_dict = checkpoint['state_dict']
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k.startswith('backbone.'):
-            new_state_dict[k[9:]] = v
-    new_checkpoint = dict(state_dict=new_state_dict)
-
-    return new_checkpoint
-
-
-def _load_checkpoint(filename, map_location=None):
-    """Load checkpoint from somewhere (modelzoo, file, url).
-
-    Args:
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str | None): Same as :func:`torch.load`. Default: None.
-
-    Returns:
-        dict | OrderedDict: The loaded checkpoint. It can be either an
-            OrderedDict storing model weights or a dict containing other
-            information, which depends on the checkpoint.
-    """
-    if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
-                      'use "torchvision://" instead')
-        model_urls = get_torchvision_models()
-        model_name = filename[11:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('torchvision://'):
-        model_urls = get_torchvision_models()
-        model_name = filename[14:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('open-mmlab://'):
-        model_urls = get_external_models()
-        model_name = filename[13:]
-        deprecated_urls = get_deprecated_model_names()
-        if model_name in deprecated_urls:
-            warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '
-                          f'of open-mmlab://{deprecated_urls[model_name]}')
-            model_name = deprecated_urls[model_name]
-        model_url = model_urls[model_name]
-        # check if is url
-        if model_url.startswith(('http://', 'https://')):
-            checkpoint = load_url_dist(model_url)
-        else:
-            filename = osp.join(_get_mmcv_home(), model_url)
-            if not osp.isfile(filename):
-                raise IOError(f'{filename} is not a checkpoint file')
-            checkpoint = torch.load(filename, map_location=map_location)
-    elif filename.startswith('mmcls://'):
-        model_urls = get_mmcls_models()
-        model_name = filename[8:]
-        checkpoint = load_url_dist(model_urls[model_name])
-        checkpoint = _process_mmcls_checkpoint(checkpoint)
-    elif filename.startswith(('http://', 'https://')):
-        checkpoint = load_url_dist(filename)
-    elif filename.startswith('pavi://'):
-        model_path = filename[7:]
-        checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
-    elif filename.startswith('s3://'):
-        checkpoint = load_fileclient_dist(
-            filename, backend='ceph', map_location=map_location)
-    else:
-        if not osp.isfile(filename):
-            raise IOError(f'{filename} is not a checkpoint file')
-        checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-
-
-def load_checkpoint(model,
-                    filename,
-                    map_location='cpu',
-                    strict=False,
-                    logger=None):
-    """Load checkpoint from a file or URI.
-
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = _load_checkpoint(filename, map_location)
-    # OrderedDict is a subclass of dict
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    # get state_dict from checkpoint
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    elif 'model' in checkpoint:
-        state_dict = checkpoint['model']
-    else:
-        state_dict = checkpoint
-    # strip prefix of state_dict
-    if list(state_dict.keys())[0].startswith('module.'):
-        state_dict = {k[7:]: v for k, v in state_dict.items()}
-
-    # for MoBY, load model of online branch
-    if sorted(list(state_dict.keys()))[0].startswith('encoder'):
-        state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
-
-    # reshape absolute position embedding
-    if state_dict.get('absolute_pos_embed') is not None:
-        absolute_pos_embed = state_dict['absolute_pos_embed']
-        N1, L, C1 = absolute_pos_embed.size()
-        N2, C2, H, W = model.absolute_pos_embed.size()
-        if N1 != N2 or C1 != C2 or L != H*W:
-            logger.warning("Error in loading absolute_pos_embed, pass")
-        else:
-            state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
-
-    # interpolate position bias table if needed
-    relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
-    for table_key in relative_position_bias_table_keys:
-        table_pretrained = state_dict[table_key]
-        table_current = model.state_dict()[table_key]
-        L1, nH1 = table_pretrained.size()
-        L2, nH2 = table_current.size()
-        if nH1 != nH2:
-            logger.warning(f"Error in loading {table_key}, pass")
-        else:
-            if L1 != L2:
-                S1 = int(L1 ** 0.5)
-                S2 = int(L2 ** 0.5)
-                table_pretrained_resized = F.interpolate(
-                     table_pretrained.permute(1, 0).view(1, nH1, S1, S1),
-                     size=(S2, S2), mode='bicubic')
-                state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
-
-    # load state_dict
-    load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-
-
-def weights_to_cpu(state_dict):
-    """Copy a model state_dict to cpu.
-
-    Args:
-        state_dict (OrderedDict): Model weights on GPU.
-
-    Returns:
-        OrderedDict: Model weights on GPU.
-    """
-    state_dict_cpu = OrderedDict()
-    for key, val in state_dict.items():
-        state_dict_cpu[key] = val.cpu()
-    return state_dict_cpu
-
-
-def _save_to_state_dict(module, destination, prefix, keep_vars):
-    """Saves module state to `destination` dictionary.
-
-    This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (dict): A dict where state will be stored.
-        prefix (str): The prefix for parameters and buffers used in this
-            module.
-    """
-    for name, param in module._parameters.items():
-        if param is not None:
-            destination[prefix + name] = param if keep_vars else param.detach()
-    for name, buf in module._buffers.items():
-        # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d
-        if buf is not None:
-            destination[prefix + name] = buf if keep_vars else buf.detach()
-
-
-def get_state_dict(module, destination=None, prefix='', keep_vars=False):
-    """Returns a dictionary containing a whole state of the module.
-
-    Both parameters and persistent buffers (e.g. running averages) are
-    included. Keys are corresponding parameter and buffer names.
-
-    This method is modified from :meth:`torch.nn.Module.state_dict` to
-    recursively check parallel module in case that the model has a complicated
-    structure, e.g., nn.Module(nn.Module(DDP)).
-
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (OrderedDict): Returned dict for the state of the
-            module.
-        prefix (str): Prefix of the key.
-        keep_vars (bool): Whether to keep the variable property of the
-            parameters. Default: False.
-
-    Returns:
-        dict: A dictionary containing a whole state of the module.
-    """
-    # recursively check parallel module in case that the model has a
-    # complicated structure, e.g., nn.Module(nn.Module(DDP))
-    if is_module_wrapper(module):
-        module = module.module
-
-    # below is the same as torch.nn.Module.state_dict()
-    if destination is None:
-        destination = OrderedDict()
-        destination._metadata = OrderedDict()
-    destination._metadata[prefix[:-1]] = local_metadata = dict(
-        version=module._version)
-    _save_to_state_dict(module, destination, prefix, keep_vars)
-    for name, child in module._modules.items():
-        if child is not None:
-            get_state_dict(
-                child, destination, prefix + name + '.', keep_vars=keep_vars)
-    for hook in module._state_dict_hooks.values():
-        hook_result = hook(module, destination, prefix, local_metadata)
-        if hook_result is not None:
-            destination = hook_result
-    return destination
-
-
-def save_checkpoint(model, filename, optimizer=None, meta=None):
-    """Save checkpoint to file.
-
-    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
-    ``optimizer``. By default ``meta`` will contain version and time info.
-
-    Args:
-        model (Module): Module whose params are to be saved.
-        filename (str): Checkpoint filename.
-        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
-        meta (dict, optional): Metadata to be saved in checkpoint.
-    """
-    if meta is None:
-        meta = {}
-    elif not isinstance(meta, dict):
-        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
-    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
-
-    if is_module_wrapper(model):
-        model = model.module
-
-    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
-        # save class name to the meta
-        meta.update(CLASSES=model.CLASSES)
-
-    checkpoint = {
-        'meta': meta,
-        'state_dict': weights_to_cpu(get_state_dict(model))
-    }
-    # save optimizer state dict in the checkpoint
-    if isinstance(optimizer, Optimizer):
-        checkpoint['optimizer'] = optimizer.state_dict()
-    elif isinstance(optimizer, dict):
-        checkpoint['optimizer'] = {}
-        for name, optim in optimizer.items():
-            checkpoint['optimizer'][name] = optim.state_dict()
-
-    if filename.startswith('pavi://'):
-        try:
-            from pavi import modelcloud
-            from pavi.exception import NodeNotFoundError
-        except ImportError:
-            raise ImportError(
-                'Please install pavi to load checkpoint from modelcloud.')
-        model_path = filename[7:]
-        root = modelcloud.Folder()
-        model_dir, model_name = osp.split(model_path)
-        try:
-            model = modelcloud.get(model_dir)
-        except NodeNotFoundError:
-            model = root.create_training_model(model_dir)
-        with TemporaryDirectory() as tmp_dir:
-            checkpoint_file = osp.join(tmp_dir, model_name)
-            with open(checkpoint_file, 'wb') as f:
-                torch.save(checkpoint, f)
-                f.flush()
-            model.create_file(checkpoint_file, name=model_name)
-    else:
-        mmcv.mkdir_or_exist(osp.dirname(filename))
-        # immediately flush buffer
-        with open(filename, 'wb') as f:
-            torch.save(checkpoint, f)
-            f.flush()
diff --git a/spaces/dirge/voicevox/voicevox_engine/metas/__init__.py b/spaces/dirge/voicevox/voicevox_engine/metas/__init__.py
deleted file mode 100644
index 4907fdf38d604dc7949dd361812938afd9db0abb..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/voicevox_engine/metas/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from . import Metas, MetasStore
-
-__all__ = [
-    "Metas",
-    "MetasStore",
-]
diff --git a/spaces/dirge/voicevox/voicevox_engine/utility/__init__.py b/spaces/dirge/voicevox/voicevox_engine/utility/__init__.py
deleted file mode 100644
index d40fea3e6c22f8bcb960ca12cf626e1f3a40afef..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/voicevox_engine/utility/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from .connect_base64_waves import (
-    ConnectBase64WavesException,
-    connect_base64_waves,
-    decode_base64_waves,
-)
-from .core_version_utility import get_latest_core_version, parse_core_version
-from .mutex_utility import mutex_wrapper
-from .path_utility import delete_file, engine_root, get_save_dir
-
-__all__ = [
-    "ConnectBase64WavesException",
-    "connect_base64_waves",
-    "decode_base64_waves",
-    "get_latest_core_version",
-    "parse_core_version",
-    "delete_file",
-    "engine_root",
-    "get_save_dir",
-    "mutex_wrapper",
-]
diff --git a/spaces/dmeck/RVC-Speakers/vits/monotonic_align/core.c b/spaces/dmeck/RVC-Speakers/vits/monotonic_align/core.c
deleted file mode 100644
index 18675a50479c5b21b0e02af12cca72a58f6d8461..0000000000000000000000000000000000000000
--- a/spaces/dmeck/RVC-Speakers/vits/monotonic_align/core.c
+++ /dev/null
@@ -1,21299 +0,0 @@
-/* Generated by Cython 0.29.21 */
-
-/* BEGIN: Cython Metadata
-{
-    "distutils": {
-        "name": "vits.monotonic_align.core",
-        "sources": [
-            "core.pyx"
-        ]
-    },
-    "module_name": "vits.monotonic_align.core"
-}
-END: Cython Metadata */
-
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-typedef int Py_tss_t;
-static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {
-  *key = PyThread_create_key();
-  return 0;
-}
-static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {
-  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));
-  *key = Py_tss_NEEDS_INIT;
-  return key;
-}
-static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {
-  PyObject_Free(key);
-}
-static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {
-  return *key != Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {
-  PyThread_delete_key(*key);
-  *key = Py_tss_NEEDS_INIT;
-}
-static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {
-  return PyThread_set_key_value(*key, value);
-}
-static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {
-  return PyThread_get_key_value(*key);
-}
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
-#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
-#else
-#define __Pyx_PyDict_NewPresized(n)  PyDict_New()
-#endif
-#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS
-#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)
-#else
-#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-  #define CYTHON_PEP393_ENABLED 1
-  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\
-                                              0 : _PyUnicode_Ready((PyObject *)(op)))
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
-  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
-  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
-  #if defined(PyUnicode_IS_READY) && defined(PyUnicode_GET_SIZE)
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
-  #else
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_LENGTH(u))
-  #endif
-#else
-  #define CYTHON_PEP393_ENABLED 0
-  #define PyUnicode_1BYTE_KIND  1
-  #define PyUnicode_2BYTE_KIND  2
-  #define PyUnicode_4BYTE_KIND  4
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
-  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
-  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
-#else
-  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
-      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
-  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
-  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
-#endif
-#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
-  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
-#endif
-#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None || (PyString_Check(b) && !PyString_CheckExact(b)))) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None || (PyUnicode_Check(b) && !PyUnicode_CheckExact(b)))) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
-#else
-  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
-  #define PyObject_ASCII(o)            PyObject_Repr(o)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBaseString_Type            PyUnicode_Type
-  #define PyStringObject               PyUnicodeObject
-  #define PyString_Type                PyUnicode_Type
-  #define PyString_Check               PyUnicode_Check
-  #define PyString_CheckExact          PyUnicode_CheckExact
-#ifndef PyObject_Unicode
-  #define PyObject_Unicode             PyObject_Str
-#endif
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
-  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
-#else
-  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
-  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
-#endif
-#ifndef PySet_CheckExact
-  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
-#endif
-#if PY_VERSION_HEX >= 0x030900A4
-  #define __Pyx_SET_REFCNT(obj, refcnt) Py_SET_REFCNT(obj, refcnt)
-  #define __Pyx_SET_SIZE(obj, size) Py_SET_SIZE(obj, size)
-#else
-  #define __Pyx_SET_REFCNT(obj, refcnt) Py_REFCNT(obj) = (refcnt)
-  #define __Pyx_SET_SIZE(obj, size) Py_SIZE(obj) = (size)
-#endif
-#if CYTHON_ASSUME_SAFE_MACROS
-  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)
-#else
-  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyIntObject                  PyLongObject
-  #define PyInt_Type                   PyLong_Type
-  #define PyInt_Check(op)              PyLong_Check(op)
-  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
-  #define PyInt_FromString             PyLong_FromString
-  #define PyInt_FromUnicode            PyLong_FromUnicode
-  #define PyInt_FromLong               PyLong_FromLong
-  #define PyInt_FromSize_t             PyLong_FromSize_t
-  #define PyInt_FromSsize_t            PyLong_FromSsize_t
-  #define PyInt_AsLong                 PyLong_AsLong
-  #define PyInt_AS_LONG                PyLong_AS_LONG
-  #define PyInt_AsSsize_t              PyLong_AsSsize_t
-  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
-  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
-  #define PyNumber_Int                 PyNumber_Long
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBoolObject                 PyLongObject
-#endif
-#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
-  #ifndef PyUnicode_InternFromString
-    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
-  #endif
-#endif
-#if PY_VERSION_HEX < 0x030200A4
-  typedef long Py_hash_t;
-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
-#else
-  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
-  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? ((void)(klass), PyMethod_New(func, self)) : __Pyx_NewRef(func))
-#else
-  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
-#endif
-#if CYTHON_USE_ASYNC_SLOTS
-  #if PY_VERSION_HEX >= 0x030500B1
-    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
-    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
-  #else
-    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
-  #endif
-#else
-  #define __Pyx_PyType_AsAsync(obj) NULL
-#endif
-#ifndef __Pyx_PyAsyncMethodsStruct
-    typedef struct {
-        unaryfunc am_await;
-        unaryfunc am_aiter;
-        unaryfunc am_anext;
-    } __Pyx_PyAsyncMethodsStruct;
-#endif
-
-#if defined(WIN32) || defined(MS_WINDOWS)
-  #define _USE_MATH_DEFINES
-#endif
-#include <math.h>
-#ifdef NAN
-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
-  float value;
-  memset(&value, 0xFF, sizeof(value));
-  return value;
-}
-#endif
-#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
-#define __Pyx_truncl trunc
-#else
-#define __Pyx_truncl truncl
-#endif
-
-#define __PYX_MARK_ERR_POS(f_index, lineno) \
-    { __pyx_filename = __pyx_f[f_index]; (void)__pyx_filename; __pyx_lineno = lineno; (void)__pyx_lineno; __pyx_clineno = __LINE__; (void)__pyx_clineno; }
-#define __PYX_ERR(f_index, lineno, Ln_error) \
-    { __PYX_MARK_ERR_POS(f_index, lineno) goto Ln_error; }
-
-#ifndef __PYX_EXTERN_C
-  #ifdef __cplusplus
-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
-#endif
-
-#define __PYX_HAVE__vits__monotonic_align__core
-#define __PYX_HAVE_API__vits__monotonic_align__core
-/* Early includes */
-#include "pythread.h"
-#include <string.h>
-#include <stdlib.h>
-#include <stdio.h>
-#include "pystate.h"
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_UTF8 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT (PY_MAJOR_VERSION >= 3 && __PYX_DEFAULT_STRING_ENCODING_IS_UTF8)
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_uchar_cast(c) ((unsigned char)c)
-#define __Pyx_long_cast(x) ((long)x)
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
-    (sizeof(type) < sizeof(Py_ssize_t))  ||\
-    (sizeof(type) > sizeof(Py_ssize_t) &&\
-          likely(v < (type)PY_SSIZE_T_MAX ||\
-                 v == (type)PY_SSIZE_T_MAX)  &&\
-          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
-                                v == (type)PY_SSIZE_T_MIN)))  ||\
-    (sizeof(type) == sizeof(Py_ssize_t) &&\
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE int __Pyx_is_valid_index(Py_ssize_t i, Py_ssize_t limit) {
-    return (size_t) i < (size_t) limit;
-}
-#if defined (__cplusplus) && __cplusplus >= 201103L
-    #include <cstdlib>
-    #define __Pyx_sst_abs(value) std::abs(value)
-#elif SIZEOF_INT >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) abs(value)
-#elif SIZEOF_LONG >= SIZEOF_SIZE_T
-    #define __Pyx_sst_abs(value) labs(value)
-#elif defined (_MSC_VER)
-    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
-#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define __Pyx_sst_abs(value) llabs(value)
-#elif defined (__GNUC__)
-    #define __Pyx_sst_abs(value) __builtin_llabs(value)
-#else
-    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))
-#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
-#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
-#define __Pyx_PySequence_Tuple(obj)\
-    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-#if CYTHON_ASSUME_SAFE_MACROS
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
-#else
-#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
-#endif
-#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c) + 1);
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
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-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *);
-
-/* GetItemInt.proto */
-#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
-               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
-#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
-/* ObjectGetItem.proto */
-#if CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key);
-#else
-#define __Pyx_PyObject_GetItem(obj, key)  PyObject_GetItem(obj, key)
-#endif
-
-/* decode_c_string_utf16.proto */
-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16(const char *s, Py_ssize_t size, const char *errors) {
-    int byteorder = 0;
-    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
-}
-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16LE(const char *s, Py_ssize_t size, const char *errors) {
-    int byteorder = -1;
-    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
-}
-static CYTHON_INLINE PyObject *__Pyx_PyUnicode_DecodeUTF16BE(const char *s, Py_ssize_t size, const char *errors) {
-    int byteorder = 1;
-    return PyUnicode_DecodeUTF16(s, size, errors, &byteorder);
-}
-
-/* decode_c_string.proto */
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
-         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
-         const char* encoding, const char* errors,
-         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
-
-/* PyErrExceptionMatches.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
-#else
-#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
-#endif
-
-/* GetAttr3.proto */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *);
-
-/* PyDictVersioning.proto */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-#define __PYX_DICT_VERSION_INIT  ((PY_UINT64_T) -1)
-#define __PYX_GET_DICT_VERSION(dict)  (((PyDictObject*)(dict))->ma_version_tag)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\
-    (version_var) = __PYX_GET_DICT_VERSION(dict);\
-    (cache_var) = (value);
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\
-        (VAR) = __pyx_dict_cached_value;\
-    } else {\
-        (VAR) = __pyx_dict_cached_value = (LOOKUP);\
-        __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\
-    }\
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj);
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj);
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version);
-#else
-#define __PYX_GET_DICT_VERSION(dict)  (0)
-#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)
-#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP)  (VAR) = (LOOKUP);
-#endif
-
-/* GetModuleGlobalName.proto */
-#if CYTHON_USE_DICT_VERSIONS
-#define __Pyx_GetModuleGlobalName(var, name)  {\
-    static PY_UINT64_T __pyx_dict_version = 0;\
-    static PyObject *__pyx_dict_cached_value = NULL;\
-    (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\
-        (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\
-        __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-}
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  {\
-    PY_UINT64_T __pyx_dict_version;\
-    PyObject *__pyx_dict_cached_value;\
-    (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\
-}
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value);
-#else
-#define __Pyx_GetModuleGlobalName(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-#define __Pyx_GetModuleGlobalNameUncached(var, name)  (var) = __Pyx__GetModuleGlobalName(name)
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name);
-#endif
-
-/* RaiseTooManyValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-/* RaiseNeedMoreValuesToUnpack.proto */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-/* RaiseNoneIterError.proto */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-/* ExtTypeTest.proto */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
-
-/* GetTopmostException.proto */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate);
-#endif
-
-/* SaveResetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
-#else
-#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
-#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
-#endif
-
-/* GetException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* SwapException.proto */
-#if CYTHON_FAST_THREAD_STATE
-#define __Pyx_ExceptionSwap(type, value, tb)  __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb)
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-#endif
-
-/* Import.proto */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
-
-/* FastTypeChecks.proto */
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
-#else
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
-#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
-#endif
-#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
-
-static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-/* ListCompAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* PyIntBinop.proto */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check);
-#else
-#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace, zerodivision_check)\
-    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
-#endif
-
-/* ListExtend.proto */
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
-    if (unlikely(!none))
-        return -1;
-    Py_DECREF(none);
-    return 0;
-#else
-    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
-#endif
-}
-
-/* ListAppend.proto */
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-/* None.proto */
-static CYTHON_INLINE long __Pyx_div_long(long, long);
-
-/* ImportFrom.proto */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
-
-/* HasAttr.proto */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *, PyObject *);
-
-/* PyObject_GenericGetAttrNoDict.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttrNoDict PyObject_GenericGetAttr
-#endif
-
-/* PyObject_GenericGetAttr.proto */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name);
-#else
-#define __Pyx_PyObject_GenericGetAttr PyObject_GenericGetAttr
-#endif
-
-/* SetVTable.proto */
-static int __Pyx_SetVtable(PyObject *dict, void *vtable);
-
-/* PyObjectGetAttrStrNoError.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name);
-
-/* SetupReduce.proto */
-static int __Pyx_setup_reduce(PyObject* type_obj);
-
-/* CLineInTraceback.proto */
-#ifdef CYTHON_CLINE_IN_TRACEBACK
-#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
-#else
-static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
-#endif
-
-/* CodeObjectCache.proto */
-typedef struct {
-    PyCodeObject* code_object;
-    int code_line;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-/* AddTraceback.proto */
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename);
-
-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
-
-
-/* BufferStructDeclare.proto */
-typedef struct {
-  Py_ssize_t shape, strides, suboffsets;
-} __Pyx_Buf_DimInfo;
-typedef struct {
-  size_t refcount;
-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
-  __Pyx_Buffer *rcbuffer;
-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
-
-/* MemviewSliceIsContig.proto */
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim);
-
-/* OverlappingSlices.proto */
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                                __Pyx_memviewslice *slice2,
-                                int ndim, size_t itemsize);
-
-/* Capsule.proto */
-static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);
-
-/* IsLittleEndian.proto */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void);
-
-/* BufferFormatCheck.proto */
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts);
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type);
-
-/* TypeInfoCompare.proto */
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-/* MemviewSliceValidateAndInit.proto */
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag);
-
-/* ObjectToMemviewSlice.proto */
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-/* CIntToPy.proto */
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-/* MemviewSliceCopyTemplate.proto */
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-/* CIntFromPy.proto */
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-/* CheckBinaryVersion.proto */
-static int __Pyx_check_binary_version(void);
-
-/* InitStrings.proto */
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
-
-static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/
-
-/* Module declarations from 'cython.view' */
-
-/* Module declarations from 'cython' */
-
-/* Module declarations from 'vits.monotonic_align.core' */
-static PyTypeObject *__pyx_array_type = 0;
-static PyTypeObject *__pyx_MemviewEnum_type = 0;
-static PyTypeObject *__pyx_memoryview_type = 0;
-static PyTypeObject *__pyx_memoryviewslice_type = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static int __pyx_memoryview_thread_locks_used;
-static PyThread_type_lock __pyx_memoryview_thread_locks[8];
-static void __pyx_f_4vits_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_4vits_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/
-static void __pyx_f_4vits_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static void *__pyx_align_pointer(void *, size_t); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/
-static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, IS_UNSIGNED(int) ? 'U' : 'I', IS_UNSIGNED(int), 0 };
-static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 };
-#define __Pyx_MODULE_NAME "vits.monotonic_align.core"
-extern int __pyx_module_is_main_vits__monotonic_align__core;
-int __pyx_module_is_main_vits__monotonic_align__core = 0;
-
-/* Implementation of 'vits.monotonic_align.core' */
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-static const char __pyx_k_O[] = "O";
-static const char __pyx_k_c[] = "c";
-static const char __pyx_k_id[] = "id";
-static const char __pyx_k_new[] = "__new__";
-static const char __pyx_k_obj[] = "obj";
-static const char __pyx_k_base[] = "base";
-static const char __pyx_k_dict[] = "__dict__";
-static const char __pyx_k_main[] = "__main__";
-static const char __pyx_k_mode[] = "mode";
-static const char __pyx_k_name[] = "name";
-static const char __pyx_k_ndim[] = "ndim";
-static const char __pyx_k_pack[] = "pack";
-static const char __pyx_k_size[] = "size";
-static const char __pyx_k_step[] = "step";
-static const char __pyx_k_stop[] = "stop";
-static const char __pyx_k_t_xs[] = "t_xs";
-static const char __pyx_k_t_ys[] = "t_ys";
-static const char __pyx_k_test[] = "__test__";
-static const char __pyx_k_ASCII[] = "ASCII";
-static const char __pyx_k_class[] = "__class__";
-static const char __pyx_k_error[] = "error";
-static const char __pyx_k_flags[] = "flags";
-static const char __pyx_k_paths[] = "paths";
-static const char __pyx_k_range[] = "range";
-static const char __pyx_k_shape[] = "shape";
-static const char __pyx_k_start[] = "start";
-static const char __pyx_k_encode[] = "encode";
-static const char __pyx_k_format[] = "format";
-static const char __pyx_k_import[] = "__import__";
-static const char __pyx_k_name_2[] = "__name__";
-static const char __pyx_k_pickle[] = "pickle";
-static const char __pyx_k_reduce[] = "__reduce__";
-static const char __pyx_k_struct[] = "struct";
-static const char __pyx_k_unpack[] = "unpack";
-static const char __pyx_k_update[] = "update";
-static const char __pyx_k_values[] = "values";
-static const char __pyx_k_fortran[] = "fortran";
-static const char __pyx_k_memview[] = "memview";
-static const char __pyx_k_Ellipsis[] = "Ellipsis";
-static const char __pyx_k_getstate[] = "__getstate__";
-static const char __pyx_k_itemsize[] = "itemsize";
-static const char __pyx_k_pyx_type[] = "__pyx_type";
-static const char __pyx_k_setstate[] = "__setstate__";
-static const char __pyx_k_TypeError[] = "TypeError";
-static const char __pyx_k_enumerate[] = "enumerate";
-static const char __pyx_k_pyx_state[] = "__pyx_state";
-static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
-static const char __pyx_k_IndexError[] = "IndexError";
-static const char __pyx_k_ValueError[] = "ValueError";
-static const char __pyx_k_pyx_result[] = "__pyx_result";
-static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static const char __pyx_k_MemoryError[] = "MemoryError";
-static const char __pyx_k_PickleError[] = "PickleError";
-static const char __pyx_k_pyx_checksum[] = "__pyx_checksum";
-static const char __pyx_k_stringsource[] = "stringsource";
-static const char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
-static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
-static const char __pyx_k_View_MemoryView[] = "View.MemoryView";
-static const char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static const char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError";
-static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
-static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum";
-static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
-static const char __pyx_k_strided_and_direct[] = "<strided and direct>";
-static const char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
-static const char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
-static const char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
-static const char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
-static const char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
-static const char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
-static const char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
-static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static const char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
-static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview";
-static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview";
-static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static const char __pyx_k_Incompatible_checksums_s_vs_0xb0[] = "Incompatible checksums (%s vs 0xb068931 = (name))";
-static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
-static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
-static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
-static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
-static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-static PyObject *__pyx_n_s_ASCII;
-static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
-static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
-static PyObject *__pyx_kp_s_Cannot_assign_to_read_only_memor;
-static PyObject *__pyx_kp_s_Cannot_create_writable_memory_vi;
-static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
-static PyObject *__pyx_n_s_Ellipsis;
-static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
-static PyObject *__pyx_kp_s_Incompatible_checksums_s_vs_0xb0;
-static PyObject *__pyx_n_s_IndexError;
-static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
-static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
-static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
-static PyObject *__pyx_n_s_MemoryError;
-static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
-static PyObject *__pyx_kp_s_MemoryView_of_r_object;
-static PyObject *__pyx_n_b_O;
-static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
-static PyObject *__pyx_n_s_PickleError;
-static PyObject *__pyx_n_s_TypeError;
-static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
-static PyObject *__pyx_n_s_ValueError;
-static PyObject *__pyx_n_s_View_MemoryView;
-static PyObject *__pyx_n_s_allocate_buffer;
-static PyObject *__pyx_n_s_base;
-static PyObject *__pyx_n_s_c;
-static PyObject *__pyx_n_u_c;
-static PyObject *__pyx_n_s_class;
-static PyObject *__pyx_n_s_cline_in_traceback;
-static PyObject *__pyx_kp_s_contiguous_and_direct;
-static PyObject *__pyx_kp_s_contiguous_and_indirect;
-static PyObject *__pyx_n_s_dict;
-static PyObject *__pyx_n_s_dtype_is_object;
-static PyObject *__pyx_n_s_encode;
-static PyObject *__pyx_n_s_enumerate;
-static PyObject *__pyx_n_s_error;
-static PyObject *__pyx_n_s_flags;
-static PyObject *__pyx_n_s_format;
-static PyObject *__pyx_n_s_fortran;
-static PyObject *__pyx_n_u_fortran;
-static PyObject *__pyx_n_s_getstate;
-static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
-static PyObject *__pyx_n_s_id;
-static PyObject *__pyx_n_s_import;
-static PyObject *__pyx_n_s_itemsize;
-static PyObject *__pyx_kp_s_itemsize_0_for_cython_array;
-static PyObject *__pyx_n_s_main;
-static PyObject *__pyx_n_s_memview;
-static PyObject *__pyx_n_s_mode;
-static PyObject *__pyx_n_s_name;
-static PyObject *__pyx_n_s_name_2;
-static PyObject *__pyx_n_s_ndim;
-static PyObject *__pyx_n_s_new;
-static PyObject *__pyx_kp_s_no_default___reduce___due_to_non;
-static PyObject *__pyx_n_s_obj;
-static PyObject *__pyx_n_s_pack;
-static PyObject *__pyx_n_s_paths;
-static PyObject *__pyx_n_s_pickle;
-static PyObject *__pyx_n_s_pyx_PickleError;
-static PyObject *__pyx_n_s_pyx_checksum;
-static PyObject *__pyx_n_s_pyx_getbuffer;
-static PyObject *__pyx_n_s_pyx_result;
-static PyObject *__pyx_n_s_pyx_state;
-static PyObject *__pyx_n_s_pyx_type;
-static PyObject *__pyx_n_s_pyx_unpickle_Enum;
-static PyObject *__pyx_n_s_pyx_vtable;
-static PyObject *__pyx_n_s_range;
-static PyObject *__pyx_n_s_reduce;
-static PyObject *__pyx_n_s_reduce_cython;
-static PyObject *__pyx_n_s_reduce_ex;
-static PyObject *__pyx_n_s_setstate;
-static PyObject *__pyx_n_s_setstate_cython;
-static PyObject *__pyx_n_s_shape;
-static PyObject *__pyx_n_s_size;
-static PyObject *__pyx_n_s_start;
-static PyObject *__pyx_n_s_step;
-static PyObject *__pyx_n_s_stop;
-static PyObject *__pyx_kp_s_strided_and_direct;
-static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
-static PyObject *__pyx_kp_s_strided_and_indirect;
-static PyObject *__pyx_kp_s_stringsource;
-static PyObject *__pyx_n_s_struct;
-static PyObject *__pyx_n_s_t_xs;
-static PyObject *__pyx_n_s_t_ys;
-static PyObject *__pyx_n_s_test;
-static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
-static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
-static PyObject *__pyx_n_s_unpack;
-static PyObject *__pyx_n_s_update;
-static PyObject *__pyx_n_s_values;
-static PyObject *__pyx_pf_4vits_15monotonic_align_4core_maximum_path_c(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
-static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
-static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
-static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject *__pyx_v___pyx_state); /* proto */
-static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_int_0;
-static PyObject *__pyx_int_1;
-static PyObject *__pyx_int_184977713;
-static PyObject *__pyx_int_neg_1;
-static float __pyx_k_;
-static PyObject *__pyx_tuple__2;
-static PyObject *__pyx_tuple__3;
-static PyObject *__pyx_tuple__4;
-static PyObject *__pyx_tuple__5;
-static PyObject *__pyx_tuple__6;
-static PyObject *__pyx_tuple__7;
-static PyObject *__pyx_tuple__8;
-static PyObject *__pyx_tuple__9;
-static PyObject *__pyx_slice__16;
-static PyObject *__pyx_tuple__10;
-static PyObject *__pyx_tuple__11;
-static PyObject *__pyx_tuple__12;
-static PyObject *__pyx_tuple__13;
-static PyObject *__pyx_tuple__14;
-static PyObject *__pyx_tuple__15;
-static PyObject *__pyx_tuple__17;
-static PyObject *__pyx_tuple__18;
-static PyObject *__pyx_tuple__19;
-static PyObject *__pyx_tuple__20;
-static PyObject *__pyx_tuple__21;
-static PyObject *__pyx_tuple__22;
-static PyObject *__pyx_tuple__23;
-static PyObject *__pyx_tuple__24;
-static PyObject *__pyx_tuple__25;
-static PyObject *__pyx_codeobj__26;
-/* Late includes */
-
-/* "vits/monotonic_align/core.pyx":7
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
- *   cdef int x
- *   cdef int y
- */
-
-static void __pyx_f_4vits_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice __pyx_v_path, __Pyx_memviewslice __pyx_v_value, int __pyx_v_t_y, int __pyx_v_t_x, struct __pyx_opt_args_4vits_15monotonic_align_4core_maximum_path_each *__pyx_optional_args) {
-  float __pyx_v_max_neg_val = __pyx_k_;
-  int __pyx_v_x;
-  int __pyx_v_y;
-  float __pyx_v_v_prev;
-  float __pyx_v_v_cur;
-  int __pyx_v_index;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  long __pyx_t_4;
-  int __pyx_t_5;
-  long __pyx_t_6;
-  long __pyx_t_7;
-  int __pyx_t_8;
-  Py_ssize_t __pyx_t_9;
-  Py_ssize_t __pyx_t_10;
-  float __pyx_t_11;
-  float __pyx_t_12;
-  float __pyx_t_13;
-  int __pyx_t_14;
-  Py_ssize_t __pyx_t_15;
-  Py_ssize_t __pyx_t_16;
-  if (__pyx_optional_args) {
-    if (__pyx_optional_args->__pyx_n > 0) {
-      __pyx_v_max_neg_val = __pyx_optional_args->max_neg_val;
-    }
-  }
-
-  /* "vits/monotonic_align/core.pyx":13
- *   cdef float v_cur
- *   cdef float tmp
- *   cdef int index = t_x - 1             # <<<<<<<<<<<<<<
- * 
- *   for y in range(t_y):
- */
-  __pyx_v_index = (__pyx_v_t_x - 1);
-
-  /* "vits/monotonic_align/core.pyx":15
- *   cdef int index = t_x - 1
- * 
- *   for y in range(t_y):             # <<<<<<<<<<<<<<
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:
- */
-  __pyx_t_1 = __pyx_v_t_y;
-  __pyx_t_2 = __pyx_t_1;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_y = __pyx_t_3;
-
-    /* "vits/monotonic_align/core.pyx":16
- * 
- *   for y in range(t_y):
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):             # <<<<<<<<<<<<<<
- *       if x == y:
- *         v_cur = max_neg_val
- */
-    __pyx_t_4 = (__pyx_v_y + 1);
-    __pyx_t_5 = __pyx_v_t_x;
-    if (((__pyx_t_4 < __pyx_t_5) != 0)) {
-      __pyx_t_6 = __pyx_t_4;
-    } else {
-      __pyx_t_6 = __pyx_t_5;
-    }
-    __pyx_t_4 = __pyx_t_6;
-    __pyx_t_5 = ((__pyx_v_t_x + __pyx_v_y) - __pyx_v_t_y);
-    __pyx_t_6 = 0;
-    if (((__pyx_t_5 > __pyx_t_6) != 0)) {
-      __pyx_t_7 = __pyx_t_5;
-    } else {
-      __pyx_t_7 = __pyx_t_6;
-    }
-    __pyx_t_6 = __pyx_t_4;
-    for (__pyx_t_5 = __pyx_t_7; __pyx_t_5 < __pyx_t_6; __pyx_t_5+=1) {
-      __pyx_v_x = __pyx_t_5;
-
-      /* "vits/monotonic_align/core.pyx":17
- *   for y in range(t_y):
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:             # <<<<<<<<<<<<<<
- *         v_cur = max_neg_val
- *       else:
- */
-      __pyx_t_8 = ((__pyx_v_x == __pyx_v_y) != 0);
-      if (__pyx_t_8) {
-
-        /* "vits/monotonic_align/core.pyx":18
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:
- *         v_cur = max_neg_val             # <<<<<<<<<<<<<<
- *       else:
- *         v_cur = value[y-1, x]
- */
-        __pyx_v_v_cur = __pyx_v_max_neg_val;
-
-        /* "vits/monotonic_align/core.pyx":17
- *   for y in range(t_y):
- *     for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
- *       if x == y:             # <<<<<<<<<<<<<<
- *         v_cur = max_neg_val
- *       else:
- */
-        goto __pyx_L7;
-      }
-
-      /* "vits/monotonic_align/core.pyx":20
- *         v_cur = max_neg_val
- *       else:
- *         v_cur = value[y-1, x]             # <<<<<<<<<<<<<<
- *       if x == 0:
- *         if y == 0:
- */
-      /*else*/ {
-        __pyx_t_9 = (__pyx_v_y - 1);
-        __pyx_t_10 = __pyx_v_x;
-        __pyx_v_v_cur = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) )));
-      }
-      __pyx_L7:;
-
-      /* "vits/monotonic_align/core.pyx":21
- *       else:
- *         v_cur = value[y-1, x]
- *       if x == 0:             # <<<<<<<<<<<<<<
- *         if y == 0:
- *           v_prev = 0.
- */
-      __pyx_t_8 = ((__pyx_v_x == 0) != 0);
-      if (__pyx_t_8) {
-
-        /* "vits/monotonic_align/core.pyx":22
- *         v_cur = value[y-1, x]
- *       if x == 0:
- *         if y == 0:             # <<<<<<<<<<<<<<
- *           v_prev = 0.
- *         else:
- */
-        __pyx_t_8 = ((__pyx_v_y == 0) != 0);
-        if (__pyx_t_8) {
-
-          /* "vits/monotonic_align/core.pyx":23
- *       if x == 0:
- *         if y == 0:
- *           v_prev = 0.             # <<<<<<<<<<<<<<
- *         else:
- *           v_prev = max_neg_val
- */
-          __pyx_v_v_prev = 0.;
-
-          /* "vits/monotonic_align/core.pyx":22
- *         v_cur = value[y-1, x]
- *       if x == 0:
- *         if y == 0:             # <<<<<<<<<<<<<<
- *           v_prev = 0.
- *         else:
- */
-          goto __pyx_L9;
-        }
-
-        /* "vits/monotonic_align/core.pyx":25
- *           v_prev = 0.
- *         else:
- *           v_prev = max_neg_val             # <<<<<<<<<<<<<<
- *       else:
- *         v_prev = value[y-1, x-1]
- */
-        /*else*/ {
-          __pyx_v_v_prev = __pyx_v_max_neg_val;
-        }
-        __pyx_L9:;
-
-        /* "vits/monotonic_align/core.pyx":21
- *       else:
- *         v_cur = value[y-1, x]
- *       if x == 0:             # <<<<<<<<<<<<<<
- *         if y == 0:
- *           v_prev = 0.
- */
-        goto __pyx_L8;
-      }
-
-      /* "vits/monotonic_align/core.pyx":27
- *           v_prev = max_neg_val
- *       else:
- *         v_prev = value[y-1, x-1]             # <<<<<<<<<<<<<<
- *       value[y, x] += max(v_prev, v_cur)
- * 
- */
-      /*else*/ {
-        __pyx_t_10 = (__pyx_v_y - 1);
-        __pyx_t_9 = (__pyx_v_x - 1);
-        __pyx_v_v_prev = (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_10 * __pyx_v_value.strides[0]) )) + __pyx_t_9)) )));
-      }
-      __pyx_L8:;
-
-      /* "vits/monotonic_align/core.pyx":28
- *       else:
- *         v_prev = value[y-1, x-1]
- *       value[y, x] += max(v_prev, v_cur)             # <<<<<<<<<<<<<<
- * 
- *   for y in range(t_y - 1, -1, -1):
- */
-      __pyx_t_11 = __pyx_v_v_cur;
-      __pyx_t_12 = __pyx_v_v_prev;
-      if (((__pyx_t_11 > __pyx_t_12) != 0)) {
-        __pyx_t_13 = __pyx_t_11;
-      } else {
-        __pyx_t_13 = __pyx_t_12;
-      }
-      __pyx_t_9 = __pyx_v_y;
-      __pyx_t_10 = __pyx_v_x;
-      *((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) )) += __pyx_t_13;
-    }
-  }
-
-  /* "vits/monotonic_align/core.pyx":30
- *       value[y, x] += max(v_prev, v_cur)
- * 
- *   for y in range(t_y - 1, -1, -1):             # <<<<<<<<<<<<<<
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- */
-  for (__pyx_t_1 = (__pyx_v_t_y - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_y = __pyx_t_1;
-
-    /* "vits/monotonic_align/core.pyx":31
- * 
- *   for y in range(t_y - 1, -1, -1):
- *     path[y, index] = 1             # <<<<<<<<<<<<<<
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- *       index = index - 1
- */
-    __pyx_t_10 = __pyx_v_y;
-    __pyx_t_9 = __pyx_v_index;
-    *((int *) ( /* dim=1 */ ((char *) (((int *) ( /* dim=0 */ (__pyx_v_path.data + __pyx_t_10 * __pyx_v_path.strides[0]) )) + __pyx_t_9)) )) = 1;
-
-    /* "vits/monotonic_align/core.pyx":32
- *   for y in range(t_y - 1, -1, -1):
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):             # <<<<<<<<<<<<<<
- *       index = index - 1
- * 
- */
-    __pyx_t_14 = ((__pyx_v_index != 0) != 0);
-    if (__pyx_t_14) {
-    } else {
-      __pyx_t_8 = __pyx_t_14;
-      goto __pyx_L13_bool_binop_done;
-    }
-    __pyx_t_14 = ((__pyx_v_index == __pyx_v_y) != 0);
-    if (!__pyx_t_14) {
-    } else {
-      __pyx_t_8 = __pyx_t_14;
-      goto __pyx_L13_bool_binop_done;
-    }
-    __pyx_t_9 = (__pyx_v_y - 1);
-    __pyx_t_10 = __pyx_v_index;
-    __pyx_t_15 = (__pyx_v_y - 1);
-    __pyx_t_16 = (__pyx_v_index - 1);
-    __pyx_t_14 = (((*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_9 * __pyx_v_value.strides[0]) )) + __pyx_t_10)) ))) < (*((float *) ( /* dim=1 */ ((char *) (((float *) ( /* dim=0 */ (__pyx_v_value.data + __pyx_t_15 * __pyx_v_value.strides[0]) )) + __pyx_t_16)) )))) != 0);
-    __pyx_t_8 = __pyx_t_14;
-    __pyx_L13_bool_binop_done:;
-    if (__pyx_t_8) {
-
-      /* "vits/monotonic_align/core.pyx":33
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):
- *       index = index - 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_index = (__pyx_v_index - 1);
-
-      /* "vits/monotonic_align/core.pyx":32
- *   for y in range(t_y - 1, -1, -1):
- *     path[y, index] = 1
- *     if index != 0 and (index == y or value[y-1, index] < value[y-1, index-1]):             # <<<<<<<<<<<<<<
- *       index = index - 1
- * 
- */
-    }
-  }
-
-  /* "vits/monotonic_align/core.pyx":7
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cdef void maximum_path_each(int[:,::1] path, float[:,::1] value, int t_y, int t_x, float max_neg_val=-1e9) nogil:             # <<<<<<<<<<<<<<
- *   cdef int x
- *   cdef int y
- */
-
-  /* function exit code */
-}
-
-/* "vits/monotonic_align/core.pyx":38
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:             # <<<<<<<<<<<<<<
- *   cdef int b = paths.shape[0]
- *   cdef int i
- */
-
-static PyObject *__pyx_pw_4vits_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static void __pyx_f_4vits_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice __pyx_v_paths, __Pyx_memviewslice __pyx_v_values, __Pyx_memviewslice __pyx_v_t_ys, __Pyx_memviewslice __pyx_v_t_xs, CYTHON_UNUSED int __pyx_skip_dispatch) {
-  CYTHON_UNUSED int __pyx_v_b;
-  int __pyx_v_i;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  __Pyx_memviewslice __pyx_t_4 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  __Pyx_memviewslice __pyx_t_5 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  Py_ssize_t __pyx_t_6;
-  Py_ssize_t __pyx_t_7;
-
-  /* "vits/monotonic_align/core.pyx":39
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:
- *   cdef int b = paths.shape[0]             # <<<<<<<<<<<<<<
- *   cdef int i
- *   for i in prange(b, nogil=True):
- */
-  __pyx_v_b = (__pyx_v_paths.shape[0]);
-
-  /* "vits/monotonic_align/core.pyx":41
- *   cdef int b = paths.shape[0]
- *   cdef int i
- *   for i in prange(b, nogil=True):             # <<<<<<<<<<<<<<
- *     maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
- */
-  {
-      #ifdef WITH_THREAD
-      PyThreadState *_save;
-      Py_UNBLOCK_THREADS
-      __Pyx_FastGIL_Remember();
-      #endif
-      /*try:*/ {
-        __pyx_t_1 = __pyx_v_b;
-        if ((1 == 0)) abort();
-        {
-            #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
-                #undef likely
-                #undef unlikely
-                #define likely(x)   (x)
-                #define unlikely(x) (x)
-            #endif
-            __pyx_t_3 = (__pyx_t_1 - 0 + 1 - 1/abs(1)) / 1;
-            if (__pyx_t_3 > 0)
-            {
-                #ifdef _OPENMP
-                #pragma omp parallel private(__pyx_t_6, __pyx_t_7) firstprivate(__pyx_t_4, __pyx_t_5)
-                #endif /* _OPENMP */
-                {
-                    #ifdef _OPENMP
-                    #pragma omp for firstprivate(__pyx_v_i) lastprivate(__pyx_v_i)
-                    #endif /* _OPENMP */
-                    for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_3; __pyx_t_2++){
-                        {
-                            __pyx_v_i = (int)(0 + 1 * __pyx_t_2);
-
-                            /* "vits/monotonic_align/core.pyx":42
- *   cdef int i
- *   for i in prange(b, nogil=True):
- *     maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])             # <<<<<<<<<<<<<<
- */
-                            __pyx_t_4.data = __pyx_v_paths.data;
-                            __pyx_t_4.memview = __pyx_v_paths.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_4, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_paths.strides[0];
-        __pyx_t_4.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_4.shape[0] = __pyx_v_paths.shape[1];
-__pyx_t_4.strides[0] = __pyx_v_paths.strides[1];
-    __pyx_t_4.suboffsets[0] = -1;
-
-__pyx_t_4.shape[1] = __pyx_v_paths.shape[2];
-__pyx_t_4.strides[1] = __pyx_v_paths.strides[2];
-    __pyx_t_4.suboffsets[1] = -1;
-
-__pyx_t_5.data = __pyx_v_values.data;
-                            __pyx_t_5.memview = __pyx_v_values.memview;
-                            __PYX_INC_MEMVIEW(&__pyx_t_5, 0);
-                            {
-    Py_ssize_t __pyx_tmp_idx = __pyx_v_i;
-    Py_ssize_t __pyx_tmp_stride = __pyx_v_values.strides[0];
-        __pyx_t_5.data += __pyx_tmp_idx * __pyx_tmp_stride;
-}
-
-__pyx_t_5.shape[0] = __pyx_v_values.shape[1];
-__pyx_t_5.strides[0] = __pyx_v_values.strides[1];
-    __pyx_t_5.suboffsets[0] = -1;
-
-__pyx_t_5.shape[1] = __pyx_v_values.shape[2];
-__pyx_t_5.strides[1] = __pyx_v_values.strides[2];
-    __pyx_t_5.suboffsets[1] = -1;
-
-__pyx_t_6 = __pyx_v_i;
-                            __pyx_t_7 = __pyx_v_i;
-                            __pyx_f_4vits_15monotonic_align_4core_maximum_path_each(__pyx_t_4, __pyx_t_5, (*((int *) ( /* dim=0 */ ((char *) (((int *) __pyx_v_t_ys.data) + __pyx_t_6)) ))), (*((int *) ( /* dim=0 */ ((char *) (((int *) __pyx_v_t_xs.data) + __pyx_t_7)) ))), NULL);
-                            __PYX_XDEC_MEMVIEW(&__pyx_t_4, 0);
-                            __pyx_t_4.memview = NULL;
-                            __pyx_t_4.data = NULL;
-                            __PYX_XDEC_MEMVIEW(&__pyx_t_5, 0);
-                            __pyx_t_5.memview = NULL;
-                            __pyx_t_5.data = NULL;
-                        }
-                    }
-                }
-            }
-        }
-        #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))
-            #undef likely
-            #undef unlikely
-            #define likely(x)   __builtin_expect(!!(x), 1)
-            #define unlikely(x) __builtin_expect(!!(x), 0)
-        #endif
-      }
-
-      /* "vits/monotonic_align/core.pyx":41
- *   cdef int b = paths.shape[0]
- *   cdef int i
- *   for i in prange(b, nogil=True):             # <<<<<<<<<<<<<<
- *     maximum_path_each(paths[i], values[i], t_ys[i], t_xs[i])
- */
-      /*finally:*/ {
-        /*normal exit:*/{
-          #ifdef WITH_THREAD
-          __Pyx_FastGIL_Forget();
-          Py_BLOCK_THREADS
-          #endif
-          goto __pyx_L5;
-        }
-        __pyx_L5:;
-      }
-  }
-
-  /* "vits/monotonic_align/core.pyx":38
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cpdef void maximum_path_c(int[:,:,::1] paths, float[:,:,::1] values, int[::1] t_ys, int[::1] t_xs) nogil:             # <<<<<<<<<<<<<<
- *   cdef int b = paths.shape[0]
- *   cdef int i
- */
-
-  /* function exit code */
-}
-
-/* Python wrapper */
-static PyObject *__pyx_pw_4vits_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static PyObject *__pyx_pw_4vits_15monotonic_align_4core_1maximum_path_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
-  __Pyx_memviewslice __pyx_v_paths = { 0, 0, { 0 }, { 0 }, { 0 } };
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-  /* function exit code */
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- *         if have_stop:             # <<<<<<<<<<<<<<
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- *                 stop += shape
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- *                     stop = 0
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- *                 stop = shape
- *         else:
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-      if (__pyx_t_2) {
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- *             elif stop > shape:
- *                 stop = shape             # <<<<<<<<<<<<<<
- *         else:
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- *                     stop = 0
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- *                 stop = shape
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- *         if have_stop:             # <<<<<<<<<<<<<<
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- *         else:
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- *             else:
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- *         if not have_step:
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- * 
- */
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- *             if (stop - start) - step * new_shape:
- */
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- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
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- * 
- */
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-
-      /* "View.MemoryView":878
- * 
- *             if (stop - start) - step * new_shape:
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- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
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- */
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- *         if new_shape < 0:             # <<<<<<<<<<<<<<
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-
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- * 
- */
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- * 
- *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
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-
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- * 
- *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
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- *     else:
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- */
-
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  Py_ssize_t __pyx_v_c_stride;
-  Py_ssize_t __pyx_v_f_stride;
-  char __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
-  /* "View.MemoryView":1121
- *     """
- *     cdef int i
- *     cdef Py_ssize_t c_stride = 0             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t f_stride = 0
- * 
- */
-  __pyx_v_c_stride = 0;
-
-  /* "View.MemoryView":1122
- *     cdef int i
- *     cdef Py_ssize_t c_stride = 0
- *     cdef Py_ssize_t f_stride = 0             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim - 1, -1, -1):
- */
-  __pyx_v_f_stride = 0;
-
-  /* "View.MemoryView":1124
- *     cdef Py_ssize_t f_stride = 0
- * 
- *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]
- */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_i = __pyx_t_1;
-
-    /* "View.MemoryView":1125
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             c_stride = mslice.strides[i]
- *             break
- */
-    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1126
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
- *             break
- * 
- */
-      __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-      /* "View.MemoryView":1127
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-      goto __pyx_L4_break;
-
-      /* "View.MemoryView":1125
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             c_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L4_break:;
-
-  /* "View.MemoryView":1129
- *             break
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- */
-  __pyx_t_1 = __pyx_v_ndim;
-  __pyx_t_3 = __pyx_t_1;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-
-    /* "View.MemoryView":1130
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1131
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
- *             break
- * 
- */
-      __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-      /* "View.MemoryView":1132
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- */
-      goto __pyx_L7_break;
-
-      /* "View.MemoryView":1130
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    }
-  }
-  __pyx_L7_break:;
-
-  /* "View.MemoryView":1134
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1135
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- *         return 'C'             # <<<<<<<<<<<<<<
- *     else:
- *         return 'F'
- */
-    __pyx_r = 'C';
-    goto __pyx_L0;
-
-    /* "View.MemoryView":1134
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  }
-
-  /* "View.MemoryView":1137
- *         return 'C'
- *     else:
- *         return 'F'             # <<<<<<<<<<<<<<
- * 
- * @cython.cdivision(True)
- */
-  /*else*/ {
-    __pyx_r = 'F';
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1116
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1140
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
-  Py_ssize_t __pyx_v_dst_extent;
-  Py_ssize_t __pyx_v_src_stride;
-  Py_ssize_t __pyx_v_dst_stride;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-  Py_ssize_t __pyx_t_6;
-
-  /* "View.MemoryView":1147
- * 
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- */
-  __pyx_v_src_extent = (__pyx_v_src_shape[0]);
-
-  /* "View.MemoryView":1148
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- */
-  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
-
-  /* "View.MemoryView":1149
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- */
-  __pyx_v_src_stride = (__pyx_v_src_strides[0]);
-
-  /* "View.MemoryView":1150
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
- * 
- *     if ndim == 1:
- */
-  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
-
-  /* "View.MemoryView":1152
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1153
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    __pyx_t_2 = ((__pyx_v_src_stride > 0) != 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-    __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
-    if (__pyx_t_2) {
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-      goto __pyx_L5_bool_binop_done;
-    }
-
-    /* "View.MemoryView":1154
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- */
-    __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
-    if (__pyx_t_2) {
-      __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
-    }
-    __pyx_t_3 = (__pyx_t_2 != 0);
-    __pyx_t_1 = __pyx_t_3;
-    __pyx_L5_bool_binop_done:;
-
-    /* "View.MemoryView":1153
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":1155
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
- *        else:
- *            for i in range(dst_extent):
- */
-      (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent)));
-
-      /* "View.MemoryView":1153
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-      goto __pyx_L4;
-    }
-
-    /* "View.MemoryView":1157
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- */
-    /*else*/ {
-      __pyx_t_4 = __pyx_v_dst_extent;
-      __pyx_t_5 = __pyx_t_4;
-      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-        __pyx_v_i = __pyx_t_6;
-
-        /* "View.MemoryView":1158
- *        else:
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
- *                src_data += src_stride
- *                dst_data += dst_stride
- */
-        (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize));
-
-        /* "View.MemoryView":1159
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride             # <<<<<<<<<<<<<<
- *                dst_data += dst_stride
- *     else:
- */
-        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-        /* "View.MemoryView":1160
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- *                dst_data += dst_stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(dst_extent):
- */
-        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-      }
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":1152
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1162
- *                dst_data += dst_stride
- *     else:
- *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *             _copy_strided_to_strided(src_data, src_strides + 1,
- *                                      dst_data, dst_strides + 1,
- */
-  /*else*/ {
-    __pyx_t_4 = __pyx_v_dst_extent;
-    __pyx_t_5 = __pyx_t_4;
-    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-      __pyx_v_i = __pyx_t_6;
-
-      /* "View.MemoryView":1163
- *     else:
- *         for i in range(dst_extent):
- *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
- *                                      dst_data, dst_strides + 1,
- *                                      src_shape + 1, dst_shape + 1,
- */
-      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
-      /* "View.MemoryView":1167
- *                                      src_shape + 1, dst_shape + 1,
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride             # <<<<<<<<<<<<<<
- *             dst_data += dst_stride
- * 
- */
-      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-      /* "View.MemoryView":1168
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride
- *             dst_data += dst_stride             # <<<<<<<<<<<<<<
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
-      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1140
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1170
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
-  /* "View.MemoryView":1173
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
- *                              src.shape, dst.shape, ndim, itemsize)
- * 
- */
-  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1170
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1177
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
-  Py_ssize_t __pyx_v_shape;
-  Py_ssize_t __pyx_v_size;
-  Py_ssize_t __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  Py_ssize_t *__pyx_t_3;
-  Py_ssize_t *__pyx_t_4;
-
-  /* "View.MemoryView":1179
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     for shape in src.shape[:ndim]:
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_size = __pyx_t_1;
-
-  /* "View.MemoryView":1181
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- * 
- *     for shape in src.shape[:ndim]:             # <<<<<<<<<<<<<<
- *         size *= shape
- * 
- */
-  __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim);
-  for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) {
-    __pyx_t_2 = __pyx_t_4;
-    __pyx_v_shape = (__pyx_t_2[0]);
-
-    /* "View.MemoryView":1182
- * 
- *     for shape in src.shape[:ndim]:
- *         size *= shape             # <<<<<<<<<<<<<<
- * 
- *     return size
- */
-    __pyx_v_size = (__pyx_v_size * __pyx_v_shape);
-  }
-
-  /* "View.MemoryView":1184
- *         size *= shape
- * 
- *     return size             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_fill_contig_strides_array')
- */
-  __pyx_r = __pyx_v_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1177
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef Py_ssize_t shape, size = src.memview.view.itemsize
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1187
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-
-  /* "View.MemoryView":1196
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
-  if (__pyx_t_1) {
-
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-    e = (*v)(p->obj, a); if (e) return e;
-  }
-  if (p->_size) {
-    e = (*v)(p->_size, a); if (e) return e;
-  }
-  if (p->_array_interface) {
-    e = (*v)(p->_array_interface, a); if (e) return e;
-  }
-  if (p->view.obj) {
-    e = (*v)(p->view.obj, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  tmp = ((PyObject*)p->obj);
-  p->obj = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_size);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_array_interface);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  Py_CLEAR(p->view.obj);
-  return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_memoryview___setitem__(o, i, v);
-  }
-  else {
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
-  {"is_c_contig", (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, 0},
-  {"is_f_contig", (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, 0},
-  {"copy", (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, 0},
-  {"copy_fortran", (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, 0},
-  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_1__reduce_cython__, METH_NOARGS, 0},
-  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryview_3__setstate_cython__, METH_O, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
-  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0},
-  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0},
-  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0},
-  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0},
-  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0},
-  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0},
-  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0},
-  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0},
-  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
-  __pyx_memoryview___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_memoryview, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
-  __pyx_memoryview___len__, /*mp_length*/
-  __pyx_memoryview___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  0, /*bf_releasebuffer*/
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "vits.monotonic_align.core.memoryview", /*tp_name*/
-  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  __pyx_memoryview___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  __pyx_memoryview___str__, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_memoryview, /*tp_traverse*/
-  __pyx_tp_clear_memoryview, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_memoryview, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_memoryview, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_memoryview, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1
-  0, /*tp_vectorcall*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-  0, /*tp_print*/
-  #endif
-};
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryviewslice_obj *p;
-  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryviewslice_obj *)o);
-  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  p->from_slice.memview = NULL;
-  return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  #if CYTHON_USE_TP_FINALIZE
-  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1);
-    __pyx_memoryviewslice___dealloc__(o);
-    __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->from_object);
-  PyObject_GC_Track(o);
-  __pyx_tp_dealloc_memoryview(o);
-}
-
-static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
-  if (p->from_object) {
-    e = (*v)(p->from_object, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  __pyx_tp_clear_memoryview(o);
-  tmp = ((PyObject*)p->from_object);
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
-  return 0;
-}
-
-static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_pw_15View_dot_MemoryView_16_memoryviewslice_4base_1__get__(o);
-}
-
-static PyMethodDef __pyx_methods__memoryviewslice[] = {
-  {"__reduce_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, METH_NOARGS, 0},
-  {"__setstate_cython__", (PyCFunction)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, METH_O, 0},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
-  {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, (char *)0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PyTypeObject __pyx_type___pyx_memoryviewslice = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "vits.monotonic_align.core._memoryviewslice", /*tp_name*/
-  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
-  #if PY_VERSION_HEX < 0x030800b4
-  0, /*tp_print*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4
-  0, /*tp_vectorcall_offset*/
-  #endif
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #endif
-  #if PY_MAJOR_VERSION >= 3
-  0, /*tp_as_async*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY
-  __pyx_memoryview___repr__, /*tp_repr*/
-  #else
-  0, /*tp_repr*/
-  #endif
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  #if CYTHON_COMPILING_IN_PYPY
-  __pyx_memoryview___str__, /*tp_str*/
-  #else
-  0, /*tp_str*/
-  #endif
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  "Internal class for passing memoryview slices to Python", /*tp_doc*/
-  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
-  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods__memoryviewslice, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets__memoryviewslice, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new__memoryviewslice, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b1
-  0, /*tp_vectorcall*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000
-  0, /*tp_print*/
-  #endif
-};
-
-static PyMethodDef __pyx_methods[] = {
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-
-#if PY_MAJOR_VERSION >= 3
-#if CYTHON_PEP489_MULTI_PHASE_INIT
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-  {Py_mod_exec, (void*)__pyx_pymod_exec_core},
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-  #if CYTHON_PEP489_MULTI_PHASE_INIT
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-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = (*call)(func, arg, kw);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyErrFetchRestore */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    tmp_type = tstate->curexc_type;
-    tmp_value = tstate->curexc_value;
-    tmp_tb = tstate->curexc_traceback;
-    tstate->curexc_type = type;
-    tstate->curexc_value = value;
-    tstate->curexc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    *type = tstate->curexc_type;
-    *value = tstate->curexc_value;
-    *tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-}
-#endif
-
-/* RaiseException */
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
-                        CYTHON_UNUSED PyObject *cause) {
-    __Pyx_PyThreadState_declare
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    if (PyType_Check(type)) {
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-    }
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                int is_subclass = PyObject_IsSubclass(instance_class, type);
-                if (!is_subclass) {
-                    instance_class = NULL;
-                } else if (unlikely(is_subclass == -1)) {
-                    goto bad;
-                } else {
-                    type = instance_class;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-    if (cause) {
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-#if CYTHON_COMPILING_IN_PYPY
-        PyObject *tmp_type, *tmp_value, *tmp_tb;
-        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
-        Py_INCREF(tb);
-        PyErr_Restore(tmp_type, tmp_value, tb);
-        Py_XDECREF(tmp_tb);
-#else
-        PyThreadState *tstate = __Pyx_PyThreadState_Current;
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-#endif
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-/* PyCFunctionFastCall */
-#if CYTHON_FAST_PYCCALL
-static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
-    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
-    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
-    PyObject *self = PyCFunction_GET_SELF(func);
-    int flags = PyCFunction_GET_FLAGS(func);
-    assert(PyCFunction_Check(func));
-    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS | METH_STACKLESS)));
-    assert(nargs >= 0);
-    assert(nargs == 0 || args != NULL);
-    /* _PyCFunction_FastCallDict() must not be called with an exception set,
-       because it may clear it (directly or indirectly) and so the
-       caller loses its exception */
-    assert(!PyErr_Occurred());
-    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
-        return (*((__Pyx_PyCFunctionFastWithKeywords)(void*)meth)) (self, args, nargs, NULL);
-    } else {
-        return (*((__Pyx_PyCFunctionFast)(void*)meth)) (self, args, nargs);
-    }
-}
-#endif
-
-/* PyFunctionFastCall */
-#if CYTHON_FAST_PYCALL
-static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
-                                               PyObject *globals) {
-    PyFrameObject *f;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    PyObject **fastlocals;
-    Py_ssize_t i;
-    PyObject *result;
-    assert(globals != NULL);
-    /* XXX Perhaps we should create a specialized
-       PyFrame_New() that doesn't take locals, but does
-       take builtins without sanity checking them.
-       */
-    assert(tstate != NULL);
-    f = PyFrame_New(tstate, co, globals, NULL);
-    if (f == NULL) {
-        return NULL;
-    }
-    fastlocals = __Pyx_PyFrame_GetLocalsplus(f);
-    for (i = 0; i < na; i++) {
-        Py_INCREF(*args);
-        fastlocals[i] = *args++;
-    }
-    result = PyEval_EvalFrameEx(f,0);
-    ++tstate->recursion_depth;
-    Py_DECREF(f);
-    --tstate->recursion_depth;
-    return result;
-}
-#if 1 || PY_VERSION_HEX < 0x030600B1
-static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) {
-    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
-    PyObject *globals = PyFunction_GET_GLOBALS(func);
-    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
-    PyObject *closure;
-#if PY_MAJOR_VERSION >= 3
-    PyObject *kwdefs;
-#endif
-    PyObject *kwtuple, **k;
-    PyObject **d;
-    Py_ssize_t nd;
-    Py_ssize_t nk;
-    PyObject *result;
-    assert(kwargs == NULL || PyDict_Check(kwargs));
-    nk = kwargs ? PyDict_Size(kwargs) : 0;
-    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
-        return NULL;
-    }
-    if (
-#if PY_MAJOR_VERSION >= 3
-            co->co_kwonlyargcount == 0 &&
-#endif
-            likely(kwargs == NULL || nk == 0) &&
-            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
-        if (argdefs == NULL && co->co_argcount == nargs) {
-            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
-            goto done;
-        }
-        else if (nargs == 0 && argdefs != NULL
-                 && co->co_argcount == Py_SIZE(argdefs)) {
-            /* function called with no arguments, but all parameters have
-               a default value: use default values as arguments .*/
-            args = &PyTuple_GET_ITEM(argdefs, 0);
-            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
-            goto done;
-        }
-    }
-    if (kwargs != NULL) {
-        Py_ssize_t pos, i;
-        kwtuple = PyTuple_New(2 * nk);
-        if (kwtuple == NULL) {
-            result = NULL;
-            goto done;
-        }
-        k = &PyTuple_GET_ITEM(kwtuple, 0);
-        pos = i = 0;
-        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
-            Py_INCREF(k[i]);
-            Py_INCREF(k[i+1]);
-            i += 2;
-        }
-        nk = i / 2;
-    }
-    else {
-        kwtuple = NULL;
-        k = NULL;
-    }
-    closure = PyFunction_GET_CLOSURE(func);
-#if PY_MAJOR_VERSION >= 3
-    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
-#endif
-    if (argdefs != NULL) {
-        d = &PyTuple_GET_ITEM(argdefs, 0);
-        nd = Py_SIZE(argdefs);
-    }
-    else {
-        d = NULL;
-        nd = 0;
-    }
-#if PY_MAJOR_VERSION >= 3
-    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, kwdefs, closure);
-#else
-    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
-                               args, (int)nargs,
-                               k, (int)nk,
-                               d, (int)nd, closure);
-#endif
-    Py_XDECREF(kwtuple);
-done:
-    Py_LeaveRecursiveCall();
-    return result;
-}
-#endif
-#endif
-
-/* PyObjectCall2Args */
-static CYTHON_UNUSED PyObject* __Pyx_PyObject_Call2Args(PyObject* function, PyObject* arg1, PyObject* arg2) {
-    PyObject *args, *result = NULL;
-    #if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyFunction_FastCall(function, args, 2);
-    }
-    #endif
-    #if CYTHON_FAST_PYCCALL
-    if (__Pyx_PyFastCFunction_Check(function)) {
-        PyObject *args[2] = {arg1, arg2};
-        return __Pyx_PyCFunction_FastCall(function, args, 2);
-    }
-    #endif
-    args = PyTuple_New(2);
-    if (unlikely(!args)) goto done;
-    Py_INCREF(arg1);
-    PyTuple_SET_ITEM(args, 0, arg1);
-    Py_INCREF(arg2);
-    PyTuple_SET_ITEM(args, 1, arg2);
-    Py_INCREF(function);
-    result = __Pyx_PyObject_Call(function, args, NULL);
-    Py_DECREF(args);
-    Py_DECREF(function);
-done:
-    return result;
-}
-
-/* PyObjectCallMethO */
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
-    PyObject *self, *result;
-    PyCFunction cfunc;
-    cfunc = PyCFunction_GET_FUNCTION(func);
-    self = PyCFunction_GET_SELF(func);
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-    result = cfunc(self, arg);
-    Py_LeaveRecursiveCall();
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-/* PyObjectCallOneArg */
-#if CYTHON_COMPILING_IN_CPYTHON
-static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_New(1);
-    if (unlikely(!args)) return NULL;
-    Py_INCREF(arg);
-    PyTuple_SET_ITEM(args, 0, arg);
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-#if CYTHON_FAST_PYCALL
-    if (PyFunction_Check(func)) {
-        return __Pyx_PyFunction_FastCall(func, &arg, 1);
-    }
-#endif
-    if (likely(PyCFunction_Check(func))) {
-        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
-            return __Pyx_PyObject_CallMethO(func, arg);
-#if CYTHON_FAST_PYCCALL
-        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
-            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
-#endif
-        }
-    }
-    return __Pyx__PyObject_CallOneArg(func, arg);
-}
-#else
-static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
-    PyObject *result;
-    PyObject *args = PyTuple_Pack(1, arg);
-    if (unlikely(!args)) return NULL;
-    result = __Pyx_PyObject_Call(func, args, NULL);
-    Py_DECREF(args);
-    return result;
-}
-#endif
-
-/* BytesEquals */
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result;
-#if CYTHON_USE_UNICODE_INTERNALS
-            Py_hash_t hash1, hash2;
-            hash1 = ((PyBytesObject*)s1)->ob_shash;
-            hash2 = ((PyBytesObject*)s2)->ob_shash;
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                return (equals == Py_NE);
-            }
-#endif
-            result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-/* UnicodeEquals */
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        if (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
-            return -1;
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-#if CYTHON_USE_UNICODE_INTERNALS
-        {
-            Py_hash_t hash1, hash2;
-        #if CYTHON_PEP393_ENABLED
-            hash1 = ((PyASCIIObject*)s1)->hash;
-            hash2 = ((PyASCIIObject*)s2)->hash;
-        #else
-            hash1 = ((PyUnicodeObject*)s1)->hash;
-            hash2 = ((PyUnicodeObject*)s2)->hash;
-        #endif
-            if (hash1 != hash2 && hash1 != -1 && hash2 != -1) {
-                goto return_ne;
-            }
-        }
-#endif
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        #if PY_MAJOR_VERSION < 3
-        Py_XDECREF(owned_ref);
-        #endif
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-/* None */
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
-    Py_ssize_t q = a / b;
-    Py_ssize_t r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* GetAttr */
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_USE_TYPE_SLOTS
-#if PY_MAJOR_VERSION >= 3
-    if (likely(PyUnicode_Check(n)))
-#else
-    if (likely(PyString_Check(n)))
-#endif
-        return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
-    return PyObject_GetAttr(o, n);
-}
-
-/* GetItemInt */
-static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (!j) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyList_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              CYTHON_NCP_UNUSED int wraparound,
-                                                              CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
-    Py_ssize_t wrapped_i = i;
-    if (wraparound & unlikely(i < 0)) {
-        wrapped_i += PyTuple_GET_SIZE(o);
-    }
-    if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
-                                                     CYTHON_NCP_UNUSED int wraparound,
-                                                     CYTHON_NCP_UNUSED int boundscheck) {
-#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
-                        return NULL;
-                    PyErr_Clear();
-                }
-            }
-            return m->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-/* ObjectGetItem */
-#if CYTHON_USE_TYPE_SLOTS
-static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {
-    PyObject *runerr;
-    Py_ssize_t key_value;
-    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;
-    if (unlikely(!(m && m->sq_item))) {
-        PyErr_Format(PyExc_TypeError, "'%.200s' object is not subscriptable", Py_TYPE(obj)->tp_name);
-        return NULL;
-    }
-    key_value = __Pyx_PyIndex_AsSsize_t(index);
-    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {
-        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);
-    }
-    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {
-        PyErr_Clear();
-        PyErr_Format(PyExc_IndexError, "cannot fit '%.200s' into an index-sized integer", Py_TYPE(index)->tp_name);
-    }
-    return NULL;
-}
-static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {
-    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;
-    if (likely(m && m->mp_subscript)) {
-        return m->mp_subscript(obj, key);
-    }
-    return __Pyx_PyObject_GetIndex(obj, key);
-}
-#endif
-
-/* decode_c_string */
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
-         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
-         const char* encoding, const char* errors,
-         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
-    Py_ssize_t length;
-    if (unlikely((start < 0) | (stop < 0))) {
-        size_t slen = strlen(cstring);
-        if (unlikely(slen > (size_t) PY_SSIZE_T_MAX)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "c-string too long to convert to Python");
-            return NULL;
-        }
-        length = (Py_ssize_t) slen;
-        if (start < 0) {
-            start += length;
-            if (start < 0)
-                start = 0;
-        }
-        if (stop < 0)
-            stop += length;
-    }
-    if (unlikely(stop <= start))
-        return __Pyx_NewRef(__pyx_empty_unicode);
-    length = stop - start;
-    cstring += start;
-    if (decode_func) {
-        return decode_func(cstring, length, errors);
-    } else {
-        return PyUnicode_Decode(cstring, length, encoding, errors);
-    }
-}
-
-/* PyErrExceptionMatches */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
-    PyObject *exc_type = tstate->curexc_type;
-    if (exc_type == err) return 1;
-    if (unlikely(!exc_type)) return 0;
-    if (unlikely(PyTuple_Check(err)))
-        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);
-    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);
-}
-#endif
-
-/* GetAttr3 */
-static PyObject *__Pyx_GetAttr3Default(PyObject *d) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        return NULL;
-    __Pyx_PyErr_Clear();
-    Py_INCREF(d);
-    return d;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) {
-    PyObject *r = __Pyx_GetAttr(o, n);
-    return (likely(r)) ? r : __Pyx_GetAttr3Default(d);
-}
-
-/* PyDictVersioning */
-#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0;
-}
-static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) {
-    PyObject **dictptr = NULL;
-    Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset;
-    if (offset) {
-#if CYTHON_COMPILING_IN_CPYTHON
-        dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj);
-#else
-        dictptr = _PyObject_GetDictPtr(obj);
-#endif
-    }
-    return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0;
-}
-static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) {
-    PyObject *dict = Py_TYPE(obj)->tp_dict;
-    if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict)))
-        return 0;
-    return obj_dict_version == __Pyx_get_object_dict_version(obj);
-}
-#endif
-
-/* GetModuleGlobalName */
-#if CYTHON_USE_DICT_VERSIONS
-static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value)
-#else
-static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name)
-#endif
-{
-    PyObject *result;
-#if !CYTHON_AVOID_BORROWED_REFS
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1
-    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    } else if (unlikely(PyErr_Occurred())) {
-        return NULL;
-    }
-#else
-    result = PyDict_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-#endif
-#else
-    result = PyObject_GetItem(__pyx_d, name);
-    __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version)
-    if (likely(result)) {
-        return __Pyx_NewRef(result);
-    }
-    PyErr_Clear();
-#endif
-    return __Pyx_GetBuiltinName(name);
-}
-
-/* RaiseTooManyValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-/* RaiseNeedMoreValuesToUnpack */
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-/* RaiseNoneIterError */
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-/* ExtTypeTest */
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(__Pyx_TypeCheck(obj, type)))
-        return 1;
-    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
-                 Py_TYPE(obj)->tp_name, type->tp_name);
-    return 0;
-}
-
-/* GetTopmostException */
-#if CYTHON_USE_EXC_INFO_STACK
-static _PyErr_StackItem *
-__Pyx_PyErr_GetTopmostException(PyThreadState *tstate)
-{
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    while ((exc_info->exc_type == NULL || exc_info->exc_type == Py_None) &&
-           exc_info->previous_item != NULL)
-    {
-        exc_info = exc_info->previous_item;
-    }
-    return exc_info;
-}
-#endif
-
-/* SaveResetException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate);
-    *type = exc_info->exc_type;
-    *value = exc_info->exc_value;
-    *tb = exc_info->exc_traceback;
-    #else
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    #endif
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-}
-static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = type;
-    exc_info->exc_value = value;
-    exc_info->exc_traceback = tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-}
-#endif
-
-/* GetException */
-#if CYTHON_FAST_THREAD_STATE
-static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb)
-#else
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
-#endif
-{
-    PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_FAST_THREAD_STATE
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_FAST_THREAD_STATE
-    #if CYTHON_USE_EXC_INFO_STACK
-    {
-        _PyErr_StackItem *exc_info = tstate->exc_info;
-        tmp_type = exc_info->exc_type;
-        tmp_value = exc_info->exc_value;
-        tmp_tb = exc_info->exc_traceback;
-        exc_info->exc_type = local_type;
-        exc_info->exc_value = local_value;
-        exc_info->exc_traceback = local_tb;
-    }
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    #endif
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-/* SwapException */
-#if CYTHON_FAST_THREAD_STATE
-static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    #if CYTHON_USE_EXC_INFO_STACK
-    _PyErr_StackItem *exc_info = tstate->exc_info;
-    tmp_type = exc_info->exc_type;
-    tmp_value = exc_info->exc_value;
-    tmp_tb = exc_info->exc_traceback;
-    exc_info->exc_type = *type;
-    exc_info->exc_value = *value;
-    exc_info->exc_traceback = *tb;
-    #else
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-    #endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#else
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-#endif
-
-/* Import */
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *empty_list = 0;
-    PyObject *module = 0;
-    PyObject *global_dict = 0;
-    PyObject *empty_dict = 0;
-    PyObject *list;
-    #if PY_MAJOR_VERSION < 3
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (!py_import)
-        goto bad;
-    #endif
-    if (from_list)
-        list = from_list;
-    else {
-        empty_list = PyList_New(0);
-        if (!empty_list)
-            goto bad;
-        list = empty_list;
-    }
-    global_dict = PyModule_GetDict(__pyx_m);
-    if (!global_dict)
-        goto bad;
-    empty_dict = PyDict_New();
-    if (!empty_dict)
-        goto bad;
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) {
-                module = PyImport_ImportModuleLevelObject(
-                    name, global_dict, empty_dict, list, 1);
-                if (!module) {
-                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0;
-        }
-        #endif
-        if (!module) {
-            #if PY_MAJOR_VERSION < 3
-            PyObject *py_level = PyInt_FromLong(level);
-            if (!py_level)
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, global_dict, empty_dict, list, py_level, (PyObject *)NULL);
-            Py_DECREF(py_level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, global_dict, empty_dict, list, level);
-            #endif
-        }
-    }
-bad:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(py_import);
-    #endif
-    Py_XDECREF(empty_list);
-    Py_XDECREF(empty_dict);
-    return module;
-}
-
-/* FastTypeChecks */
-#if CYTHON_COMPILING_IN_CPYTHON
-static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {
-    while (a) {
-        a = a->tp_base;
-        if (a == b)
-            return 1;
-    }
-    return b == &PyBaseObject_Type;
-}
-static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {
-    PyObject *mro;
-    if (a == b) return 1;
-    mro = a->tp_mro;
-    if (likely(mro)) {
-        Py_ssize_t i, n;
-        n = PyTuple_GET_SIZE(mro);
-        for (i = 0; i < n; i++) {
-            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)
-                return 1;
-        }
-        return 0;
-    }
-    return __Pyx_InBases(a, b);
-}
-#if PY_MAJOR_VERSION == 2
-static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {
-    PyObject *exception, *value, *tb;
-    int res;
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    __Pyx_ErrFetch(&exception, &value, &tb);
-    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;
-    if (unlikely(res == -1)) {
-        PyErr_WriteUnraisable(err);
-        res = 0;
-    }
-    if (!res) {
-        res = PyObject_IsSubclass(err, exc_type2);
-        if (unlikely(res == -1)) {
-            PyErr_WriteUnraisable(err);
-            res = 0;
-        }
-    }
-    __Pyx_ErrRestore(exception, value, tb);
-    return res;
-}
-#else
-static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {
-    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;
-    if (!res) {
-        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);
-    }
-    return res;
-}
-#endif
-static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {
-    Py_ssize_t i, n;
-    assert(PyExceptionClass_Check(exc_type));
-    n = PyTuple_GET_SIZE(tuple);
-#if PY_MAJOR_VERSION >= 3
-    for (i=0; i<n; i++) {
-        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;
-    }
-#endif
-    for (i=0; i<n; i++) {
-        PyObject *t = PyTuple_GET_ITEM(tuple, i);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(exc_type == t)) return 1;
-        #endif
-        if (likely(PyExceptionClass_Check(t))) {
-            if (__Pyx_inner_PyErr_GivenExceptionMatches2(exc_type, NULL, t)) return 1;
-        } else {
-        }
-    }
-    return 0;
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {
-    if (likely(err == exc_type)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        if (likely(PyExceptionClass_Check(exc_type))) {
-            return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);
-        } else if (likely(PyTuple_Check(exc_type))) {
-            return __Pyx_PyErr_GivenExceptionMatchesTuple(err, exc_type);
-        } else {
-        }
-    }
-    return PyErr_GivenExceptionMatches(err, exc_type);
-}
-static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {
-    assert(PyExceptionClass_Check(exc_type1));
-    assert(PyExceptionClass_Check(exc_type2));
-    if (likely(err == exc_type1 || err == exc_type2)) return 1;
-    if (likely(PyExceptionClass_Check(err))) {
-        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);
-    }
-    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));
-}
-#endif
-
-/* PyIntBinop */
-#if !CYTHON_COMPILING_IN_PYPY
-static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
-    (void)inplace;
-    (void)zerodivision_check;
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(op1))) {
-        const long b = intval;
-        long x;
-        long a = PyInt_AS_LONG(op1);
-            x = (long)((unsigned long)a + b);
-            if (likely((x^a) >= 0 || (x^b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_add(op1, op2);
-    }
-    #endif
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact(op1))) {
-        const long b = intval;
-        long a, x;
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG llb = intval;
-        PY_LONG_LONG lla, llx;
-#endif
-        const digit* digits = ((PyLongObject*)op1)->ob_digit;
-        const Py_ssize_t size = Py_SIZE(op1);
-        if (likely(__Pyx_sst_abs(size) <= 1)) {
-            a = likely(size) ? digits[0] : 0;
-            if (size == -1) a = -a;
-        } else {
-            switch (size) {
-                case -2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 2:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 3:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                case 4:
-                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
-                        break;
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
-                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
-                        goto long_long;
-#endif
-                    }
-                    CYTHON_FALLTHROUGH;
-                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
-            }
-        }
-                x = a + b;
-            return PyLong_FromLong(x);
-#ifdef HAVE_LONG_LONG
-        long_long:
-                llx = lla + llb;
-            return PyLong_FromLongLong(llx);
-#endif
-        
-        
-    }
-    #endif
-    if (PyFloat_CheckExact(op1)) {
-        const long b = intval;
-        double a = PyFloat_AS_DOUBLE(op1);
-            double result;
-            PyFPE_START_PROTECT("add", return NULL)
-            result = ((double)a) + (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-    }
-    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
-}
-#endif
-
-/* None */
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-/* ImportFrom */
-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
-    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
-    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-        PyErr_Format(PyExc_ImportError,
-        #if PY_MAJOR_VERSION < 3
-            "cannot import name %.230s", PyString_AS_STRING(name));
-        #else
-            "cannot import name %S", name);
-        #endif
-    }
-    return value;
-}
-
-/* HasAttr */
-static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) {
-    PyObject *r;
-    if (unlikely(!__Pyx_PyBaseString_Check(n))) {
-        PyErr_SetString(PyExc_TypeError,
-                        "hasattr(): attribute name must be string");
-        return -1;
-    }
-    r = __Pyx_GetAttr(o, n);
-    if (unlikely(!r)) {
-        PyErr_Clear();
-        return 0;
-    } else {
-        Py_DECREF(r);
-        return 1;
-    }
-}
-
-/* PyObject_GenericGetAttrNoDict */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) {
-    PyErr_Format(PyExc_AttributeError,
-#if PY_MAJOR_VERSION >= 3
-                 "'%.50s' object has no attribute '%U'",
-                 tp->tp_name, attr_name);
-#else
-                 "'%.50s' object has no attribute '%.400s'",
-                 tp->tp_name, PyString_AS_STRING(attr_name));
-#endif
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) {
-    PyObject *descr;
-    PyTypeObject *tp = Py_TYPE(obj);
-    if (unlikely(!PyString_Check(attr_name))) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    assert(!tp->tp_dictoffset);
-    descr = _PyType_Lookup(tp, attr_name);
-    if (unlikely(!descr)) {
-        return __Pyx_RaiseGenericGetAttributeError(tp, attr_name);
-    }
-    Py_INCREF(descr);
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS)))
-    #endif
-    {
-        descrgetfunc f = Py_TYPE(descr)->tp_descr_get;
-        if (unlikely(f)) {
-            PyObject *res = f(descr, obj, (PyObject *)tp);
-            Py_DECREF(descr);
-            return res;
-        }
-    }
-    return descr;
-}
-#endif
-
-/* PyObject_GenericGetAttr */
-#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000
-static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) {
-    if (unlikely(Py_TYPE(obj)->tp_dictoffset)) {
-        return PyObject_GenericGetAttr(obj, attr_name);
-    }
-    return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name);
-}
-#endif
-
-/* SetVTable */
-static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
-#if PY_VERSION_HEX >= 0x02070000
-    PyObject *ob = PyCapsule_New(vtable, 0, 0);
-#else
-    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
-#endif
-    if (!ob)
-        goto bad;
-    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
-        goto bad;
-    Py_DECREF(ob);
-    return 0;
-bad:
-    Py_XDECREF(ob);
-    return -1;
-}
-
-/* PyObjectGetAttrStrNoError */
-static void __Pyx_PyObject_GetAttrStr_ClearAttributeError(void) {
-    __Pyx_PyThreadState_declare
-    __Pyx_PyThreadState_assign
-    if (likely(__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError)))
-        __Pyx_PyErr_Clear();
-}
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name) {
-    PyObject *result;
-#if CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_TYPE_SLOTS && PY_VERSION_HEX >= 0x030700B1
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_getattro == PyObject_GenericGetAttr)) {
-        return _PyObject_GenericGetAttrWithDict(obj, attr_name, NULL, 1);
-    }
-#endif
-    result = __Pyx_PyObject_GetAttrStr(obj, attr_name);
-    if (unlikely(!result)) {
-        __Pyx_PyObject_GetAttrStr_ClearAttributeError();
-    }
-    return result;
-}
-
-/* SetupReduce */
-static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
-  int ret;
-  PyObject *name_attr;
-  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name_2);
-  if (likely(name_attr)) {
-      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
-  } else {
-      ret = -1;
-  }
-  if (unlikely(ret < 0)) {
-      PyErr_Clear();
-      ret = 0;
-  }
-  Py_XDECREF(name_attr);
-  return ret;
-}
-static int __Pyx_setup_reduce(PyObject* type_obj) {
-    int ret = 0;
-    PyObject *object_reduce = NULL;
-    PyObject *object_reduce_ex = NULL;
-    PyObject *reduce = NULL;
-    PyObject *reduce_ex = NULL;
-    PyObject *reduce_cython = NULL;
-    PyObject *setstate = NULL;
-    PyObject *setstate_cython = NULL;
-#if CYTHON_USE_PYTYPE_LOOKUP
-    if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
-#else
-    if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto __PYX_GOOD;
-#endif
-#if CYTHON_USE_PYTYPE_LOOKUP
-    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#else
-    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD;
-#endif
-    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD;
-    if (reduce_ex == object_reduce_ex) {
-#if CYTHON_USE_PYTYPE_LOOKUP
-        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#else
-        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD;
-#endif
-        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD;
-        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
-            reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython);
-            if (likely(reduce_cython)) {
-                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-            } else if (reduce == object_reduce || PyErr_Occurred()) {
-                goto __PYX_BAD;
-            }
-            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
-            if (!setstate) PyErr_Clear();
-            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
-                setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython);
-                if (likely(setstate_cython)) {
-                    ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                    ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD;
-                } else if (!setstate || PyErr_Occurred()) {
-                    goto __PYX_BAD;
-                }
-            }
-            PyType_Modified((PyTypeObject*)type_obj);
-        }
-    }
-    goto __PYX_GOOD;
-__PYX_BAD:
-    if (!PyErr_Occurred())
-        PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
-    ret = -1;
-__PYX_GOOD:
-#if !CYTHON_USE_PYTYPE_LOOKUP
-    Py_XDECREF(object_reduce);
-    Py_XDECREF(object_reduce_ex);
-#endif
-    Py_XDECREF(reduce);
-    Py_XDECREF(reduce_ex);
-    Py_XDECREF(reduce_cython);
-    Py_XDECREF(setstate);
-    Py_XDECREF(setstate_cython);
-    return ret;
-}
-
-/* CLineInTraceback */
-#ifndef CYTHON_CLINE_IN_TRACEBACK
-static int __Pyx_CLineForTraceback(CYTHON_NCP_UNUSED PyThreadState *tstate, int c_line) {
-    PyObject *use_cline;
-    PyObject *ptype, *pvalue, *ptraceback;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject **cython_runtime_dict;
-#endif
-    if (unlikely(!__pyx_cython_runtime)) {
-        return c_line;
-    }
-    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);
-#if CYTHON_COMPILING_IN_CPYTHON
-    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
-    if (likely(cython_runtime_dict)) {
-        __PYX_PY_DICT_LOOKUP_IF_MODIFIED(
-            use_cline, *cython_runtime_dict,
-            __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback))
-    } else
-#endif
-    {
-      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
-      if (use_cline_obj) {
-        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
-        Py_DECREF(use_cline_obj);
-      } else {
-        PyErr_Clear();
-        use_cline = NULL;
-      }
-    }
-    if (!use_cline) {
-        c_line = 0;
-        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
-    }
-    else if (use_cline == Py_False || (use_cline != Py_True && PyObject_Not(use_cline) != 0)) {
-        c_line = 0;
-    }
-    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);
-    return c_line;
-}
-#endif
-
-/* CodeObjectCache */
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = start + (end - start) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-
-/* AddTraceback */
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyObject *py_srcfile = 0;
-    PyObject *py_funcname = 0;
-    #if PY_MAJOR_VERSION < 3
-    py_srcfile = PyString_FromString(filename);
-    #else
-    py_srcfile = PyUnicode_FromString(filename);
-    #endif
-    if (!py_srcfile) goto bad;
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        #else
-        py_funcname = PyUnicode_FromString(funcname);
-        #endif
-    }
-    if (!py_funcname) goto bad;
-    py_code = __Pyx_PyCode_New(
-        0,
-        0,
-        0,
-        0,
-        0,
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    Py_DECREF(py_funcname);
-    return py_code;
-bad:
-    Py_XDECREF(py_srcfile);
-    Py_XDECREF(py_funcname);
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-    PyThreadState *tstate = __Pyx_PyThreadState_Current;
-    if (c_line) {
-        c_line = __Pyx_CLineForTraceback(tstate, c_line);
-    }
-    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
-    if (!py_code) {
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) goto bad;
-        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
-    }
-    py_frame = PyFrame_New(
-        tstate,            /*PyThreadState *tstate,*/
-        py_code,           /*PyCodeObject *code,*/
-        __pyx_d,    /*PyObject *globals,*/
-        0                  /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (__Pyx_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
-    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-    if ((0)) {}
-    view->obj = NULL;
-    Py_DECREF(obj);
-}
-#endif
-
-
-/* MemviewSliceIsContig */
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice mvs, char order, int ndim)
-{
-    int i, index, step, start;
-    Py_ssize_t itemsize = mvs.memview->view.itemsize;
-    if (order == 'F') {
-        step = 1;
-        start = 0;
-    } else {
-        step = -1;
-        start = ndim - 1;
-    }
-    for (i = 0; i < ndim; i++) {
-        index = start + step * i;
-        if (mvs.suboffsets[index] >= 0 || mvs.strides[index] != itemsize)
-            return 0;
-        itemsize *= mvs.shape[index];
-    }
-    return 1;
-}
-
-/* OverlappingSlices */
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
-                               void **out_start, void **out_end,
-                               int ndim, size_t itemsize)
-{
-    char *start, *end;
-    int i;
-    start = end = slice->data;
-    for (i = 0; i < ndim; i++) {
-        Py_ssize_t stride = slice->strides[i];
-        Py_ssize_t extent = slice->shape[i];
-        if (extent == 0) {
-            *out_start = *out_end = start;
-            return;
-        } else {
-            if (stride > 0)
-                end += stride * (extent - 1);
-            else
-                start += stride * (extent - 1);
-        }
-    }
-    *out_start = start;
-    *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                     __Pyx_memviewslice *slice2,
-                     int ndim, size_t itemsize)
-{
-    void *start1, *end1, *start2, *end2;
-    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
-    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
-    return (start1 < end2) && (start2 < end1);
-}
-
-/* Capsule */
-static CYTHON_INLINE PyObject *
-__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
-{
-    PyObject *cobj;
-#if PY_VERSION_HEX >= 0x02070000
-    cobj = PyCapsule_New(p, sig, NULL);
-#else
-    cobj = PyCObject_FromVoidPtr(p, NULL);
-#endif
-    return cobj;
-}
-
-/* IsLittleEndian */
-static CYTHON_INLINE int __Pyx_Is_Little_Endian(void)
-{
-  union {
-    uint32_t u32;
-    uint8_t u8[4];
-  } S;
-  S.u32 = 0x01020304;
-  return S.u8[0] == 4;
-}
-
-/* BufferFormatCheck */
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t <= '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1)
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case '?': return "'bool'";
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparseable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case '?': case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count;
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break;
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue;
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number, ndim;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    ndim = ctx->head->field->type->ndim;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_Is_Little_Endian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T':
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}':
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0;
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-        CYTHON_FALLTHROUGH;
-      case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) &&
-            (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-        CYTHON_FALLTHROUGH;
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-
-/* TypeInfoCompare */
-  static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
-    int i;
-    if (!a || !b)
-        return 0;
-    if (a == b)
-        return 1;
-    if (a->size != b->size || a->typegroup != b->typegroup ||
-            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
-        if (a->typegroup == 'H' || b->typegroup == 'H') {
-            return a->size == b->size;
-        } else {
-            return 0;
-        }
-    }
-    if (a->ndim) {
-        for (i = 0; i < a->ndim; i++)
-            if (a->arraysize[i] != b->arraysize[i])
-                return 0;
-    }
-    if (a->typegroup == 'S') {
-        if (a->flags != b->flags)
-            return 0;
-        if (a->fields || b->fields) {
-            if (!(a->fields && b->fields))
-                return 0;
-            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
-                __Pyx_StructField *field_a = a->fields + i;
-                __Pyx_StructField *field_b = b->fields + i;
-                if (field_a->offset != field_b->offset ||
-                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
-                    return 0;
-            }
-            return !a->fields[i].type && !b->fields[i].type;
-        }
-    }
-    return 1;
-}
-
-/* MemviewSliceValidateAndInit */
-  static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    if (buf->shape[dim] <= 1)
-        return 1;
-    if (buf->strides) {
-        if (spec & __Pyx_MEMVIEW_CONTIG) {
-            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
-                if (unlikely(buf->strides[dim] != sizeof(void *))) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "Buffer is not indirectly contiguous "
-                                 "in dimension %d.", dim);
-                    goto fail;
-                }
-            } else if (unlikely(buf->strides[dim] != buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-        if (spec & __Pyx_MEMVIEW_FOLLOW) {
-            Py_ssize_t stride = buf->strides[dim];
-            if (stride < 0)
-                stride = -stride;
-            if (unlikely(stride < buf->itemsize)) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-    } else {
-        if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not contiguous in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not indirect in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (unlikely(buf->suboffsets)) {
-            PyErr_SetString(PyExc_ValueError,
-                            "Buffer exposes suboffsets but no strides");
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
-{
-    if (spec & __Pyx_MEMVIEW_DIRECT) {
-        if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer not compatible with direct access "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    if (spec & __Pyx_MEMVIEW_PTR) {
-        if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer is not indirectly accessible "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
-    int i;
-    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = 0; i < ndim; i++) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not fortran contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = ndim - 1; i >- 1; i--) {
-            if (unlikely(stride * buf->itemsize != buf->strides[i]  &&  buf->shape[i] > 1)) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not C contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj)
-{
-    struct __pyx_memoryview_obj *memview, *new_memview;
-    __Pyx_RefNannyDeclarations
-    Py_buffer *buf;
-    int i, spec = 0, retval = -1;
-    __Pyx_BufFmt_Context ctx;
-    int from_memoryview = __pyx_memoryview_check(original_obj);
-    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
-    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
-                                                            original_obj)->typeinfo)) {
-        memview = (struct __pyx_memoryview_obj *) original_obj;
-        new_memview = NULL;
-    } else {
-        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                            original_obj, buf_flags, 0, dtype);
-        new_memview = memview;
-        if (unlikely(!memview))
-            goto fail;
-    }
-    buf = &memview->view;
-    if (unlikely(buf->ndim != ndim)) {
-        PyErr_Format(PyExc_ValueError,
-                "Buffer has wrong number of dimensions (expected %d, got %d)",
-                ndim, buf->ndim);
-        goto fail;
-    }
-    if (new_memview) {
-        __Pyx_BufFmt_Init(&ctx, stack, dtype);
-        if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail;
-    }
-    if (unlikely((unsigned) buf->itemsize != dtype->size)) {
-        PyErr_Format(PyExc_ValueError,
-                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
-                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
-                     buf->itemsize,
-                     (buf->itemsize > 1) ? "s" : "",
-                     dtype->name,
-                     dtype->size,
-                     (dtype->size > 1) ? "s" : "");
-        goto fail;
-    }
-    if (buf->len > 0) {
-        for (i = 0; i < ndim; i++) {
-            spec = axes_specs[i];
-            if (unlikely(!__pyx_check_strides(buf, i, ndim, spec)))
-                goto fail;
-            if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec)))
-                goto fail;
-        }
-        if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag)))
-            goto fail;
-    }
-    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
-                                         new_memview != NULL) == -1)) {
-        goto fail;
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    Py_XDECREF(new_memview);
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3,
-                                                 &__Pyx_TypeInfo_float, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* ObjectToMemviewSlice */
-  static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG,
-                                                 (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1,
-                                                 &__Pyx_TypeInfo_int, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-    const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-/* CIntFromPyVerify */
-  #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
-#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
-    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
-#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
-    {\
-        func_type value = func_value;\
-        if (sizeof(target_type) < sizeof(func_type)) {\
-            if (unlikely(value != (func_type) (target_type) value)) {\
-                func_type zero = 0;\
-                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
-                    return (target_type) -1;\
-                if (is_unsigned && unlikely(value < zero))\
-                    goto raise_neg_overflow;\
-                else\
-                    goto raise_overflow;\
-            }\
-        }\
-        return (target_type) value;\
-    }
-
-/* CIntToPy */
-  static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
-#endif
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-#ifdef HAVE_LONG_LONG
-        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-            return PyLong_FromLongLong((PY_LONG_LONG) value);
-#endif
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-/* MemviewSliceCopyTemplate */
-  static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object)
-{
-    __Pyx_RefNannyDeclarations
-    int i;
-    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
-    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
-    Py_buffer *buf = &from_memview->view;
-    PyObject *shape_tuple = NULL;
-    PyObject *temp_int = NULL;
-    struct __pyx_array_obj *array_obj = NULL;
-    struct __pyx_memoryview_obj *memview_obj = NULL;
-    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
-    for (i = 0; i < ndim; i++) {
-        if (unlikely(from_mvs->suboffsets[i] >= 0)) {
-            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
-                                           "indirect dimensions (axis %d)", i);
-            goto fail;
-        }
-    }
-    shape_tuple = PyTuple_New(ndim);
-    if (unlikely(!shape_tuple)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(shape_tuple);
-    for(i = 0; i < ndim; i++) {
-        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
-        if(unlikely(!temp_int)) {
-            goto fail;
-        } else {
-            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
-            temp_int = NULL;
-        }
-    }
-    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
-    if (unlikely(!array_obj)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(array_obj);
-    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                    (PyObject *) array_obj, contig_flag,
-                                    dtype_is_object,
-                                    from_mvs->memview->typeinfo);
-    if (unlikely(!memview_obj))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
-        goto fail;
-    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
-                                                dtype_is_object) < 0))
-        goto fail;
-    goto no_fail;
-fail:
-    __Pyx_XDECREF(new_mvs.memview);
-    new_mvs.memview = NULL;
-    new_mvs.data = NULL;
-no_fail:
-    __Pyx_XDECREF(shape_tuple);
-    __Pyx_XDECREF(temp_int);
-    __Pyx_XDECREF(array_obj);
-    __Pyx_RefNannyFinishContext();
-    return new_mvs;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-    const int neg_one = (int) ((int) 0 - (int) 1), const_zero = (int) 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
-                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
-                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
-                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (int) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (int) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
-                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            int val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to int");
-    return (int) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to int");
-    return (int) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-    const long neg_one = (long) ((long) 0 - (long) 1), const_zero = (long) 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
-                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
-                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
-                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (long) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (long) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
-                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            long val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to long");
-    return (long) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to long");
-    return (long) -1;
-}
-
-/* CIntFromPy */
-  static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-    const char neg_one = (char) ((char) 0 - (char) 1), const_zero = (char) 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(char) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x))
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                goto raise_neg_overflow;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case  1: __PYX_VERIFY_RETURN_INT(char, digit, digits[0])
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 2 * PyLong_SHIFT) {
-                            return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 3 * PyLong_SHIFT) {
-                            return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) >= 4 * PyLong_SHIFT) {
-                            return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]));
-                        }
-                    }
-                    break;
-            }
-#endif
-#if CYTHON_COMPILING_IN_CPYTHON
-            if (unlikely(Py_SIZE(x) < 0)) {
-                goto raise_neg_overflow;
-            }
-#else
-            {
-                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
-                if (unlikely(result < 0))
-                    return (char) -1;
-                if (unlikely(result == 1))
-                    goto raise_neg_overflow;
-            }
-#endif
-            if (sizeof(char) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(unsigned PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
-#endif
-            }
-        } else {
-#if CYTHON_USE_PYLONG_INTERNALS
-            const digit* digits = ((PyLongObject*)x)->ob_digit;
-            switch (Py_SIZE(x)) {
-                case  0: return (char) 0;
-                case -1: __PYX_VERIFY_RETURN_INT(char, sdigit, (sdigit) (-(sdigit)digits[0]))
-                case  1: __PYX_VERIFY_RETURN_INT(char,  digit, +digits[0])
-                case -2:
-                    if (8 * sizeof(char) - 1 > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 2:
-                    if (8 * sizeof(char) > 1 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                            return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -3:
-                    if (8 * sizeof(char) - 1 > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 3:
-                    if (8 * sizeof(char) > 2 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                            return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case -4:
-                    if (8 * sizeof(char) - 1 > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-                case 4:
-                    if (8 * sizeof(char) > 3 * PyLong_SHIFT) {
-                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
-                            __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
-                        } else if (8 * sizeof(char) - 1 > 4 * PyLong_SHIFT) {
-                            return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])));
-                        }
-                    }
-                    break;
-            }
-#endif
-            if (sizeof(char) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x))
-#ifdef HAVE_LONG_LONG
-            } else if (sizeof(char) <= sizeof(PY_LONG_LONG)) {
-                __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x))
-#endif
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            char val;
-            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (char) -1;
-        }
-    } else {
-        char val;
-        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-raise_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "value too large to convert to char");
-    return (char) -1;
-raise_neg_overflow:
-    PyErr_SetString(PyExc_OverflowError,
-        "can't convert negative value to char");
-    return (char) -1;
-}
-
-/* CheckBinaryVersion */
-  static int __Pyx_check_binary_version(void) {
-    char ctversion[4], rtversion[4];
-    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
-    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
-        char message[200];
-        PyOS_snprintf(message, sizeof(message),
-                      "compiletime version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        return PyErr_WarnEx(NULL, message, 1);
-    }
-    return 0;
-}
-
-/* InitStrings */
-  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION < 3
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        #else
-        if (t->is_unicode | t->is_str) {
-            if (t->intern) {
-                *t->p = PyUnicode_InternFromString(t->s);
-            } else if (t->encoding) {
-                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
-            } else {
-                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
-            }
-        } else {
-            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
-        }
-        #endif
-        if (!*t->p)
-            return -1;
-        if (PyObject_Hash(*t->p) == -1)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-#if !CYTHON_PEP393_ENABLED
-static const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    char* defenc_c;
-    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-    if (!defenc) return NULL;
-    defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    {
-        char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-        char* c;
-        for (c = defenc_c; c < end; c++) {
-            if ((unsigned char) (*c) >= 128) {
-                PyUnicode_AsASCIIString(o);
-                return NULL;
-            }
-        }
-    }
-#endif
-    *length = PyBytes_GET_SIZE(defenc);
-    return defenc_c;
-}
-#else
-static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-    if (likely(PyUnicode_IS_ASCII(o))) {
-        *length = PyUnicode_GET_LENGTH(o);
-        return PyUnicode_AsUTF8(o);
-    } else {
-        PyUnicode_AsASCIIString(o);
-        return NULL;
-    }
-#else
-    return PyUnicode_AsUTF8AndSize(o, length);
-#endif
-}
-#endif
-#endif
-static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-        return __Pyx_PyUnicode_AsStringAndSize(o, length);
-    } else
-#endif
-#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrueAndDecref(PyObject* x) {
-    int retval;
-    if (unlikely(!x)) return -1;
-    retval = __Pyx_PyObject_IsTrue(x);
-    Py_DECREF(x);
-    return retval;
-}
-static PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {
-#if PY_MAJOR_VERSION >= 3
-    if (PyLong_Check(result)) {
-        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,
-                "__int__ returned non-int (type %.200s).  "
-                "The ability to return an instance of a strict subclass of int "
-                "is deprecated, and may be removed in a future version of Python.",
-                Py_TYPE(result)->tp_name)) {
-            Py_DECREF(result);
-            return NULL;
-        }
-        return result;
-    }
-#endif
-    PyErr_Format(PyExc_TypeError,
-                 "__%.4s__ returned non-%.4s (type %.200s)",
-                 type_name, type_name, Py_TYPE(result)->tp_name);
-    Py_DECREF(result);
-    return NULL;
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
-#if CYTHON_USE_TYPE_SLOTS
-  PyNumberMethods *m;
-#endif
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_Check(x) || PyLong_Check(x)))
-#else
-  if (likely(PyLong_Check(x)))
-#endif
-    return __Pyx_NewRef(x);
-#if CYTHON_USE_TYPE_SLOTS
-  m = Py_TYPE(x)->tp_as_number;
-  #if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = m->nb_long(x);
-  }
-  #else
-  if (likely(m && m->nb_int)) {
-    name = "int";
-    res = m->nb_int(x);
-  }
-  #endif
-#else
-  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {
-    res = PyNumber_Int(x);
-  }
-#endif
-  if (likely(res)) {
-#if PY_MAJOR_VERSION < 3
-    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {
-#else
-    if (unlikely(!PyLong_CheckExact(res))) {
-#endif
-        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b))) {
-    if (sizeof(Py_ssize_t) >= sizeof(long))
-        return PyInt_AS_LONG(b);
-    else
-        return PyInt_AsSsize_t(b);
-  }
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_USE_PYLONG_INTERNALS
-    const digit* digits = ((PyLongObject*)b)->ob_digit;
-    const Py_ssize_t size = Py_SIZE(b);
-    if (likely(__Pyx_sst_abs(size) <= 1)) {
-        ival = likely(size) ? digits[0] : 0;
-        if (size == -1) ival = -ival;
-        return ival;
-    } else {
-      switch (size) {
-         case 2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -2:
-           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -3:
-           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case 4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-         case -4:
-           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
-             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
-           }
-           break;
-      }
-    }
-    #endif
-    return PyLong_AsSsize_t(b);
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) {
-  return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False);
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-    return PyInt_FromSize_t(ival);
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/spaces/docs-demos/flaubert_small_cased/app.py b/spaces/docs-demos/flaubert_small_cased/app.py
deleted file mode 100644
index 675904c919a2c92cc554bb64cf33ea1a78db4027..0000000000000000000000000000000000000000
--- a/spaces/docs-demos/flaubert_small_cased/app.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import gradio as gr
-
-title = "FlauBERT"
-
-description = "Gradio Demo for FlauBERT. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1912.05372' target='_blank'>FlauBERT: Unsupervised Language Model Pre-training for French</a></p>"
-
-examples = [
-    ["Paris est la <special1> de la France.","flaubert_small_cased"]
-]
-
-
-io1 = gr.Interface.load("huggingface/flaubert/flaubert_small_cased")
-
-io2 = gr.Interface.load("huggingface/flaubert/flaubert_base_cased")
-
-def inference(inputtext, model):
-    if model == "flaubert_small_cased":
-        outlabel = io1(inputtext)
-    else:
-        outlabel = io2(inputtext)
-    return outlabel
-     
-
-gr.Interface(
-    inference, 
-    [gr.inputs.Textbox(label="Context",lines=10),gr.inputs.Dropdown(choices=["flaubert_small_cased","flaubert_base_cased"], type="value", default="flaubert_small_cased", label="model")], 
-    [gr.outputs.Label(label="Output")],
-    examples=examples,
-    article=article,
-    title=title,
-    description=description).launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/dongsiqie/lobe-chat/README.md b/spaces/dongsiqie/lobe-chat/README.md
deleted file mode 100644
index 609c591435b95a1bf3d7204d04a63fe2a550381c..0000000000000000000000000000000000000000
--- a/spaces/dongsiqie/lobe-chat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: lobe-chat
-emoji: ⚡
-colorFrom: blue
-colorTo: yellow
-sdk: docker
-pinned: false
-license: mit
-app_port: 3210
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/dream-textures/README/README.md b/spaces/dream-textures/README/README.md
deleted file mode 100644
index bec3859ec80e81271d86fac043abf2236abf4dcc..0000000000000000000000000000000000000000
--- a/spaces/dream-textures/README/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: README
-emoji: 🌖
-colorFrom: yellow
-colorTo: red
-sdk: static
-pinned: false
----
-
-[![Dream Textures, subtitle: Stable Diffusion built-in to Blender](https://github.com/carson-katri/dream-textures/raw/main/docs/assets/banner.png)](https://github.com/carson-katri/dream-textures)
-
-* [Get the latest release](https://github.com/carson-katri/dream-textures/releases/latest)
-* [Join the Discord server](https://discord.gg/EmDJ8CaWZ7)
\ No newline at end of file
diff --git a/spaces/duong11111/ChatGPT4.0/README.md b/spaces/duong11111/ChatGPT4.0/README.md
deleted file mode 100644
index 8d6ab41947d8936f326263f141650644d2301ea5..0000000000000000000000000000000000000000
--- a/spaces/duong11111/ChatGPT4.0/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Chat-with-GPT4
-emoji: 🚀
-colorFrom: red
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: duong11111/ChatGPT4
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/eddydecena/cat-vs-dog/src/models.py b/spaces/eddydecena/cat-vs-dog/src/models.py
deleted file mode 100644
index 3adedf97088841452f37d8002ce2bd77e7b3d18f..0000000000000000000000000000000000000000
--- a/spaces/eddydecena/cat-vs-dog/src/models.py
+++ /dev/null
@@ -1,77 +0,0 @@
-from typing import Tuple
-from typing import Optional
-
-import tensorflow as tf
-from tensorflow.keras import layers
-from keras_tuner import HyperModel
-
-class MakeHyperModel(HyperModel):
-    def __init__(self, input_shape: Tuple[int, int, int], num_classes: int, data_augmentation: Optional[tf.keras.Sequential] = None) -> None:
-        self.input_shape = input_shape
-        self.num_classes = num_classes
-        self.data_augmentation = data_augmentation
-    
-    def build(self, hp) -> tf.keras.Model:
-        inputs = tf.keras.Input(shape=self.input_shape)
-        
-        if self.data_augmentation != None:
-            x = self.data_augmentation(inputs)
-        else:
-            x = inputs
-        
-        x = layers.Rescaling(1.0/255)(x)
-        x = layers.Conv2D(32, 3, strides=2, padding='same')(x)
-        x = layers.BatchNormalization()(x)
-        x = layers.Activation('relu')(x)
-        
-        x = layers.Conv2D(64, 3,  padding='same')(x)
-        x = layers.BatchNormalization()(x)
-        x = layers.Activation('relu')(x)
-        
-        previous_block_activation = x
-        
-        for size in [128, 256, 512, 728]:
-            x = layers.Activation('relu')(x)
-            x = layers.SeparableConv2D(size, 3, padding='same')(x)
-            x = layers.BatchNormalization()(x)
-            
-            x = layers.Activation("relu")(x)
-            x = layers.SeparableConv2D(size, 3, padding='same')(x)
-            x = layers.BatchNormalization()(x)
-            
-            x = layers.MaxPooling2D(3, strides=2, padding='same')(x)
-            
-            residual = layers.Conv2D(size, 1, strides=2, padding='same')(previous_block_activation)
-            
-            x = layers.add([x, residual])
-            previous_block_activation = x
-        
-        x = layers.SeparableConv2D(1024, 3, padding='same')(x)
-        x = layers.BatchNormalization()(x)
-        x = layers.Activation('relu')(x)
-        
-        x = layers.GlobalAveragePooling2D()(x)
-        
-        if self.num_classes == 2:
-            activation = 'sigmoid'
-            loss_fn = 'binary_crossentropy'
-            units = 1
-        else:
-            activation = 'softmax'
-            loss_fn = 'categorical_crossentropy'
-            units = self.num_classes
-        
-        x = layers.Dropout(0.5)(x)
-        outputs = layers.Dense(units, activation=activation)(x)
-        
-        model = tf.keras.Model(inputs, outputs)
-        
-        model.compile(
-            optimizer=tf.keras.optimizers.Adam(
-                hp.Choice("learning_rate", values=[1e-2, 1e-3, 1e-4])
-            ),
-            loss=loss_fn,
-            metrics=['accuracy']
-        )
-        
-        return model
\ No newline at end of file
diff --git a/spaces/facebook/ov-seg/open_vocab_seg/modeling/clip_adapter/clip/clip.py b/spaces/facebook/ov-seg/open_vocab_seg/modeling/clip_adapter/clip/clip.py
deleted file mode 100644
index 6d733edfac02d81ba3e402eb7e702764728bdaa2..0000000000000000000000000000000000000000
--- a/spaces/facebook/ov-seg/open_vocab_seg/modeling/clip_adapter/clip/clip.py
+++ /dev/null
@@ -1,285 +0,0 @@
-import hashlib
-import os
-import urllib
-import warnings
-from collections import OrderedDict
-from typing import Union, List
-
-import torch
-from PIL import Image
-from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
-from tqdm import tqdm
-
-from .model import build_model
-from .simple_tokenizer import SimpleTokenizer as _Tokenizer
-
-try:
-    from torchvision.transforms import InterpolationMode
-
-    BICUBIC = InterpolationMode.BICUBIC
-except ImportError:
-    BICUBIC = Image.BICUBIC
-
-
-if torch.__version__.split(".") < ["1", "7", "1"]:
-    warnings.warn("PyTorch version 1.7.1 or higher is recommended")
-
-
-__all__ = ["available_models", "load", "tokenize"]
-_tokenizer = _Tokenizer()
-
-_MODELS = {
-    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
-    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
-    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
-    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
-    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
-    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
-    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
-    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
-}
-
-
-def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
-    os.makedirs(root, exist_ok=True)
-    filename = os.path.basename(url)
-
-    expected_sha256 = url.split("/")[-2]
-    download_target = os.path.join(root, filename)
-
-    if os.path.exists(download_target) and not os.path.isfile(download_target):
-        raise RuntimeError(f"{download_target} exists and is not a regular file")
-
-    if os.path.isfile(download_target):
-        if (
-            hashlib.sha256(open(download_target, "rb").read()).hexdigest()
-            == expected_sha256
-        ):
-            return download_target
-        else:
-            warnings.warn(
-                f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
-            )
-
-    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
-        with tqdm(
-            total=int(source.info().get("Content-Length")),
-            ncols=80,
-            unit="iB",
-            unit_scale=True,
-        ) as loop:
-            while True:
-                buffer = source.read(8192)
-                if not buffer:
-                    break
-
-                output.write(buffer)
-                loop.update(len(buffer))
-
-    if (
-        hashlib.sha256(open(download_target, "rb").read()).hexdigest()
-        != expected_sha256
-    ):
-        raise RuntimeError(
-            f"Model has been downloaded but the SHA256 checksum does not not match"
-        )
-
-    return download_target
-
-
-def _transform(n_px):
-    return Compose(
-        [
-            Resize(n_px, interpolation=BICUBIC),
-            CenterCrop(n_px),
-            lambda image: image.convert("RGB"),
-            ToTensor(),
-            Normalize(
-                (0.48145466, 0.4578275, 0.40821073),
-                (0.26862954, 0.26130258, 0.27577711),
-            ),
-        ]
-    )
-
-
-def available_models() -> List[str]:
-    """Returns the names of available CLIP models"""
-    return list(_MODELS.keys())
-
-
-def load(
-    name: str,
-    mask_prompt_depth: int = 0,
-    device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
-    jit=False,
-):
-    """Load a CLIP model
-
-    Parameters
-    ----------
-    name : str
-        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
-
-    device : Union[str, torch.device]
-        The device to put the loaded model
-
-    jit : bool
-        Whether to load the optimized JIT model or more hackable non-JIT model (default).
-
-    Returns
-    -------
-    model : torch.nn.Module
-        The CLIP model
-
-    preprocess : Callable[[PIL.Image], torch.Tensor]
-        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
-    """
-    if name in _MODELS:
-        model_path = _download(_MODELS[name])
-    elif os.path.isfile(name):
-        model_path = name
-    else:
-        raise RuntimeError(
-            f"Model {name} not found; available models = {available_models()}"
-        )
-
-    try:
-        # loading JIT archive
-        model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
-        state_dict = None
-    except RuntimeError:
-        # loading saved state dict
-        if jit:
-            warnings.warn(
-                f"File {model_path} is not a JIT archive. Loading as a state dict instead"
-            )
-            jit = False
-        state_dict = torch.load(model_path, map_location="cpu")
-        if 'state_dict' in state_dict:
-            new_state_dict = OrderedDict()
-            for k, v in state_dict['state_dict'].items():
-                if k.startswith('module.'):
-                    name = k[7:]  # remove `module.`
-                    new_state_dict[name] = v
-            state_dict = new_state_dict
-
-    if not jit:
-        model = build_model(state_dict or model.state_dict(), mask_prompt_depth).to(device)
-        if str(device) == "cpu":
-            model.float()
-        return model, _transform(model.visual.input_resolution)
-
-    # patch the device names
-    device_holder = torch.jit.trace(
-        lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]
-    )
-    device_node = [
-        n
-        for n in device_holder.graph.findAllNodes("prim::Constant")
-        if "Device" in repr(n)
-    ][-1]
-
-    def patch_device(module):
-        try:
-            graphs = [module.graph] if hasattr(module, "graph") else []
-        except RuntimeError:
-            graphs = []
-
-        if hasattr(module, "forward1"):
-            graphs.append(module.forward1.graph)
-
-        for graph in graphs:
-            for node in graph.findAllNodes("prim::Constant"):
-                if "value" in node.attributeNames() and str(node["value"]).startswith(
-                    "cuda"
-                ):
-                    node.copyAttributes(device_node)
-
-    model.apply(patch_device)
-    patch_device(model.encode_image)
-    patch_device(model.encode_text)
-
-    # patch dtype to float32 on CPU
-    if str(device) == "cpu":
-        float_holder = torch.jit.trace(
-            lambda: torch.ones([]).float(), example_inputs=[]
-        )
-        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
-        float_node = float_input.node()
-
-        def patch_float(module):
-            try:
-                graphs = [module.graph] if hasattr(module, "graph") else []
-            except RuntimeError:
-                graphs = []
-
-            if hasattr(module, "forward1"):
-                graphs.append(module.forward1.graph)
-
-            for graph in graphs:
-                for node in graph.findAllNodes("aten::to"):
-                    inputs = list(node.inputs())
-                    for i in [
-                        1,
-                        2,
-                    ]:  # dtype can be the second or third argument to aten::to()
-                        if inputs[i].node()["value"] == 5:
-                            inputs[i].node().copyAttributes(float_node)
-
-        model.apply(patch_float)
-        patch_float(model.encode_image)
-        patch_float(model.encode_text)
-
-        model.float()
-
-    return model, _transform(model.input_resolution.item())
-
-
-def tokenize(
-    texts: Union[str, List[str]],
-    context_length: int = 77,
-    truncate: bool = False,
-    return_length: bool = False,
-) -> torch.LongTensor:
-    """
-    Returns the tokenized representation of given input string(s)
-
-    Parameters
-    ----------
-    texts : Union[str, List[str]]
-        An input string or a list of input strings to tokenize
-
-    context_length : int
-        The context length to use; all CLIP models use 77 as the context length
-
-    truncate: bool
-        Whether to truncate the text in case its encoding is longer than the context length
-
-    Returns
-    -------
-    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
-    """
-    if isinstance(texts, str):
-        texts = [texts]
-
-    sot_token = _tokenizer.encoder["<|startoftext|>"]
-    eot_token = _tokenizer.encoder["<|endoftext|>"]
-    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
-    result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
-    length = []
-    for i, tokens in enumerate(all_tokens):
-        if len(tokens) > context_length:
-            if truncate:
-                tokens = tokens[:context_length]
-                tokens[-1] = eot_token
-                length.append(context_length)
-            else:
-                raise RuntimeError(
-                    f"Input {texts[i]} is too long for context length {context_length}"
-                )
-        else:
-            length.append(len(tokens))
-        result[i, : len(tokens)] = torch.tensor(tokens)
-    if return_length:
-        return result, length
-    return result
diff --git a/spaces/falterWliame/Face_Mask_Detection/Geant 2500hd Probleme Boot ((FULL)).md b/spaces/falterWliame/Face_Mask_Detection/Geant 2500hd Probleme Boot ((FULL)).md
deleted file mode 100644
index 70eb054f65d7bb7087d31a72b2d369d74fd99f8f..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Geant 2500hd Probleme Boot ((FULL)).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Geant 2500hd probleme boot</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://urlca.com/2uDcZn">https://urlca.com/2uDcZn</a></b></p><br /><br />
-
- 8a78ff9644<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/falterWliame/Face_Mask_Detection/RAD Studio 10.3.3 Android Services Patch LINK.md b/spaces/falterWliame/Face_Mask_Detection/RAD Studio 10.3.3 Android Services Patch LINK.md
deleted file mode 100644
index 28f3eb968a7a4cc26a4d69c8f74b7481b1a67245..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/RAD Studio 10.3.3 Android Services Patch LINK.md	
+++ /dev/null
@@ -1,36 +0,0 @@
-
-```html
-<h1>RAD Studio 10.3.3 Android Services Patch: What You Need to Know</h1>
-<meta name="description" content="Learn how to install and use the RAD Studio 10.3.3 Android Services Patch to fix issues with Android services and notifications in your Delphi and C++Builder apps.">
-<p>RAD Studio 10.3.3 is the latest version of the popular IDE for developing cross-platform applications with Delphi and C++Builder. However, some users have reported issues with Android services and notifications in their apps built with RAD Studio 10.3.3.</p>
-<h2>RAD Studio 10.3.3 Android Services Patch</h2><br /><p><b><b>Download Zip</b> &#9913; <a href="https://urlca.com/2uDcMh">https://urlca.com/2uDcMh</a></b></p><br /><br />
-<p>Fortunately, Embarcadero has released a patch that addresses these issues and improves the stability and performance of Android services and notifications in RAD Studio 10.3.3. In this article, we will show you how to install and use the RAD Studio 10.3.3 Android Services Patch and what benefits it brings to your app development.</p>
-<h2>How to Install the RAD Studio 10.3.3 Android Services Patch</h2>
-<p>The RAD Studio 10.3.3 Android Services Patch is available for download from the <a href="https://cc.embarcadero.com/item/30915">Embarcadero CodeCentral</a> website. You need to have a valid license for RAD Studio 10.3.3 to access the patch.</p>
-<p>To install the patch, follow these steps:</p>
-<ol>
-<li>Close RAD Studio 10.3.3 if it is running.</li>
-<li>Download the patch file from the CodeCentral website.</li>
-<li>Extract the contents of the zip file to a temporary folder.</li>
-<li>Run the Install.exe file as administrator.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Restart RAD Studio 10.3.3 and verify that the patch has been applied by checking the About box.</li>
-</ol>
-<h2>How to Use the RAD Studio 10.3.3 Android Services Patch</h2>
-<p>The RAD Studio 10.3.3 Android Services Patch fixes several issues related to Android services and notifications in Delphi and C++Builder apps, such as:</p>
-<p></p>
-<ul>
-<li>Android services not starting or stopping correctly.</li>
-<li>Android services not receiving intents or broadcasting events.</li>
-<li>Android notifications not showing or updating properly.</li>
-<li>Android notifications not working on devices running Android 8 or higher.</li>
-</ul>
-<p>To use the patch, you need to rebuild your Android apps with RAD Studio 10.3.3 after installing the patch. You do not need to make any changes to your code or project settings.</p>
-<p>The patch also includes updated versions of the <a href="https://docwiki.embarcadero.com/RADStudio/Sydney/en/Android_Notification_Center">Android Notification Center</a> and <a href="https://docwiki.embarcadero.com/RADStudio/Sydney/en/Using_the_Android_Alarm_Manager">Android Alarm Manager</a> components that are compatible with the patch and provide enhanced functionality for managing notifications and alarms on Android devices.</p>
-<h2>Conclusion</h2>
-<p>The RAD Studio 10.3.3 Android Services Patch is a must-have update for anyone developing Android apps with Delphi or C++Builder using RAD Studio 10.3.3. It fixes several issues that affect the reliability and usability of Android services and notifications in your apps and improves your app development experience.</p>
-<p>To download and install the patch, visit the <a href="https://cc.embarcadero.com/item/30915">Embarcadero CodeCentral</a> website today and enjoy the benefits of the patch in your next Android app project.</p>
-
-```</p> d5da3c52bf<br />
-<br />
-<br />
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diff --git a/spaces/fatiXbelha/sd/Badminton League Hack APK Unlimited Money and No Ads.md b/spaces/fatiXbelha/sd/Badminton League Hack APK Unlimited Money and No Ads.md
deleted file mode 100644
index 3981b28f4fa4a585efde68621edeae982ea3ab4a..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Badminton League Hack APK Unlimited Money and No Ads.md	
+++ /dev/null
@@ -1,122 +0,0 @@
-<br />
-<h1>Badminton League Hack Apk: A Cheat or a Challenge?</h1>
- <p>Badminton is one of the most popular and enjoyable sports in the world. It is a fast-paced, thrilling, and skillful game that can be played by anyone, anywhere. However, some people may find it too hard or too boring to play badminton without some extra help. That's why they resort to using badminton league hack apk, a modified version of the original badminton league game that gives them unlimited access to coins, gems, items, modes, and more.</p>
-<h2>badminton league hack apk</h2><br /><p><b><b>Download File</b> ===> <a href="https://urllie.com/2uNy2x">https://urllie.com/2uNy2x</a></b></p><br /><br />
- <p>But is using badminton league hack apk really worth it? What are the benefits and drawbacks of using it? And how can you play badminton better with or without it? In this article, we will answer these questions and provide you with some tips and tricks to improve your badminton skills. Whether you are a beginner or a pro, a hacker or a fair player, this article will help you enjoy badminton more.</p>
- <h2>How to Download and Install Badminton League Hack Apk?</h2>
- <p>If you want to try out badminton league hack apk, you will need to download it from a third-party website that offers it for free. However, you should be careful when doing so, as some websites may contain viruses, malware, or spyware that can harm your device or steal your personal information. Here are some steps you should follow to download and install badminton league hack apk safely:</p>
- <ol>
-<li>Make sure your device has enough storage space and battery life.</li>
-<li>Go to your device settings and enable the option to install apps from unknown sources.</li>
-<li>Search for a reliable website that offers badminton league hack apk download link.</li>
-<li>Click on the download link and wait for the file to be downloaded.</li>
-<li>Locate the file in your device's file manager and tap on it to install it.</li>
-<li>Follow the instructions on the screen and grant the necessary permissions.</li>
-<li>Launch the app and enjoy playing badminton with unlimited resources.</li>
-</ol>
- <p><b>Note:</b> You may need to uninstall the original badminton league game before installing the hacked version. You may also need to turn off your internet connection while playing the hacked version to avoid detection by the game developers.</p>
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- <h2>How to Use Badminton League Hack Apk Features?</h2>
- <p>Once you have installed badminton league hack apk on your device, you can start using its features to enhance your gaming experience. Here are some of the features that you can use:</p>
- <ul>
-<li><b>Unlimited coins and gems:</b> With this feature, you can get as many coins and gems as you want without spending any real money. You can use these currencies to buy new rackets, shuttlecocks, outfits, accessories, and more. You can also use them to upgrade your skills, unlock new modes, and participate in tournaments.</li>
-<li><b>Unlock all items and modes:</b> With this feature, you can access all the items and modes that are normally locked or require a lot of coins and gems to unlock. You can enjoy playing different modes such as 1v1, 2v2, tournament, and super series. You can also customize your character with various items such as hats, glasses, masks, and more.</li>
-<li><b>Remove ads and malware:</b> With this feature, you can get rid of the annoying ads and pop-ups that interrupt your gameplay. You can also protect your device from any potential malware or spyware that may come with the hacked version.</li>
-</ul>
- <p>However, you should also be aware of the drawbacks of using badminton league hack apk. Some of them are:</p>
- <ul>
-<li><b>Risk of getting banned:</b> If the game developers detect that you are using a hacked version, they may ban your account or device from playing the game. You may lose all your progress and achievements, and you may not be able to play the game again.</li>
-<li><b>Lack of challenge and fun:</b> If you use badminton league hack apk to get unlimited resources and access to everything, you may lose the sense of challenge and fun that comes with playing the game. You may not feel the satisfaction of earning coins and gems, unlocking items and modes, and improving your skills. You may also lose the respect and admiration of other players who play fairly.</li>
-<li><b>Possibility of viruses and bugs:</b> As mentioned earlier, some websites that offer badminton league hack apk may contain viruses, malware, or spyware that can harm your device or steal your personal information. Even if you download it from a safe website, the hacked version may still have some bugs or glitches that can affect your gameplay or cause your app to crash.</li>
-</ul>
- <h2>How to Play Badminton Better With or Without Badminton League Hack Apk?</h2>
- <p>Whether you decide to use badminton league hack apk or not, you can still improve your badminton skills and enjoy the game more by following some tips and tricks. Here are some of them:</p>
- <h3>Master the Basics of Badminton Rules and Scoring</h3>
- <p>Before you start playing badminton, you should know the basic rules and scoring system of the game. Here are some of them:</p>
- <ul>
-<li>The game is played on a rectangular court divided by a net in the middle. The court has two service courts on each side, marked by lines.</li>
-<li>The game can be played by two players (singles) or four players (doubles).</li>
-<li>The game starts with a toss to decide who will serve first. The server must hit the shuttlecock (also called birdie) over the net to the opponent's service court diagonally.</li>
-<li>The rally continues until the shuttlecock lands on the ground, goes out of bounds, hits the net, or is hit by a player before it crosses the net.</li>
-<li>The player who wins the rally gets a point and serves again. If the server loses the rally, the serve passes to the opponent.</li>
-<li>The game is played to 21 points, with a two-point difference required to win. If the score reaches 20-20, the game continues until one player has a two-point lead or reaches 30 points.</li>
-<li>A match consists of three games, with the winner being the first to win two games.</li>
-</ul>
- <h3>Improve Your Speed, Reflexes, and Flexibility</h3>
- <p>Badminton is a fast-paced sport that requires quick movements, reactions, and adjustments. To play better, you should improve your speed, reflexes, and flexibility by doing some exercises such as:</p>
- <ul>
-<li>Running: Running can help you increase your stamina, endurance, and speed. You can run on a treadmill, on a track, or on a road. You can also do some interval training by alternating between fast and slow running.</li>
-<li>Sprinting: Sprinting can help you improve your acceleration, agility, and explosiveness. You can sprint on a flat surface or on an incline. You can also do some drills such as skipping, hopping, jumping, or shuffling.</li>
-<li>Stretching: Stretching can help you improve your flexibility, mobility, and range of motion. You can stretch your muscles before and after playing badminton. You can also do some yoga poses such as downward dog, cobra pose, or warrior pose.</li>
-</ul>
- <h3>Practice Your Serve, Backhand, and Footwork</h3>
- <p>Badminton is a skillful sport that requires good technique, accuracy, and consistency. To play better, you should practice your serve, backhand, and footwork by doing some exercises such as:</p>
- <ul>
-<li>Serve: The serve is the first and most important shot in badminton. It can set the tone and pace of the rally. You should practice your serve by aiming at different targets on the court, such as the corners, the center, or the lines. You should also practice different types of serves, such as high, low, flick, or drive.</li>
-<li>Backhand: The backhand is one of the most difficult and essential shots in badminton. It can help you defend and attack from any position on the court. You should practice your backhand by hitting the shuttlecock with your racket's back face, using your thumb and index finger to grip the handle. You should also practice different angles and directions of your backhand, such as cross-court, straight, or drop.</li>
-<li>Footwork: The footwork is the foundation of your movement and balance on the court. It can help you reach and return any shot with ease and efficiency. You should practice your footwork by using small and quick steps, keeping your feet close to the ground, and moving in a diagonal or lateral direction. You should also practice different patterns and rhythms of your footwork, such as shuffle, chasse, or split step.</li>
-</ul>
- <h2>Use Strategies and Tactics to Exploit Your Opponent's Weaknesses</h2>
- <p>Badminton is a strategic sport that requires smart thinking and planning. To play better, you should use strategies and tactics to exploit your opponent's weaknesses by doing some actions such as:</p>
- <ul>
-<li>Analyzing: Before and during the game, you should analyze your opponent's strengths and weaknesses, such as their preferred shots, their dominant hand, their speed, their stamina, their temperament, etc. You should also analyze the court conditions, such as the wind, the lighting, the surface, etc.</li>
-<li>Varying: During the game, you should vary your shots and pace to keep your opponent guessing and off-balance. You should use different types of shots, such as smashes, drops, clears, drives, net shots, etc. You should also use different speeds and heights of your shots, such as fast or slow, high or low.</li>
-<li>Attacking: During the game, you should attack your opponent's weak spots to gain an advantage and score points. You should aim for their backhand side, their body, their baseline, their corners, etc. You should also use deception and feints to trick them into making mistakes.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Badminton league hack apk is a modified version of the original badminton league game that gives you unlimited access to coins, gems, items, modes, and more. However, it also comes with some risks and drawbacks that may ruin your gaming experience or harm your device. Therefore, you should be careful when using it and weigh the pros and cons before downloading it.</p>
- <p>If you want to play badminton better with or without badminton league hack apk, you should follow some tips and tricks that can help you improve your skills and enjoy the game more. You should master the basics of badminton rules and scoring, improve your speed, reflexes, and flexibility, practice your serve, backhand, and footwork, and use strategies and tactics to exploit your opponent's weaknesses.</p>
- <p>Badminton is a fun and exciting sport that can be played by anyone, anywhere. Whether you are a hacker or a fair player, a beginner or a pro, you can always find ways to challenge yourself and have fun. So what are you waiting for? Grab your racket and shuttlecock and start playing badminton today!</p>
- <p><b>Play Badminton, Stay Fit, Have Fun!</b></p>
- <h2>FAQs</h2>
- <h3>What is the origin and history of badminton?</h3>
- <p>Badminton is believed to have originated in ancient China as a game called ti jian zi (shuttle kicking). It was later introduced to India by British soldiers as a game called poona (derived from Pune). It was then brought to England by British officers as a game called badminton (named after Badminton House). It was officially recognized as a sport by the International Badminton Federation (IBF) in 1934.</p>
- <h3>What are the best badminton rackets and shuttlecocks to use?</h3>
- <p>The best badminton rackets and shuttlecocks to use depend on your personal preference and playing style. However, some general factors to consider are: - Weight: The weight of the racket affects its balance, power, and maneuverability. Generally, lighter rackets are easier to swing and control, while heavier rackets are more stable and powerful. The weight range of rackets is usually between 80 to 100 grams. - Balance: The balance of the racket refers to the distribution of weight between the head and the handle. Generally, head-heavy rackets are more suitable for players who prefer smashing and attacking, while head-light rackets are more suitable for players who prefer defending and rallying. The balance point of rackets is usually measured in millimeters from the bottom of the handle. - Shaft: The shaft of the racket affects its flexibility, stiffness, and responsiveness. Generally, flexible shafts are more forgiving and generate more power, while stiff shafts are more accurate and generate more speed. The shaft stiffness of rackets is usually classified as extra stiff, stiff, medium, flexible, or extra flexible. - String: The string of the racket affects its tension, durability, and feel. Generally, higher tension strings provide more control and accuracy, while lower tension strings provide more power and comfort. The string tension of rackets is usually measured in pounds per square inch (psi) or kilograms per square centimeter (kg/cm2). - Grip: The grip of the racket affects its size, shape, and material. Generally, smaller grips are easier to change and rotate, while larger grips are more stable and comfortable. The grip size of rackets is usually classified as G1 (largest), G2, G3, G4, or G5 (smallest). The grip shape of rackets is usually either oval or rectangular. The grip material of rackets is usually either synthetic or natural. - Shuttlecock: The shuttlecock is the projectile that is hit by the racket in badminton. It consists of a cork base and a cone of feathers or synthetic material. Generally, feather shuttlecocks are more expensive and fragile, but also more aerodynamic and consistent, while synthetic shuttlecocks are cheaper and durable, but also less responsive and variable. The speed of shuttlecocks is usually classified as 75 (slowest), 76, 77, 78, or 79 (fastest).</p>
- <h3>How to join a badminton club or tournament?</h3>
- <p>If you want to join a badminton club or tournament, you should do some research online or offline to find out the options available in your area. You should consider some factors such as: - Location: You should choose a club or tournament that is convenient and accessible for you. - Level: You should choose a club or tournament that matches your skill level and goals. You can join a recreational club or tournament if you want to play for fun and socialize with other players. You can join a competitive club or tournament if you want to play for challenge and improvement. - Schedule: You should choose a club or tournament that fits your availability and preference. You can join a regular club or tournament if you want to play on a fixed day and time every week. You can join a flexible club or tournament if you want to play on different days and times depending on your schedule. - Fee: You should choose a club or tournament that suits your budget and expectations. You can join a free or low-cost club or tournament if you want to save money and play casually. You can join a paid or high-cost club or tournament if you want to get more benefits and services.</p>
- <h3>How to avoid injuries and stay healthy while playing badminton?</h3>
- <p>Badminton is a physical sport that can cause injuries and health problems if not played properly or safely. To avoid injuries and stay healthy while playing badminton, you should do some actions such as: - Warming up: Before playing badminton, you should warm up your body by doing some light exercises such as jogging, stretching, jumping, or skipping. This can help you increase your blood circulation, loosen your muscles, and prevent stiffness and cramps. - Cooling down: After playing badminton, you should cool down your body by doing some gentle exercises such as walking, breathing, or relaxing. This can help you lower your heart rate, release your tension, and reduce soreness and fatigue. - Hydrating: During and after playing badminton, you should hydrate your body by drinking plenty of water or sports drinks. This can help you replenish your fluids, electrolytes, and energy, and prevent dehydration and heatstroke. - Eating: Before and after playing badminton, you should eat some healthy and balanced meals or snacks. This can help you provide your body with the nutrients, carbohydrates, proteins, and fats it needs to perform well and recover fast. - Resting: Between and after playing badminton, you should rest your body by taking some breaks or naps. This can help you restore your strength, stamina, and concentration, and prevent overexertion and injury. - Treating: If you get injured or feel unwell while playing badminton, you should treat your condition as soon as possible by applying some first aid or seeking medical attention. You should also stop playing badminton until you are fully healed and cleared by a doctor.</p>
- <h3>How to watch live streams and highlights of badminton matches?</h3>
- <p>If you want to watch live streams and highlights of badminton matches, you have several options to choose from. You can watch them on: - Television: You can watch live streams and highlights of badminton matches on various television channels that broadcast them, such as ESPN, NBC Sports, Fox Sports, BBC Sport, etc. You can check the schedule and availability of these channels on your cable or satellite provider. - Internet: You can watch live streams and highlights of badminton matches on various websites that stream them, such as YouTube, Facebook, Twitch, BWF TV, etc. You can access these websites on your computer, smartphone, tablet, or smart TV. You may need to register or pay a fee to watch some of these streams. - App: You can watch live streams and highlights of badminton matches on various apps that offer them, such as Badminton Live Score, Badminton World TV, Badminton League, etc. You can download these apps on your smartphone or tablet from the Google Play Store or the Apple App Store. You may need to create an account or purchase a subscription to watch some of these streams.</p> 197e85843d<br />
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-<p>Bingo is a game of chance in which each player matches the numbers printed in different arrangements on cards. The game host (a caller) draws at random, marking the selected numbers with tiles. When a player finds the selected numbers are arranged on their card in a row, they call out "Bingo!" and win a prize. Bingo is one of the most popular games in the United States, with millions of people playing it every week. Bingo originated in Italy in the 16th century, and later spread to France, Germany, and Britain. It was brought to America by Edwin Lowe in 1929, who popularized it by selling bingo sets and organizing games at churches and fairs.</p>
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-<p>One of the main reasons why you should play bingo on your laptop is convenience. You can play bingo anytime, anywhere, without needing physical cards or equipment. All you need is a laptop and an internet connection. You can play bingo in your pajamas, in your bed, or even while traveling. You don't have to worry about losing your cards, running out of ink, or missing a number. You can also pause and resume your game whenever you want.</p>
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-<h3>Find a Reliable Website</h3>
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-<ul>
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-<li>The customer service and support of the website</li>
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-<p>Some examples of websites that offer free bingo games for laptop are:</p>
-<table>
-<tr><th>Website</th><th>Features</th></tr>
-<tr><td>Bingo Blitz</td><td>- Over 100 bingo rooms with different themes and challenges<br>- Daily credits, power-ups, and gifts<br>- Chat and play with millions of players worldwide<br>- Collect items and complete quests in each room<br>- Available on Windows, Mac, iOS, and Android</td></tr>
-<tr><td>Bingo Bash</td><td>- Over 30 bingo rooms with different themes and modes<br>- Daily chips, power-ups, and coins<br>- Chat and play with thousands of players online<br>- Collect items and unlock new rooms<br>- Available on Windows, Mac, iOS, and Android</td></tr>
-<tr><td>Bingo Pop</td><td>- Over 20 bingo rooms with different themes and jackpots<br>- Daily tickets, cherries, and coins<br>- Chat and play with hundreds of players online<br>- Collect items and win prizes in each room<br>- Available on Windows, Mac, iOS, and Android</td></tr>
-</table> <h3>Choose a Bingo Game</h3>
-<p>The second step to download free bingo game for laptop is to choose a bingo game that suits your preferences. You can browse through the available bingo games on the website, and select one that appeals to you. You can look at the screenshots, descriptions, and ratings of the bingo games, and see what features they offer. You can also try playing the demo version of the bingo games, if available, to get a feel of how they work. Some of the factors that you should consider when choosing a bingo game are:</p>
-<ul>
-<li>The theme and design of the bingo game</li>
-<li>The number and type of bingo cards</li>
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-<li>The prizes and jackpots of the bingo game</li>
-</ul>
-<p>Some examples of bingo games that you can download for free for laptop are:</p>
-<table>
-<tr><th>Bingo Game</th><th>Features</th></tr>
-<tr><td>Bingo Party</td><td>- 8 classic and special bingo rooms with different themes<br>- Up to 4 bingo cards per game<br>- Adjustable difficulty and speed settings<br>- Huge prizes and jackpots in each room<br>- Available on Windows, Mac, iOS, and Android</td></tr>
-<tr><td>Bingo Quest</td><td>- 4 unique bingo worlds with different themes and challenges<br>- Up to 2 bingo cards per game<br>- Fixed difficulty and speed settings<br>- Collectible items and rewards in each world<br>- Available on Windows, Mac, iOS, and Android</td></tr>
-<tr><td>Bingo Showdown</td><td>- 6 western-themed bingo rooms with different modes<br>- Up to 8 bingo cards per game<br>- Random difficulty and speed settings<br>- Power-ups and mini-games in each room<br>- Available on Windows, Mac, iOS, and Android</td></tr>
-</table> <h3>Download and Install the Game</h3>
-<p>The third and final step to download free bingo game for laptop is to follow the instructions on the website to download and install the bingo game on your laptop. You can usually find the download link or button on the website, and click on it to start the download process. You may need to agree to the terms and conditions of the website and the game before downloading. You may also need to create an account or sign in to the website to access the game. The download time may vary depending on your internet speed and the size of the game file. Once the download is complete, you can open the file and run the installation wizard. You may need to choose a destination folder for the game, and create a shortcut on your desktop. The installation time may also vary depending on your laptop specifications and the game requirements. Once the installation is complete, you can launch the game and start playing bingo on your laptop.</p>
-<h2>Tips and Tricks for Playing Bingo on Laptop</h2>
-<h3>Use Multiple Cards</h3>
-<p>One of the tips and tricks for playing bingo on laptop is to use multiple cards. Playing with more than one bingo card at a time can increase your chances of winning, as you have more numbers to match. However, you should also be careful not to use too many cards, as it can be hard to keep track of them all. You should also make sure that you can afford to buy multiple cards, as some online bingo games may charge you per card. You can usually see how many cards you can play with in each game, and select or deselect them accordingly.</p>
-<h3>Use Auto-Daub Feature</h3>
-<p>Another tip and trick for playing bingo on laptop is to use the auto-daub feature. This is a feature that marks your cards automatically when a number is called, so you don't have to do it manually. This can save you time and avoid missing numbers, especially if you are playing with multiple cards. However, you should also be aware that some online bingo games may not have this feature, or may charge you extra for using it. You should also check if using this feature affects your chances of winning, as some games may reward manual daubing more than auto-daubing. You can usually see if a game has this feature, and turn it on or off in the settings.</p>
-<h3>Claim Your Bonuses and Rewards</h3>
-<p>A third tip and trick for playing bingo on laptop is to claim your bonuses and rewards. Most online bingo games offer freebies and incentives for playing, such as credits, power-ups, gifts, and more. These can help you enhance your bingo experience, as you can use them to buy more cards, boost your chances of winning, or unlock new features. However, you should also be careful not to fall for scams or spam offers that promise you free bingo games or prizes, as they may be trying to steal your personal or financial information. You should only claim bonuses and rewards from reputable websites and games that you trust.</p>
-<h2>Conclusion</h2>
-<p>In conclusion, playing bingo on laptop can be a fun and rewarding activity that you can enjoy anytime, anywhere. You can play different bingo games, themes, and modes online, and chat and interact with other bingo players from around the world. You can also download free bingo games for laptop easily by following these steps:</p>
-<ol>
-<li>Find a reliable website that offers free bingo games for laptop</li>
-<li>Choose a bingo game that suits your preferences</li>
-<li>Download and install the game on your laptop</li>
-</ol>
-<p>You can also improve your bingo skills by using these tips and tricks:</p>
-<ul>
-<li>Use multiple cards</li>
-<li>Use auto-daub feature</li>
-<li>Claim your bonuses and rewards</li>
-</ul>
-<p>We hope that this article has helped you learn more about playing bingo on laptop, and encouraged you to try it out for yourself. Bingo is a great way to have fun and win prizes, so why not give it a shot? You might just get lucky and shout "Bingo!"</p>
-<h2>FAQs</h2>
-<p>Here are some common questions that readers might have about playing bingo on laptop:</p>
-<ol>
-<li>Q: Is playing bingo on laptop safe?<br>A: Playing bingo on laptop is generally safe, as long as you play on reputable websites and games that have proper security measures in place. You should also protect your personal and financial information by using strong passwords, antivirus software, and secure payment methods.</li>
-<li>Q: Is playing bingo on laptop legal?<br>A: Playing bingo on laptop is legal in most countries, as long as it is not considered gambling or betting. You should check the laws and regulations of your country before playing online bingo games, especially if they involve real money or prizes. You should also play responsibly and avoid addiction or overspending.</li>
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diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cara Download Z Library di PC dan Android Langkah Demi Langkah.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cara Download Z Library di PC dan Android Langkah Demi Langkah.md
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- <h3>Cara Mendownload Buku dari Komputer</h3>
-<ol>
-<li>Klik judul buku yang ingin Anda download untuk melihat detailnya.</li>
-<li>Pilih format buku yang Anda inginkan dari daftar yang tersedia di sebelah kanan judul buku. Misalnya, PDF, EPUB, MOBI, DJVU, dan lainnya.</li>
-<li>Pilih bahasa buku yang Anda inginkan dari daftar yang tersedia di sebelah kanan format buku. Misalnya, Inggris, Indonesia, Jerman, Prancis, dan lainnya.</li>
-<li>Pilih cermin atau server yang ingin Anda gunakan untuk mendownload buku dari daftar yang tersedia di sebelah kanan bahasa buku. Misalnya, 1lib.us, b-ok.cc, b-ok.asia, dan lainnya.</li>
-<li>Klik tombol "Download" di sebelah kanan cermin atau server yang Anda pilih. Tunggu hingga proses download selesai.</li>
-<li>Buka file buku yang telah Anda download di komputer Anda dengan menggunakan aplikasi pembaca buku digital yang sesuai dengan formatnya. Misalnya, Adobe Reader untuk PDF, Calibre untuk EPUB atau MOBI, WinDJView untuk DJVU, dan lainnya.</li>
-</ol>
-<p>Berikut adalah contoh screenshot cara mendownload buku dari komputer:</p>
-<img src="https://i.imgur.com/9fX8x4u.png" alt="Screenshot of downloading a book from computer" width="600" height="400">
- <h3>Cara Mendownload Buku dari Ponsel</h3>
-<ol>
-<li>Buka situs Z Library di browser ponsel Anda.</li>
-<li>Ketuk ikon menu di pojok kiri atas situs untuk membuka menu navigasi.</li>
-<li>Ketuk opsi "Mobile version" untuk beralih ke versi situs yang dioptimalkan untuk ponsel.</li>
-<li>Ketikkan judul, pengarang, atau kata kunci yang berkaitan dengan buku yang Anda cari di kotak pencarian di bagian atas situs. Ketuk tombol "Search" untuk mencari buku.</li>
-<li>Ketuk judul buku yang ingin Anda download untuk melihat detailnya.</li>
-<li>Ketuk format buku yang Anda inginkan dari daftar yang tersedia di bawah judul buku. Misalnya, PDF, EPUB, MOBI, DJVU, dan lainnya.</li>
-<li>Ketuk bahasa buku yang Anda inginkan dari daftar yang tersedia di bawah format buku. Misalnya, Inggris, Indonesia, Jerman, Prancis, dan lainnya.</li>
-<li>Ketuk cermin atau server yang ingin Anda gunakan untuk mendownload buku dari daftar yang tersedia di bawah bahasa buku. Misalnya, 1lib.us, b-ok.cc, b-ok.asia, dan lainnya.</li>
-<li>Ketuk tombol "Download" di bawah cermin atau server yang Anda pilih. Tunggu hingga proses download selesai.</li>
-<li>Buka file buku yang telah Anda download di ponsel Anda dengan menggunakan aplikasi pembaca buku digital yang sesuai dengan formatnya. Misalnya, Google Play Books untuk PDF atau EPUB, Kindle untuk MOBI, EBookDroid untuk DJVU, dan lainnya.</li>
-</ol>
-<p>Berikut adalah contoh screenshot cara mendownload buku dari ponsel:</p>
-<img src="https://i.imgur.com/0cL9wXk.png" alt="Screenshot of downloading a book from mobile" width="300" height="500">
- <h2>Tips dan Trik untuk Menggunakan Z Library dengan Efektif</h2>
-<p>Selain cara download z library yang telah dijelaskan di atas, ada beberapa tips dan trik yang dapat Anda gunakan untuk meningkatkan pengalaman Anda dalam menggunakan situs ini. Berikut adalah beberapa di antaranya:</p>
-<ul>
-<li>Anda dapat membuat akun gratis di Z Library dengan mengklik menu "Sign in" di bagian atas situs. Dengan memiliki akun, Anda dapat menandai buku yang Anda sukai, mengunggah buku sendiri, mengatur preferensi situs, dan mengakses fitur lainnya.</li>
-<li>Anda dapat berdonasi ke Z Library dengan mengklik menu "Donate" di bagian atas situs. Dengan berdonasi, Anda dapat membantu proyek ini tetap hidup dan berkembang. Anda juga dapat mendapatkan akses ke beberapa fitur premium, seperti download tanpa captcha, download tanpa iklan, dan download tanpa batas waktu.</li>
-<li>Anda dapat mengunduh aplikasi Z Library untuk Android dengan mengklik menu "Mobile version" di bagian atas situs. Dengan aplikasi ini, Anda dapat mencari dan mendownload buku dari ponsel Anda dengan lebih mudah dan cepat.</li>
-<li>Anda dapat menghubungi tim Z Library dengan mengklik menu "Contact us" di bagian atas situs. Anda dapat memberikan saran, kritik, pertanyaan, atau laporan masalah yang Anda temui saat menggunakan situs ini.</li>
-<li>Anda dapat bergabung dengan komunitas Z Library dengan mengklik menu "Forum" di bagian atas situs. Anda dapat berdiskusi, berbagi, dan belajar dari pengguna lain yang juga tertarik dengan buku dan perpustakaan bayangan.</li>
-</ul>
- <h2>Kesimpulan</h2>
-<p>Z Library adalah salah satu proyek perpustakaan bayangan terbesar di dunia yang menyediakan akses file-sharing untuk jurnal ilmiah, teks akademik, dan buku umum. Anda dapat mendownload ribuan buku dari berbagai genre, bahasa, dan format dengan mudah dan cepat.</p>
-<p>Cara download z library sangat sederhana dan praktis. Anda hanya perlu mencari buku yang Anda inginkan, memilih format, bahasa, dan cermin yang sesuai dengan preferensi Anda, dan mengklik tombol download. Anda juga dapat menggunakan beberapa tips dan trik untuk menggunakan situs ini dengan lebih efektif.</p>
-<p>Jika Anda seorang pecinta buku yang ingin membaca lebih banyak buku tanpa harus mengeluarkan banyak uang, maka Z Library adalah situs yang tepat untuk Anda. Jangan ragu untuk mencobanya dan menikmati berbagai buku yang tersedia di sana. Selamat membaca!</p>
- <h2>FAQ</h2>
-<ol>
-<li>Apa itu perpustakaan bayangan?</li>
-<p>Perpustakaan bayangan adalah istilah yang digunakan untuk menyebut proyek-proyek perpustakaan digital yang menyediakan akses file-sharing ke jurnal ilmiah, teks akademik, dan buku umum yang biasanya tidak tersedia secara bebas atau legal bagi publik.</p>
- <li>Apa bedanya Z Library dengan Library Genesis?</li>
-<p>Z Library adalah salah satu cermin dari Library Genesis, yaitu proyek perpustakaan bayangan yang menyediakan lebih dari 2,5 juta buku dan 58 juta artikel. Z Library memiliki beberapa keunggulan dibandingkan dengan Library Genesis, seperti antarmuka yang lebih ramah pengguna, fitur pencarian yang lebih canggih, dan kecepatan download yang lebih tinggi.</p>
- <li>Apakah Z Library legal dan aman?</li>
-<p>Z Library tidak sepenuhnya legal dan aman, karena sebagian besar buku yang disediakannya melanggar hak cipta dan lisensi dari penerbit atau pengarang. Oleh karena itu, Anda harus berhati-hati saat menggunakan situs ini dan menghindari mendownload buku yang melanggar hukum di negara Anda. Anda juga harus menggunakan VPN atau proxy untuk melindungi privasi dan keamanan Anda saat mengakses situs ini.</p>
- <li>Apakah Z Library memiliki batasan download?</li>
-<p>Z Library tidak memiliki batasan download secara teknis, tetapi ada beberapa hal yang dapat mempengaruhi jumlah buku yang dapat Anda download. Pertama, Anda harus memasukkan captcha setiap kali Anda ingin mendownload buku, yang dapat mengganggu proses download Anda. Kedua, Anda harus menunggu beberapa detik sebelum Anda dapat mendownload buku lainnya, yang dapat memperlambat kecepatan download Anda. Ketiga, Anda harus memastikan bahwa cermin atau server yang Anda pilih tidak sibuk atau bermasalah, yang dapat mempengaruhi ketersediaan dan kualitas buku yang dapat Anda download.</p>
- <li>Bagaimana cara mengatasi masalah saat mendownload buku dari Z Library?</li>
-<p>Jika Anda mengalami masalah saat mendownload buku dari Z Library, seperti file buku yang rusak, tidak lengkap, atau tidak sesuai dengan deskripsi, Anda dapat mencoba beberapa solusi berikut:</p>
-<ul>
-<li>Coba download buku dari cermin atau server lain yang tersedia.</li>
-<li>Coba download buku dengan format atau bahasa lain yang tersedia.</li>
-<li>Coba download buku dengan menggunakan browser atau perangkat lain yang berbeda.</li>
-<li>Coba download buku dengan menggunakan aplikasi Z Library untuk Android.</li>
-<li>Coba hubungi tim Z Library dengan mengklik menu "Contact us" di bagian atas situs.</li>
-</ul>
- <li>Bagaimana cara mendukung proyek Z Library?</li>
-<p>Anda dapat mendukung proyek Z Library dengan beberapa cara, seperti:</p>
-<ul>
-<li>Berdonasi ke Z Library dengan mengklik menu "Donate" di bagian atas situs.</li>
-<li>Mengunggah buku sendiri ke Z Library dengan mengklik menu "Upload" di bagian atas situs.</li>
-<li>Membagikan situs Z Library ke teman-teman atau keluarga Anda yang juga tertarik dengan buku dan perpustakaan bayangan.</li>
-<li>Mengikuti media sosial Z Library di Facebook, Twitter, atau Telegram untuk mendapatkan informasi terbaru tentang proyek ini.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download MW and Enjoy the Most Realistic Campaign Ever.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download MW and Enjoy the Most Realistic Campaign Ever.md
deleted file mode 100644
index 68d8972f7f5ebfd838ce49b4488a0ce408e1f92f..0000000000000000000000000000000000000000
--- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download MW and Enjoy the Most Realistic Campaign Ever.md	
+++ /dev/null
@@ -1,104 +0,0 @@
-<br />
-<h1>How to Download MW: A Guide for Gamers</h1> | <p>If you are a fan of first-person shooter games, you may have heard of Call of Duty: Modern Warfare, or MW for short. MW is one of the most popular and critically acclaimed games in the Call of Duty series, offering a realistic and immersive experience that will keep you on the edge of your seat. Whether you want to play solo or with friends, online or offline, MW has something for everyone. But how do you download MW on your PC or console? In this article, we will show you how to download MW and enjoy its amazing features.</p>
-<h2>download mw</h2><br /><p><b><b>Download</b> &#10022; <a href="https://gohhs.com/2uPtN9">https://gohhs.com/2uPtN9</a></b></p><br /><br /> | | <h2>What is MW?</h2> | <p>MW is a 2019 first-person shooter game developed by Infinity Ward and published by Activision. It is a reboot of the Modern Warfare sub-series, which started with Call of Duty 4: Modern Warfare in 2007. The game takes place in a realistic and modern setting, where you can play as a CIA officer, a British SAS commando, or a rebel fighter from a fictional Middle Eastern country. The game has three main modes: Campaign, Multiplayer, and Spec Ops.</p> | | <h3>MW Campaign</h3> | <p>The campaign mode is a single-player mode that follows a gripping story that spans multiple locations around the world. You will face various enemies and moral dilemmas as you try to stop a terrorist group from using chemical weapons. The campaign mode is praised for its cinematic presentation, intense action, and provocative themes. However, some critics have also pointed out its controversial depiction of certain events and characters.</p> | | <h3>MW Multiplayer</h3> | <p>The multiplayer mode is an online mode that allows you to compete with other players in various modes and maps. You can customize your loadout, weapons, perks, killstreaks, and more to suit your playstyle. You <p>You can also play with your friends in a cross-play feature that lets you join players from different platforms. The multiplayer mode has four sub-modes: Ground War, Realism, Gunfight, and Warzone.</p>
- <h4>Ground War</h4>
-<p>Ground War is a large-scale mode that supports up to 64 players in two teams. You can use vehicles, such as tanks, helicopters, and ATVs, to traverse the map and engage the enemy. The objective is to capture and hold flags or eliminate the enemy team. Ground War is similar to the Conquest mode from the Battlefield series.</p>
- <h4>Realism</h4>
-<p>Realism is a mode that removes the heads-up display (HUD) and increases the damage of weapons. You will have to rely on your senses and skills to survive and communicate with your team. Realism is similar to the Hardcore mode from previous Call of Duty games, but with some differences, such as no friendly fire and no killcams.</p>
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- <h4>Gunfight</h4>
-<p>Gunfight is a mode that pits two teams of two players in small maps. You will have a random loadout that changes every two rounds. The first team to win six rounds wins the match. Gunfight is a fast-paced and tactical mode that requires coordination and quick reflexes.</p>
- <h4>Warzone</h4>
-<p>Warzone is a free-to-play battle royale mode that supports up to 150 players in a massive map. You can play solo, duo, trio, or quad, and loot weapons, armor, and items from buildings and crates. You will have to avoid the gas circle that shrinks over time and eliminate other players or teams. You can also buy or find contracts that give you objectives and rewards, such as money, loot, or intel. Warzone also has a unique feature called the Gulag, where you can fight for a chance to respawn if you die for the first time.</p>
- <h3>MW Spec Ops</h3>
-<p>The Spec Ops mode is a cooperative mode that allows you to team up with up to three other players and complete various missions. You can choose from four operations that have multiple objectives and challenges, or four classic missions that are based on previous Call of Duty games. The Spec Ops mode is connected to the campaign mode and expands on its story and characters.</p>
- <h2>How to Download MW on PC</h2>
-<p>If you want to download MW on your PC, you will need to meet the system requirements and choose a download option. Here are the steps you need to follow:</p>
- <h3>System Requirements</h3>
-<p>Before you download MW on your PC, you should check if your PC can run the game smoothly. Here are the minimum and recommended specifications for PC:</p>
- <table>
-<tr><th>Minimum</th><th>Recommended</th></tr>
-<tr><td>OS: Windows 7 64-bit or Windows 10 64-bit</td><td>OS: Windows 10 64-bit</td></tr>
-<tr><td>CPU: Intel Core i3-4340 or AMD FX-6300</td><td>CPU: Intel Core i5-2500K or AMD Ryzen R5 1600X</td></tr>
-<tr><td>RAM: 8 GB</td><td>RAM: 12 GB</td></tr>
-<tr><td>GPU: NVIDIA GeForce GTX 670 or GTX 1650 or AMD Radeon HD 7950</td><td>GPU: NVIDIA GeForce GTX 970 or GTX 1660 or AMD Radeon R9 390 or RX 580</td></tr>
-<tr><td>HDD: 175 GB available space</td><td>HDD: 175 GB available space</td></tr>
-<tr><td>DirectX: Version 12</td><td>DirectX: Version 12</td></tr>
-<tr><td>Network: Broadband Internet connection</td><td>Network: Broadband Internet connection</td></tr>
-<tr><td>Sound Card: DirectX Compatible</td><td>Sound Card: DirectX Compatible</td></tr>
-</table>
- <p>If your PC meets or exceeds these specifications, you can proceed to download MW on your PC.</p>
- <h3>Download Options</h3>
-<p>You have several options to download MW on your PC. You can use the official platform, Battle.net, or an alternative platform, Steam. You can also try to download MW from other sources, but we do not recommend this option for various reasons.</p>
- <h4>Battle.net</h4>
-<p>Battle.net is the official platform for downloading and playing MW on PC. It is owned by Activision Blizzard, the parent company of Activision. To download MW on Battle.net, you will need to create an account and install the <p>Battle.net app on your PC. Then, you will need to buy MW from the Battle.net shop or redeem a code if you have one. After that, you can download and install MW on your PC and launch it from the Battle.net app. You will also need to link your Battle.net account with your Activision account to play MW online.</p>
- <p>Battle.net is the best option to download MW on PC because it is the most secure, reliable, and updated platform. You will also get access to exclusive features, such as cross-play, cross-progression, and in-game rewards. However, Battle.net also has some drawbacks, such as high prices, large file sizes, and limited customer support.</p>
- <h4>Steam</h4>
-<p>Steam is an alternative platform for downloading and playing MW on PC. It is owned by Valve Corporation, a video game developer and distributor. To download MW on Steam, you will need to create an account and install the Steam app on your PC. Then, you will need to buy MW from the Steam store or redeem a code if you have one. After that, you can download and install MW on your PC and launch it from the Steam app.</p>
- <p>Steam is a popular option to download MW on PC because it has many advantages, such as lower prices, frequent sales, user reviews, achievements, cloud saves, and community features. However, Steam also has some disadvantages, such as compatibility issues, performance problems, and delayed updates. You will also need to link your Steam account with your Activision account to play MW online.</p>
- <h4>Other Sources</h4>
-<p>You can also try to download MW from other sources, such as torrent sites, file-sharing platforms, or third-party sellers. However, we do not recommend this option for various reasons. First of all, downloading MW from other sources is illegal and unethical, as it violates the intellectual property rights of the developers and publishers. Second of all, downloading MW from other sources is risky and dangerous, as it may expose your PC to viruses, malware, or hackers. Third of all, downloading MW from other sources is unreliable and unsatisfactory, as it may result in incomplete files, corrupted data, or poor quality. Finally, downloading MW from other sources is unfair and disrespectful, as it harms the gaming industry and the gaming community.</p>
- <p>Therefore, we strongly advise you to avoid downloading MW from other sources and stick to the official or alternative platforms.</p>
- <h2>How to Download MW on Console</h2>
-<p>If you want to download MW on your console, you will need to follow a similar process as on PC. However, there are some differences depending on whether you have a PS4 or an Xbox One. Here are the steps you need to follow:</p>
- <h3>PS4</h3>
-<p>To download MW on your PS4, you will need to create a PlayStation Network (PSN) account and connect your PS4 to the internet. Then, you will need to buy MW from the PlayStation Store or insert a disc if you have one. After that, <p>you can download and install MW on your PS4 and launch it from the home screen. You will also need to link your PSN account with your Activision account to play MW online.</p>
- <p>Downloading MW on your PS4 is a convenient and easy option because it is the official platform for the game. You will also get access to exclusive features, such as early access, bonus content, and PlayStation Plus benefits. However, downloading MW on your PS4 also has some drawbacks, such as high prices, large file sizes, and limited customer support.</p>
- <h3>Xbox One</h3>
-<p>To download MW on your Xbox One, you will need to create an Xbox Live account and connect your Xbox One to the internet. Then, you will need to buy MW from the Microsoft Store or insert a disc if you have one. After that, you can download and install MW on your Xbox One and launch it from the home screen. You will also need to link your Xbox Live account with your Activision account to play MW online.</p>
- <p>Downloading MW on your Xbox One is a similar option to the PS4, but with some differences. You will also get access to exclusive features, such as cross-play, cross-progression, and Xbox Live Gold benefits. However, downloading MW on your Xbox One also has some drawbacks, such as compatibility issues, performance problems, and delayed updates.</p>
- <h2>Conclusion</h2>
-<p>Downloading MW is a simple and straightforward process that depends on your platform of choice. Whether you have a PC, a PS4, or an Xbox One, you can enjoy the game and its features by following the steps we have outlined in this article. However, you should also be aware of the pros and cons of each option and choose the one that suits you best. We hope this guide has helped you learn how to download MW and have fun playing it.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about downloading MW:</p>
- <h3>How much does MW cost?</h3>
-<p>The price of MW varies depending on the platform and the edition. The standard edition costs $59.99 on PC, PS4, and Xbox One. The operator edition costs $79.99 on PC and $89.99 on PS4 and Xbox One. The operator enhanced edition costs $99.99 on PC and $109.99 on PS4 and Xbox One.</p>
- <h3>How long does it take to download MW?</h3>
-<p>The download time of MW depends on your internet speed and the file size of the game. The file size of MW is about 175 GB on PC, 110 GB on PS4, and 137 GB on Xbox One. The download time can range from a few hours to a few days depending on your connection.</p>
- <h3>Can I play MW offline?</h3>
-<p>You can play MW offline only in the campaign mode and some spec ops missions. You will need an internet connection to play the multiplayer mode and most spec ops missions.</p>
- <h3>Can I play MW with friends?</h3>
-<p>You can play MW with friends in all modes except for the campaign mode. You can invite your friends to join your party or lobby and play together online. You can also use the cross-play feature to play with friends who have different platforms.</p>
- <h3>Can I download MW for free?</h3>
-<p>You cannot download MW for free unless you have a code or a gift from someone who bought the game. However, you can download Warzone for free from Battle.net, PlayStation Store, or Microsoft Store. Warzone is a standalone mode that does not require MW to play.</p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/flax-community/SentenceSimplifier/README.md b/spaces/flax-community/SentenceSimplifier/README.md
deleted file mode 100644
index 2c9859a7864978e34f17397cb39443a3d605dcca..0000000000000000000000000000000000000000
--- a/spaces/flax-community/SentenceSimplifier/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Sentence Simplifier
-emoji: 🧠
-colorFrom: blue
-colorTo: pink
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/flax-community/alberti/app.py b/spaces/flax-community/alberti/app.py
deleted file mode 100644
index 645d2cd4eeac3cff5c7d4ffd99bb0615905e33d7..0000000000000000000000000000000000000000
--- a/spaces/flax-community/alberti/app.py
+++ /dev/null
@@ -1,202 +0,0 @@
-import random
-import re
-from poems import SAMPLE_POEMS
-
-import langid
-import numpy as np
-import streamlit as st
-import torch
-
-from icu_tokenizer import Tokenizer
-from transformers import pipeline
-
-MODELS = {
-    "ALBERTI": "flax-community/alberti-bert-base-multilingual-cased",
-    "mBERT": "bert-base-multilingual-cased"
-}
-
-TOPK = 50
-st.set_page_config(layout="wide")
-
-
-def mask_line(line, language="es", restrictive=True):
-    tokenizer = Tokenizer(lang=language)
-    token_list = tokenizer.tokenize(line)
-    if lang != "zh":
-        restrictive = not all([len(token) <= 3 for token in token_list])
-    random_num = random.randint(0, len(token_list) - 1)
-    random_word = token_list[random_num]
-    if not restrictive:
-        token_list[random_num] = "[MASK]"
-        masked_l = " ".join(token_list)
-        return masked_l
-    elif len(random_word) > 3 or (lang == "zh" and random_word.isalpha()):
-        token_list[random_num] = "[MASK]"
-        masked_l = " ".join(token_list)
-        return masked_l
-    else:
-        return mask_line(line, language)
-
-
-def filter_candidates(candidates, get_any_candidate=False):
-    cand_list = []
-    score_list = []
-    for candidate in candidates:
-        if not get_any_candidate and candidate["token_str"][:2] != "##" and candidate["token_str"].isalpha():
-            cand = candidate["sequence"]
-            score = candidate["score"]
-            cand_list.append(cand)
-            score_list.append('{0:.5f}'.format(score))
-        elif get_any_candidate:
-            cand = candidate["sequence"]
-            score = candidate["score"]
-            cand_list.append(cand)
-            score_list.append('{0:.5f}'.format(score))
-        if len(score_list) == TOPK:
-            break
-    if len(cand_list) < 1:
-        return filter_candidates(candidates, get_any_candidate=True)
-    else:
-        return cand_list[0]
-
-
-def infer_candidates(nlp, line):
-    line = re.sub("–", "-", line)
-    line = re.sub("—", "-", line)
-    line = re.sub("’", "'", line)
-    line = re.sub("…", "...", line)
-    inputs = nlp._parse_and_tokenize(line)
-    outputs = nlp._forward(inputs, return_tensors=True)
-    input_ids = inputs["input_ids"][0]
-    masked_index = torch.nonzero(input_ids == nlp.tokenizer.mask_token_id,
-                                 as_tuple=False)
-    logits = outputs[0, masked_index.item(), :]
-    probs = logits.softmax(dim=0)
-    values, predictions = probs.topk(TOPK)
-    result = []
-    for v, p in zip(values.tolist(), predictions.tolist()):
-        tokens = input_ids.numpy()
-        tokens[masked_index] = p
-        # Filter padding out:
-        tokens = tokens[np.where(tokens != nlp.tokenizer.pad_token_id)]
-        l = []
-        token_list = [nlp.tokenizer.decode([token], skip_special_tokens=True) for token in tokens]
-        for idx, token in enumerate(token_list):
-            if token.startswith('##'):
-                l[-1] += token[2:]
-            elif idx == masked_index.item():
-                l += ['<b style="color: #ff0000;">', token, "</b>"]
-            else:
-                l += [token]
-        sequence = " ".join(l).strip()
-        result.append(
-            {
-                "sequence": sequence,
-                "score": v,
-                "token": p,
-                "token_str": nlp.tokenizer.decode(p),
-                "masked_index": masked_index.item()
-            }
-        )
-    return result
-
-
-def rewrite_poem(poem, ml_model=MODELS["ALBERTI"], masking=True, language="es"):
-    nlp = pipeline("fill-mask", model=ml_model)
-    unmasked_lines = []
-    masked_lines = []
-    for line in poem:
-        if line == "":
-            unmasked_lines.append("")
-            masked_lines.append("")
-            continue
-        if masking:
-            masked_line = mask_line(line, language)
-        else:
-            masked_line = line
-        masked_lines.append(masked_line)
-        unmasked_line_candidates = infer_candidates(nlp, masked_line)
-        unmasked_line = filter_candidates(unmasked_line_candidates)
-        unmasked_lines.append(unmasked_line)
-        unmasked_poem = "<br>".join(unmasked_lines)
-    return unmasked_poem, masked_lines
-
-
-instructions_text_0 = st.sidebar.markdown(
-    """# ALBERTI vs BERT 🥊
-
-We present ALBERTI, our BERT-based multilingual model for poetry.""")
-
-instructions_text_1 = st.sidebar.markdown(
-    """We have trained bert on a huge (for poetry, that is) corpus of
-multilingual poetry to try to get a more 'poetic' model. This is the result
-of our work.
-
-You can find more information on the [project's site](https://huggingface.co/flax-community/alberti-bert-base-multilingual-cased)""")
-
-sample_chooser = st.sidebar.selectbox(
-    "Choose a poem",
-    list(SAMPLE_POEMS.keys())
-)
-
-instructions_text_2 = st.sidebar.markdown("""# How to use
-
-You can choose from a list of example poems in Spanish, English, French, German,
-Chinese and Arabic, but you can also paste a poem, or write it yourself!
-
-Then click on 'Rewrite!' to do the masking and the fill-mask task on the chosen
-poem, randomly masking one word per verse, and get the two new versions for each of the models.
-
-The list of languages used on the training of ALBERTI are:
-
-* Arabic
-* Chinese
-* Czech
-* English
-* Finnish
-* French
-* German
-* Hungarian
-* Italian
-* Portuguese
-* Russian
-* Spanish""")
-
-col1, col2, col3 = st.columns(3)
-
-st.markdown(
-    """
-    <style>
-        label {
-        font-size: 1rem !important;
-        font-weight: bold !important;
-        }
-        .block-container {
-        padding-left: 1rem !important;
-        padding-right: 1rem !important;
-        }
-    </style>
-    """, unsafe_allow_html=True)
-
-if sample_chooser:
-    model_list = set(MODELS.values())
-    user_input = col1.text_area("Input poem",
-                                "\n".join(SAMPLE_POEMS[sample_chooser]),
-                                height=600)
-    poem = user_input.split("\n")
-    rewrite_button = col1.button("Rewrite!")
-    if "[MASK]" in user_input or "<mask>" in user_input:
-        col1.error("You don't have to mask the poem, we'll do it for you!")
-
-if rewrite_button:
-    lang = langid.classify(user_input)[0]
-    unmasked_poem, masked_poem = rewrite_poem(poem, language=lang)
-    user_input_2 = col2.write(f"""<b>Output poem from ALBERTI</b>
-
-
-{unmasked_poem}""", unsafe_allow_html=True)
-    unmasked_poem_2, _ = rewrite_poem(masked_poem, ml_model=MODELS["mBERT"],
-                                      masking=False)
-    user_input_3 = col3.write(f"""<b>Output poem from mBERT</b>
-
-{unmasked_poem_2}""", unsafe_allow_html=True)
diff --git a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/case_studies_envs/formatscasestudyenvs.py b/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/case_studies_envs/formatscasestudyenvs.py
deleted file mode 100644
index 252aa334fe00d702783b45505f812b7b069800e4..0000000000000000000000000000000000000000
--- a/spaces/flowers-team/SocialAISchool/gym-minigrid/gym_minigrid/social_ai_envs/case_studies_envs/formatscasestudyenvs.py
+++ /dev/null
@@ -1,253 +0,0 @@
-from gym_minigrid.social_ai_envs.socialaiparamenv import SocialAIParamEnv
-from gym_minigrid.parametric_env import *
-from gym_minigrid.register import register
-
-'''
-These are the environments for the formats case study: 
-
-All the environments use the Language Feedback cue type.
-
-There are four Intro sequences:
-- no (N)
-- eye contact (E)
-- ask (A)
-- ask during eye contact (AE)
-
-The Training environments all have the 6 problems.
-There will be four registered training corresponding to the 4 different introductory sequences.
-
-The Testing environments are always one problem per env - i.e no testing on two problems at the same time
-registered testing envs : problems x {N, A, E, AE}
-'''
-
-PROBLEMS = ["Boxes", "Switches", "Generators", "Levers", "Doors", "Marble"]
-CUES = ["LangFeedback"]
-INTRO_SEC = ["N", "E", "A", "AE"]
-
-# training
-class TrainingFormatsCSParamEnv(SocialAIParamEnv):
-
-    def __init__(self, cue, intro_sec, scaffolding=False, **kwargs):
-
-        if cue not in CUES:
-            raise ValueError(f"Cue {cue} undefined.")
-        self.cue = cue
-
-        if intro_sec not in INTRO_SEC:
-            raise ValueError(f"Cue {intro_sec} undefined.")
-        self.intro_sec = intro_sec
-
-        self.scaffolding = scaffolding
-
-        super(TrainingFormatsCSParamEnv, self).__init__(**kwargs)
-
-    def construct_tree(self):
-        tree = ParameterTree()
-
-        env_type_nd = tree.add_node("Env_type", type="param")
-
-        # Information seeking
-        inf_seeking_nd = tree.add_node("Information_seeking", parent=env_type_nd, type="value")
-
-        prag_fr_compl_nd = tree.add_node("Pragmatic_frame_complexity", parent=inf_seeking_nd, type="param")
-
-        if self.scaffolding:
-            tree.add_node("No", parent=prag_fr_compl_nd, type="value")
-            tree.add_node("Eye_contact", parent=prag_fr_compl_nd, type="value")
-            tree.add_node("Ask", parent=prag_fr_compl_nd, type="value")
-            tree.add_node("Ask_Eye_contact", parent=prag_fr_compl_nd, type="value")
-
-        else:
-            if self.intro_sec == "N":
-                tree.add_node("No", parent=prag_fr_compl_nd, type="value")
-            elif self.intro_sec == "E":
-                tree.add_node("Eye_contact", parent=prag_fr_compl_nd, type="value")
-            elif self.intro_sec == "A":
-                tree.add_node("Ask", parent=prag_fr_compl_nd, type="value")
-            elif self.intro_sec == "AE":
-                tree.add_node("Ask_Eye_contact", parent=prag_fr_compl_nd, type="value")
-
-        # scaffolding
-        scaffolding_nd = tree.add_node("Scaffolding", parent=inf_seeking_nd, type="param")
-        scaffolding_N_nd = tree.add_node("N", parent=scaffolding_nd, type="value")
-        if self.scaffolding:
-            scaffolding_Y_nd = tree.add_node("Y", parent=scaffolding_nd, type="value")
-
-        cue_type_nd = tree.add_node("Cue_type", parent=scaffolding_N_nd, type="param")
-
-        if self.cue == "Pointing":
-            tree.add_node("Pointing", parent=cue_type_nd, type="value")
-        elif self.cue == "LangColor":
-            tree.add_node("Language_Color", parent=cue_type_nd, type="value")
-        elif self.cue == "LangFeedback":
-            tree.add_node("Language_Feedback", parent=cue_type_nd, type="value")
-
-        problem_nd = tree.add_node("Problem", parent=inf_seeking_nd, type="param")
-
-        boxes_nd = tree.add_node("Boxes", parent=problem_nd, type="value")
-        version_nd = tree.add_node("N", parent=boxes_nd, type="param")
-        tree.add_node("2", parent=version_nd, type="value")
-        peer_nd = tree.add_node("Peer", parent=boxes_nd, type="param")
-        tree.add_node("Y", parent=peer_nd, type="value")
-
-        switches_nd = tree.add_node("Switches", parent=problem_nd, type="value")
-        version_nd = tree.add_node("N", parent=switches_nd, type="param")
-        tree.add_node("2", parent=version_nd, type="value")
-        peer_nd = tree.add_node("Peer", parent=switches_nd, type="param")
-        tree.add_node("Y", parent=peer_nd, type="value")
-
-        generators_nd = tree.add_node("Generators", parent=problem_nd, type="value")
-        version_nd = tree.add_node("N", parent=generators_nd, type="param")
-        tree.add_node("2", parent=version_nd, type="value")
-        peer_nd = tree.add_node("Peer", parent=generators_nd, type="param")
-        tree.add_node("Y", parent=peer_nd, type="value")
-
-        levers_nd = tree.add_node("Levers", parent=problem_nd, type="value")
-        version_nd = tree.add_node("N", parent=levers_nd, type="param")
-        tree.add_node("2", parent=version_nd, type="value")
-        peer_nd = tree.add_node("Peer", parent=levers_nd, type="param")
-        tree.add_node("Y", parent=peer_nd, type="value")
-
-        doors_nd = tree.add_node("Doors", parent=problem_nd, type="value")
-        version_nd = tree.add_node("N", parent=doors_nd, type="param")
-        tree.add_node("2", parent=version_nd, type="value")
-        peer_nd = tree.add_node("Peer", parent=doors_nd, type="param")
-        tree.add_node("Y", parent=peer_nd, type="value")
-
-        marble_nd = tree.add_node("Marble", parent=problem_nd, type="value")
-        version_nd = tree.add_node("N", parent=marble_nd, type="param")
-        tree.add_node("2", parent=version_nd, type="value")
-        peer_nd = tree.add_node("Peer", parent=marble_nd, type="param")
-        tree.add_node("Y", parent=peer_nd, type="value")
-
-        return tree
-
-# testing
-class TestingFormatsCSParamEnv(SocialAIParamEnv):
-
-    def __init__(self, cue, intro_sec, problem, **kwargs):
-
-        if cue not in CUES:
-            raise ValueError(f"Cue {cue} undefined.")
-        self.cue = cue
-
-        if intro_sec not in INTRO_SEC:
-            raise ValueError(f"Cue {intro_sec} undefined.")
-        self.intro_sec = intro_sec
-
-        self.problem = problem
-        if self.problem not in PROBLEMS:
-            raise ValueError(f"Problem {self.problem} undefined.")
-
-        super(TestingFormatsCSParamEnv, self).__init__(**kwargs)
-
-    def construct_tree(self):
-        tree = ParameterTree()
-
-        env_type_nd = tree.add_node("Env_type", type="param")
-
-        # Information seeking
-        inf_seeking_nd = tree.add_node("Information_seeking", parent=env_type_nd, type="value")
-
-        prag_fr_compl_nd = tree.add_node("Pragmatic_frame_complexity", parent=inf_seeking_nd, type="param")
-
-        if self.intro_sec == "N":
-            tree.add_node("No", parent=prag_fr_compl_nd, type="value")
-        elif self.intro_sec == "E":
-            tree.add_node("Eye_contact", parent=prag_fr_compl_nd, type="value")
-        elif self.intro_sec == "A":
-            tree.add_node("Ask", parent=prag_fr_compl_nd, type="value")
-        elif self.intro_sec == "AE":
-            tree.add_node("Ask_Eye_contact", parent=prag_fr_compl_nd, type="value")
-
-        # scaffolding
-        scaffolding_nd = tree.add_node("Scaffolding", parent=inf_seeking_nd, type="param")
-        scaffolding_N_nd = tree.add_node("N", parent=scaffolding_nd, type="value")
-
-        cue_type_nd = tree.add_node("Cue_type", parent=scaffolding_N_nd, type="param")
-        if self.cue == "Pointing":
-            tree.add_node("Pointing", parent=cue_type_nd, type="value")
-        elif self.cue == "LangColor":
-            tree.add_node("Language_Color", parent=cue_type_nd, type="value")
-        elif self.cue == "LangFeedback":
-            tree.add_node("Language_Feedback", parent=cue_type_nd, type="value")
-
-        problem_nd = tree.add_node("Problem", parent=inf_seeking_nd, type="param")
-
-        boxes_nd = tree.add_node(self.problem, parent=problem_nd, type="value")
-
-        version_nd = tree.add_node("N", parent=boxes_nd, type="param")
-        tree.add_node("2", parent=version_nd, type="value")
-
-        peer_nd = tree.add_node("Peer", parent=boxes_nd, type="param")
-        tree.add_node("Y", parent=peer_nd, type="value")
-
-        return tree
-
-
-
-# register training envs
-for cue in CUES:
-    for intro_sec in INTRO_SEC:
-        env_name = f'{intro_sec}{cue}TrainFormatsCSParamEnv'
-
-        assert cue == "LangFeedback"
-
-        register(
-            id='SocialAI-{}-v1'.format(env_name),
-            entry_point='gym_minigrid.social_ai_envs:TrainingFormatsCSParamEnv',
-            kwargs={
-                'intro_sec': intro_sec,
-                'cue': cue,
-            }
-        )
-
-for intro_sec in INTRO_SEC:
-    # scaffolding train env
-    for cue in CUES:
-        # intro_sec = "AE"
-        env_name = f'{intro_sec}{cue}TrainScaffoldingCSParamEnv'
-
-        assert cue == "LangFeedback"
-
-        register(
-            id='SocialAI-{}-v1'.format(env_name),
-            entry_point='gym_minigrid.social_ai_envs:TrainingFormatsCSParamEnv',
-            kwargs={
-                'intro_sec': intro_sec,
-                'cue': cue,
-                'scaffolding': True
-            }
-        )
-
-
-# register testing envs : cues x problems x {social, asocial} x {joint attention, no}
-for cue in CUES:
-    for problem in PROBLEMS:
-        for intro_sec in INTRO_SEC:
-            env_name = f'{intro_sec}{cue}{problem}TestFormatsCSParamEnv'
-
-            assert cue == "LangFeedback"
-
-            register(
-                id='SocialAI-{}-v1'.format(env_name),
-                entry_point='gym_minigrid.social_ai_envs:TestingFormatsCSParamEnv',
-                kwargs={
-                    'problem': problem,
-                    'cue': cue,
-                    'intro_sec': intro_sec
-                }
-            )
-
-N_formats_test_set = [
-    f"SocialAI-NLangFeedback{problem}TestFormatsCSParamEnv-v1" for problem in PROBLEMS
-]
-E_formats_test_set = [
-    f"SocialAI-ELangFeedback{problem}TestFormatsCSParamEnv-v1" for problem in PROBLEMS
-]
-A_formats_test_set = [
-    f"SocialAI-ALangFeedback{problem}TestFormatsCSParamEnv-v1" for problem in PROBLEMS
-]
-AE_formats_test_set = [
-    f"SocialAI-AELangFeedback{problem}TestFormatsCSParamEnv-v1" for problem in PROBLEMS
-]
diff --git a/spaces/freddyaboulton/gradio_foliumtest/src/backend/gradio_foliumtest/templates/component/index.js b/spaces/freddyaboulton/gradio_foliumtest/src/backend/gradio_foliumtest/templates/component/index.js
deleted file mode 100644
index ed8de8986f512760e40df5b8faa5f7ca80d1815c..0000000000000000000000000000000000000000
--- a/spaces/freddyaboulton/gradio_foliumtest/src/backend/gradio_foliumtest/templates/component/index.js
+++ /dev/null
@@ -1,2377 +0,0 @@
-const {
-  SvelteComponent: pt,
-  assign: kt,
-  create_slot: yt,
-  detach: vt,
-  element: Ct,
-  get_all_dirty_from_scope: qt,
-  get_slot_changes: Ft,
-  get_spread_update: Lt,
-  init: Mt,
-  insert: St,
-  safe_not_equal: zt,
-  set_dynamic_element_data: ze,
-  set_style: S,
-  toggle_class: E,
-  transition_in: st,
-  transition_out: ot,
-  update_slot_base: Vt
-} = window.__gradio__svelte__internal;
-function Nt(n) {
-  let e, t, l;
-  const i = (
-    /*#slots*/
-    n[17].default
-  ), f = yt(
-    i,
-    n,
-    /*$$scope*/
-    n[16],
-    null
-  );
-  let s = [
-    { "data-testid": (
-      /*test_id*/
-      n[7]
-    ) },
-    { id: (
-      /*elem_id*/
-      n[2]
-    ) },
-    {
-      class: t = "block " + /*elem_classes*/
-      n[3].join(" ") + " svelte-1t38q2d"
-    }
-  ], o = {};
-  for (let r = 0; r < s.length; r += 1)
-    o = kt(o, s[r]);
-  return {
-    c() {
-      e = Ct(
-        /*tag*/
-        n[14]
-      ), f && f.c(), ze(
-        /*tag*/
-        n[14]
-      )(e, o), E(
-        e,
-        "hidden",
-        /*visible*/
-        n[10] === !1
-      ), E(
-        e,
-        "padded",
-        /*padding*/
-        n[6]
-      ), E(
-        e,
-        "border_focus",
-        /*border_mode*/
-        n[5] === "focus"
-      ), E(e, "hide-container", !/*explicit_call*/
-      n[8] && !/*container*/
-      n[9]), S(e, "height", typeof /*height*/
-      n[0] == "number" ? (
-        /*height*/
-        n[0] + "px"
-      ) : void 0), S(e, "width", typeof /*width*/
-      n[1] == "number" ? `calc(min(${/*width*/
-      n[1]}px, 100%))` : void 0), S(
-        e,
-        "border-style",
-        /*variant*/
-        n[4]
-      ), S(
-        e,
-        "overflow",
-        /*allow_overflow*/
-        n[11] ? "visible" : "hidden"
-      ), S(
-        e,
-        "flex-grow",
-        /*scale*/
-        n[12]
-      ), S(e, "min-width", `calc(min(${/*min_width*/
-      n[13]}px, 100%))`), S(e, "border-width", "var(--block-border-width)");
-    },
-    m(r, a) {
-      St(r, e, a), f && f.m(e, null), l = !0;
-    },
-    p(r, a) {
-      f && f.p && (!l || a & /*$$scope*/
-      65536) && Vt(
-        f,
-        i,
-        r,
-        /*$$scope*/
-        r[16],
-        l ? Ft(
-          i,
-          /*$$scope*/
-          r[16],
-          a,
-          null
-        ) : qt(
-          /*$$scope*/
-          r[16]
-        ),
-        null
-      ), ze(
-        /*tag*/
-        r[14]
-      )(e, o = Lt(s, [
-        (!l || a & /*test_id*/
-        128) && { "data-testid": (
-          /*test_id*/
-          r[7]
-        ) },
-        (!l || a & /*elem_id*/
-        4) && { id: (
-          /*elem_id*/
-          r[2]
-        ) },
-        (!l || a & /*elem_classes*/
-        8 && t !== (t = "block " + /*elem_classes*/
-        r[3].join(" ") + " svelte-1t38q2d")) && { class: t }
-      ])), E(
-        e,
-        "hidden",
-        /*visible*/
-        r[10] === !1
-      ), E(
-        e,
-        "padded",
-        /*padding*/
-        r[6]
-      ), E(
-        e,
-        "border_focus",
-        /*border_mode*/
-        r[5] === "focus"
-      ), E(e, "hide-container", !/*explicit_call*/
-      r[8] && !/*container*/
-      r[9]), a & /*height*/
-      1 && S(e, "height", typeof /*height*/
-      r[0] == "number" ? (
-        /*height*/
-        r[0] + "px"
-      ) : void 0), a & /*width*/
-      2 && S(e, "width", typeof /*width*/
-      r[1] == "number" ? `calc(min(${/*width*/
-      r[1]}px, 100%))` : void 0), a & /*variant*/
-      16 && S(
-        e,
-        "border-style",
-        /*variant*/
-        r[4]
-      ), a & /*allow_overflow*/
-      2048 && S(
-        e,
-        "overflow",
-        /*allow_overflow*/
-        r[11] ? "visible" : "hidden"
-      ), a & /*scale*/
-      4096 && S(
-        e,
-        "flex-grow",
-        /*scale*/
-        r[12]
-      ), a & /*min_width*/
-      8192 && S(e, "min-width", `calc(min(${/*min_width*/
-      r[13]}px, 100%))`);
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-  src_url_equal: it,
-  transition_in: B,
-  transition_out: Z
-} = window.__gradio__svelte__internal;
-function ft(n) {
-  let e, t;
-  const l = [
-    {
-      autoscroll: (
-        /*gradio*/
-        n[10].autoscroll
-      )
-    },
-    { i18n: (
-      /*gradio*/
-      n[10].i18n
-    ) },
-    /*loading_status*/
-    n[7]
-  ];
-  let i = {};
-  for (let f = 0; f < l.length; f += 1)
-    i = cn(i, l[f]);
-  return e = new on({ props: i }), {
-    c() {
-      ie(e.$$.fragment);
-    },
-    m(f, s) {
-      se(e, f, s), t = !0;
-    },
-    p(f, s) {
-      const o = s & /*gradio, loading_status*/
-      1152 ? bn(l, [
-        s & /*gradio*/
-        1024 && {
-          autoscroll: (
-            /*gradio*/
-            f[10].autoscroll
-          )
-        },
-        s & /*gradio*/
-        1024 && { i18n: (
-          /*gradio*/
-          f[10].i18n
-        ) },
-        s & /*loading_status*/
-        128 && mn(
-          /*loading_status*/
-          f[7]
-        )
-      ]) : {};
-      e.$set(o);
-    },
-    i(f) {
-      t || (B(e.$$.fragment, f), t = !0);
-    },
-    o(f) {
-      Z(e.$$.fragment, f), t = !1;
-    },
-    d(f) {
-      fe(e, f);
-    }
-  };
-}
-function wn(n) {
-  let e, t;
-  return e = new dl({
-    props: {
-      unpadded_box: !0,
-      size: "large",
-      $$slots: { default: [kn] },
-      $$scope: { ctx: n }
-    }
-  }), {
-    c() {
-      ie(e.$$.fragment);
-    },
-    m(l, i) {
-      se(e, l, i), t = !0;
-    },
-    p(l, i) {
-      const f = {};
-      i & /*$$scope*/
-      32768 && (f.$$scope = { dirty: i, ctx: l }), e.$set(f);
-    },
-    i(l) {
-      t || (B(e.$$.fragment, l), t = !0);
-    },
-    o(l) {
-      Z(e.$$.fragment, l), t = !1;
-    },
-    d(l) {
-      fe(e, l);
-    }
-  };
-}
-function pn(n) {
-  let e, t, l, i;
-  return {
-    c() {
-      e = un("iframe"), it(e.src, t = /*new_value*/
-      n[11].url) || U(e, "src", t), U(e, "title", l = /*label*/
-      n[8] ?? "Folium Map"), U(e, "height", i = /*height*/
-      n[9] + "px"), U(e, "class", "svelte-12n2kv6");
-    },
-    m(f, s) {
-      de(f, e, s);
-    },
-    p(f, s) {
-      s & /*new_value*/
-      2048 && !it(e.src, t = /*new_value*/
-      f[11].url) && U(e, "src", t), s & /*label*/
-      256 && l !== (l = /*label*/
-      f[8] ?? "Folium Map") && U(e, "title", l), s & /*height*/
-      512 && i !== (i = /*height*/
-      f[9] + "px") && U(e, "height", i);
-    },
-    i: lt,
-    o: lt,
-    d(f) {
-      f && ue(e);
-    }
-  };
-}
-function kn(n) {
-  let e, t;
-  return e = new rt({}), {
-    c() {
-      ie(e.$$.fragment);
-    },
-    m(l, i) {
-      se(e, l, i), t = !0;
-    },
-    i(l) {
-      t || (B(e.$$.fragment, l), t = !0);
-    },
-    o(l) {
-      Z(e.$$.fragment, l), t = !1;
-    },
-    d(l) {
-      fe(e, l);
-    }
-  };
-}
-function yn(n) {
-  let e, t, l, i, f, s, o, r = (
-    /*loading_status*/
-    n[7] && ft(n)
-  );
-  t = new Qt({
-    props: {
-      show_label: !0,
-      Icon: rt,
-      label: (
-        /*label*/
-        n[8] || "Folium Map"
-      )
-    }
-  });
-  const a = [pn, wn], _ = [];
-  function u(c, m) {
-    return (
-      /*value*/
-      c[3] ? 0 : 1
-    );
-  }
-  return i = u(n), f = _[i] = a[i](n), {
-    c() {
-      r && r.c(), e = nt(), ie(t.$$.fragment), l = nt(), f.c(), s = dn();
-    },
-    m(c, m) {
-      r && r.m(c, m), de(c, e, m), se(t, c, m), de(c, l, m), _[i].m(c, m), de(c, s, m), o = !0;
-    },
-    p(c, m) {
-      /*loading_status*/
-      c[7] ? r ? (r.p(c, m), m & /*loading_status*/
-      128 && B(r, 1)) : (r = ft(c), r.c(), B(r, 1), r.m(e.parentNode, e)) : r && (tt(), Z(r, 1, 1, () => {
-        r = null;
-      }), et());
-      const h = {};
-      m & /*label*/
-      256 && (h.label = /*label*/
-      c[8] || "Folium Map"), t.$set(h);
-      let L = i;
-      i = u(c), i === L ? _[i].p(c, m) : (tt(), Z(_[L], 1, 1, () => {
-        _[L] = null;
-      }), et(), f = _[i], f ? f.p(c, m) : (f = _[i] = a[i](c), f.c()), B(f, 1), f.m(s.parentNode, s));
-    },
-    i(c) {
-      o || (B(r), B(t.$$.fragment, c), B(f), o = !0);
-    },
-    o(c) {
-      Z(r), Z(t.$$.fragment, c), Z(f), o = !1;
-    },
-    d(c) {
-      c && (ue(e), ue(l), ue(s)), r && r.d(c), fe(t, c), _[i].d(c);
-    }
-  };
-}
-function vn(n) {
-  let e, t;
-  return e = new It({
-    props: {
-      visible: (
-        /*visible*/
-        n[2]
-      ),
-      elem_id: (
-        /*elem_id*/
-        n[0]
-      ),
-      elem_classes: (
-        /*elem_classes*/
-        n[1]
-      ),
-      container: (
-        /*container*/
-        n[4]
-      ),
-      scale: (
-        /*scale*/
-        n[5]
-      ),
-      min_width: (
-        /*min_width*/
-        n[6]
-      ),
-      $$slots: { default: [yn] },
-      $$scope: { ctx: n }
-    }
-  }), {
-    c() {
-      ie(e.$$.fragment);
-    },
-    m(l, i) {
-      se(e, l, i), t = !0;
-    },
-    p(l, [i]) {
-      const f = {};
-      i & /*visible*/
-      4 && (f.visible = /*visible*/
-      l[2]), i & /*elem_id*/
-      1 && (f.elem_id = /*elem_id*/
-      l[0]), i & /*elem_classes*/
-      2 && (f.elem_classes = /*elem_classes*/
-      l[1]), i & /*container*/
-      16 && (f.container = /*container*/
-      l[4]), i & /*scale*/
-      32 && (f.scale = /*scale*/
-      l[5]), i & /*min_width*/
-      64 && (f.min_width = /*min_width*/
-      l[6]), i & /*$$scope, new_value, label, height, value, gradio, loading_status*/
-      36744 && (f.$$scope = { dirty: i, ctx: l }), e.$set(f);
-    },
-    i(l) {
-      t || (B(e.$$.fragment, l), t = !0);
-    },
-    o(l) {
-      Z(e.$$.fragment, l), t = !1;
-    },
-    d(l) {
-      fe(e, l);
-    }
-  };
-}
-function Cn(n, e, t) {
-  let { elem_id: l = "" } = e, { elem_classes: i = [] } = e, { visible: f = !0 } = e, { value: s } = e, { container: o = !0 } = e, { scale: r = null } = e, { min_width: a = void 0 } = e, { loading_status: _ } = e, { label: u } = e, { root: c } = e, { proxy_url: m } = e, { height: h = 500 } = e, { gradio: L } = e, C, M = C;
-  return n.$$set = (b) => {
-    "elem_id" in b && t(0, l = b.elem_id), "elem_classes" in b && t(1, i = b.elem_classes), "visible" in b && t(2, f = b.visible), "value" in b && t(3, s = b.value), "container" in b && t(4, o = b.container), "scale" in b && t(5, r = b.scale), "min_width" in b && t(6, a = b.min_width), "loading_status" in b && t(7, _ = b.loading_status), "label" in b && t(8, u = b.label), "root" in b && t(12, c = b.root), "proxy_url" in b && t(13, m = b.proxy_url), "height" in b && t(9, h = b.height), "gradio" in b && t(10, L = b.gradio);
-  }, n.$$.update = () => {
-    n.$$.dirty & /*value, root, proxy_url*/
-    12296 && t(11, C = bt(s, c, m)), n.$$.dirty & /*new_value, old_value, gradio*/
-    19456 && JSON.stringify(C) !== JSON.stringify(M) && (L.dispatch("change"), t(14, M = C));
-  }, [
-    l,
-    i,
-    f,
-    s,
-    o,
-    r,
-    a,
-    _,
-    u,
-    h,
-    L,
-    C,
-    c,
-    m,
-    M
-  ];
-}
-class qn extends _n {
-  constructor(e) {
-    super(), gn(this, e, Cn, vn, hn, {
-      elem_id: 0,
-      elem_classes: 1,
-      visible: 2,
-      value: 3,
-      container: 4,
-      scale: 5,
-      min_width: 6,
-      loading_status: 7,
-      label: 8,
-      root: 12,
-      proxy_url: 13,
-      height: 9,
-      gradio: 10
-    });
-  }
-}
-export {
-  qn as default
-};
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Provider.py b/spaces/g4f/freegpt-webui/g4f/Provider/Provider.py
deleted file mode 100644
index d24df76b6a6ccfc9b244f13a51bfc124b398a271..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Provider.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import os
-from ..typing import sha256, Dict, get_type_hints
-
-url = None
-model = None
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    return
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/gotiQspiryo/whisper-ui/core.py b/spaces/gotiQspiryo/whisper-ui/core.py
deleted file mode 100644
index 331e895dafdedd052a7ba586adf5dc7c654cdc38..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/core.py
+++ /dev/null
@@ -1,307 +0,0 @@
-"""Thin wrapper class to manage Media objects."""
-import shutil
-from datetime import datetime, timedelta
-from functools import lru_cache
-from pathlib import Path
-from typing import Any, List, Union
-
-import ffmpeg
-import numpy as np
-import whisper
-from config import MEDIA_DIR
-from db import ENGINE, Media, Segment, Transcript
-from pytube import Playlist, YouTube
-from sqlalchemy.orm import Session
-
-
-# Whisper transcription functions
-# ----------------
-@lru_cache(maxsize=1)
-def get_whisper_model(whisper_model: str):
-    """Get a whisper model from the cache or download it if it doesn't exist"""
-    model = whisper.load_model(whisper_model)
-    return model
-
-
-# Media manager class
-# -------------------
-class MediaManager:
-    """A class to act as a primary interface to manage media objects and related data"""
-
-    def __init__(self, media_dir: Path = MEDIA_DIR):
-        self.media_dir = media_dir
-        # Create the media directory if it doesn't exist
-        self.media_dir.mkdir(exist_ok=True, parents=True)
-        self.session = Session(ENGINE)
-
-    def _transcribe(self, audio_path: str, whisper_model: str, **whisper_args):
-        """Transcribe the audio file using whisper"""
-
-        # Get whisper model
-        # NOTE: If mulitple models are selected, this may keep all of them in memory depending on the cache size
-        transcriber = get_whisper_model(whisper_model)
-
-        # Set configs & transcribe
-        if whisper_args["temperature_increment_on_fallback"] is not None:
-            whisper_args["temperature"] = tuple(
-                np.arange(whisper_args["temperature"], 1.0 + 1e-6, whisper_args["temperature_increment_on_fallback"])
-            )
-        else:
-            whisper_args["temperature"] = [whisper_args["temperature"]]
-
-        del whisper_args["temperature_increment_on_fallback"]
-
-        transcript = transcriber.transcribe(
-            audio_path,
-            **whisper_args,
-            language='es'
-        )
-
-        return transcript
-
-    def _transcribe_and_save(self, media_obj: Media, whisper_model: str, **whisper_args):
-        """Transcribe the audio file using whisper and save the transcript to the database"""
-
-        transcript = self._transcribe(media_obj.filepath, whisper_model, **whisper_args)
-
-        # Write transcripts into the same directory as the audio file
-        audio_dir = Path(media_obj.filepath).parent
-        writer = whisper.utils.get_writer("all", audio_dir)
-        writer(transcript, "transcript")
-
-        # Add transcript to the database
-        self.session.add(
-            Transcript(
-                media_id=media_obj.id,
-                media=media_obj,
-                text=transcript["text"],
-                language=transcript["language"],
-                generated_by=f"whisper-{whisper_model}",
-            )
-        )
-
-        # Add all the segments to the database
-        for segment in transcript["segments"]:
-            self.session.add(
-                Segment(
-                    media_id=media_obj.id,
-                    media=media_obj,
-                    number=segment["id"],
-                    text=segment["text"],
-                    start=segment["start"],
-                    end=segment["end"],
-                    generated_by=f"whisper-{whisper_model}",
-                    temperature=segment["temperature"],
-                    avg_logprob=segment["avg_logprob"],
-                    compression_ratio=segment["compression_ratio"],
-                    no_speech_prob=segment["no_speech_prob"],
-                )
-            )
-        self.session.commit()
-
-    def _create(self, source_list: List[Union[str, Any]], source_type: str, whisper_model: str, **whisper_args):
-        "Download audio files from the source and save it to the media directory"
-
-        # Iterate over each source in the list & download the file
-        for source in source_list:
-            # If it is a youtube file, download it with pytube
-            if source_type == "youtube":
-                yc = YouTube(source)
-                source_name = yc.title
-                # Remove any non-alphanumeric characters from the source name and replace them with a hyphen for the directory name
-                source_dirname = "".join([c if c.isalnum() else "-" for c in source_name])
-                # itag = 140 is the audio only version
-                # Check if directory already exists. If it does, append the current date and time to the directory name
-                save_dir = self.media_dir / f"{source_dirname}"
-                save_filename = "audio.mp4"
-                if save_dir.exists():
-                    save_dir = self.media_dir / f"""{source_dirname}-{datetime.now().strftime("%Y-%m-%d %H-%M-%S")}"""
-                # Download the audio file
-                yc.streams.get_by_itag(140).download(save_dir, filename=save_filename)
-            elif source_type == "upload":
-                # Parse the file name from the source
-                tokens = source.name.split(".")
-                source_name = ".".join(tokens[:-1])
-                # Remove any non-alphanumeric characters from the source name and replace them with a hyphen
-                source_dirname = "".join([c if c.isalnum() else "-" for c in source_name])
-                source_format = tokens[-1]
-                # Check if directory already exists. If it does, append the current date and time to the directory name
-                save_dir = self.media_dir / f"{source_dirname}"
-                if save_dir.exists():
-                    save_dir = self.media_dir / f"""{source_dirname}-{datetime.now().strftime("%Y-%m-%d %H-%M-%S")}"""
-                # Create the directory
-                save_dir.mkdir(exist_ok=True)
-                save_filename = f"audio.{source_format}"
-                # Save the audio file
-                with open(save_dir / save_filename, "wb") as f:
-                    f.write(source.read())
-
-            # Save the media object to the database
-            media_obj = Media(
-                source_name=source_name,
-                source_type=source_type,
-                filepath=str(save_dir / save_filename),
-            )
-            # Add source link if it is a youtube file
-            if source_type == "youtube":
-                media_obj.source_link = source
-
-            self.session.add(media_obj)
-
-            # Transcribe the audio file
-            self._transcribe_and_save(media_obj, whisper_model, **whisper_args)
-
-        # Commit the changes to the database
-        self.session.commit()
-
-    def add(self, source: Union[str, Any], source_type: str, **whisper_args):
-        "Adds a set of media objects to the database from YouTube or uploaded files"
-        # If the source is a YouTube URL, expand it to a list of URLs
-        if source_type == "youtube":
-            # If the source is a playlist, download all the videos in the playlist
-            if "list=" in source:
-                source_list = list(Playlist(source))
-            else:
-                source_list = [source]
-        elif source_type == "upload":
-            source_list = source if type(source) == list else [source]
-
-        # Download & save the audio files and add them to the database
-        self._create(source_list=source_list, source_type=source_type, **whisper_args)
-
-    def delete(self, media_id: str):
-        "Delete a media object from the database"
-        # Get the media object
-        media_obj = self.session.query(Media).filter(Media.id == media_id).first()
-        # Delete the media object
-        self.session.delete(media_obj)
-        # Commit the changes to the database
-        self.session.commit()
-        # Remove the audio file directory
-        shutil.rmtree(Path(media_obj.filepath).parent)
-
-    def get_segments(self, **filters):
-        "Get all the segments in the database"
-
-        filter_args = []
-
-        if "start_date" in filters:
-            filter_args.append(Segment.created >= filters["start_date"])
-        if "end_date" in filters:
-            # NOTE: Since dates are stored as timezone aware datetime strings, add 1 day to the end date
-            # and filter until the end of that day
-            end_date = datetime.strptime(filters["end_date"], "%Y-%m-%d")
-            end_date = end_date + timedelta(days=1)
-            end_date = end_date.strftime("%Y-%m-%d")
-            filter_args.append(Segment.created < end_date)
-
-        if "source_type" in filters:
-            filter_args.append(Media.source_type == filters["source_type"])
-
-        if "search_by_name" in filters:
-            filter_args.append(Media.source_name.like(f"""%{filters["search_by_name"]}%"""))
-
-        if "search_by_transcript" in filters:
-            filter_args.append(Segment.text.like(f"""%{filters["search_by_transcript"]}%"""))
-
-        # Get the media objects
-        segment_objs = self.session.query(Segment).join(Media)
-
-        # Filter the media objects
-        if len(filter_args) > 0:
-            segment_objs = segment_objs.filter(*filter_args)
-
-        # Sort the media objects by creation date
-        segment_objs = segment_objs.order_by(Media.created.desc()).limit(filters.get("limit", 10))
-
-        # Finally, format media objects into a list of dictionaries
-        formatted_segment_list = [self._format_segment(segment_obj) for segment_obj in segment_objs.all()]
-
-        return formatted_segment_list
-
-    def get_list(self, **filters):
-        "List all the media objects in the database"
-
-        # Get media objects & filter them as needed
-        filter_args = []
-
-        if "start_date" in filters:
-            filter_args.append(Media.created >= filters["start_date"])
-        if "end_date" in filters:
-            # NOTE: Since dates are stored as timezone aware datetime strings, add 1 day to the end date
-            # and filter until the end of that day
-            end_date = datetime.strptime(filters["end_date"], "%Y-%m-%d")
-            end_date = end_date + timedelta(days=1)
-            end_date = end_date.strftime("%Y-%m-%d")
-            filter_args.append(Media.created < end_date)
-
-        if "source_type" in filters:
-            filter_args.append(Media.source_type == filters["source_type"])
-
-        if "search_by_name" in filters:
-            filter_args.append(Media.source_name.like(f"""%{filters["search_by_name"]}%"""))
-
-        if "search_by_transcript" in filters:
-            filter_args.append(Transcript.text.like(f"""%{filters["search_by_transcript"]}%"""))
-
-        # Get the media objects
-        media_objs = self.session.query(Media).join(Transcript)
-
-        # Filter the media objects
-        if len(filter_args) > 0:
-            media_objs = media_objs.filter(*filter_args)
-
-        # Sort the media objects by creation date
-        media_objs = media_objs.order_by(Media.created.desc()).limit(filters.get("limit", 10))
-
-        # Finally, format media objects into a list of dictionaries
-        formatted_media_list = [self._format_media_base(media_obj) for media_obj in media_objs.all()]
-
-        return formatted_media_list
-
-    def get_detail(self, media_id: str):
-        "Get the details of a media object"
-        media_obj = self.session.query(Media).filter(Media.id == media_id).first()
-        return self._format_media_detail(media_obj)
-
-    def _format_media_base(self, media_obj: Media):
-        "Formats the media object to a dictionary"
-        return {
-            "id": media_obj.id,
-            "source_name": media_obj.source_name,
-            "source_type": media_obj.source_type,
-            "source_link": media_obj.source_link,
-            "filepath": media_obj.filepath,
-            "created": media_obj.created,
-            "language": media_obj.transcript.language,
-            "generated_by": media_obj.transcript.generated_by,
-        }
-
-    def _format_media_detail(self, media_obj: Media):
-        "Formats the media object to a dictionary"
-        base = self._format_media_base(media_obj)
-        details = {
-            "transcript": media_obj.transcript.text,
-            "segments": [
-                {
-                    "number": segment.number,
-                    "start": segment.start,
-                    "end": segment.end,
-                    "text": segment.text,
-                }
-                for segment in media_obj.segments
-            ],
-        }
-        base.update(details)
-        return base
-
-    def _format_segment(self, segment_obj: Segment):
-        """Formats the segment object to a dictionary"""
-        return {
-            "id": segment_obj.id,
-            "number": segment_obj.number,
-            "start": segment_obj.start,
-            "end": segment_obj.end,
-            "text": segment_obj.text,
-            "media": self._format_media_base(segment_obj.media),
-        }
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/720p Alibaba Aur 41 Chor Download A Classic Arabian Nights Story in High Definition.md b/spaces/gotiQspiryo/whisper-ui/examples/720p Alibaba Aur 41 Chor Download A Classic Arabian Nights Story in High Definition.md
deleted file mode 100644
index 1cdd06dbb7c601ebb34249447896a79dfda9676f..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/720p Alibaba Aur 41 Chor Download A Classic Arabian Nights Story in High Definition.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>720p Alibaba Aur 41 Chor Download</h2><br /><p><b><b>Download File</b> &#9889; <a href="https://urlgoal.com/2uyMZC">https://urlgoal.com/2uyMZC</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/gradio/HuBERT/fairseq/data/legacy/masked_lm_dataset.py b/spaces/gradio/HuBERT/fairseq/data/legacy/masked_lm_dataset.py
deleted file mode 100644
index dd8ea2c60aff306ab3a756223a298a28d41a4991..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/data/legacy/masked_lm_dataset.py
+++ /dev/null
@@ -1,303 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from typing import Dict, List, Tuple
-
-import numpy as np
-import torch
-from fairseq.data import Dictionary, FairseqDataset, data_utils
-from fairseq.data.concat_dataset import ConcatDataset
-from fairseq.data.legacy.block_pair_dataset import BlockPairDataset
-from fairseq.data.token_block_dataset import TokenBlockDataset
-
-
-class MaskedLMDataset(FairseqDataset):
-    """
-    A wrapper Dataset for masked language modelling. The dataset
-    wraps around TokenBlockDataset or BlockedPairDataset and creates a batch
-    where the input blocks are masked according to the specified masking
-    probability. Additionally the batch can also contain sentence level targets
-    if this is specified.
-
-    Args:
-        dataset: Dataset which generates blocks of data. Only BlockPairDataset
-            and TokenBlockDataset are supported.
-        sizes: Sentence lengths
-        vocab: Dictionary with the vocabulary and special tokens.
-        pad_idx: Id of padding token in dictionary
-        mask_idx: Id of mask token in dictionary
-        classif_token_idx: Id of classification token in dictionary. This is the
-            token associated with the sentence embedding (Eg: CLS for BERT)
-        sep_token_idx: Id of separator token in dictionary
-            (Eg: SEP in BERT)
-        seed: Seed for random number generator for reproducibility.
-        shuffle: Shuffle the elements before batching.
-        has_pairs: Specifies whether the underlying dataset
-            generates a pair of blocks along with a sentence_target or not.
-            Setting it to True assumes that the underlying dataset generates a
-            label for the pair of sentences which is surfaced as
-            sentence_target. The default value assumes a single block with no
-            sentence target.
-        segment_id: An optional segment id for filling in the segment labels
-            when we are in the single block setting (Eg: XLM). Default is 0.
-        masking_ratio: specifies what percentage of the blocks should be masked.
-        masking_prob: specifies the probability of a given token being
-            replaced with the "MASK" token.
-        random_token_prob: specifies the probability of a given token being
-            replaced by a random token from the vocabulary.
-    """
-
-    def __init__(
-        self,
-        dataset: FairseqDataset,
-        sizes: np.ndarray,
-        vocab: Dictionary,
-        pad_idx: int,
-        mask_idx: int,
-        classif_token_idx: int,
-        sep_token_idx: int,
-        seed: int = 1,
-        shuffle: bool = True,
-        has_pairs: bool = True,
-        segment_id: int = 0,
-        masking_ratio: float = 0.15,
-        masking_prob: float = 0.8,
-        random_token_prob: float = 0.1,
-    ):
-        # Make sure the input datasets are the ones supported
-        assert (
-            isinstance(dataset, TokenBlockDataset)
-            or isinstance(dataset, BlockPairDataset)
-            or isinstance(dataset, ConcatDataset)
-        ), (
-            "MaskedLMDataset only wraps TokenBlockDataset or BlockPairDataset or "
-            "ConcatDataset"
-        )
-
-        self.dataset = dataset
-        self.sizes = np.array(sizes)
-        self.vocab = vocab
-        self.pad_idx = pad_idx
-        self.mask_idx = mask_idx
-        self.classif_token_idx = classif_token_idx
-        self.sep_token_idx = sep_token_idx
-        self.shuffle = shuffle
-        self.seed = seed
-        self.has_pairs = has_pairs
-        self.segment_id = segment_id
-        self.masking_ratio = masking_ratio
-        self.masking_prob = masking_prob
-        self.random_token_prob = random_token_prob
-
-        # If we have only one block then sizes needs to be updated to include
-        # the classification token
-        if not has_pairs:
-            self.sizes = self.sizes + 1
-
-    def __getitem__(self, index: int):
-        # if has_pairs, then expect 2 blocks and a sentence target
-        if self.has_pairs:
-            (block_one, block_two, sentence_target) = self.dataset[index]
-        else:
-            block_one = self.dataset[index]
-
-        return {
-            "id": index,
-            "block_one": block_one,
-            "block_two": block_two if self.has_pairs else None,
-            "sentence_target": sentence_target if self.has_pairs else None,
-        }
-
-    def __len__(self):
-        return len(self.dataset)
-
-    def _mask_block(
-        self,
-        sentence: np.ndarray,
-        mask_idx: int,
-        pad_idx: int,
-        dictionary_token_range: Tuple,
-    ):
-        """
-        Mask tokens for Masked Language Model training
-        Samples mask_ratio tokens that will be predicted by LM.
-
-        Note:This function may not be efficient enough since we had multiple
-        conversions between np and torch, we can replace them with torch
-        operators later.
-
-        Args:
-            sentence: 1d tensor to be masked
-            mask_idx: index to use for masking the sentence
-            pad_idx: index to use for masking the target for tokens we aren't
-                predicting
-            dictionary_token_range: range of indices in dictionary which can
-                be used for random word replacement
-                (e.g. without special characters)
-        Return:
-            masked_sent: masked sentence
-            target: target with words which we are not predicting replaced
-                by pad_idx
-        """
-        masked_sent = np.copy(sentence)
-        sent_length = len(sentence)
-        mask_num = math.ceil(sent_length * self.masking_ratio)
-        mask = np.random.choice(sent_length, mask_num, replace=False)
-        target = np.copy(sentence)
-
-        for i in range(sent_length):
-            if i in mask:
-                rand = np.random.random()
-
-                # replace with mask if probability is less than masking_prob
-                # (Eg: 0.8)
-                if rand < self.masking_prob:
-                    masked_sent[i] = mask_idx
-
-                # replace with random token if probability is less than
-                # masking_prob + random_token_prob (Eg: 0.9)
-                elif rand < (self.masking_prob + self.random_token_prob):
-                    # sample random token from dictionary
-                    masked_sent[i] = np.random.randint(
-                        dictionary_token_range[0], dictionary_token_range[1]
-                    )
-            else:
-                target[i] = pad_idx
-
-        return masked_sent, target
-
-    def _collate(self, samples: List[Dict], pad_idx: int, eos_idx: int):
-        """
-        Does the heavy lifting for creating a batch from the input list of
-        examples. The logic is as follows:
-            1. Mask the input blocks. In case has_pair is True then we have 2
-               blocks to mask.
-            2. Prepend the first masked block tensor with the special token
-               used as sentence embedding. Eg: CLS in BERT. This happens
-               irrespective of the value of has_pair.
-            3. If has_pair is True, then append the first masked block with the
-               special separator token (eg: SEP for BERT) and compute segment
-               label accordingly. In this case, also append the second masked
-               block with this special separator token and compute its segment
-               label.
-            4. For the targets tensor, prepend and append with padding index
-               accordingly.
-            5. Concatenate all tensors.
-        """
-        if len(samples) == 0:
-            return {}
-        # To ensure determinism, we reset the state of the PRNG after every
-        # batch based on the seed and the first id of the batch. This ensures
-        # that across epochs we get the same mask for the same example. This
-        # is needed for reproducibility and is how BERT does masking
-        # TODO: Can we add deteminism without this constraint?
-        with data_utils.numpy_seed(self.seed + samples[0]["id"]):
-            for s in samples:
-
-                # token range is needed for replacing with random token during
-                # masking
-                token_range = (self.vocab.nspecial, len(self.vocab))
-
-                # mask according to specified probabilities.
-                masked_blk_one, masked_tgt_one = self._mask_block(
-                    s["block_one"],
-                    self.mask_idx,
-                    self.pad_idx,
-                    token_range,
-                )
-
-                tokens = np.concatenate([[self.classif_token_idx], masked_blk_one])
-                targets = np.concatenate([[self.pad_idx], masked_tgt_one])
-                segments = np.ones(len(tokens)) * self.segment_id
-
-                # if has_pairs is True then we need to add the SEP token to both
-                # the blocks after masking and re-compute segments based on the new
-                # lengths.
-                if self.has_pairs:
-                    tokens_one = np.concatenate([tokens, [self.sep_token_idx]])
-                    targets_one = np.concatenate([targets, [self.pad_idx]])
-
-                    masked_blk_two, masked_tgt_two = self._mask_block(
-                        s["block_two"], self.mask_idx, self.pad_idx, token_range
-                    )
-                    tokens_two = np.concatenate([masked_blk_two, [self.sep_token_idx]])
-                    targets_two = np.concatenate([masked_tgt_two, [self.pad_idx]])
-
-                    # block + 1 sep + 1 special (CLS)
-                    segments_one = np.zeros(len(tokens_one))
-                    # block + 1 sep
-                    segments_two = np.ones(len(tokens_two))
-
-                    tokens = np.concatenate([tokens_one, tokens_two])
-                    targets = np.concatenate([targets_one, targets_two])
-                    segments = np.concatenate([segments_one, segments_two])
-
-                s["source"] = torch.LongTensor(tokens)
-                s["segment_labels"] = torch.LongTensor(segments)
-                s["lm_target"] = torch.LongTensor(targets)
-
-        def merge(key):
-            return data_utils.collate_tokens(
-                [s[key] for s in samples], pad_idx, eos_idx, left_pad=False
-            )
-
-        return {
-            "id": torch.LongTensor([s["id"] for s in samples]),
-            "ntokens": sum(len(s["source"]) for s in samples),
-            "net_input": {
-                "src_tokens": merge("source"),
-                "segment_labels": merge("segment_labels"),
-            },
-            "lm_target": merge("lm_target"),
-            "sentence_target": torch.LongTensor([s["sentence_target"] for s in samples])
-            if self.has_pairs
-            else None,
-            "nsentences": len(samples),
-        }
-
-    def collater(self, samples: List[Dict]):
-        """Merge a list of samples to form a mini-batch.
-
-        Args:
-            samples (List[dict]): samples to collate
-
-        Returns:
-            dict: a mini-batch of data
-        """
-        return self._collate(samples, self.vocab.pad(), self.vocab.eos())
-
-    def num_tokens(self, index: int):
-        """
-        Return the number of tokens in a sample. This value is used to
-        enforce max-tokens during batching.
-        """
-        return self.sizes[index]
-
-    def size(self, index: int):
-        """
-        Return an example's size as a float or tuple. This value is used when
-        filtering a dataset with max-positions.
-        """
-        return self.sizes[index]
-
-    def ordered_indices(self):
-        """
-        Return an ordered list of indices. Batches will be constructed based
-        on this order.
-        """
-        if self.shuffle:
-            return np.random.permutation(len(self))
-        else:
-            order = [np.arange(len(self))]
-            order.append(self.sizes)
-            return np.lexsort(order)
-
-    @property
-    def supports_prefetch(self):
-        return getattr(self.dataset, "supports_prefetch", False)
-
-    def prefetch(self, indices):
-        self.dataset.prefetch(indices)
diff --git a/spaces/gradio/blocks_style/run.py b/spaces/gradio/blocks_style/run.py
deleted file mode 100644
index a4feb0e0b34fb7ded12ef97f6542eff8738bb5e8..0000000000000000000000000000000000000000
--- a/spaces/gradio/blocks_style/run.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import gradio as gr
-
-with gr.Blocks(title="Styling Examples") as demo:
-    with gr.Column(variant="box"):
-        txt = gr.Textbox(label="Small Textbox", lines=1)
-        num = gr.Number(label="Number", show_label=False)
-        slider = gr.Slider(label="Slider", show_label=False)
-        check = gr.Checkbox(label="Checkbox", show_label=False)
-        check_g = gr.CheckboxGroup(
-            label="Checkbox Group",
-            choices=["One", "Two", "Three"],
-            show_label=False,
-        )
-        radio = gr.Radio(
-            label="Radio",
-            choices=["One", "Two", "Three"],
-            show_label=False,
-        )
-        drop = gr.Dropdown(
-            label="Dropdown", choices=["One", "Two", "Three"], show_label=False
-        )
-        image = gr.Image(show_label=False)
-        video = gr.Video(show_label=False)
-        audio = gr.Audio(show_label=False)
-        file = gr.File(show_label=False)
-        df = gr.Dataframe(show_label=False)
-        label = gr.Label(container=False)
-        highlight = gr.HighlightedText(
-            [("hello", None), ("goodbye", "-")],
-            color_map={"+": "green", "-": "red"},
-            container=False,
-        )
-        json = gr.JSON(container=False)
-        html = gr.HTML(show_label=False)
-        gallery = gr.Gallery(
-            columns=(3, 3, 1),
-            height="auto",
-            container=False,
-        )
-        chat = gr.Chatbot([("hi", "good bye")])
-
-        model = gr.Model3D()
-
-        md = gr.Markdown(show_label=False)
-
-        highlight = gr.HighlightedText()
-
-        btn = gr.Button("Run")
-
-        gr.Dataset(components=[txt, num])
-
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/gwang-kim/DATID-3D/pose_estimation/check_pose.py b/spaces/gwang-kim/DATID-3D/pose_estimation/check_pose.py
deleted file mode 100644
index d537c99aa892f6e6cebe64e37149d900344637ec..0000000000000000000000000000000000000000
--- a/spaces/gwang-kim/DATID-3D/pose_estimation/check_pose.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import numpy as np
-import scipy.io
-def euler2rot(euler):
-    sin, cos = np.sin, np.cos
-    phi, theta, psi = euler[0], euler[1], euler[2]
-    R1 = np.array([[1, 0, 0],
-                   [0, cos(phi), sin(phi)],
-                   [0, -sin(phi), cos(phi)]])
-    R2 = np.array([[cos(theta), 0, -sin(theta)],
-                   [0, 1, 0],
-                   [sin(theta), 0, cos(theta)]])
-    R3 = np.array([[cos(psi), sin(psi), 0],
-                   [-sin(psi), cos(psi), 0],
-                   [0, 0, 1]])
-    R = R1 @ R2 @ R3
-    return R
-
-
-import json
-import os
-from glob import glob
-import sys
-
-temp_folder = sys.argv[1]
-output_folder = sys.argv[2]
-
-import shutil
-pose_template = np.load('util/pose_template.npy')
-glob_names= sorted(glob('%s/cropped_images/*.png'%(temp_folder)))
-
-for name_all in glob_names:
-
- if os.path.isfile('%s/cropped_images/cameras.json'%(temp_folder)):
-       with open('%s/cropped_images/cameras.json'%(temp_folder), 'r') as file:     
-              labels = json.load(file)#['labels']
-              predict_pose = labels
- name = os.path.basename(name_all)[:-4]
- pose= np.array(predict_pose[name+'.png']['pose']).reshape(16)
- pose_template[:16] = pose
-
- np.save('%s/'%(output_folder)+name+'.npy',pose_template)
- shutil.copy('%s/cropped_images/'%(temp_folder)+name+'.png','%s/'%(output_folder)+name+'.png')
-
-
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/docs/Dataset.md b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/docs/Dataset.md
deleted file mode 100644
index ef6c56cedab89f3ab09306826240b075af244899..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/docs/Dataset.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# SHHQ Dataset
-<img src="../img/preview_samples1.png" width="96%" height="96%">
-
-## Overview
-SHHQ is a dataset with high-quality full-body human images in a resolution of 1024 × 512.
-Since we need to follow a rigorous legal review in our institute, we can not release all of the data at once.
-
-For now, SHHQ-1.0 with 40K images is released! More data will be released in the later versions.
-
-
-## Data Sources
-Images are collected in two main ways: 
-1) From the Internet. 
-We developed a crawler tool with an official API, mainly downloading images from Flickr, Pixabay and Pexels. So you need to meet all the following licenses when using the dataset: CC0, [Pixabay License](https://pixabay.com/service/license/), and [Pexels Licenses](https://www.pexels.com/license/).
-2) From the data providers. 
-We purchased images from databases of individual photographers, modeling agencies and other suppliers.
-Images were reviewed by our legal team prior to purchase to ensure permission for use in research.
-
-### Note: 
-The composition of SHHQ-1.0: 
-
-1) Images obtained from the above sources.
-2) Processed 9991 DeepFashion [[1]](#1) images (retain only full body images).
-3) 1940 African images from the InFashAI [[2]](#2) dataset to increase data diversity.
-
-## Data License
-We are aware of privacy concerns and seriously treat the license and privacy issues. All released data will be ensured under the license of CC0 and free for research use. Also, persons in the dataset are anonymised without additional private or sensitive metadata.
-
-## Agreement
-The SHHQ is available for non-commercial research purposes only. 
-
-You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit any portion of the images and any portion of the derived data for commercial purposes. 
-
-You agree NOT to further copy, publish or distribute any portion of SHHQ to any third party for any purpose. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
-
-Shanghai AI Lab reserves the right to terminate your access to the SHHQ at any time.
-
-## Dataset Preview
-For those interested in our dataset, we provide a preview version with 100 images randomly sampled from SHHQ-1.0: [SHHQ-1.0_samples](https://drive.google.com/file/d/1tnNFfmFtzRbYL3qEnNXQ_ShaN9YV5tI5/view?usp=sharing).
-
-In SHHQ-1.0, we provide aligned raw images along with machine-calculated segmentation masks. Later we are planning to release manually annotated human-parsing version of these 40,000 images. Please stay tuned. 
- 
-> We also provide script [bg_white.py](../bg_white.py) to whiten the background of the raw image using its segmentation mask.
-
-If you want to access the full SHHQ-1.0, please read the following instructions.
-
-## Model trained using SHHQ-1.0
-
-| Structure | 1024x512 | Metric | Scores |  512x256 | Metric | Scores | 
-| --------- |:----------:| :----------:| :----------:|  :-----: |  :-----: |  :-----: | 
-| StyleGAN1 | to be released | - | - | to be released | - | - |
-| StyleGAN2 | [SHHQ-1.0_sg2_1024.pkl](https://drive.google.com/file/d/1PuvE72xpc69Zq4y58dohuKbG9dFnnjEX/view?usp=sharing) | fid50k_full | 3.56 | [SHHQ-1.0_sg2_512.pkl](https://drive.google.com/file/d/170t2FRWxR8_TG3_y0nVtDBogLPOClnyf/view?usp=sharing) | fid50k_full | 3.68 |
-| StyleGAN3 | to be released | - | - |to be released | - | - |
-
-
-## Download Instructions
-Please download the SHHQ Dataset Release Agreement from [link](./SHHQ_Dataset_Release_Agreement.pdf).
-Read it carefully, complete and sign it appropriately. 
-
-Please send the completed form to Jianglin Fu (arlenefu@outlook.com) and Shikai Li (lishikai@pjlab.org.cn), and cc to Wayne Wu (wuwenyan0503@gmail.com) using institutional email address. The email Subject Title is "SHHQ Dataset Release Agreement". We will verify your request and contact you with the dataset link and password to unzip the image data.
-
-Note:
-
-1. We are currently facing large incoming applications, and we need to carefully verify all the applicants, please be patient, and we will reply to you as soon as possible.
-
-2. The signature in the agreement should be hand-written.
-
-## References
-<a id="1">[1]</a> 
-Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou. DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations. CVPR (2016)
-
-<a id="2">[2]</a> 
-Hacheme, Gilles and Sayouti, Noureini. Neural fashion image captioning: Accounting for data diversity. arXiv preprint arXiv:2106.12154 (2021)
-
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/interpolation.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/interpolation.py
deleted file mode 100644
index b578881834f4333d7e386e5a8f142e3a98a3252c..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/interpolation.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# interpolate between two z code
-# score all middle latent code
-# https://www.aiuai.cn/aifarm1929.html
-
-import os
-import re
-from typing import List
-from tqdm import tqdm
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import torch
-import click
-import legacy
-import random
-from typing import List, Optional
-
-
-def lerp(code1, code2, alpha):
-    return code1 * alpha + code2 * (1 - alpha)
-
-# Taken and adapted from wikipedia's slerp article
-# https://en.wikipedia.org/wiki/Slerp
-
-
-def slerp(code1, code2, alpha, DOT_THRESHOLD=0.9995):  # Spherical linear interpolation
-    code1_copy = np.copy(code1)
-    code2_copy = np.copy(code2)
-
-    code1 = code1 / np.linalg.norm(code1)
-    code2 = code2 / np.linalg.norm(code2)
-    dot = np.sum(code1 * code2)
-    if np.abs(dot) > DOT_THRESHOLD:
-        return lerp(code1_copy, code2_copy, alpha)
-
-    # Calculate initial angle between v0 and v1
-    theta_0 = np.arccos(dot)
-    sin_theta_0 = np.sin(theta_0)
-    # Angle at timestep t
-    theta_t = theta_0 * alpha
-    sin_theta_t = np.sin(theta_t)
-
-    s0 = np.sin(theta_0 - theta_t) / sin_theta_0
-    s1 = sin_theta_t / sin_theta_0
-    code3 = s0 * code1_copy + s1 * code2_copy
-    return code3
-
-
-def generate_image_from_z(G, z, noise_mode, truncation_psi, device):
-    label = torch.zeros([1, G.c_dim], device=device)
-    w = G.mapping(z, label, truncation_psi=truncation_psi)
-    img = G.synthesis(w, noise_mode=noise_mode, force_fp32=True)
-    img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-    img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')
-    return img
-
-
-def get_concat_h(im1, im2):
-    dst = PIL.Image.new('RGB', (im1.width + im2.width, im1.height))
-    dst.paste(im1, (0, 0))
-    dst.paste(im2, (im1.width, 0))
-    return dst
-
-
-def make_latent_interp_animation(G, code1, code2, img1, img2, num_interps, noise_mode, save_mid_image, truncation_psi, device, outdir, fps):
-    step_size = 1.0/num_interps
-
-    all_imgs = []
-    amounts = np.arange(0, 1, step_size)
-    for seed_idx, alpha in enumerate(tqdm(amounts)):
-        interpolated_latent_code = lerp(code1, code2, alpha)
-        image = generate_image_from_z(
-            G, interpolated_latent_code, noise_mode, truncation_psi, device)
-        interp_latent_image = image.resize((512, 1024))
-        if not os.path.exists(os.path.join(outdir, 'img')):
-            os.makedirs(os.path.join(outdir, 'img'), exist_ok=True)
-        if save_mid_image:
-            interp_latent_image.save(f'{outdir}/img/seed{seed_idx:04d}.png')
-
-        frame = get_concat_h(img2, interp_latent_image)
-        frame = get_concat_h(frame, img1)
-        all_imgs.append(frame)
-
-    save_name = os.path.join(outdir, 'latent_space_traversal.gif')
-    all_imgs[0].save(save_name, save_all=True,
-                     append_images=all_imgs[1:], duration=1000/fps, loop=0)
-
-
-"""
-Create interpolated images between two given seeds using pretrained network pickle.
-
-Examples:
-
-\b
-python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl  --seeds=85,100 --outdir=outputs/inter_gifs
-    
-"""
-
-
-@click.command()
-@click.pass_context
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=legacy.num_range, help='List of 2 random seeds, e.g. 1,2')
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=0.8, show_default=True)
-@click.option('--noise-mode', 'noise_mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--outdir', default='outputs/inter_gifs', help='Where to save the output images', type=str, required=True, metavar='DIR')
-@click.option('--save_mid_image', default=True, type=bool, help='select True if you want to save all interpolated images')
-@click.option('--fps', default=15, help='FPS for GIF', type=int)
-@click.option('--num_interps', default=100, help='Number of interpolation images', type=int)
-def main(
-    ctx: click.Context,
-    network_pkl: str,
-    seeds: Optional[List[int]],
-    truncation_psi: float,
-    noise_mode: str,
-    outdir: str,
-    save_mid_image: bool,
-    fps: int,
-    num_interps: int
-):
-
-    device = torch.device('cuda')
-    with dnnlib.util.open_url(network_pkl) as f:
-        G = legacy.load_network_pkl(f)['G_ema'].to(device)  # type: ignore
-
-    outdir = os.path.join(outdir)
-    if not os.path.exists(outdir):
-        os.makedirs(outdir, exist_ok=True)
-        os.makedirs(os.path.join(outdir, 'img'), exist_ok=True)
-
-    if len(seeds) > 2:
-        print("Receiving more than two seeds, only use the first two.")
-        seeds = seeds[0:2]
-    elif len(seeds) == 1:
-        print('Require two seeds, randomly generate two now.')
-        seeds = [seeds[0], random.randint(0, 10000)]
-
-    z1 = torch.from_numpy(np.random.RandomState(
-        seeds[0]).randn(1, G.z_dim)).to(device)
-    z2 = torch.from_numpy(np.random.RandomState(
-        seeds[1]).randn(1, G.z_dim)).to(device)
-    img1 = generate_image_from_z(G, z1, noise_mode, truncation_psi, device)
-    img2 = generate_image_from_z(G, z2, noise_mode, truncation_psi, device)
-    img1.save(f'{outdir}/seed{seeds[0]:04d}.png')
-    img2.save(f'{outdir}/seed{seeds[1]:04d}.png')
-
-    make_latent_interp_animation(G, z1, z2, img1, img2, num_interps,
-                                 noise_mode, save_mid_image, truncation_psi, device, outdir, fps)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/h2oai/wave-tour/examples/stat_wide_pie.py b/spaces/h2oai/wave-tour/examples/stat_wide_pie.py
deleted file mode 100644
index 4688eff7af6aae3f46c9541c17669d43e0dc3fc7..0000000000000000000000000000000000000000
--- a/spaces/h2oai/wave-tour/examples/stat_wide_pie.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Stat / Pie / Wide
-# Create a wide stat pie card displaying a pie chart.
-# ---
-from h2o_wave import site, ui
-
-page = site['/demo']
-
-page.add('example', ui.wide_pie_stat_card(
-    box='1 1 3 3',
-    title='Wide Pie Stat',
-    pies=[
-        ui.pie(label='Category 1', value='35%', fraction=0.35, color='#2cd0f5', aux_value='$ 35'),
-        ui.pie(label='Category 2', value='65%', fraction=0.65, color='$themePrimary', aux_value='$ 65'),
-    ],
-))
-
-page.save()
diff --git a/spaces/hanstyle/tts/hq_wav2lip_train.py b/spaces/hanstyle/tts/hq_wav2lip_train.py
deleted file mode 100644
index c384ad9d80c82fd6bff51ea395eeefe93e1e0997..0000000000000000000000000000000000000000
--- a/spaces/hanstyle/tts/hq_wav2lip_train.py
+++ /dev/null
@@ -1,443 +0,0 @@
-from os.path import dirname, join, basename, isfile
-from tqdm import tqdm
-
-from models import SyncNet_color as SyncNet
-from models import Wav2Lip, Wav2Lip_disc_qual
-import audio
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch import optim
-import torch.backends.cudnn as cudnn
-from torch.utils import data as data_utils
-import numpy as np
-
-from glob import glob
-
-import os, random, cv2, argparse
-from hparams import hparams, get_image_list
-
-parser = argparse.ArgumentParser(description='Code to train the Wav2Lip model WITH the visual quality discriminator')
-
-parser.add_argument("--data_root", help="Root folder of the preprocessed LRS2 dataset", required=True, type=str)
-
-parser.add_argument('--checkpoint_dir', help='Save checkpoints to this directory', required=True, type=str)
-parser.add_argument('--syncnet_checkpoint_path', help='Load the pre-trained Expert discriminator', required=True, type=str)
-
-parser.add_argument('--checkpoint_path', help='Resume generator from this checkpoint', default=None, type=str)
-parser.add_argument('--disc_checkpoint_path', help='Resume quality disc from this checkpoint', default=None, type=str)
-
-args = parser.parse_args()
-
-
-global_step = 0
-global_epoch = 0
-use_cuda = torch.cuda.is_available()
-print('use_cuda: {}'.format(use_cuda))
-
-syncnet_T = 5
-syncnet_mel_step_size = 16
-
-class Dataset(object):
-    def __init__(self, split):
-        self.all_videos = get_image_list(args.data_root, split)
-
-    def get_frame_id(self, frame):
-        return int(basename(frame).split('.')[0])
-
-    def get_window(self, start_frame):
-        start_id = self.get_frame_id(start_frame)
-        vidname = dirname(start_frame)
-
-        window_fnames = []
-        for frame_id in range(start_id, start_id + syncnet_T):
-            frame = join(vidname, '{}.jpg'.format(frame_id))
-            if not isfile(frame):
-                return None
-            window_fnames.append(frame)
-        return window_fnames
-
-    def read_window(self, window_fnames):
-        if window_fnames is None: return None
-        window = []
-        for fname in window_fnames:
-            img = cv2.imread(fname)
-            if img is None:
-                return None
-            try:
-                img = cv2.resize(img, (hparams.img_size, hparams.img_size))
-            except Exception as e:
-                return None
-
-            window.append(img)
-
-        return window
-
-    def crop_audio_window(self, spec, start_frame):
-        if type(start_frame) == int:
-            start_frame_num = start_frame
-        else:
-            start_frame_num = self.get_frame_id(start_frame)
-        start_idx = int(80. * (start_frame_num / float(hparams.fps)))
-        
-        end_idx = start_idx + syncnet_mel_step_size
-
-        return spec[start_idx : end_idx, :]
-
-    def get_segmented_mels(self, spec, start_frame):
-        mels = []
-        assert syncnet_T == 5
-        start_frame_num = self.get_frame_id(start_frame) + 1 # 0-indexing ---> 1-indexing
-        if start_frame_num - 2 < 0: return None
-        for i in range(start_frame_num, start_frame_num + syncnet_T):
-            m = self.crop_audio_window(spec, i - 2)
-            if m.shape[0] != syncnet_mel_step_size:
-                return None
-            mels.append(m.T)
-
-        mels = np.asarray(mels)
-
-        return mels
-
-    def prepare_window(self, window):
-        # 3 x T x H x W
-        x = np.asarray(window) / 255.
-        x = np.transpose(x, (3, 0, 1, 2))
-
-        return x
-
-    def __len__(self):
-        return len(self.all_videos)
-
-    def __getitem__(self, idx):
-        while 1:
-            idx = random.randint(0, len(self.all_videos) - 1)
-            vidname = self.all_videos[idx]
-            img_names = list(glob(join(vidname, '*.jpg')))
-            if len(img_names) <= 3 * syncnet_T:
-                continue
-            
-            img_name = random.choice(img_names)
-            wrong_img_name = random.choice(img_names)
-            while wrong_img_name == img_name:
-                wrong_img_name = random.choice(img_names)
-
-            window_fnames = self.get_window(img_name)
-            wrong_window_fnames = self.get_window(wrong_img_name)
-            if window_fnames is None or wrong_window_fnames is None:
-                continue
-
-            window = self.read_window(window_fnames)
-            if window is None:
-                continue
-
-            wrong_window = self.read_window(wrong_window_fnames)
-            if wrong_window is None:
-                continue
-
-            try:
-                wavpath = join(vidname, "audio.wav")
-                wav = audio.load_wav(wavpath, hparams.sample_rate)
-
-                orig_mel = audio.melspectrogram(wav).T
-            except Exception as e:
-                continue
-
-            mel = self.crop_audio_window(orig_mel.copy(), img_name)
-            
-            if (mel.shape[0] != syncnet_mel_step_size):
-                continue
-
-            indiv_mels = self.get_segmented_mels(orig_mel.copy(), img_name)
-            if indiv_mels is None: continue
-
-            window = self.prepare_window(window)
-            y = window.copy()
-            window[:, :, window.shape[2]//2:] = 0.
-
-            wrong_window = self.prepare_window(wrong_window)
-            x = np.concatenate([window, wrong_window], axis=0)
-
-            x = torch.FloatTensor(x)
-            mel = torch.FloatTensor(mel.T).unsqueeze(0)
-            indiv_mels = torch.FloatTensor(indiv_mels).unsqueeze(1)
-            y = torch.FloatTensor(y)
-            return x, indiv_mels, mel, y
-
-def save_sample_images(x, g, gt, global_step, checkpoint_dir):
-    x = (x.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
-    g = (g.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
-    gt = (gt.detach().cpu().numpy().transpose(0, 2, 3, 4, 1) * 255.).astype(np.uint8)
-
-    refs, inps = x[..., 3:], x[..., :3]
-    folder = join(checkpoint_dir, "samples_step{:09d}".format(global_step))
-    if not os.path.exists(folder): os.mkdir(folder)
-    collage = np.concatenate((refs, inps, g, gt), axis=-2)
-    for batch_idx, c in enumerate(collage):
-        for t in range(len(c)):
-            cv2.imwrite('{}/{}_{}.jpg'.format(folder, batch_idx, t), c[t])
-
-logloss = nn.BCELoss()
-def cosine_loss(a, v, y):
-    d = nn.functional.cosine_similarity(a, v)
-    loss = logloss(d.unsqueeze(1), y)
-
-    return loss
-
-device = torch.device("cuda" if use_cuda else "cpu")
-syncnet = SyncNet().to(device)
-for p in syncnet.parameters():
-    p.requires_grad = False
-
-recon_loss = nn.L1Loss()
-def get_sync_loss(mel, g):
-    g = g[:, :, :, g.size(3)//2:]
-    g = torch.cat([g[:, :, i] for i in range(syncnet_T)], dim=1)
-    # B, 3 * T, H//2, W
-    a, v = syncnet(mel, g)
-    y = torch.ones(g.size(0), 1).float().to(device)
-    return cosine_loss(a, v, y)
-
-def train(device, model, disc, train_data_loader, test_data_loader, optimizer, disc_optimizer,
-          checkpoint_dir=None, checkpoint_interval=None, nepochs=None):
-    global global_step, global_epoch
-    resumed_step = global_step
-
-    while global_epoch < nepochs:
-        print('Starting Epoch: {}'.format(global_epoch))
-        running_sync_loss, running_l1_loss, disc_loss, running_perceptual_loss = 0., 0., 0., 0.
-        running_disc_real_loss, running_disc_fake_loss = 0., 0.
-        prog_bar = tqdm(enumerate(train_data_loader))
-        for step, (x, indiv_mels, mel, gt) in prog_bar:
-            disc.train()
-            model.train()
-
-            x = x.to(device)
-            mel = mel.to(device)
-            indiv_mels = indiv_mels.to(device)
-            gt = gt.to(device)
-
-            ### Train generator now. Remove ALL grads. 
-            optimizer.zero_grad()
-            disc_optimizer.zero_grad()
-
-            g = model(indiv_mels, x)
-
-            if hparams.syncnet_wt > 0.:
-                sync_loss = get_sync_loss(mel, g)
-            else:
-                sync_loss = 0.
-
-            if hparams.disc_wt > 0.:
-                perceptual_loss = disc.perceptual_forward(g)
-            else:
-                perceptual_loss = 0.
-
-            l1loss = recon_loss(g, gt)
-
-            loss = hparams.syncnet_wt * sync_loss + hparams.disc_wt * perceptual_loss + \
-                                    (1. - hparams.syncnet_wt - hparams.disc_wt) * l1loss
-
-            loss.backward()
-            optimizer.step()
-
-            ### Remove all gradients before Training disc
-            disc_optimizer.zero_grad()
-
-            pred = disc(gt)
-            disc_real_loss = F.binary_cross_entropy(pred, torch.ones((len(pred), 1)).to(device))
-            disc_real_loss.backward()
-
-            pred = disc(g.detach())
-            disc_fake_loss = F.binary_cross_entropy(pred, torch.zeros((len(pred), 1)).to(device))
-            disc_fake_loss.backward()
-
-            disc_optimizer.step()
-
-            running_disc_real_loss += disc_real_loss.item()
-            running_disc_fake_loss += disc_fake_loss.item()
-
-            if global_step % checkpoint_interval == 0:
-                save_sample_images(x, g, gt, global_step, checkpoint_dir)
-
-            # Logs
-            global_step += 1
-            cur_session_steps = global_step - resumed_step
-
-            running_l1_loss += l1loss.item()
-            if hparams.syncnet_wt > 0.:
-                running_sync_loss += sync_loss.item()
-            else:
-                running_sync_loss += 0.
-
-            if hparams.disc_wt > 0.:
-                running_perceptual_loss += perceptual_loss.item()
-            else:
-                running_perceptual_loss += 0.
-
-            if global_step == 1 or global_step % checkpoint_interval == 0:
-                save_checkpoint(
-                    model, optimizer, global_step, checkpoint_dir, global_epoch)
-                save_checkpoint(disc, disc_optimizer, global_step, checkpoint_dir, global_epoch, prefix='disc_')
-
-
-            if global_step % hparams.eval_interval == 0:
-                with torch.no_grad():
-                    average_sync_loss = eval_model(test_data_loader, global_step, device, model, disc)
-
-                    if average_sync_loss < .75:
-                        hparams.set_hparam('syncnet_wt', 0.03)
-
-            prog_bar.set_description('L1: {}, Sync: {}, Percep: {} | Fake: {}, Real: {}'.format(running_l1_loss / (step + 1),
-                                                                                        running_sync_loss / (step + 1),
-                                                                                        running_perceptual_loss / (step + 1),
-                                                                                        running_disc_fake_loss / (step + 1),
-                                                                                        running_disc_real_loss / (step + 1)))
-
-        global_epoch += 1
-
-def eval_model(test_data_loader, global_step, device, model, disc):
-    eval_steps = 300
-    print('Evaluating for {} steps'.format(eval_steps))
-    running_sync_loss, running_l1_loss, running_disc_real_loss, running_disc_fake_loss, running_perceptual_loss = [], [], [], [], []
-    while 1:
-        for step, (x, indiv_mels, mel, gt) in enumerate((test_data_loader)):
-            model.eval()
-            disc.eval()
-
-            x = x.to(device)
-            mel = mel.to(device)
-            indiv_mels = indiv_mels.to(device)
-            gt = gt.to(device)
-
-            pred = disc(gt)
-            disc_real_loss = F.binary_cross_entropy(pred, torch.ones((len(pred), 1)).to(device))
-
-            g = model(indiv_mels, x)
-            pred = disc(g)
-            disc_fake_loss = F.binary_cross_entropy(pred, torch.zeros((len(pred), 1)).to(device))
-
-            running_disc_real_loss.append(disc_real_loss.item())
-            running_disc_fake_loss.append(disc_fake_loss.item())
-
-            sync_loss = get_sync_loss(mel, g)
-            
-            if hparams.disc_wt > 0.:
-                perceptual_loss = disc.perceptual_forward(g)
-            else:
-                perceptual_loss = 0.
-
-            l1loss = recon_loss(g, gt)
-
-            loss = hparams.syncnet_wt * sync_loss + hparams.disc_wt * perceptual_loss + \
-                                    (1. - hparams.syncnet_wt - hparams.disc_wt) * l1loss
-
-            running_l1_loss.append(l1loss.item())
-            running_sync_loss.append(sync_loss.item())
-            
-            if hparams.disc_wt > 0.:
-                running_perceptual_loss.append(perceptual_loss.item())
-            else:
-                running_perceptual_loss.append(0.)
-
-            if step > eval_steps: break
-
-        print('L1: {}, Sync: {}, Percep: {} | Fake: {}, Real: {}'.format(sum(running_l1_loss) / len(running_l1_loss),
-                                                            sum(running_sync_loss) / len(running_sync_loss),
-                                                            sum(running_perceptual_loss) / len(running_perceptual_loss),
-                                                            sum(running_disc_fake_loss) / len(running_disc_fake_loss),
-                                                             sum(running_disc_real_loss) / len(running_disc_real_loss)))
-        return sum(running_sync_loss) / len(running_sync_loss)
-
-
-def save_checkpoint(model, optimizer, step, checkpoint_dir, epoch, prefix=''):
-    checkpoint_path = join(
-        checkpoint_dir, "{}checkpoint_step{:09d}.pth".format(prefix, global_step))
-    optimizer_state = optimizer.state_dict() if hparams.save_optimizer_state else None
-    torch.save({
-        "state_dict": model.state_dict(),
-        "optimizer": optimizer_state,
-        "global_step": step,
-        "global_epoch": epoch,
-    }, checkpoint_path)
-    print("Saved checkpoint:", checkpoint_path)
-
-def _load(checkpoint_path):
-    if use_cuda:
-        checkpoint = torch.load(checkpoint_path)
-    else:
-        checkpoint = torch.load(checkpoint_path,
-                                map_location=lambda storage, loc: storage)
-    return checkpoint
-
-
-def load_checkpoint(path, model, optimizer, reset_optimizer=False, overwrite_global_states=True):
-    global global_step
-    global global_epoch
-
-    print("Load checkpoint from: {}".format(path))
-    checkpoint = _load(path)
-    s = checkpoint["state_dict"]
-    new_s = {}
-    for k, v in s.items():
-        new_s[k.replace('module.', '')] = v
-    model.load_state_dict(new_s)
-    if not reset_optimizer:
-        optimizer_state = checkpoint["optimizer"]
-        if optimizer_state is not None:
-            print("Load optimizer state from {}".format(path))
-            optimizer.load_state_dict(checkpoint["optimizer"])
-    if overwrite_global_states:
-        global_step = checkpoint["global_step"]
-        global_epoch = checkpoint["global_epoch"]
-
-    return model
-
-if __name__ == "__main__":
-    checkpoint_dir = args.checkpoint_dir
-
-    # Dataset and Dataloader setup
-    train_dataset = Dataset('train')
-    test_dataset = Dataset('val')
-
-    train_data_loader = data_utils.DataLoader(
-        train_dataset, batch_size=hparams.batch_size, shuffle=True,
-        num_workers=hparams.num_workers)
-
-    test_data_loader = data_utils.DataLoader(
-        test_dataset, batch_size=hparams.batch_size,
-        num_workers=4)
-
-    device = torch.device("cuda" if use_cuda else "cpu")
-
-     # Model
-    model = Wav2Lip().to(device)
-    disc = Wav2Lip_disc_qual().to(device)
-
-    print('total trainable params {}'.format(sum(p.numel() for p in model.parameters() if p.requires_grad)))
-    print('total DISC trainable params {}'.format(sum(p.numel() for p in disc.parameters() if p.requires_grad)))
-
-    optimizer = optim.Adam([p for p in model.parameters() if p.requires_grad],
-                           lr=hparams.initial_learning_rate, betas=(0.5, 0.999))
-    disc_optimizer = optim.Adam([p for p in disc.parameters() if p.requires_grad],
-                           lr=hparams.disc_initial_learning_rate, betas=(0.5, 0.999))
-
-    if args.checkpoint_path is not None:
-        load_checkpoint(args.checkpoint_path, model, optimizer, reset_optimizer=False)
-
-    if args.disc_checkpoint_path is not None:
-        load_checkpoint(args.disc_checkpoint_path, disc, disc_optimizer, 
-                                reset_optimizer=False, overwrite_global_states=False)
-        
-    load_checkpoint(args.syncnet_checkpoint_path, syncnet, None, reset_optimizer=True, 
-                                overwrite_global_states=False)
-
-    if not os.path.exists(checkpoint_dir):
-        os.mkdir(checkpoint_dir)
-
-    # Train!
-    train(device, model, disc, train_data_loader, test_data_loader, optimizer, disc_optimizer,
-              checkpoint_dir=checkpoint_dir,
-              checkpoint_interval=hparams.checkpoint_interval,
-              nepochs=hparams.nepochs)
diff --git a/spaces/hanzportgas/rvc-models/infer_pack/commons.py b/spaces/hanzportgas/rvc-models/infer_pack/commons.py
deleted file mode 100644
index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000
--- a/spaces/hanzportgas/rvc-models/infer_pack/commons.py
+++ /dev/null
@@ -1,166 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def init_weights(m, mean=0.0, std=0.01):
-    classname = m.__class__.__name__
-    if classname.find("Conv") != -1:
-        m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-    return int((kernel_size * dilation - dilation) / 2)
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-    """KL(P||Q)"""
-    kl = (logs_q - logs_p) - 0.5
-    kl += (
-        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
-    )
-    return kl
-
-
-def rand_gumbel(shape):
-    """Sample from the Gumbel distribution, protect from overflows."""
-    uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-    return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-    g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-    return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, :, idx_str:idx_end]
-    return ret
-
-
-def slice_segments2(x, ids_str, segment_size=4):
-    ret = torch.zeros_like(x[:, :segment_size])
-    for i in range(x.size(0)):
-        idx_str = ids_str[i]
-        idx_end = idx_str + segment_size
-        ret[i] = x[i, idx_str:idx_end]
-    return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-    b, d, t = x.size()
-    if x_lengths is None:
-        x_lengths = t
-    ids_str_max = x_lengths - segment_size + 1
-    ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-    ret = slice_segments(x, ids_str, segment_size)
-    return ret, ids_str
-
-
-def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4):
-    position = torch.arange(length, dtype=torch.float)
-    num_timescales = channels // 2
-    log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / (
-        num_timescales - 1
-    )
-    inv_timescales = min_timescale * torch.exp(
-        torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment
-    )
-    scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-    signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-    signal = F.pad(signal, [0, 0, 0, channels % 2])
-    signal = signal.view(1, channels, length)
-    return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-    b, channels, length = x.size()
-    signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-    return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-    mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-    return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-    n_channels_int = n_channels[0]
-    in_act = input_a + input_b
-    t_act = torch.tanh(in_act[:, :n_channels_int, :])
-    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-    acts = t_act * s_act
-    return acts
-
-
-def convert_pad_shape(pad_shape):
-    l = pad_shape[::-1]
-    pad_shape = [item for sublist in l for item in sublist]
-    return pad_shape
-
-
-def shift_1d(x):
-    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-    return x
-
-
-def sequence_mask(length, max_length=None):
-    if max_length is None:
-        max_length = length.max()
-    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-    return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-    """
-    duration: [b, 1, t_x]
-    mask: [b, 1, t_y, t_x]
-    """
-    device = duration.device
-
-    b, _, t_y, t_x = mask.shape
-    cum_duration = torch.cumsum(duration, -1)
-
-    cum_duration_flat = cum_duration.view(b * t_x)
-    path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-    path = path.view(b, t_x, t_y)
-    path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-    path = path.unsqueeze(1).transpose(2, 3) * mask
-    return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-    if isinstance(parameters, torch.Tensor):
-        parameters = [parameters]
-    parameters = list(filter(lambda p: p.grad is not None, parameters))
-    norm_type = float(norm_type)
-    if clip_value is not None:
-        clip_value = float(clip_value)
-
-    total_norm = 0
-    for p in parameters:
-        param_norm = p.grad.data.norm(norm_type)
-        total_norm += param_norm.item() ** norm_type
-        if clip_value is not None:
-            p.grad.data.clamp_(min=-clip_value, max=clip_value)
-    total_norm = total_norm ** (1.0 / norm_type)
-    return total_norm
diff --git a/spaces/haoqi7/research/README.md b/spaces/haoqi7/research/README.md
deleted file mode 100644
index 107a9c0ef807de9aecbad0ce311443d09014b703..0000000000000000000000000000000000000000
--- a/spaces/haoqi7/research/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Research
-emoji: 👀
-colorFrom: indigo
-colorTo: red
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp
deleted file mode 100644
index 432bf7c6c118ee0676659e7fb04881351ebd8642..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/csrc/cpu/soft_nms.cpp
+++ /dev/null
@@ -1,117 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-#include "cpu/vision.h"
-
-
-template <typename scalar_t>
-std::pair<at::Tensor, at::Tensor> soft_nms_cpu_kernel(const at::Tensor& dets,
-                                                      const at::Tensor& scores,
-                                                      const float threshold,
-                                                      const float sigma) {
-  AT_ASSERTM(!dets.device().is_cuda(), "dets must be a CPU tensor");
-  AT_ASSERTM(!scores.device().is_cuda(), "scores must be a CPU tensor");
-  AT_ASSERTM(dets.type() == scores.type(), "dets should have the same type as scores");
-
-  if (dets.numel() == 0) {
-    return std::make_pair(at::empty({0}, dets.options().dtype(at::kLong).device(at::kCPU)),
-                          at::empty({0}, scores.options().dtype(at::kFloat).device(at::kCPU)));
-  }
-
-  auto x1_t = dets.select(1, 0).contiguous();
-  auto y1_t = dets.select(1, 1).contiguous();
-  auto x2_t = dets.select(1, 2).contiguous();
-  auto y2_t = dets.select(1, 3).contiguous();
-
-  auto scores_t = scores.clone();
-
-  at::Tensor areas_t = (x2_t - x1_t + 1) * (y2_t - y1_t + 1);
-  auto ndets = dets.size(0);
-  auto inds_t = at::arange(ndets, dets.options().dtype(at::kLong).device(at::kCPU));
-
-  auto x1 = x1_t.data_ptr<scalar_t>();
-  auto y1 = y1_t.data_ptr<scalar_t>();
-  auto x2 = x2_t.data_ptr<scalar_t>();
-  auto y2 = y2_t.data_ptr<scalar_t>();
-  auto s = scores_t.data_ptr<scalar_t>();
-  auto inds = inds_t.data_ptr<int64_t>();
-  auto areas = areas_t.data_ptr<scalar_t>();
-
-  for (int64_t i = 0; i < ndets; i++) {
-
-    auto ix1 = x1[i];
-    auto iy1 = y1[i];
-    auto ix2 = x2[i];
-    auto iy2 = y2[i];
-    auto is = s[i];
-    auto ii = inds[i];
-    auto iarea = areas[i];
-
-    auto maxpos = scores_t.slice(0, i, ndets).argmax().item<int64_t>() + i;
-
-    // add max box as a detection
-    x1[i] = x1[maxpos];
-    y1[i] = y1[maxpos];
-    x2[i] = x2[maxpos];
-    y2[i] = y2[maxpos];
-    s[i] = s[maxpos];
-    inds[i] = inds[maxpos];
-    areas[i] = areas[maxpos];
-
-    // swap ith box with position of max box
-    x1[maxpos] = ix1;
-    y1[maxpos] = iy1;
-    x2[maxpos] = ix2;
-    y2[maxpos] = iy2;
-    s[maxpos] = is;
-    inds[maxpos] = ii;
-    areas[maxpos] = iarea;
-
-    ix1 = x1[i];
-    iy1 = y1[i];
-    ix2 = x2[i];
-    iy2 = y2[i];
-    iarea = areas[i];
-
-    // NMS iterations, note that ndets changes if detection boxes
-    // fall below threshold
-    for (int64_t j = i + 1; j < ndets; j++) {
-      auto xx1 = std::max(ix1, x1[j]);
-      auto yy1 = std::max(iy1, y1[j]);
-      auto xx2 = std::min(ix2, x2[j]);
-      auto yy2 = std::min(iy2, y2[j]);
-
-      auto w = std::max(static_cast<scalar_t>(0), xx2 - xx1 + 1);
-      auto h = std::max(static_cast<scalar_t>(0), yy2 - yy1 + 1);
-
-      auto inter = w * h;
-      auto ovr = inter / (iarea + areas[j] - inter);
-
-      s[j] = s[j] * std::exp(- std::pow(ovr, 2.0) / sigma);
-
-      // if box score falls below threshold, discard the box by
-      // swapping with last box update ndets
-      if (s[j] < threshold) {
-        x1[j] = x1[ndets - 1];
-        y1[j] = y1[ndets - 1];
-        x2[j] = x2[ndets - 1];
-        y2[j] = y2[ndets - 1];
-        s[j] = s[ndets - 1];
-        inds[j] = inds[ndets - 1];
-        areas[j] = areas[ndets - 1];
-        j--;
-        ndets--;
-      }
-    }
-  }
-  return std::make_pair(inds_t.slice(0, 0, ndets), scores_t.slice(0, 0, ndets));
-}
-
-std::pair<at::Tensor, at::Tensor> soft_nms_cpu(const at::Tensor& dets,
-                                               const at::Tensor& scores,
-                                               const float threshold,
-                                               const float sigma) {
-  std::pair<at::Tensor, at::Tensor> result;
-  AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "soft_nms", [&] {
-    result = soft_nms_cpu_kernel<scalar_t>(dets, scores, threshold, sigma);
-  });
-  return result;
-}
\ No newline at end of file
diff --git a/spaces/hasibzunair/masksup-segmentation-demo/README.md b/spaces/hasibzunair/masksup-segmentation-demo/README.md
deleted file mode 100644
index c09c33dbbc4099ed6fbfec6100361dde104d9835..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/masksup-segmentation-demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Masksup Segmentation Demo
-emoji: 🛋️ 🛏️ 📦
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.45.2
-app_file: app.py
-pinned: false
-license: afl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hjs8/CogVideo/model.py b/spaces/hjs8/CogVideo/model.py
deleted file mode 100644
index 1510b1c7339bfd981371a2cc0a1fa9e639844552..0000000000000000000000000000000000000000
--- a/spaces/hjs8/CogVideo/model.py
+++ /dev/null
@@ -1,1243 +0,0 @@
-# This code is adapted from https://github.com/THUDM/CogVideo/blob/ff423aa169978fb2f636f761e348631fa3178b03/cogvideo_pipeline.py
-
-from __future__ import annotations
-
-import argparse
-import logging
-import os
-import pathlib
-import shutil
-import subprocess
-import sys
-import tempfile
-import time
-import zipfile
-from typing import Any
-
-if os.getenv('SYSTEM') == 'spaces':
-    subprocess.run('pip install icetk==0.0.4'.split())
-    subprocess.run('pip install SwissArmyTransformer==0.2.9'.split())
-    subprocess.run(
-        'pip install git+https://github.com/Sleepychord/Image-Local-Attention@43fee31'
-        .split())
-    #subprocess.run('git clone https://github.com/NVIDIA/apex'.split())
-    #subprocess.run('git checkout 1403c21'.split(), cwd='apex')
-    #with open('patch.apex') as f:
-    #    subprocess.run('patch -p1'.split(), cwd='apex', stdin=f)
-    #subprocess.run(
-    #    'pip install -v --disable-pip-version-check --no-cache-dir --global-option --cpp_ext --global-option --cuda_ext ./'
-    #    .split(),
-    #    cwd='apex')
-    #subprocess.run('rm -rf apex'.split())
-    with open('patch') as f:
-        subprocess.run('patch -p1'.split(), cwd='CogVideo', stdin=f)
-
-    from huggingface_hub import hf_hub_download
-
-    def download_and_extract_icetk_models() -> None:
-        icetk_model_dir = pathlib.Path('/home/user/.icetk_models')
-        icetk_model_dir.mkdir()
-        path = hf_hub_download('THUDM/icetk',
-                               'models.zip',
-                               use_auth_token=os.getenv('HF_TOKEN'))
-        with zipfile.ZipFile(path) as f:
-            f.extractall(path=icetk_model_dir.as_posix())
-
-    def download_and_extract_cogvideo_models(name: str) -> None:
-        path = hf_hub_download('THUDM/CogVideo',
-                               name,
-                               use_auth_token=os.getenv('HF_TOKEN'))
-        with zipfile.ZipFile(path) as f:
-            f.extractall('pretrained')
-        os.remove(path)
-
-    def download_and_extract_cogview2_models(name: str) -> None:
-        path = hf_hub_download('THUDM/CogView2', name)
-        with zipfile.ZipFile(path) as f:
-            f.extractall()
-        shutil.move('/home/user/app/sharefs/cogview-new/cogview2-dsr',
-                    'pretrained')
-        shutil.rmtree('/home/user/app/sharefs/')
-        os.remove(path)
-
-    download_and_extract_icetk_models()
-    download_and_extract_cogvideo_models('cogvideo-stage1.zip')
-    #download_and_extract_cogvideo_models('cogvideo-stage2.zip')
-    #download_and_extract_cogview2_models('cogview2-dsr.zip')
-
-    os.environ['SAT_HOME'] = '/home/user/app/pretrained'
-
-import gradio as gr
-import imageio.v2 as iio
-import numpy as np
-import torch
-from icetk import IceTokenizer
-from SwissArmyTransformer import get_args
-from SwissArmyTransformer.arguments import set_random_seed
-from SwissArmyTransformer.generation.sampling_strategies import BaseStrategy
-from SwissArmyTransformer.resources import auto_create
-
-app_dir = pathlib.Path(__file__).parent
-submodule_dir = app_dir / 'CogVideo'
-sys.path.insert(0, submodule_dir.as_posix())
-
-from coglm_strategy import CoglmStrategy
-from models.cogvideo_cache_model import CogVideoCacheModel
-from sr_pipeline import DirectSuperResolution
-
-formatter = logging.Formatter(
-    '[%(asctime)s] %(name)s %(levelname)s: %(message)s',
-    datefmt='%Y-%m-%d %H:%M:%S')
-stream_handler = logging.StreamHandler(stream=sys.stdout)
-stream_handler.setLevel(logging.INFO)
-stream_handler.setFormatter(formatter)
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.INFO)
-logger.propagate = False
-logger.addHandler(stream_handler)
-
-ICETK_MODEL_DIR = app_dir / 'icetk_models'
-
-
-def get_masks_and_position_ids_stage1(data, textlen, framelen):
-    # Extract batch size and sequence length.
-    tokens = data
-    seq_length = len(data[0])
-    # Attention mask (lower triangular).
-    attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
-                                device=data.device)
-    attention_mask[:, :textlen, textlen:] = 0
-    attention_mask[:, textlen:, textlen:].tril_()
-    attention_mask.unsqueeze_(1)
-    # Unaligned version
-    position_ids = torch.zeros(seq_length,
-                               dtype=torch.long,
-                               device=data.device)
-    torch.arange(textlen,
-                 out=position_ids[:textlen],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512,
-                 512 + seq_length - textlen,
-                 out=position_ids[textlen:],
-                 dtype=torch.long,
-                 device=data.device)
-    position_ids = position_ids.unsqueeze(0)
-
-    return tokens, attention_mask, position_ids
-
-
-def get_masks_and_position_ids_stage2(data, textlen, framelen):
-    # Extract batch size and sequence length.
-    tokens = data
-    seq_length = len(data[0])
-
-    # Attention mask (lower triangular).
-    attention_mask = torch.ones((1, textlen + framelen, textlen + framelen),
-                                device=data.device)
-    attention_mask[:, :textlen, textlen:] = 0
-    attention_mask[:, textlen:, textlen:].tril_()
-    attention_mask.unsqueeze_(1)
-
-    # Unaligned version
-    position_ids = torch.zeros(seq_length,
-                               dtype=torch.long,
-                               device=data.device)
-    torch.arange(textlen,
-                 out=position_ids[:textlen],
-                 dtype=torch.long,
-                 device=data.device)
-    frame_num = (seq_length - textlen) // framelen
-    assert frame_num == 5
-    torch.arange(512,
-                 512 + framelen,
-                 out=position_ids[textlen:textlen + framelen],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * 2,
-                 512 + framelen * 3,
-                 out=position_ids[textlen + framelen:textlen + framelen * 2],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * (frame_num - 1),
-                 512 + framelen * frame_num,
-                 out=position_ids[textlen + framelen * 2:textlen +
-                                  framelen * 3],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * 1,
-                 512 + framelen * 2,
-                 out=position_ids[textlen + framelen * 3:textlen +
-                                  framelen * 4],
-                 dtype=torch.long,
-                 device=data.device)
-    torch.arange(512 + framelen * 3,
-                 512 + framelen * 4,
-                 out=position_ids[textlen + framelen * 4:textlen +
-                                  framelen * 5],
-                 dtype=torch.long,
-                 device=data.device)
-
-    position_ids = position_ids.unsqueeze(0)
-
-    return tokens, attention_mask, position_ids
-
-
-def my_update_mems(hiddens, mems_buffers, mems_indexs,
-                   limited_spatial_channel_mem, text_len, frame_len):
-    if hiddens is None:
-        return None, mems_indexs
-    mem_num = len(hiddens)
-    ret_mem = []
-    with torch.no_grad():
-        for id in range(mem_num):
-            if hiddens[id][0] is None:
-                ret_mem.append(None)
-            else:
-                if id == 0 and limited_spatial_channel_mem and mems_indexs[
-                        id] + hiddens[0][0].shape[1] >= text_len + frame_len:
-                    if mems_indexs[id] == 0:
-                        for layer, hidden in enumerate(hiddens[id]):
-                            mems_buffers[id][
-                                layer, :, :text_len] = hidden.expand(
-                                    mems_buffers[id].shape[1], -1,
-                                    -1)[:, :text_len]
-                    new_mem_len_part2 = (mems_indexs[id] +
-                                         hiddens[0][0].shape[1] -
-                                         text_len) % frame_len
-                    if new_mem_len_part2 > 0:
-                        for layer, hidden in enumerate(hiddens[id]):
-                            mems_buffers[id][
-                                layer, :, text_len:text_len +
-                                new_mem_len_part2] = hidden.expand(
-                                    mems_buffers[id].shape[1], -1,
-                                    -1)[:, -new_mem_len_part2:]
-                    mems_indexs[id] = text_len + new_mem_len_part2
-                else:
-                    for layer, hidden in enumerate(hiddens[id]):
-                        mems_buffers[id][layer, :,
-                                         mems_indexs[id]:mems_indexs[id] +
-                                         hidden.shape[1]] = hidden.expand(
-                                             mems_buffers[id].shape[1], -1, -1)
-                    mems_indexs[id] += hidden.shape[1]
-                ret_mem.append(mems_buffers[id][:, :, :mems_indexs[id]])
-    return ret_mem, mems_indexs
-
-
-def calc_next_tokens_frame_begin_id(text_len, frame_len, total_len):
-    # The fisrt token's position id of the frame that the next token belongs to;
-    if total_len < text_len:
-        return None
-    return (total_len - text_len) // frame_len * frame_len + text_len
-
-
-def my_filling_sequence(
-        model,
-        tokenizer,
-        args,
-        seq,
-        batch_size,
-        get_masks_and_position_ids,
-        text_len,
-        frame_len,
-        strategy=BaseStrategy(),
-        strategy2=BaseStrategy(),
-        mems=None,
-        log_text_attention_weights=0,  # default to 0: no artificial change
-        mode_stage1=True,
-        enforce_no_swin=False,
-        guider_seq=None,
-        guider_text_len=0,
-        guidance_alpha=1,
-        limited_spatial_channel_mem=False,  # 空间通道的存储限制在本帧内
-        **kw_args):
-    '''
-        seq: [2, 3, 5, ..., -1(to be generated), -1, ...]
-        mems: [num_layers, batch_size, len_mems(index), mem_hidden_size]
-            cache, should be first mems.shape[1] parts of context_tokens.
-            mems are the first-level citizens here, but we don't assume what is memorized.
-            input mems are used when multi-phase generation.
-    '''
-    if guider_seq is not None:
-        logger.debug('Using Guidance In Inference')
-    if limited_spatial_channel_mem:
-        logger.debug("Limit spatial-channel's mem to current frame")
-    assert len(seq.shape) == 2
-
-    # building the initial tokens, attention_mask, and position_ids
-    actual_context_length = 0
-
-    while seq[-1][
-            actual_context_length] >= 0:  # the last seq has least given tokens
-        actual_context_length += 1  # [0, context_length-1] are given
-    assert actual_context_length > 0
-    current_frame_num = (actual_context_length - text_len) // frame_len
-    assert current_frame_num >= 0
-    context_length = text_len + current_frame_num * frame_len
-
-    tokens, attention_mask, position_ids = get_masks_and_position_ids(
-        seq, text_len, frame_len)
-    tokens = tokens[..., :context_length]
-    input_tokens = tokens.clone()
-
-    if guider_seq is not None:
-        guider_index_delta = text_len - guider_text_len
-        guider_tokens, guider_attention_mask, guider_position_ids = get_masks_and_position_ids(
-            guider_seq, guider_text_len, frame_len)
-        guider_tokens = guider_tokens[..., :context_length -
-                                      guider_index_delta]
-        guider_input_tokens = guider_tokens.clone()
-
-    for fid in range(current_frame_num):
-        input_tokens[:, text_len + 400 * fid] = tokenizer['<start_of_image>']
-        if guider_seq is not None:
-            guider_input_tokens[:, guider_text_len +
-                                400 * fid] = tokenizer['<start_of_image>']
-
-    attention_mask = attention_mask.type_as(next(
-        model.parameters()))  # if fp16
-    # initialize generation
-    counter = context_length - 1  # Last fixed index is ``counter''
-    index = 0  # Next forward starting index, also the length of cache.
-    mems_buffers_on_GPU = False
-    mems_indexs = [0, 0]
-    mems_len = [(400 + 74) if limited_spatial_channel_mem else 5 * 400 + 74,
-                5 * 400 + 74]
-    mems_buffers = [
-        torch.zeros(args.num_layers,
-                    batch_size,
-                    mem_len,
-                    args.hidden_size * 2,
-                    dtype=next(model.parameters()).dtype)
-        for mem_len in mems_len
-    ]
-
-    if guider_seq is not None:
-        guider_attention_mask = guider_attention_mask.type_as(
-            next(model.parameters()))  # if fp16
-        guider_mems_buffers = [
-            torch.zeros(args.num_layers,
-                        batch_size,
-                        mem_len,
-                        args.hidden_size * 2,
-                        dtype=next(model.parameters()).dtype)
-            for mem_len in mems_len
-        ]
-        guider_mems_indexs = [0, 0]
-        guider_mems = None
-
-    torch.cuda.empty_cache()
-    # step-by-step generation
-    while counter < len(seq[0]) - 1:
-        # we have generated counter+1 tokens
-        # Now, we want to generate seq[counter + 1],
-        # token[:, index: counter+1] needs forwarding.
-        if index == 0:
-            group_size = 2 if (input_tokens.shape[0] == batch_size
-                               and not mode_stage1) else batch_size
-
-            logits_all = None
-            for batch_idx in range(0, input_tokens.shape[0], group_size):
-                logits, *output_per_layers = model(
-                    input_tokens[batch_idx:batch_idx + group_size, index:],
-                    position_ids[..., index:counter + 1],
-                    attention_mask,  # TODO memlen
-                    mems=mems,
-                    text_len=text_len,
-                    frame_len=frame_len,
-                    counter=counter,
-                    log_text_attention_weights=log_text_attention_weights,
-                    enforce_no_swin=enforce_no_swin,
-                    **kw_args)
-                logits_all = torch.cat(
-                    (logits_all,
-                     logits), dim=0) if logits_all is not None else logits
-                mem_kv01 = [[o['mem_kv'][0] for o in output_per_layers],
-                            [o['mem_kv'][1] for o in output_per_layers]]
-                next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
-                    text_len, frame_len, mem_kv01[0][0].shape[1])
-                for id, mem_kv in enumerate(mem_kv01):
-                    for layer, mem_kv_perlayer in enumerate(mem_kv):
-                        if limited_spatial_channel_mem and id == 0:
-                            mems_buffers[id][
-                                layer, batch_idx:batch_idx + group_size, :
-                                text_len] = mem_kv_perlayer.expand(
-                                    min(group_size,
-                                        input_tokens.shape[0] - batch_idx), -1,
-                                    -1)[:, :text_len]
-                            mems_buffers[id][layer, batch_idx:batch_idx+group_size, text_len:text_len+mem_kv_perlayer.shape[1]-next_tokens_frame_begin_id] =\
-                                mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, next_tokens_frame_begin_id:]
-                        else:
-                            mems_buffers[id][
-                                layer, batch_idx:batch_idx +
-                                group_size, :mem_kv_perlayer.
-                                shape[1]] = mem_kv_perlayer.expand(
-                                    min(group_size,
-                                        input_tokens.shape[0] - batch_idx), -1,
-                                    -1)
-                mems_indexs[0], mems_indexs[1] = mem_kv01[0][0].shape[
-                    1], mem_kv01[1][0].shape[1]
-                if limited_spatial_channel_mem:
-                    mems_indexs[0] -= (next_tokens_frame_begin_id - text_len)
-
-            mems = [
-                mems_buffers[id][:, :, :mems_indexs[id]] for id in range(2)
-            ]
-            logits = logits_all
-
-            # Guider
-            if guider_seq is not None:
-                guider_logits_all = None
-                for batch_idx in range(0, guider_input_tokens.shape[0],
-                                       group_size):
-                    guider_logits, *guider_output_per_layers = model(
-                        guider_input_tokens[batch_idx:batch_idx + group_size,
-                                            max(index -
-                                                guider_index_delta, 0):],
-                        guider_position_ids[
-                            ...,
-                            max(index - guider_index_delta, 0):counter + 1 -
-                            guider_index_delta],
-                        guider_attention_mask,
-                        mems=guider_mems,
-                        text_len=guider_text_len,
-                        frame_len=frame_len,
-                        counter=counter - guider_index_delta,
-                        log_text_attention_weights=log_text_attention_weights,
-                        enforce_no_swin=enforce_no_swin,
-                        **kw_args)
-                    guider_logits_all = torch.cat(
-                        (guider_logits_all, guider_logits), dim=0
-                    ) if guider_logits_all is not None else guider_logits
-                    guider_mem_kv01 = [[
-                        o['mem_kv'][0] for o in guider_output_per_layers
-                    ], [o['mem_kv'][1] for o in guider_output_per_layers]]
-                    for id, guider_mem_kv in enumerate(guider_mem_kv01):
-                        for layer, guider_mem_kv_perlayer in enumerate(
-                                guider_mem_kv):
-                            if limited_spatial_channel_mem and id == 0:
-                                guider_mems_buffers[id][
-                                    layer, batch_idx:batch_idx + group_size, :
-                                    guider_text_len] = guider_mem_kv_perlayer.expand(
-                                        min(group_size,
-                                            input_tokens.shape[0] - batch_idx),
-                                        -1, -1)[:, :guider_text_len]
-                                guider_next_tokens_frame_begin_id = calc_next_tokens_frame_begin_id(
-                                    guider_text_len, frame_len,
-                                    guider_mem_kv_perlayer.shape[1])
-                                guider_mems_buffers[id][layer, batch_idx:batch_idx+group_size, guider_text_len:guider_text_len+guider_mem_kv_perlayer.shape[1]-guider_next_tokens_frame_begin_id] =\
-                                    guider_mem_kv_perlayer.expand(min(group_size, input_tokens.shape[0]-batch_idx), -1, -1)[:, guider_next_tokens_frame_begin_id:]
-                            else:
-                                guider_mems_buffers[id][
-                                    layer, batch_idx:batch_idx +
-                                    group_size, :guider_mem_kv_perlayer.
-                                    shape[1]] = guider_mem_kv_perlayer.expand(
-                                        min(group_size,
-                                            input_tokens.shape[0] - batch_idx),
-                                        -1, -1)
-                    guider_mems_indexs[0], guider_mems_indexs[
-                        1] = guider_mem_kv01[0][0].shape[1], guider_mem_kv01[
-                            1][0].shape[1]
-                    if limited_spatial_channel_mem:
-                        guider_mems_indexs[0] -= (
-                            guider_next_tokens_frame_begin_id -
-                            guider_text_len)
-                guider_mems = [
-                    guider_mems_buffers[id][:, :, :guider_mems_indexs[id]]
-                    for id in range(2)
-                ]
-                guider_logits = guider_logits_all
-        else:
-            if not mems_buffers_on_GPU:
-                if not mode_stage1:
-                    torch.cuda.empty_cache()
-                    for idx, mem in enumerate(mems):
-                        mems[idx] = mem.to(next(model.parameters()).device)
-                    if guider_seq is not None:
-                        for idx, mem in enumerate(guider_mems):
-                            guider_mems[idx] = mem.to(
-                                next(model.parameters()).device)
-                else:
-                    torch.cuda.empty_cache()
-                    for idx, mem_buffer in enumerate(mems_buffers):
-                        mems_buffers[idx] = mem_buffer.to(
-                            next(model.parameters()).device)
-                    mems = [
-                        mems_buffers[id][:, :, :mems_indexs[id]]
-                        for id in range(2)
-                    ]
-                    if guider_seq is not None:
-                        for idx, guider_mem_buffer in enumerate(
-                                guider_mems_buffers):
-                            guider_mems_buffers[idx] = guider_mem_buffer.to(
-                                next(model.parameters()).device)
-                        guider_mems = [
-                            guider_mems_buffers[id]
-                            [:, :, :guider_mems_indexs[id]] for id in range(2)
-                        ]
-                    mems_buffers_on_GPU = True
-
-            logits, *output_per_layers = model(
-                input_tokens[:, index:],
-                position_ids[..., index:counter + 1],
-                attention_mask,  # TODO memlen
-                mems=mems,
-                text_len=text_len,
-                frame_len=frame_len,
-                counter=counter,
-                log_text_attention_weights=log_text_attention_weights,
-                enforce_no_swin=enforce_no_swin,
-                limited_spatial_channel_mem=limited_spatial_channel_mem,
-                **kw_args)
-            mem_kv0, mem_kv1 = [o['mem_kv'][0] for o in output_per_layers
-                                ], [o['mem_kv'][1] for o in output_per_layers]
-
-            if guider_seq is not None:
-                guider_logits, *guider_output_per_layers = model(
-                    guider_input_tokens[:,
-                                        max(index - guider_index_delta, 0):],
-                    guider_position_ids[...,
-                                        max(index -
-                                            guider_index_delta, 0):counter +
-                                        1 - guider_index_delta],
-                    guider_attention_mask,
-                    mems=guider_mems,
-                    text_len=guider_text_len,
-                    frame_len=frame_len,
-                    counter=counter - guider_index_delta,
-                    log_text_attention_weights=0,
-                    enforce_no_swin=enforce_no_swin,
-                    limited_spatial_channel_mem=limited_spatial_channel_mem,
-                    **kw_args)
-                guider_mem_kv0, guider_mem_kv1 = [
-                    o['mem_kv'][0] for o in guider_output_per_layers
-                ], [o['mem_kv'][1] for o in guider_output_per_layers]
-
-            if not mems_buffers_on_GPU:
-                torch.cuda.empty_cache()
-                for idx, mem_buffer in enumerate(mems_buffers):
-                    mems_buffers[idx] = mem_buffer.to(
-                        next(model.parameters()).device)
-                if guider_seq is not None:
-                    for idx, guider_mem_buffer in enumerate(
-                            guider_mems_buffers):
-                        guider_mems_buffers[idx] = guider_mem_buffer.to(
-                            next(model.parameters()).device)
-                mems_buffers_on_GPU = True
-
-            mems, mems_indexs = my_update_mems([mem_kv0, mem_kv1],
-                                               mems_buffers, mems_indexs,
-                                               limited_spatial_channel_mem,
-                                               text_len, frame_len)
-            if guider_seq is not None:
-                guider_mems, guider_mems_indexs = my_update_mems(
-                    [guider_mem_kv0, guider_mem_kv1], guider_mems_buffers,
-                    guider_mems_indexs, limited_spatial_channel_mem,
-                    guider_text_len, frame_len)
-
-        counter += 1
-        index = counter
-
-        logits = logits[:, -1].expand(batch_size,
-                                      -1)  # [batch size, vocab size]
-        tokens = tokens.expand(batch_size, -1)
-        if guider_seq is not None:
-            guider_logits = guider_logits[:, -1].expand(batch_size, -1)
-            guider_tokens = guider_tokens.expand(batch_size, -1)
-
-        if seq[-1][counter].item() < 0:
-            # sampling
-            guided_logits = guider_logits + (
-                logits - guider_logits
-            ) * guidance_alpha if guider_seq is not None else logits
-            if mode_stage1 and counter < text_len + 400:
-                tokens, mems = strategy.forward(guided_logits, tokens, mems)
-            else:
-                tokens, mems = strategy2.forward(guided_logits, tokens, mems)
-            if guider_seq is not None:
-                guider_tokens = torch.cat((guider_tokens, tokens[:, -1:]),
-                                          dim=1)
-
-            if seq[0][counter].item() >= 0:
-                for si in range(seq.shape[0]):
-                    if seq[si][counter].item() >= 0:
-                        tokens[si, -1] = seq[si, counter]
-                        if guider_seq is not None:
-                            guider_tokens[si,
-                                          -1] = guider_seq[si, counter -
-                                                           guider_index_delta]
-
-        else:
-            tokens = torch.cat(
-                (tokens, seq[:, counter:counter + 1].clone().expand(
-                    tokens.shape[0], 1).to(device=tokens.device,
-                                           dtype=tokens.dtype)),
-                dim=1)
-            if guider_seq is not None:
-                guider_tokens = torch.cat(
-                    (guider_tokens,
-                     guider_seq[:, counter - guider_index_delta:counter + 1 -
-                                guider_index_delta].clone().expand(
-                                    guider_tokens.shape[0], 1).to(
-                                        device=guider_tokens.device,
-                                        dtype=guider_tokens.dtype)),
-                    dim=1)
-
-        input_tokens = tokens.clone()
-        if guider_seq is not None:
-            guider_input_tokens = guider_tokens.clone()
-        if (index - text_len - 1) // 400 < (input_tokens.shape[-1] - text_len -
-                                            1) // 400:
-            boi_idx = ((index - text_len - 1) // 400 + 1) * 400 + text_len
-            while boi_idx < input_tokens.shape[-1]:
-                input_tokens[:, boi_idx] = tokenizer['<start_of_image>']
-                if guider_seq is not None:
-                    guider_input_tokens[:, boi_idx -
-                                        guider_index_delta] = tokenizer[
-                                            '<start_of_image>']
-                boi_idx += 400
-
-        if strategy.is_done:
-            break
-    return strategy.finalize(tokens, mems)
-
-
-class InferenceModel_Sequential(CogVideoCacheModel):
-    def __init__(self, args, transformer=None, parallel_output=True):
-        super().__init__(args,
-                         transformer=transformer,
-                         parallel_output=parallel_output,
-                         window_size=-1,
-                         cogvideo_stage=1)
-
-    # TODO: check it
-
-    def final_forward(self, logits, **kwargs):
-        logits_parallel = logits
-        logits_parallel = torch.nn.functional.linear(
-            logits_parallel.float(),
-            self.transformer.word_embeddings.weight[:20000].float())
-        return logits_parallel
-
-
-class InferenceModel_Interpolate(CogVideoCacheModel):
-    def __init__(self, args, transformer=None, parallel_output=True):
-        super().__init__(args,
-                         transformer=transformer,
-                         parallel_output=parallel_output,
-                         window_size=10,
-                         cogvideo_stage=2)
-
-    # TODO: check it
-
-    def final_forward(self, logits, **kwargs):
-        logits_parallel = logits
-        logits_parallel = torch.nn.functional.linear(
-            logits_parallel.float(),
-            self.transformer.word_embeddings.weight[:20000].float())
-        return logits_parallel
-
-
-def get_default_args() -> argparse.Namespace:
-    known = argparse.Namespace(generate_frame_num=5,
-                               coglm_temperature2=0.89,
-                               use_guidance_stage1=True,
-                               use_guidance_stage2=False,
-                               guidance_alpha=3.0,
-                               stage_1=True,
-                               stage_2=False,
-                               both_stages=False,
-                               parallel_size=1,
-                               stage1_max_inference_batch_size=-1,
-                               multi_gpu=False,
-                               layout='64, 464, 2064',
-                               window_size=10,
-                               additional_seqlen=2000,
-                               cogvideo_stage=1)
-
-    args_list = [
-        '--tokenizer-type',
-        'fake',
-        '--mode',
-        'inference',
-        '--distributed-backend',
-        'nccl',
-        '--fp16',
-        '--model-parallel-size',
-        '1',
-        '--temperature',
-        '1.05',
-        '--top_k',
-        '12',
-        '--sandwich-ln',
-        '--seed',
-        '1234',
-        '--num-workers',
-        '0',
-        '--batch-size',
-        '1',
-        '--max-inference-batch-size',
-        '8',
-    ]
-    args = get_args(args_list)
-    args = argparse.Namespace(**vars(args), **vars(known))
-    args.layout = [int(x) for x in args.layout.split(',')]
-    args.do_train = False
-    return args
-
-
-class Model:
-    def __init__(self, only_first_stage: bool = False):
-        self.args = get_default_args()
-        if only_first_stage:
-            self.args.stage_1 = True
-            self.args.both_stages = False
-        else:
-            self.args.stage_1 = False
-            self.args.both_stages = True
-
-        self.tokenizer = self.load_tokenizer()
-
-        self.model_stage1, self.args = self.load_model_stage1()
-        self.model_stage2, self.args = self.load_model_stage2()
-
-        self.strategy_cogview2, self.strategy_cogvideo = self.load_strategies()
-        self.dsr = self.load_dsr()
-
-        self.device = torch.device(self.args.device)
-
-    def load_tokenizer(self) -> IceTokenizer:
-        logger.info('--- load_tokenizer ---')
-        start = time.perf_counter()
-
-        tokenizer = IceTokenizer(ICETK_MODEL_DIR.as_posix())
-        tokenizer.add_special_tokens(
-            ['<start_of_image>', '<start_of_english>', '<start_of_chinese>'])
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return tokenizer
-
-    def load_model_stage1(
-            self) -> tuple[CogVideoCacheModel, argparse.Namespace]:
-        logger.info('--- load_model_stage1 ---')
-        start = time.perf_counter()
-
-        args = self.args
-        model_stage1, args = InferenceModel_Sequential.from_pretrained(
-            args, 'cogvideo-stage1')
-        model_stage1.eval()
-        if args.both_stages:
-            model_stage1 = model_stage1.cpu()
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return model_stage1, args
-
-    def load_model_stage2(
-            self) -> tuple[CogVideoCacheModel | None, argparse.Namespace]:
-        logger.info('--- load_model_stage2 ---')
-        start = time.perf_counter()
-
-        args = self.args
-        if args.both_stages:
-            model_stage2, args = InferenceModel_Interpolate.from_pretrained(
-                args, 'cogvideo-stage2')
-            model_stage2.eval()
-            if args.both_stages:
-                model_stage2 = model_stage2.cpu()
-        else:
-            model_stage2 = None
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return model_stage2, args
-
-    def load_strategies(self) -> tuple[CoglmStrategy, CoglmStrategy]:
-        logger.info('--- load_strategies ---')
-        start = time.perf_counter()
-
-        invalid_slices = [slice(self.tokenizer.num_image_tokens, None)]
-        strategy_cogview2 = CoglmStrategy(invalid_slices,
-                                          temperature=1.0,
-                                          top_k=16)
-        strategy_cogvideo = CoglmStrategy(
-            invalid_slices,
-            temperature=self.args.temperature,
-            top_k=self.args.top_k,
-            temperature2=self.args.coglm_temperature2)
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return strategy_cogview2, strategy_cogvideo
-
-    def load_dsr(self) -> DirectSuperResolution | None:
-        logger.info('--- load_dsr ---')
-        start = time.perf_counter()
-
-        if self.args.both_stages:
-            path = auto_create('cogview2-dsr', path=None)
-            dsr = DirectSuperResolution(self.args,
-                                        path,
-                                        max_bz=12,
-                                        onCUDA=False)
-        else:
-            dsr = None
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return dsr
-
-    @torch.inference_mode()
-    def process_stage1(self,
-                       model,
-                       seq_text,
-                       duration,
-                       video_raw_text=None,
-                       video_guidance_text='视频',
-                       image_text_suffix='',
-                       batch_size=1,
-                       image_prompt=None):
-        process_start_time = time.perf_counter()
-
-        generate_frame_num = self.args.generate_frame_num
-        tokenizer = self.tokenizer
-        use_guide = self.args.use_guidance_stage1
-
-        if next(model.parameters()).device != self.device:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage 1 model to cuda')
-
-            model = model.to(self.device)
-
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
-
-        if video_raw_text is None:
-            video_raw_text = seq_text
-        mbz = self.args.stage1_max_inference_batch_size if self.args.stage1_max_inference_batch_size > 0 else self.args.max_inference_batch_size
-        assert batch_size < mbz or batch_size % mbz == 0
-        frame_len = 400
-
-        # generate the first frame:
-        enc_text = tokenizer.encode(seq_text + image_text_suffix)
-        seq_1st = enc_text + [tokenizer['<start_of_image>']] + [-1] * 400
-        logger.info(
-            f'[Generating First Frame with CogView2] Raw text: {tokenizer.decode(enc_text):s}'
-        )
-        text_len_1st = len(seq_1st) - frame_len * 1 - 1
-
-        seq_1st = torch.tensor(seq_1st, dtype=torch.long,
-                               device=self.device).unsqueeze(0)
-        if image_prompt is None:
-            output_list_1st = []
-            for tim in range(max(batch_size // mbz, 1)):
-                start_time = time.perf_counter()
-                output_list_1st.append(
-                    my_filling_sequence(
-                        model,
-                        tokenizer,
-                        self.args,
-                        seq_1st.clone(),
-                        batch_size=min(batch_size, mbz),
-                        get_masks_and_position_ids=
-                        get_masks_and_position_ids_stage1,
-                        text_len=text_len_1st,
-                        frame_len=frame_len,
-                        strategy=self.strategy_cogview2,
-                        strategy2=self.strategy_cogvideo,
-                        log_text_attention_weights=1.4,
-                        enforce_no_swin=True,
-                        mode_stage1=True,
-                    )[0])
-                elapsed = time.perf_counter() - start_time
-                logger.info(f'[First Frame] Elapsed: {elapsed:.2f}')
-            output_tokens_1st = torch.cat(output_list_1st, dim=0)
-            given_tokens = output_tokens_1st[:, text_len_1st + 1:text_len_1st +
-                                            401].unsqueeze(
-                                                1
-                                            )  # given_tokens.shape: [bs, frame_num, 400]
-        else:
-            given_tokens = tokenizer.encode(image_path=image_prompt, image_size=160).repeat(batch_size, 1).unsqueeze(1)
-
-        # generate subsequent frames:
-        total_frames = generate_frame_num
-        enc_duration = tokenizer.encode(f'{float(duration)}秒')
-        if use_guide:
-            video_raw_text = video_raw_text + ' 视频'
-        enc_text_video = tokenizer.encode(video_raw_text)
-        seq = enc_duration + [tokenizer['<n>']] + enc_text_video + [
-            tokenizer['<start_of_image>']
-        ] + [-1] * 400 * generate_frame_num
-        guider_seq = enc_duration + [tokenizer['<n>']] + tokenizer.encode(
-            video_guidance_text) + [tokenizer['<start_of_image>']
-                                    ] + [-1] * 400 * generate_frame_num
-        logger.info(
-            f'[Stage1: Generating Subsequent Frames, Frame Rate {4/duration:.1f}] raw text: {tokenizer.decode(enc_text_video):s}'
-        )
-
-        text_len = len(seq) - frame_len * generate_frame_num - 1
-        guider_text_len = len(guider_seq) - frame_len * generate_frame_num - 1
-        seq = torch.tensor(seq, dtype=torch.long,
-                           device=self.device).unsqueeze(0).repeat(
-                               batch_size, 1)
-        guider_seq = torch.tensor(guider_seq,
-                                  dtype=torch.long,
-                                  device=self.device).unsqueeze(0).repeat(
-                                      batch_size, 1)
-
-        for given_frame_id in range(given_tokens.shape[1]):
-            seq[:, text_len + 1 + given_frame_id * 400:text_len + 1 +
-                (given_frame_id + 1) * 400] = given_tokens[:, given_frame_id]
-            guider_seq[:, guider_text_len + 1 +
-                       given_frame_id * 400:guider_text_len + 1 +
-                       (given_frame_id + 1) *
-                       400] = given_tokens[:, given_frame_id]
-        output_list = []
-
-        if use_guide:
-            video_log_text_attention_weights = 0
-        else:
-            guider_seq = None
-            video_log_text_attention_weights = 1.4
-
-        for tim in range(max(batch_size // mbz, 1)):
-            input_seq = seq[:min(batch_size, mbz)].clone(
-            ) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
-            guider_seq2 = (guider_seq[:min(batch_size, mbz)].clone()
-                           if tim == 0 else guider_seq[mbz * tim:mbz *
-                                                       (tim + 1)].clone()
-                           ) if guider_seq is not None else None
-            output_list.append(
-                my_filling_sequence(
-                    model,
-                    tokenizer,
-                    self.args,
-                    input_seq,
-                    batch_size=min(batch_size, mbz),
-                    get_masks_and_position_ids=
-                    get_masks_and_position_ids_stage1,
-                    text_len=text_len,
-                    frame_len=frame_len,
-                    strategy=self.strategy_cogview2,
-                    strategy2=self.strategy_cogvideo,
-                    log_text_attention_weights=video_log_text_attention_weights,
-                    guider_seq=guider_seq2,
-                    guider_text_len=guider_text_len,
-                    guidance_alpha=self.args.guidance_alpha,
-                    limited_spatial_channel_mem=True,
-                    mode_stage1=True,
-                )[0])
-
-        output_tokens = torch.cat(output_list, dim=0)[:, 1 + text_len:]
-
-        if self.args.both_stages:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage 1 model to cpu')
-            model = model.cpu()
-            torch.cuda.empty_cache()
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving in model1 takes time: {elapsed:.2f}')
-
-        # decoding
-        res = []
-        for seq in output_tokens:
-            decoded_imgs = [
-                self.postprocess(
-                    torch.nn.functional.interpolate(tokenizer.decode(
-                        image_ids=seq.tolist()[i * 400:(i + 1) * 400]),
-                                                    size=(480, 480))[0])
-                for i in range(total_frames)
-            ]
-            res.append(decoded_imgs)  # only the last image (target)
-
-        assert len(res) == batch_size
-        tokens = output_tokens[:, :+total_frames * 400].reshape(
-            -1, total_frames, 400).cpu()
-
-        elapsed = time.perf_counter() - process_start_time
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return tokens, res[0]
-
-    @torch.inference_mode()
-    def process_stage2(self,
-                       model,
-                       seq_text,
-                       duration,
-                       parent_given_tokens,
-                       video_raw_text=None,
-                       video_guidance_text='视频',
-                       gpu_rank=0,
-                       gpu_parallel_size=1):
-        process_start_time = time.perf_counter()
-
-        generate_frame_num = self.args.generate_frame_num
-        tokenizer = self.tokenizer
-        use_guidance = self.args.use_guidance_stage2
-
-        stage2_start_time = time.perf_counter()
-
-        if next(model.parameters()).device != self.device:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage-2 model to cuda')
-
-            model = model.to(self.device)
-
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving in stage-2 model takes time: {elapsed:.2f}')
-
-        try:
-            sample_num_allgpu = parent_given_tokens.shape[0]
-            sample_num = sample_num_allgpu // gpu_parallel_size
-            assert sample_num * gpu_parallel_size == sample_num_allgpu
-            parent_given_tokens = parent_given_tokens[gpu_rank *
-                                                      sample_num:(gpu_rank +
-                                                                  1) *
-                                                      sample_num]
-        except:
-            logger.critical('No frame_tokens found in interpolation, skip')
-            return False, []
-
-        # CogVideo Stage2 Generation
-        while duration >= 0.5:  # TODO: You can change the boundary to change the frame rate
-            parent_given_tokens_num = parent_given_tokens.shape[1]
-            generate_batchsize_persample = (parent_given_tokens_num - 1) // 2
-            generate_batchsize_total = generate_batchsize_persample * sample_num
-            total_frames = generate_frame_num
-            frame_len = 400
-            enc_text = tokenizer.encode(seq_text)
-            enc_duration = tokenizer.encode(str(float(duration)) + '秒')
-            seq = enc_duration + [tokenizer['<n>']] + enc_text + [
-                tokenizer['<start_of_image>']
-            ] + [-1] * 400 * generate_frame_num
-            text_len = len(seq) - frame_len * generate_frame_num - 1
-
-            logger.info(
-                f'[Stage2: Generating Frames, Frame Rate {int(4/duration):d}] raw text: {tokenizer.decode(enc_text):s}'
-            )
-
-            # generation
-            seq = torch.tensor(seq, dtype=torch.long,
-                               device=self.device).unsqueeze(0).repeat(
-                                   generate_batchsize_total, 1)
-            for sample_i in range(sample_num):
-                for i in range(generate_batchsize_persample):
-                    seq[sample_i * generate_batchsize_persample +
-                        i][text_len + 1:text_len + 1 +
-                           400] = parent_given_tokens[sample_i][2 * i]
-                    seq[sample_i * generate_batchsize_persample +
-                        i][text_len + 1 + 400:text_len + 1 +
-                           800] = parent_given_tokens[sample_i][2 * i + 1]
-                    seq[sample_i * generate_batchsize_persample +
-                        i][text_len + 1 + 800:text_len + 1 +
-                           1200] = parent_given_tokens[sample_i][2 * i + 2]
-
-            if use_guidance:
-                guider_seq = enc_duration + [
-                    tokenizer['<n>']
-                ] + tokenizer.encode(video_guidance_text) + [
-                    tokenizer['<start_of_image>']
-                ] + [-1] * 400 * generate_frame_num
-                guider_text_len = len(
-                    guider_seq) - frame_len * generate_frame_num - 1
-                guider_seq = torch.tensor(
-                    guider_seq, dtype=torch.long,
-                    device=self.device).unsqueeze(0).repeat(
-                        generate_batchsize_total, 1)
-                for sample_i in range(sample_num):
-                    for i in range(generate_batchsize_persample):
-                        guider_seq[sample_i * generate_batchsize_persample +
-                                   i][text_len + 1:text_len + 1 +
-                                      400] = parent_given_tokens[sample_i][2 *
-                                                                           i]
-                        guider_seq[sample_i * generate_batchsize_persample +
-                                   i][text_len + 1 + 400:text_len + 1 +
-                                      800] = parent_given_tokens[sample_i][2 *
-                                                                           i +
-                                                                           1]
-                        guider_seq[sample_i * generate_batchsize_persample +
-                                   i][text_len + 1 + 800:text_len + 1 +
-                                      1200] = parent_given_tokens[sample_i][2 *
-                                                                            i +
-                                                                            2]
-                video_log_text_attention_weights = 0
-            else:
-                guider_seq = None
-                guider_text_len = 0
-                video_log_text_attention_weights = 1.4
-
-            mbz = self.args.max_inference_batch_size
-
-            assert generate_batchsize_total < mbz or generate_batchsize_total % mbz == 0
-            output_list = []
-            start_time = time.perf_counter()
-            for tim in range(max(generate_batchsize_total // mbz, 1)):
-                input_seq = seq[:min(generate_batchsize_total, mbz)].clone(
-                ) if tim == 0 else seq[mbz * tim:mbz * (tim + 1)].clone()
-                guider_seq2 = (
-                    guider_seq[:min(generate_batchsize_total, mbz)].clone()
-                    if tim == 0 else guider_seq[mbz * tim:mbz *
-                                                (tim + 1)].clone()
-                ) if guider_seq is not None else None
-                output_list.append(
-                    my_filling_sequence(
-                        model,
-                        tokenizer,
-                        self.args,
-                        input_seq,
-                        batch_size=min(generate_batchsize_total, mbz),
-                        get_masks_and_position_ids=
-                        get_masks_and_position_ids_stage2,
-                        text_len=text_len,
-                        frame_len=frame_len,
-                        strategy=self.strategy_cogview2,
-                        strategy2=self.strategy_cogvideo,
-                        log_text_attention_weights=
-                        video_log_text_attention_weights,
-                        mode_stage1=False,
-                        guider_seq=guider_seq2,
-                        guider_text_len=guider_text_len,
-                        guidance_alpha=self.args.guidance_alpha,
-                        limited_spatial_channel_mem=True,
-                    )[0])
-            elapsed = time.perf_counter() - start_time
-            logger.info(f'Duration {duration:.2f}, Elapsed: {elapsed:.2f}\n')
-
-            output_tokens = torch.cat(output_list, dim=0)
-            output_tokens = output_tokens[:, text_len + 1:text_len + 1 +
-                                          (total_frames) * 400].reshape(
-                                              sample_num, -1,
-                                              400 * total_frames)
-            output_tokens_merge = torch.cat(
-                (output_tokens[:, :, :1 * 400], output_tokens[:, :,
-                                                              400 * 3:4 * 400],
-                 output_tokens[:, :, 400 * 1:2 * 400],
-                 output_tokens[:, :, 400 * 4:(total_frames) * 400]),
-                dim=2).reshape(sample_num, -1, 400)
-
-            output_tokens_merge = torch.cat(
-                (output_tokens_merge, output_tokens[:, -1:, 400 * 2:3 * 400]),
-                dim=1)
-            duration /= 2
-            parent_given_tokens = output_tokens_merge
-
-        if self.args.both_stages:
-            move_start_time = time.perf_counter()
-            logger.debug('moving stage 2 model to cpu')
-            model = model.cpu()
-            torch.cuda.empty_cache()
-            elapsed = time.perf_counter() - move_start_time
-            logger.debug(f'moving out model2 takes time: {elapsed:.2f}')
-
-        elapsed = time.perf_counter() - stage2_start_time
-        logger.info(f'CogVideo Stage2 completed. Elapsed: {elapsed:.2f}\n')
-
-        # direct super-resolution by CogView2
-        logger.info('[Direct super-resolution]')
-        dsr_start_time = time.perf_counter()
-
-        enc_text = tokenizer.encode(seq_text)
-        frame_num_per_sample = parent_given_tokens.shape[1]
-        parent_given_tokens_2d = parent_given_tokens.reshape(-1, 400)
-        text_seq = torch.tensor(enc_text, dtype=torch.long,
-                                device=self.device).unsqueeze(0).repeat(
-                                    parent_given_tokens_2d.shape[0], 1)
-        sred_tokens = self.dsr(text_seq, parent_given_tokens_2d)
-
-        decoded_sr_videos = []
-        for sample_i in range(sample_num):
-            decoded_sr_imgs = []
-            for frame_i in range(frame_num_per_sample):
-                decoded_sr_img = tokenizer.decode(
-                    image_ids=sred_tokens[frame_i + sample_i *
-                                          frame_num_per_sample][-3600:])
-                decoded_sr_imgs.append(
-                    self.postprocess(
-                        torch.nn.functional.interpolate(decoded_sr_img,
-                                                        size=(480, 480))[0]))
-            decoded_sr_videos.append(decoded_sr_imgs)
-
-        elapsed = time.perf_counter() - dsr_start_time
-        logger.info(
-            f'Direct super-resolution completed. Elapsed: {elapsed:.2f}')
-
-        elapsed = time.perf_counter() - process_start_time
-        logger.info(f'--- done ({elapsed=:.3f}) ---')
-        return True, decoded_sr_videos[0]
-
-    @staticmethod
-    def postprocess(tensor: torch.Tensor) -> np.ndarray:
-        return tensor.cpu().mul(255).add_(0.5).clamp_(0, 255).permute(
-            1, 2, 0).to(torch.uint8).numpy()
-
-    def run(self, text: str, seed: int,
-            only_first_stage: bool,image_prompt: None) -> list[np.ndarray]:
-        logger.info('==================== run ====================')
-        start = time.perf_counter()
-
-        set_random_seed(seed)
-        self.args.seed = seed
-
-        if only_first_stage:
-            self.args.stage_1 = True
-            self.args.both_stages = False
-        else:
-            self.args.stage_1 = False
-            self.args.both_stages = True
-
-        parent_given_tokens, res = self.process_stage1(
-            self.model_stage1,
-            text,
-            duration=4.0,
-            video_raw_text=text,
-            video_guidance_text='视频',
-            image_text_suffix=' 高清摄影',
-            batch_size=self.args.batch_size,
-            image_prompt=image_prompt)
-        if not only_first_stage:
-            _, res = self.process_stage2(
-                self.model_stage2,
-                text,
-                duration=2.0,
-                parent_given_tokens=parent_given_tokens,
-                video_raw_text=text + ' 视频',
-                video_guidance_text='视频',
-                gpu_rank=0,
-                gpu_parallel_size=1)  # TODO: 修改
-
-        elapsed = time.perf_counter() - start
-        logger.info(f'Elapsed: {elapsed:.3f}')
-        logger.info('==================== done ====================')
-        return res
-
-
-class AppModel(Model):
-    def __init__(self, only_first_stage: bool):
-        super().__init__(only_first_stage)
-        self.translator = gr.Interface.load(
-            'spaces/chinhon/translation_eng2ch')
-
-    def to_video(self, frames: list[np.ndarray]) -> str:
-        out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
-        if self.args.stage_1:
-            fps = 4
-        else:
-            fps = 8
-        writer = iio.get_writer(out_file.name, fps=fps)
-        for frame in frames:
-            writer.append_data(frame)
-        writer.close()
-        return out_file.name
-
-    def run_with_translation(
-            self, text: str, translate: bool, seed: int,
-            only_first_stage: bool,image_prompt: None) -> tuple[str | None, str | None]:
-            
-        logger.info(f'{text=}, {translate=}, {seed=}, {only_first_stage=},{image_prompt=}')
-        if translate:
-            text = translated_text = self.translator(text)
-        else:
-            translated_text = None
-        frames = self.run(text, seed, only_first_stage,image_prompt)
-        video_path = self.to_video(frames)
-        return translated_text, video_path
diff --git a/spaces/huggingface-projects/InstructPix2Pix-Chatbot-ui/frontend/src/lib/initImage.ts b/spaces/huggingface-projects/InstructPix2Pix-Chatbot-ui/frontend/src/lib/initImage.ts
deleted file mode 100644
index e2248f084c219bd8eff7c1d17d86790cd89e5c72..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/InstructPix2Pix-Chatbot-ui/frontend/src/lib/initImage.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-export const initImage =
-  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";
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/options/__init__.py b/spaces/iamironman4279/SadTalker/src/face3d/options/__init__.py
deleted file mode 100644
index e7eedebe54aa70169fd25951b3034d819e396c90..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/options/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""
diff --git a/spaces/ifey/chatdemo/AppPub/User/Bean/User_Data.py b/spaces/ifey/chatdemo/AppPub/User/Bean/User_Data.py
deleted file mode 100644
index 05a8aa00e949a0808a294266cdcc303f12a0ddc1..0000000000000000000000000000000000000000
--- a/spaces/ifey/chatdemo/AppPub/User/Bean/User_Data.py
+++ /dev/null
@@ -1,20 +0,0 @@
-"""
-用戶data裡面的json數據bean
-"""
-
-
-class User_Data:
-    def __init__(
-            self,
-            name,
-            picture: str = "",
-    ):
-        self.name = name
-        self.picture = picture
-
-    # 定义一个自定义的JSON序列化方法
-    def to_json(self):
-        return {
-            "name": self.name,
-            "picture": self.picture
-        }
diff --git a/spaces/ikechan8370/meme-generator/Dockerfile b/spaces/ikechan8370/meme-generator/Dockerfile
deleted file mode 100644
index 84a94a115da71ff8a675e8c932f95aec8d83f795..0000000000000000000000000000000000000000
--- a/spaces/ikechan8370/meme-generator/Dockerfile
+++ /dev/null
@@ -1,39 +0,0 @@
-FROM ubuntu:20.04
-
-RUN apt update && apt install -y python3-pip fonts-noto-cjk fonts-noto-color-emoji git fontconfig
-
-RUN git clone https://github.com/MeetWq/meme-generator.git && mkdir /usr/share/fonts/meme && mv meme-generator/resources/fonts/* /usr/share/fonts/meme
-
-RUN fc-cache -fv
-
-RUN rm -rf meme-generator
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-
-WORKDIR $HOME/app
-
-COPY --chown=user . $HOME/app
-
-RUN git clone https://github.com/MeetWq/meme-generator.git
-
-
-RUN pip install poetry
-
-RUN git clone https://github.com/MeetWq/meme-generator-contrib  && mkdir $HOME/meme-extend && mv meme-generator-contrib/memes/* $HOME/meme-extend
-
-ADD config.toml $HOME/.config/meme_generator/config.toml
-
-RUN cd meme-generator && poetry install && . .venv/bin/activate && python -m meme_generator.download && cd ..
-
-RUN rm -rf meme-generator-contrib && rm -rf $HOME/meme-generator
-
-ADD utils.py meme-generator/meme_generator
-
-ADD dianzhongdian/__init__.py meme-generator/meme_generator/memes/dianzhongdian/
-# 如果有自己扩展包
-# ADD extends/ /meme-extend
-
-CMD cd meme-generator && . .venv/bin/activate && python3 -m meme_generator.app
diff --git a/spaces/ikun12/ikun/upcunet_v3.py b/spaces/ikun12/ikun/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/ikun12/ikun/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)
diff --git a/spaces/imamnurby/RecipeGen/utils.py b/spaces/imamnurby/RecipeGen/utils.py
deleted file mode 100644
index f99b91b414644d2aac132d9bc74c609b5ab12f98..0000000000000000000000000000000000000000
--- a/spaces/imamnurby/RecipeGen/utils.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import pandas as pd
-import string
-
-
-def get_function_mapping(df_metadata, split_ref):
-    df_cp = df_metadata[['function', 'desc', 'split']].copy()
-    df_cp.drop_duplicates(inplace=True)
-    return {function:desc for function, desc, split in df_cp.values if split==split_ref}
-
-def get_channel_mapping(df_metadata):
-    df_cp = df_metadata[['channel', 'channel_desc']].copy()
-    df_cp.drop_duplicates(inplace=True)
-    return {channel:desc for channel, desc in df_cp.values}
-
-def get_valid_field(df_metadata):
-    df_cp = df_metadata[["function", "field"]].copy()
-    df_cp.drop_duplicates(inplace=True)
-    field_list = df_cp.field.tolist()
-    function_list = df_cp.function.tolist()
-    field_list_out=[]
-    field_mapping={}
-    for temp_field, temp_function in zip(field_list, function_list):
-        temp_field = temp_field.split(" ### ")
-        temp_field = [x.strip().lower() for x in temp_field if x.strip() != '']
-        temp_field = [x.translate(str.maketrans(' ', '_', string.punctuation)) for x in temp_field]
-        for item in temp_field:
-            if item not in field_list_out:
-                field_list_out.append(item)
-        temp_field = "(" + ", ".join(temp_field) + ")"
-        field_mapping[temp_function] = temp_field
-    return field_list_out, field_mapping
-
-def get_channel_to_function_mapping(df_metadata):
-    df_cp = df_metadata[["channel", "function"]].copy()
-    df_cp.drop_duplicates(inplace=True)
-    out_dict={}
-    for temp_channel, temp_function in df_cp.values:
-        if temp_channel not in out_dict.keys():
-            out_dict[temp_channel] = []
-        temp_function = temp_function.split(".")[-1]
-        out_dict[temp_channel].append(temp_function)
-    out_dict = {k:", ".join(v) for k,v in out_dict.items()}
-    return out_dict
-
-def get_metadata(path):
-    df_metadata = pd.read_csv(path)
-    df_metadata = df_metadata.fillna('')
-    function_dict_trigger = get_function_mapping(df_metadata=df_metadata, split_ref='trigger')
-    function_dict_action = get_function_mapping(df_metadata=df_metadata, split_ref='action')
-    channel_dict = get_channel_mapping(df_metadata=df_metadata)
-    valid_field,field_mapping = get_valid_field(df_metadata=df_metadata)
-    channel_to_function_dict = get_channel_to_function_mapping(df_metadata=df_metadata)
-    return channel_dict, function_dict_trigger, function_dict_action, field_mapping, valid_field, channel_to_function_dict
-
-def append_prefix(desc, prefix):
-    return prefix + desc
-
-def append_suffix(desc, suffix):
-    return desc + suffix
-
-def process_field(raw_field):
-    field = raw_field.split(" ### ")
-    field = [x.strip().lower() for x in field if x.strip() != '']
-    field = [x.translate(str.maketrans(' ', '_', string.punctuation)) for x in field]
-    return field
\ No newline at end of file
diff --git a/spaces/inamXcontru/PoeticTTS/Autodesk For Mac Compare and Contrast Native and Virtualized Software Solutions.md b/spaces/inamXcontru/PoeticTTS/Autodesk For Mac Compare and Contrast Native and Virtualized Software Solutions.md
deleted file mode 100644
index dfb20ebd851052980e8d918f23286d10b42ba1d2..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Autodesk For Mac Compare and Contrast Native and Virtualized Software Solutions.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>oa1 swords of the daimyo pdf download</h2><br /><p><b><b>Download</b> &#10001; &#10001; &#10001; <a href="https://gohhs.com/2uz4GH">https://gohhs.com/2uz4GH</a></b></p><br /><br />
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diff --git a/spaces/innnky/nyaru4.0/inference/slicer.py b/spaces/innnky/nyaru4.0/inference/slicer.py
deleted file mode 100644
index b05840bcf6bdced0b6e2adbecb1a1dd5b3dee462..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru4.0/inference/slicer.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import librosa
-import torch
-import torchaudio
-
-
-class Slicer:
-    def __init__(self,
-                 sr: int,
-                 threshold: float = -40.,
-                 min_length: int = 5000,
-                 min_interval: int = 300,
-                 hop_size: int = 20,
-                 max_sil_kept: int = 5000):
-        if not min_length >= min_interval >= hop_size:
-            raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
-        if not max_sil_kept >= hop_size:
-            raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
-        min_interval = sr * min_interval / 1000
-        self.threshold = 10 ** (threshold / 20.)
-        self.hop_size = round(sr * hop_size / 1000)
-        self.win_size = min(round(min_interval), 4 * self.hop_size)
-        self.min_length = round(sr * min_length / 1000 / self.hop_size)
-        self.min_interval = round(min_interval / self.hop_size)
-        self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
-
-    def _apply_slice(self, waveform, begin, end):
-        if len(waveform.shape) > 1:
-            return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
-        else:
-            return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
-
-    # @timeit
-    def slice(self, waveform):
-        if len(waveform.shape) > 1:
-            samples = librosa.to_mono(waveform)
-        else:
-            samples = waveform
-        if samples.shape[0] <= self.min_length:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
-        sil_tags = []
-        silence_start = None
-        clip_start = 0
-        for i, rms in enumerate(rms_list):
-            # Keep looping while frame is silent.
-            if rms < self.threshold:
-                # Record start of silent frames.
-                if silence_start is None:
-                    silence_start = i
-                continue
-            # Keep looping while frame is not silent and silence start has not been recorded.
-            if silence_start is None:
-                continue
-            # Clear recorded silence start if interval is not enough or clip is too short
-            is_leading_silence = silence_start == 0 and i > self.max_sil_kept
-            need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
-            if not is_leading_silence and not need_slice_middle:
-                silence_start = None
-                continue
-            # Need slicing. Record the range of silent frames to be removed.
-            if i - silence_start <= self.max_sil_kept:
-                pos = rms_list[silence_start: i + 1].argmin() + silence_start
-                if silence_start == 0:
-                    sil_tags.append((0, pos))
-                else:
-                    sil_tags.append((pos, pos))
-                clip_start = pos
-            elif i - silence_start <= self.max_sil_kept * 2:
-                pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
-                pos += i - self.max_sil_kept
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                    clip_start = pos_r
-                else:
-                    sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
-                    clip_start = max(pos_r, pos)
-            else:
-                pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
-                pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
-                if silence_start == 0:
-                    sil_tags.append((0, pos_r))
-                else:
-                    sil_tags.append((pos_l, pos_r))
-                clip_start = pos_r
-            silence_start = None
-        # Deal with trailing silence.
-        total_frames = rms_list.shape[0]
-        if silence_start is not None and total_frames - silence_start >= self.min_interval:
-            silence_end = min(total_frames, silence_start + self.max_sil_kept)
-            pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
-            sil_tags.append((pos, total_frames + 1))
-        # Apply and return slices.
-        if len(sil_tags) == 0:
-            return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
-        else:
-            chunks = []
-            # 第一段静音并非从头开始，补上有声片段
-            if sil_tags[0][0]:
-                chunks.append(
-                    {"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
-            for i in range(0, len(sil_tags)):
-                # 标识有声片段（跳过第一段）
-                if i:
-                    chunks.append({"slice": False,
-                                   "split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
-                # 标识所有静音片段
-                chunks.append({"slice": True,
-                               "split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
-            # 最后一段静音并非结尾，补上结尾片段
-            if sil_tags[-1][1] * self.hop_size < len(waveform):
-                chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
-            chunk_dict = {}
-            for i in range(len(chunks)):
-                chunk_dict[str(i)] = chunks[i]
-            return chunk_dict
-
-
-def cut(audio_path, db_thresh=-30, min_len=5000):
-    audio, sr = librosa.load(audio_path, sr=None)
-    slicer = Slicer(
-        sr=sr,
-        threshold=db_thresh,
-        min_length=min_len
-    )
-    chunks = slicer.slice(audio)
-    return chunks
-
-
-def chunks2audio(audio_path, chunks):
-    chunks = dict(chunks)
-    audio, sr = torchaudio.load(audio_path)
-    if len(audio.shape) == 2 and audio.shape[1] >= 2:
-        audio = torch.mean(audio, dim=0).unsqueeze(0)
-    audio = audio.cpu().numpy()[0]
-    result = []
-    for k, v in chunks.items():
-        tag = v["split_time"].split(",")
-        if tag[0] != tag[1]:
-            result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
-    return result, sr
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Dr Rajkumar And Kalpana Mimicry.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Dr Rajkumar And Kalpana Mimicry.md
deleted file mode 100644
index 460acbe537845c923558a63bf82d76126842e575..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Dr Rajkumar And Kalpana Mimicry.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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index 67eaf612d197eb3e44e5f833532ec48dd20e2e01..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Eset Nod32 Antivirus 6 Activation Key Lists.md	
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index 5b0b58723bd281f4bbfdc0a9b8817f4a85ef19e6..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Feedsoft Professional 3.19 Crack.md	
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diff --git a/spaces/jbilcke-hf/Panoremix/src/lib/triggerDownload.ts b/spaces/jbilcke-hf/Panoremix/src/lib/triggerDownload.ts
deleted file mode 100644
index e5627a26a4bba34bdf28279d265c6a71440d8136..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/Panoremix/src/lib/triggerDownload.ts
+++ /dev/null
@@ -1,12 +0,0 @@
-export function triggerDownload(filename: string, text: string) {
-  var element = document.createElement('a');
-  element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(text));
-  element.setAttribute('download', filename);
-
-  element.style.display = 'none';
-  document.body.appendChild(element);
-
-  element.click();
-
-  document.body.removeChild(element);
-}
\ No newline at end of file
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/attrs/validators.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/attrs/validators.py
deleted file mode 100644
index ab2c9b3024714d3b1caeb2f0773a0274dfc10f01..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/attrs/validators.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-from attr.validators import *  # noqa
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bson/buffer.c b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bson/buffer.c
deleted file mode 100644
index cc75202746a4273dc1333436009fa5620544b023..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/bson/buffer.c
+++ /dev/null
@@ -1,157 +0,0 @@
-/*
- * Copyright 2009-2015 MongoDB, Inc.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/* Include Python.h so we can set Python's error indicator. */
-#define PY_SSIZE_T_CLEAN
-#include "Python.h"
-
-#include <stdlib.h>
-#include <string.h>
-
-#include "buffer.h"
-
-#define INITIAL_BUFFER_SIZE 256
-
-struct buffer {
-    char* buffer;
-    int size;
-    int position;
-};
-
-/* Set Python's error indicator to MemoryError.
- * Called after allocation failures. */
-static void set_memory_error(void) {
-    PyErr_NoMemory();
-}
-
-/* Allocate and return a new buffer.
- * Return NULL and sets MemoryError on allocation failure. */
-buffer_t pymongo_buffer_new(void) {
-    buffer_t buffer;
-    buffer = (buffer_t)malloc(sizeof(struct buffer));
-    if (buffer == NULL) {
-        set_memory_error();
-        return NULL;
-    }
-
-    buffer->size = INITIAL_BUFFER_SIZE;
-    buffer->position = 0;
-    buffer->buffer = (char*)malloc(sizeof(char) * INITIAL_BUFFER_SIZE);
-    if (buffer->buffer == NULL) {
-        free(buffer);
-        set_memory_error();
-        return NULL;
-    }
-
-    return buffer;
-}
-
-/* Free the memory allocated for `buffer`.
- * Return non-zero on failure. */
-int pymongo_buffer_free(buffer_t buffer) {
-    if (buffer == NULL) {
-        return 1;
-    }
-    /* Buffer will be NULL when buffer_grow fails. */
-    if (buffer->buffer != NULL) {
-        free(buffer->buffer);
-    }
-    free(buffer);
-    return 0;
-}
-
-/* Grow `buffer` to at least `min_length`.
- * Return non-zero and sets MemoryError on allocation failure. */
-static int buffer_grow(buffer_t buffer, int min_length) {
-    int old_size = 0;
-    int size = buffer->size;
-    char* old_buffer = buffer->buffer;
-    if (size >= min_length) {
-        return 0;
-    }
-    while (size < min_length) {
-        old_size = size;
-        size *= 2;
-        if (size <= old_size) {
-           /* Size did not increase. Could be an overflow
-            * or size < 1. Just go with min_length. */
-           size = min_length;
-        }
-    }
-    buffer->buffer = (char*)realloc(buffer->buffer, sizeof(char) * size);
-    if (buffer->buffer == NULL) {
-        free(old_buffer);
-        set_memory_error();
-        return 1;
-    }
-    buffer->size = size;
-    return 0;
-}
-
-/* Assure that `buffer` has at least `size` free bytes (and grow if needed).
- * Return non-zero and sets MemoryError on allocation failure.
- * Return non-zero and sets ValueError if `size` would exceed 2GiB. */
-static int buffer_assure_space(buffer_t buffer, int size) {
-    int new_size = buffer->position + size;
-    /* Check for overflow. */
-    if (new_size < buffer->position) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Document would overflow BSON size limit");
-        return 1;
-    }
-
-    if (new_size <= buffer->size) {
-        return 0;
-    }
-    return buffer_grow(buffer, new_size);
-}
-
-/* Save `size` bytes from the current position in `buffer` (and grow if needed).
- * Return offset for writing, or -1 on failure.
- * Sets MemoryError or ValueError on failure. */
-buffer_position pymongo_buffer_save_space(buffer_t buffer, int size) {
-    int position = buffer->position;
-    if (buffer_assure_space(buffer, size) != 0) {
-        return -1;
-    }
-    buffer->position += size;
-    return position;
-}
-
-/* Write `size` bytes from `data` to `buffer` (and grow if needed).
- * Return non-zero on failure.
- * Sets MemoryError or ValueError on failure. */
-int pymongo_buffer_write(buffer_t buffer, const char* data, int size) {
-    if (buffer_assure_space(buffer, size) != 0) {
-        return 1;
-    }
-
-    memcpy(buffer->buffer + buffer->position, data, size);
-    buffer->position += size;
-    return 0;
-}
-
-int pymongo_buffer_get_position(buffer_t buffer) {
-    return buffer->position;
-}
-
-char* pymongo_buffer_get_buffer(buffer_t buffer) {
-    return buffer->buffer;
-}
-
-void pymongo_buffer_update_position(buffer_t buffer, buffer_position new_position) {
-    buffer->position = new_position;
-}
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/decorators.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/decorators.py
deleted file mode 100644
index d9bba9502ca353bca5136f43c92436ff584f06e1..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/click/decorators.py
+++ /dev/null
@@ -1,561 +0,0 @@
-import inspect
-import types
-import typing as t
-from functools import update_wrapper
-from gettext import gettext as _
-
-from .core import Argument
-from .core import Command
-from .core import Context
-from .core import Group
-from .core import Option
-from .core import Parameter
-from .globals import get_current_context
-from .utils import echo
-
-if t.TYPE_CHECKING:
-    import typing_extensions as te
-
-    P = te.ParamSpec("P")
-
-R = t.TypeVar("R")
-T = t.TypeVar("T")
-_AnyCallable = t.Callable[..., t.Any]
-FC = t.TypeVar("FC", bound=t.Union[_AnyCallable, Command])
-
-
-def pass_context(f: "t.Callable[te.Concatenate[Context, P], R]") -> "t.Callable[P, R]":
-    """Marks a callback as wanting to receive the current context
-    object as first argument.
-    """
-
-    def new_func(*args: "P.args", **kwargs: "P.kwargs") -> "R":
-        return f(get_current_context(), *args, **kwargs)
-
-    return update_wrapper(new_func, f)
-
-
-def pass_obj(f: "t.Callable[te.Concatenate[t.Any, P], R]") -> "t.Callable[P, R]":
-    """Similar to :func:`pass_context`, but only pass the object on the
-    context onwards (:attr:`Context.obj`).  This is useful if that object
-    represents the state of a nested system.
-    """
-
-    def new_func(*args: "P.args", **kwargs: "P.kwargs") -> "R":
-        return f(get_current_context().obj, *args, **kwargs)
-
-    return update_wrapper(new_func, f)
-
-
-def make_pass_decorator(
-    object_type: t.Type[T], ensure: bool = False
-) -> t.Callable[["t.Callable[te.Concatenate[T, P], R]"], "t.Callable[P, R]"]:
-    """Given an object type this creates a decorator that will work
-    similar to :func:`pass_obj` but instead of passing the object of the
-    current context, it will find the innermost context of type
-    :func:`object_type`.
-
-    This generates a decorator that works roughly like this::
-
-        from functools import update_wrapper
-
-        def decorator(f):
-            @pass_context
-            def new_func(ctx, *args, **kwargs):
-                obj = ctx.find_object(object_type)
-                return ctx.invoke(f, obj, *args, **kwargs)
-            return update_wrapper(new_func, f)
-        return decorator
-
-    :param object_type: the type of the object to pass.
-    :param ensure: if set to `True`, a new object will be created and
-                   remembered on the context if it's not there yet.
-    """
-
-    def decorator(f: "t.Callable[te.Concatenate[T, P], R]") -> "t.Callable[P, R]":
-        def new_func(*args: "P.args", **kwargs: "P.kwargs") -> "R":
-            ctx = get_current_context()
-
-            obj: t.Optional[T]
-            if ensure:
-                obj = ctx.ensure_object(object_type)
-            else:
-                obj = ctx.find_object(object_type)
-
-            if obj is None:
-                raise RuntimeError(
-                    "Managed to invoke callback without a context"
-                    f" object of type {object_type.__name__!r}"
-                    " existing."
-                )
-
-            return ctx.invoke(f, obj, *args, **kwargs)
-
-        return update_wrapper(new_func, f)
-
-    return decorator  # type: ignore[return-value]
-
-
-def pass_meta_key(
-    key: str, *, doc_description: t.Optional[str] = None
-) -> "t.Callable[[t.Callable[te.Concatenate[t.Any, P], R]], t.Callable[P, R]]":
-    """Create a decorator that passes a key from
-    :attr:`click.Context.meta` as the first argument to the decorated
-    function.
-
-    :param key: Key in ``Context.meta`` to pass.
-    :param doc_description: Description of the object being passed,
-        inserted into the decorator's docstring. Defaults to "the 'key'
-        key from Context.meta".
-
-    .. versionadded:: 8.0
-    """
-
-    def decorator(f: "t.Callable[te.Concatenate[t.Any, P], R]") -> "t.Callable[P, R]":
-        def new_func(*args: "P.args", **kwargs: "P.kwargs") -> R:
-            ctx = get_current_context()
-            obj = ctx.meta[key]
-            return ctx.invoke(f, obj, *args, **kwargs)
-
-        return update_wrapper(new_func, f)
-
-    if doc_description is None:
-        doc_description = f"the {key!r} key from :attr:`click.Context.meta`"
-
-    decorator.__doc__ = (
-        f"Decorator that passes {doc_description} as the first argument"
-        " to the decorated function."
-    )
-    return decorator  # type: ignore[return-value]
-
-
-CmdType = t.TypeVar("CmdType", bound=Command)
-
-
-# variant: no call, directly as decorator for a function.
-@t.overload
-def command(name: _AnyCallable) -> Command:
-    ...
-
-
-# variant: with positional name and with positional or keyword cls argument:
-# @command(namearg, CommandCls, ...) or @command(namearg, cls=CommandCls, ...)
-@t.overload
-def command(
-    name: t.Optional[str],
-    cls: t.Type[CmdType],
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], CmdType]:
-    ...
-
-
-# variant: name omitted, cls _must_ be a keyword argument, @command(cls=CommandCls, ...)
-@t.overload
-def command(
-    name: None = None,
-    *,
-    cls: t.Type[CmdType],
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], CmdType]:
-    ...
-
-
-# variant: with optional string name, no cls argument provided.
-@t.overload
-def command(
-    name: t.Optional[str] = ..., cls: None = None, **attrs: t.Any
-) -> t.Callable[[_AnyCallable], Command]:
-    ...
-
-
-def command(
-    name: t.Union[t.Optional[str], _AnyCallable] = None,
-    cls: t.Optional[t.Type[CmdType]] = None,
-    **attrs: t.Any,
-) -> t.Union[Command, t.Callable[[_AnyCallable], t.Union[Command, CmdType]]]:
-    r"""Creates a new :class:`Command` and uses the decorated function as
-    callback.  This will also automatically attach all decorated
-    :func:`option`\s and :func:`argument`\s as parameters to the command.
-
-    The name of the command defaults to the name of the function with
-    underscores replaced by dashes.  If you want to change that, you can
-    pass the intended name as the first argument.
-
-    All keyword arguments are forwarded to the underlying command class.
-    For the ``params`` argument, any decorated params are appended to
-    the end of the list.
-
-    Once decorated the function turns into a :class:`Command` instance
-    that can be invoked as a command line utility or be attached to a
-    command :class:`Group`.
-
-    :param name: the name of the command.  This defaults to the function
-                 name with underscores replaced by dashes.
-    :param cls: the command class to instantiate.  This defaults to
-                :class:`Command`.
-
-    .. versionchanged:: 8.1
-        This decorator can be applied without parentheses.
-
-    .. versionchanged:: 8.1
-        The ``params`` argument can be used. Decorated params are
-        appended to the end of the list.
-    """
-
-    func: t.Optional[t.Callable[[_AnyCallable], t.Any]] = None
-
-    if callable(name):
-        func = name
-        name = None
-        assert cls is None, "Use 'command(cls=cls)(callable)' to specify a class."
-        assert not attrs, "Use 'command(**kwargs)(callable)' to provide arguments."
-
-    if cls is None:
-        cls = t.cast(t.Type[CmdType], Command)
-
-    def decorator(f: _AnyCallable) -> CmdType:
-        if isinstance(f, Command):
-            raise TypeError("Attempted to convert a callback into a command twice.")
-
-        attr_params = attrs.pop("params", None)
-        params = attr_params if attr_params is not None else []
-
-        try:
-            decorator_params = f.__click_params__  # type: ignore
-        except AttributeError:
-            pass
-        else:
-            del f.__click_params__  # type: ignore
-            params.extend(reversed(decorator_params))
-
-        if attrs.get("help") is None:
-            attrs["help"] = f.__doc__
-
-        if t.TYPE_CHECKING:
-            assert cls is not None
-            assert not callable(name)
-
-        cmd = cls(
-            name=name or f.__name__.lower().replace("_", "-"),
-            callback=f,
-            params=params,
-            **attrs,
-        )
-        cmd.__doc__ = f.__doc__
-        return cmd
-
-    if func is not None:
-        return decorator(func)
-
-    return decorator
-
-
-GrpType = t.TypeVar("GrpType", bound=Group)
-
-
-# variant: no call, directly as decorator for a function.
-@t.overload
-def group(name: _AnyCallable) -> Group:
-    ...
-
-
-# variant: with positional name and with positional or keyword cls argument:
-# @group(namearg, GroupCls, ...) or @group(namearg, cls=GroupCls, ...)
-@t.overload
-def group(
-    name: t.Optional[str],
-    cls: t.Type[GrpType],
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], GrpType]:
-    ...
-
-
-# variant: name omitted, cls _must_ be a keyword argument, @group(cmd=GroupCls, ...)
-@t.overload
-def group(
-    name: None = None,
-    *,
-    cls: t.Type[GrpType],
-    **attrs: t.Any,
-) -> t.Callable[[_AnyCallable], GrpType]:
-    ...
-
-
-# variant: with optional string name, no cls argument provided.
-@t.overload
-def group(
-    name: t.Optional[str] = ..., cls: None = None, **attrs: t.Any
-) -> t.Callable[[_AnyCallable], Group]:
-    ...
-
-
-def group(
-    name: t.Union[str, _AnyCallable, None] = None,
-    cls: t.Optional[t.Type[GrpType]] = None,
-    **attrs: t.Any,
-) -> t.Union[Group, t.Callable[[_AnyCallable], t.Union[Group, GrpType]]]:
-    """Creates a new :class:`Group` with a function as callback.  This
-    works otherwise the same as :func:`command` just that the `cls`
-    parameter is set to :class:`Group`.
-
-    .. versionchanged:: 8.1
-        This decorator can be applied without parentheses.
-    """
-    if cls is None:
-        cls = t.cast(t.Type[GrpType], Group)
-
-    if callable(name):
-        return command(cls=cls, **attrs)(name)
-
-    return command(name, cls, **attrs)
-
-
-def _param_memo(f: t.Callable[..., t.Any], param: Parameter) -> None:
-    if isinstance(f, Command):
-        f.params.append(param)
-    else:
-        if not hasattr(f, "__click_params__"):
-            f.__click_params__ = []  # type: ignore
-
-        f.__click_params__.append(param)  # type: ignore
-
-
-def argument(
-    *param_decls: str, cls: t.Optional[t.Type[Argument]] = None, **attrs: t.Any
-) -> t.Callable[[FC], FC]:
-    """Attaches an argument to the command.  All positional arguments are
-    passed as parameter declarations to :class:`Argument`; all keyword
-    arguments are forwarded unchanged (except ``cls``).
-    This is equivalent to creating an :class:`Argument` instance manually
-    and attaching it to the :attr:`Command.params` list.
-
-    For the default argument class, refer to :class:`Argument` and
-    :class:`Parameter` for descriptions of parameters.
-
-    :param cls: the argument class to instantiate.  This defaults to
-                :class:`Argument`.
-    :param param_decls: Passed as positional arguments to the constructor of
-        ``cls``.
-    :param attrs: Passed as keyword arguments to the constructor of ``cls``.
-    """
-    if cls is None:
-        cls = Argument
-
-    def decorator(f: FC) -> FC:
-        _param_memo(f, cls(param_decls, **attrs))
-        return f
-
-    return decorator
-
-
-def option(
-    *param_decls: str, cls: t.Optional[t.Type[Option]] = None, **attrs: t.Any
-) -> t.Callable[[FC], FC]:
-    """Attaches an option to the command.  All positional arguments are
-    passed as parameter declarations to :class:`Option`; all keyword
-    arguments are forwarded unchanged (except ``cls``).
-    This is equivalent to creating an :class:`Option` instance manually
-    and attaching it to the :attr:`Command.params` list.
-
-    For the default option class, refer to :class:`Option` and
-    :class:`Parameter` for descriptions of parameters.
-
-    :param cls: the option class to instantiate.  This defaults to
-                :class:`Option`.
-    :param param_decls: Passed as positional arguments to the constructor of
-        ``cls``.
-    :param attrs: Passed as keyword arguments to the constructor of ``cls``.
-    """
-    if cls is None:
-        cls = Option
-
-    def decorator(f: FC) -> FC:
-        _param_memo(f, cls(param_decls, **attrs))
-        return f
-
-    return decorator
-
-
-def confirmation_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--yes`` option which shows a prompt before continuing if
-    not passed. If the prompt is declined, the program will exit.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--yes"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value:
-            ctx.abort()
-
-    if not param_decls:
-        param_decls = ("--yes",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("callback", callback)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("prompt", "Do you want to continue?")
-    kwargs.setdefault("help", "Confirm the action without prompting.")
-    return option(*param_decls, **kwargs)
-
-
-def password_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--password`` option which prompts for a password, hiding
-    input and asking to enter the value again for confirmation.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--password"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-    if not param_decls:
-        param_decls = ("--password",)
-
-    kwargs.setdefault("prompt", True)
-    kwargs.setdefault("confirmation_prompt", True)
-    kwargs.setdefault("hide_input", True)
-    return option(*param_decls, **kwargs)
-
-
-def version_option(
-    version: t.Optional[str] = None,
-    *param_decls: str,
-    package_name: t.Optional[str] = None,
-    prog_name: t.Optional[str] = None,
-    message: t.Optional[str] = None,
-    **kwargs: t.Any,
-) -> t.Callable[[FC], FC]:
-    """Add a ``--version`` option which immediately prints the version
-    number and exits the program.
-
-    If ``version`` is not provided, Click will try to detect it using
-    :func:`importlib.metadata.version` to get the version for the
-    ``package_name``. On Python < 3.8, the ``importlib_metadata``
-    backport must be installed.
-
-    If ``package_name`` is not provided, Click will try to detect it by
-    inspecting the stack frames. This will be used to detect the
-    version, so it must match the name of the installed package.
-
-    :param version: The version number to show. If not provided, Click
-        will try to detect it.
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--version"``.
-    :param package_name: The package name to detect the version from. If
-        not provided, Click will try to detect it.
-    :param prog_name: The name of the CLI to show in the message. If not
-        provided, it will be detected from the command.
-    :param message: The message to show. The values ``%(prog)s``,
-        ``%(package)s``, and ``%(version)s`` are available. Defaults to
-        ``"%(prog)s, version %(version)s"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    :raise RuntimeError: ``version`` could not be detected.
-
-    .. versionchanged:: 8.0
-        Add the ``package_name`` parameter, and the ``%(package)s``
-        value for messages.
-
-    .. versionchanged:: 8.0
-        Use :mod:`importlib.metadata` instead of ``pkg_resources``. The
-        version is detected based on the package name, not the entry
-        point name. The Python package name must match the installed
-        package name, or be passed with ``package_name=``.
-    """
-    if message is None:
-        message = _("%(prog)s, version %(version)s")
-
-    if version is None and package_name is None:
-        frame = inspect.currentframe()
-        f_back = frame.f_back if frame is not None else None
-        f_globals = f_back.f_globals if f_back is not None else None
-        # break reference cycle
-        # https://docs.python.org/3/library/inspect.html#the-interpreter-stack
-        del frame
-
-        if f_globals is not None:
-            package_name = f_globals.get("__name__")
-
-            if package_name == "__main__":
-                package_name = f_globals.get("__package__")
-
-            if package_name:
-                package_name = package_name.partition(".")[0]
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value or ctx.resilient_parsing:
-            return
-
-        nonlocal prog_name
-        nonlocal version
-
-        if prog_name is None:
-            prog_name = ctx.find_root().info_name
-
-        if version is None and package_name is not None:
-            metadata: t.Optional[types.ModuleType]
-
-            try:
-                from importlib import metadata  # type: ignore
-            except ImportError:
-                # Python < 3.8
-                import importlib_metadata as metadata  # type: ignore
-
-            try:
-                version = metadata.version(package_name)  # type: ignore
-            except metadata.PackageNotFoundError:  # type: ignore
-                raise RuntimeError(
-                    f"{package_name!r} is not installed. Try passing"
-                    " 'package_name' instead."
-                ) from None
-
-        if version is None:
-            raise RuntimeError(
-                f"Could not determine the version for {package_name!r} automatically."
-            )
-
-        echo(
-            message % {"prog": prog_name, "package": package_name, "version": version},
-            color=ctx.color,
-        )
-        ctx.exit()
-
-    if not param_decls:
-        param_decls = ("--version",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("is_eager", True)
-    kwargs.setdefault("help", _("Show the version and exit."))
-    kwargs["callback"] = callback
-    return option(*param_decls, **kwargs)
-
-
-def help_option(*param_decls: str, **kwargs: t.Any) -> t.Callable[[FC], FC]:
-    """Add a ``--help`` option which immediately prints the help page
-    and exits the program.
-
-    This is usually unnecessary, as the ``--help`` option is added to
-    each command automatically unless ``add_help_option=False`` is
-    passed.
-
-    :param param_decls: One or more option names. Defaults to the single
-        value ``"--help"``.
-    :param kwargs: Extra arguments are passed to :func:`option`.
-    """
-
-    def callback(ctx: Context, param: Parameter, value: bool) -> None:
-        if not value or ctx.resilient_parsing:
-            return
-
-        echo(ctx.get_help(), color=ctx.color)
-        ctx.exit()
-
-    if not param_decls:
-        param_decls = ("--help",)
-
-    kwargs.setdefault("is_flag", True)
-    kwargs.setdefault("expose_value", False)
-    kwargs.setdefault("is_eager", True)
-    kwargs.setdefault("help", _("Show this message and exit."))
-    kwargs["callback"] = callback
-    return option(*param_decls, **kwargs)
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/macRes.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/macRes.py
deleted file mode 100644
index f5a6cfe4789a351204d0ce6fa2abb5651487c5c0..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/misc/macRes.py
+++ /dev/null
@@ -1,261 +0,0 @@
-from io import BytesIO
-import struct
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytesjoin, tostr
-from collections import OrderedDict
-from collections.abc import MutableMapping
-
-
-class ResourceError(Exception):
-    pass
-
-
-class ResourceReader(MutableMapping):
-    """Reader for Mac OS resource forks.
-
-    Parses a resource fork and returns resources according to their type.
-    If run on OS X, this will open the resource fork in the filesystem.
-    Otherwise, it will open the file itself and attempt to read it as
-    though it were a resource fork.
-
-    The returned object can be indexed by type and iterated over,
-    returning in each case a list of py:class:`Resource` objects
-    representing all the resources of a certain type.
-
-    """
-
-    def __init__(self, fileOrPath):
-        """Open a file
-
-        Args:
-                fileOrPath: Either an object supporting a ``read`` method, an
-                        ``os.PathLike`` object, or a string.
-        """
-        self._resources = OrderedDict()
-        if hasattr(fileOrPath, "read"):
-            self.file = fileOrPath
-        else:
-            try:
-                # try reading from the resource fork (only works on OS X)
-                self.file = self.openResourceFork(fileOrPath)
-                self._readFile()
-                return
-            except (ResourceError, IOError):
-                # if it fails, use the data fork
-                self.file = self.openDataFork(fileOrPath)
-        self._readFile()
-
-    @staticmethod
-    def openResourceFork(path):
-        if hasattr(path, "__fspath__"):  # support os.PathLike objects
-            path = path.__fspath__()
-        with open(path + "/..namedfork/rsrc", "rb") as resfork:
-            data = resfork.read()
-        infile = BytesIO(data)
-        infile.name = path
-        return infile
-
-    @staticmethod
-    def openDataFork(path):
-        with open(path, "rb") as datafork:
-            data = datafork.read()
-        infile = BytesIO(data)
-        infile.name = path
-        return infile
-
-    def _readFile(self):
-        self._readHeaderAndMap()
-        self._readTypeList()
-
-    def _read(self, numBytes, offset=None):
-        if offset is not None:
-            try:
-                self.file.seek(offset)
-            except OverflowError:
-                raise ResourceError("Failed to seek offset ('offset' is too large)")
-            if self.file.tell() != offset:
-                raise ResourceError("Failed to seek offset (reached EOF)")
-        try:
-            data = self.file.read(numBytes)
-        except OverflowError:
-            raise ResourceError("Cannot read resource ('numBytes' is too large)")
-        if len(data) != numBytes:
-            raise ResourceError("Cannot read resource (not enough data)")
-        return data
-
-    def _readHeaderAndMap(self):
-        self.file.seek(0)
-        headerData = self._read(ResourceForkHeaderSize)
-        sstruct.unpack(ResourceForkHeader, headerData, self)
-        # seek to resource map, skip reserved
-        mapOffset = self.mapOffset + 22
-        resourceMapData = self._read(ResourceMapHeaderSize, mapOffset)
-        sstruct.unpack(ResourceMapHeader, resourceMapData, self)
-        self.absTypeListOffset = self.mapOffset + self.typeListOffset
-        self.absNameListOffset = self.mapOffset + self.nameListOffset
-
-    def _readTypeList(self):
-        absTypeListOffset = self.absTypeListOffset
-        numTypesData = self._read(2, absTypeListOffset)
-        (self.numTypes,) = struct.unpack(">H", numTypesData)
-        absTypeListOffset2 = absTypeListOffset + 2
-        for i in range(self.numTypes + 1):
-            resTypeItemOffset = absTypeListOffset2 + ResourceTypeItemSize * i
-            resTypeItemData = self._read(ResourceTypeItemSize, resTypeItemOffset)
-            item = sstruct.unpack(ResourceTypeItem, resTypeItemData)
-            resType = tostr(item["type"], encoding="mac-roman")
-            refListOffset = absTypeListOffset + item["refListOffset"]
-            numRes = item["numRes"] + 1
-            resources = self._readReferenceList(resType, refListOffset, numRes)
-            self._resources[resType] = resources
-
-    def _readReferenceList(self, resType, refListOffset, numRes):
-        resources = []
-        for i in range(numRes):
-            refOffset = refListOffset + ResourceRefItemSize * i
-            refData = self._read(ResourceRefItemSize, refOffset)
-            res = Resource(resType)
-            res.decompile(refData, self)
-            resources.append(res)
-        return resources
-
-    def __getitem__(self, resType):
-        return self._resources[resType]
-
-    def __delitem__(self, resType):
-        del self._resources[resType]
-
-    def __setitem__(self, resType, resources):
-        self._resources[resType] = resources
-
-    def __len__(self):
-        return len(self._resources)
-
-    def __iter__(self):
-        return iter(self._resources)
-
-    def keys(self):
-        return self._resources.keys()
-
-    @property
-    def types(self):
-        """A list of the types of resources in the resource fork."""
-        return list(self._resources.keys())
-
-    def countResources(self, resType):
-        """Return the number of resources of a given type."""
-        try:
-            return len(self[resType])
-        except KeyError:
-            return 0
-
-    def getIndices(self, resType):
-        """Returns a list of indices of resources of a given type."""
-        numRes = self.countResources(resType)
-        if numRes:
-            return list(range(1, numRes + 1))
-        else:
-            return []
-
-    def getNames(self, resType):
-        """Return list of names of all resources of a given type."""
-        return [res.name for res in self.get(resType, []) if res.name is not None]
-
-    def getIndResource(self, resType, index):
-        """Return resource of given type located at an index ranging from 1
-        to the number of resources for that type, or None if not found.
-        """
-        if index < 1:
-            return None
-        try:
-            res = self[resType][index - 1]
-        except (KeyError, IndexError):
-            return None
-        return res
-
-    def getNamedResource(self, resType, name):
-        """Return the named resource of given type, else return None."""
-        name = tostr(name, encoding="mac-roman")
-        for res in self.get(resType, []):
-            if res.name == name:
-                return res
-        return None
-
-    def close(self):
-        if not self.file.closed:
-            self.file.close()
-
-
-class Resource(object):
-    """Represents a resource stored within a resource fork.
-
-    Attributes:
-            type: resource type.
-            data: resource data.
-            id: ID.
-            name: resource name.
-            attr: attributes.
-    """
-
-    def __init__(
-        self, resType=None, resData=None, resID=None, resName=None, resAttr=None
-    ):
-        self.type = resType
-        self.data = resData
-        self.id = resID
-        self.name = resName
-        self.attr = resAttr
-
-    def decompile(self, refData, reader):
-        sstruct.unpack(ResourceRefItem, refData, self)
-        # interpret 3-byte dataOffset as (padded) ULONG to unpack it with struct
-        (self.dataOffset,) = struct.unpack(">L", bytesjoin([b"\0", self.dataOffset]))
-        absDataOffset = reader.dataOffset + self.dataOffset
-        (dataLength,) = struct.unpack(">L", reader._read(4, absDataOffset))
-        self.data = reader._read(dataLength)
-        if self.nameOffset == -1:
-            return
-        absNameOffset = reader.absNameListOffset + self.nameOffset
-        (nameLength,) = struct.unpack("B", reader._read(1, absNameOffset))
-        (name,) = struct.unpack(">%ss" % nameLength, reader._read(nameLength))
-        self.name = tostr(name, encoding="mac-roman")
-
-
-ResourceForkHeader = """
-		> # big endian
-		dataOffset:     L
-		mapOffset:      L
-		dataLen:        L
-		mapLen:         L
-"""
-
-ResourceForkHeaderSize = sstruct.calcsize(ResourceForkHeader)
-
-ResourceMapHeader = """
-		> # big endian
-		attr:              H
-		typeListOffset:    H
-		nameListOffset:    H
-"""
-
-ResourceMapHeaderSize = sstruct.calcsize(ResourceMapHeader)
-
-ResourceTypeItem = """
-		> # big endian
-		type:              4s
-		numRes:            H
-		refListOffset:     H
-"""
-
-ResourceTypeItemSize = sstruct.calcsize(ResourceTypeItem)
-
-ResourceRefItem = """
-		> # big endian
-		id:                h
-		nameOffset:        h
-		attr:              B
-		dataOffset:        3s
-		reserved:          L
-"""
-
-ResourceRefItemSize = sstruct.calcsize(ResourceRefItem)
diff --git a/spaces/jpoptum/1-SimPhysics/README.md b/spaces/jpoptum/1-SimPhysics/README.md
deleted file mode 100644
index b31a6ae704f0373db4e2fb14898ade5c42afb191..0000000000000000000000000000000000000000
--- a/spaces/jpoptum/1-SimPhysics/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: 🏖️PlayCanvas Simulation Vehicle Physics⛱️🌊 Live HTML5
-emoji: 1-Sim🌊
-colorFrom: green
-colorTo: gray
-sdk: static
-pinned: false
----
-
-Inspired by Danny Lange, VP AI and ML at Unity 
-Reference: https://youtu.be/YsEDv13W1RI?t=48 
-
-Quote on MLAgents: ... if you think about what I just said about evolution and that the creation of tools for intelligence yeah so you have the basic nature you have the 3d spatial environment you have gravity and you have inertia and the physics engine and now we throw in ml agents which is a machine learning system
-
diff --git a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/data_objects/utterance.py b/spaces/keithhon/Real-Time-Voice-Cloning/encoder/data_objects/utterance.py
deleted file mode 100644
index 0768c3420f422a7464f305b4c1fb6752c57ceda7..0000000000000000000000000000000000000000
--- a/spaces/keithhon/Real-Time-Voice-Cloning/encoder/data_objects/utterance.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-
-
-class Utterance:
-    def __init__(self, frames_fpath, wave_fpath):
-        self.frames_fpath = frames_fpath
-        self.wave_fpath = wave_fpath
-        
-    def get_frames(self):
-        return np.load(self.frames_fpath)
-
-    def random_partial(self, n_frames):
-        """
-        Crops the frames into a partial utterance of n_frames
-        
-        :param n_frames: The number of frames of the partial utterance
-        :return: the partial utterance frames and a tuple indicating the start and end of the 
-        partial utterance in the complete utterance.
-        """
-        frames = self.get_frames()
-        if frames.shape[0] == n_frames:
-            start = 0
-        else:
-            start = np.random.randint(0, frames.shape[0] - n_frames)
-        end = start + n_frames
-        return frames[start:end], (start, end)
\ No newline at end of file
diff --git a/spaces/ken4005/Uhi-ChatGPT/custom.css b/spaces/ken4005/Uhi-ChatGPT/custom.css
deleted file mode 100644
index 5143eb138ea2469d8c457c71cb210fd3fb7cbe15..0000000000000000000000000000000000000000
--- a/spaces/ken4005/Uhi-ChatGPT/custom.css
+++ /dev/null
@@ -1,162 +0,0 @@
-:root {
-    --chatbot-color-light: #F3F3F3;
-    --chatbot-color-dark: #121111;
-}
-
-/* status_display */
-#status_display {
-    display: flex;
-    min-height: 2.5em;
-    align-items: flex-end;
-    justify-content: flex-end;
-}
-#status_display p {
-    font-size: .85em;
-    font-family: monospace;
-    color: var(--body-text-color-subdued);
-}
-
-#chuanhu_chatbot, #status_display {
-    transition: all 0.6s;
-}
-/* list */
-ol:not(.options), ul:not(.options) {
-    padding-inline-start: 2em !important;
-}
-
-/* 亮色 */
-#chuanhu_chatbot {
-    background-color: var(--chatbot-color-light) !important;
-}
-[data-testid = "bot"] {
-    background-color: #FFFFFF !important;
-}
-[data-testid = "user"] {
-    background-color: #95EC69 !important;
-}
-/* 对话气泡 */
-[class *= "message"] {
-    border-radius: var(--radius-xl) !important;
-    border: none;
-    padding: var(--spacing-xl) !important;
-    font-size: var(--text-md) !important;
-    line-height: var(--line-md) !important;
-    min-height: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-    min-width: calc(var(--text-md)*var(--line-md) + 2*var(--spacing-xl));
-}
-[data-testid = "bot"] {
-    max-width: 85%;
-    border-bottom-left-radius: 0 !important;
-}
-[data-testid = "user"] {
-    max-width: 85%;
-    width: auto !important;
-    border-bottom-right-radius: 0 !important;
-}
-/* 表格 */
-table {
-    margin: 1em 0;
-    border-collapse: collapse;
-    empty-cells: show;
-}
-td,th {
-    border: 1.2px solid var(--border-color-primary) !important;
-    padding: 0.2em;
-}
-thead {
-    background-color: rgba(175,184,193,0.2);
-}
-thead th {
-    padding: .5em .2em;
-}
-/* 行内代码 */
-code {
-    display: inline;
-    white-space: break-spaces;
-    border-radius: 6px;
-    margin: 0 2px 0 2px;
-    padding: .2em .4em .1em .4em;
-    background-color: rgba(175,184,193,0.2);
-}
-/* 代码块 */
-pre code {
-    display: block;
-    overflow: auto;
-    white-space: pre;
-    background-color: hsla(0, 0%, 0%, 80%)!important;
-    border-radius: 10px;
-    padding: 1.4em 1.2em 0em 1.4em;
-    margin: 1.2em 2em 1.2em 0.5em;
-    color: #FFF;
-    box-shadow: 6px 6px 16px hsla(0, 0%, 0%, 0.2);
-}
-/* 代码高亮样式 */
-.highlight .hll { background-color: #49483e }
-.highlight .c { color: #75715e } /* Comment */
-.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
-.highlight .k { color: #66d9ef } /* Keyword */
-.highlight .l { color: #ae81ff } /* Literal */
-.highlight .n { color: #f8f8f2 } /* Name */
-.highlight .o { color: #f92672 } /* Operator */
-.highlight .p { color: #f8f8f2 } /* Punctuation */
-.highlight .ch { color: #75715e } /* Comment.Hashbang */
-.highlight .cm { color: #75715e } /* Comment.Multiline */
-.highlight .cp { color: #75715e } /* Comment.Preproc */
-.highlight .cpf { color: #75715e } /* Comment.PreprocFile */
-.highlight .c1 { color: #75715e } /* Comment.Single */
-.highlight .cs { color: #75715e } /* Comment.Special */
-.highlight .gd { color: #f92672 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gi { color: #a6e22e } /* Generic.Inserted */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #75715e } /* Generic.Subheading */
-.highlight .kc { color: #66d9ef } /* Keyword.Constant */
-.highlight .kd { color: #66d9ef } /* Keyword.Declaration */
-.highlight .kn { color: #f92672 } /* Keyword.Namespace */
-.highlight .kp { color: #66d9ef } /* Keyword.Pseudo */
-.highlight .kr { color: #66d9ef } /* Keyword.Reserved */
-.highlight .kt { color: #66d9ef } /* Keyword.Type */
-.highlight .ld { color: #e6db74 } /* Literal.Date */
-.highlight .m { color: #ae81ff } /* Literal.Number */
-.highlight .s { color: #e6db74 } /* Literal.String */
-.highlight .na { color: #a6e22e } /* Name.Attribute */
-.highlight .nb { color: #f8f8f2 } /* Name.Builtin */
-.highlight .nc { color: #a6e22e } /* Name.Class */
-.highlight .no { color: #66d9ef } /* Name.Constant */
-.highlight .nd { color: #a6e22e } /* Name.Decorator */
-.highlight .ni { color: #f8f8f2 } /* Name.Entity */
-.highlight .ne { color: #a6e22e } /* Name.Exception */
-.highlight .nf { color: #a6e22e } /* Name.Function */
-.highlight .nl { color: #f8f8f2 } /* Name.Label */
-.highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-.highlight .nx { color: #a6e22e } /* Name.Other */
-.highlight .py { color: #f8f8f2 } /* Name.Property */
-.highlight .nt { color: #f92672 } /* Name.Tag */
-.highlight .nv { color: #f8f8f2 } /* Name.Variable */
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-.highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-.highlight .mb { color: #ae81ff } /* Literal.Number.Bin */
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diff --git a/spaces/keras-io/Monocular-Depth-Estimation/layers.py b/spaces/keras-io/Monocular-Depth-Estimation/layers.py
deleted file mode 100644
index 6a67d2b8e8424a4dddae3bb6d5f3f210f68b5460..0000000000000000000000000000000000000000
--- a/spaces/keras-io/Monocular-Depth-Estimation/layers.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from tensorflow.keras.layers import Layer, InputSpec
-import keras.utils.conv_utils as conv_utils
-import tensorflow as tf
-import tensorflow.keras.backend as K
-
-
-def normalize_data_format(value):
-    if value is None:
-        value = K.image_data_format()
-    data_format = value.lower()
-    if data_format not in {'channels_first', 'channels_last'}:
-        raise ValueError('The `data_format` argument must be one of '
-                         '"channels_first", "channels_last". Received: ' +
-                         str(value))
-    return data_format
-
-
-class BilinearUpSampling2D(Layer):
-    def __init__(self, size=(2, 2), data_format=None, **kwargs):
-        super(BilinearUpSampling2D, self).__init__(**kwargs)
-        self.data_format = normalize_data_format(data_format)
-        self.size = conv_utils.normalize_tuple(size, 2, 'size')
-        self.input_spec = InputSpec(ndim=4)
-
-    def compute_output_shape(self, input_shape):
-        if self.data_format == 'channels_first':
-            height = self.size[0] * input_shape[2] if input_shape[2] is not None else None
-            width = self.size[1] * input_shape[3] if input_shape[3] is not None else None
-            return (input_shape[0],
-                    input_shape[1],
-                    height,
-                    width)
-        elif self.data_format == 'channels_last':
-            height = self.size[0] * input_shape[1] if input_shape[1] is not None else None
-            width = self.size[1] * input_shape[2] if input_shape[2] is not None else None
-            return (input_shape[0],
-                    height,
-                    width,
-                    input_shape[3])
-
-    def call(self, inputs):
-        input_shape = K.shape(inputs)
-        if self.data_format == 'channels_first':
-            height = self.size[0] * input_shape[2] if input_shape[2] is not None else None
-            width = self.size[1] * input_shape[3] if input_shape[3] is not None else None
-        elif self.data_format == 'channels_last':
-            height = self.size[0] * input_shape[1] if input_shape[1] is not None else None
-            width = self.size[1] * input_shape[2] if input_shape[2] is not None else None
-
-        return tf.image.resize(inputs, [height, width], method=tf.image.ResizeMethod.BILINEAR)
-
-    def get_config(self):
-        config = {'size': self.size, 'data_format': self.data_format}
-        base_config = super(BilinearUpSampling2D, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))
diff --git a/spaces/kesally/anime-remove-background/app.py b/spaces/kesally/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/kesally/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def rmbg_fn(img):
-    mask = get_mask(img)
-    img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    return mask, img
-
-
-if __name__ == "__main__":
-    providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-    model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
-    rmbg_model = rt.InferenceSession(model_path, providers=providers)
-    app = gr.Blocks()
-    with app:
-        gr.Markdown("# Anime Remove Background\n\n"
-                    "![visitor badge](https://visitor-badge.glitch.me/badge?page_id=skytnt.animeseg)\n\n"
-                    "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
-        with gr.Row():
-            with gr.Column():
-                input_img = gr.Image(label="input image")
-                examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
-                examples = gr.Dataset(components=[input_img], samples=examples_data)
-            run_btn = gr.Button(variant="primary")
-            output_mask = gr.Image(label="mask")
-            output_img = gr.Image(label="result", image_mode="RGBA")
-        examples.click(lambda x: x[0], [examples], [input_img])
-        run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
-    app.launch()
diff --git a/spaces/kevinwang676/M4Singer/data_gen/tts/base_binarizer.py b/spaces/kevinwang676/M4Singer/data_gen/tts/base_binarizer.py
deleted file mode 100644
index da3c8e1e6e3854e74032e41a2ab23dddb9fb5c16..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/M4Singer/data_gen/tts/base_binarizer.py
+++ /dev/null
@@ -1,224 +0,0 @@
-import os
-os.environ["OMP_NUM_THREADS"] = "1"
-
-from utils.multiprocess_utils import chunked_multiprocess_run
-import random
-import traceback
-import json
-from resemblyzer import VoiceEncoder
-from tqdm import tqdm
-from data_gen.tts.data_gen_utils import get_mel2ph, get_pitch, build_phone_encoder
-from utils.hparams import set_hparams, hparams
-import numpy as np
-from utils.indexed_datasets import IndexedDatasetBuilder
-from vocoders.base_vocoder import VOCODERS
-import pandas as pd
-
-
-class BinarizationError(Exception):
-    pass
-
-
-class BaseBinarizer:
-    def __init__(self, processed_data_dir=None):
-        if processed_data_dir is None:
-            processed_data_dir = hparams['processed_data_dir']
-        self.processed_data_dirs = processed_data_dir.split(",")
-        self.binarization_args = hparams['binarization_args']
-        self.pre_align_args = hparams['pre_align_args']
-        self.forced_align = self.pre_align_args['forced_align']
-        tg_dir = None
-        if self.forced_align == 'mfa':
-            tg_dir = 'mfa_outputs'
-        if self.forced_align == 'kaldi':
-            tg_dir = 'kaldi_outputs'
-        self.item2txt = {}
-        self.item2ph = {}
-        self.item2wavfn = {}
-        self.item2tgfn = {}
-        self.item2spk = {}
-        for ds_id, processed_data_dir in enumerate(self.processed_data_dirs):
-            self.meta_df = pd.read_csv(f"{processed_data_dir}/metadata_phone.csv", dtype=str)
-            for r_idx, r in self.meta_df.iterrows():
-                item_name = raw_item_name = r['item_name']
-                if len(self.processed_data_dirs) > 1:
-                    item_name = f'ds{ds_id}_{item_name}'
-                self.item2txt[item_name] = r['txt']
-                self.item2ph[item_name] = r['ph']
-                self.item2wavfn[item_name] = os.path.join(hparams['raw_data_dir'], 'wavs', os.path.basename(r['wav_fn']).split('_')[1])
-                self.item2spk[item_name] = r.get('spk', 'SPK1')
-                if len(self.processed_data_dirs) > 1:
-                    self.item2spk[item_name] = f"ds{ds_id}_{self.item2spk[item_name]}"
-                if tg_dir is not None:
-                    self.item2tgfn[item_name] = f"{processed_data_dir}/{tg_dir}/{raw_item_name}.TextGrid"
-        self.item_names = sorted(list(self.item2txt.keys()))
-        if self.binarization_args['shuffle']:
-            random.seed(1234)
-            random.shuffle(self.item_names)
-
-    @property
-    def train_item_names(self):
-        return self.item_names[hparams['test_num']+hparams['valid_num']:]
-
-    @property
-    def valid_item_names(self):
-        return self.item_names[0: hparams['test_num']+hparams['valid_num']]  #
-
-    @property
-    def test_item_names(self):
-        return self.item_names[0: hparams['test_num']]  # Audios for MOS testing are in 'test_ids'
-
-    def build_spk_map(self):
-        spk_map = set()
-        for item_name in self.item_names:
-            spk_name = self.item2spk[item_name]
-            spk_map.add(spk_name)
-        spk_map = {x: i for i, x in enumerate(sorted(list(spk_map)))}
-        assert len(spk_map) == 0 or len(spk_map) <= hparams['num_spk'], len(spk_map)
-        return spk_map
-
-    def item_name2spk_id(self, item_name):
-        return self.spk_map[self.item2spk[item_name]]
-
-    def _phone_encoder(self):
-        ph_set_fn = f"{hparams['binary_data_dir']}/phone_set.json"
-        ph_set = []
-        if hparams['reset_phone_dict'] or not os.path.exists(ph_set_fn):
-            for processed_data_dir in self.processed_data_dirs:
-                ph_set += [x.split(' ')[0] for x in open(f'{processed_data_dir}/dict.txt').readlines()]
-            ph_set = sorted(set(ph_set))
-            json.dump(ph_set, open(ph_set_fn, 'w'))
-        else:
-            ph_set = json.load(open(ph_set_fn, 'r'))
-        print("| phone set: ", ph_set)
-        return build_phone_encoder(hparams['binary_data_dir'])
-
-    def meta_data(self, prefix):
-        if prefix == 'valid':
-            item_names = self.valid_item_names
-        elif prefix == 'test':
-            item_names = self.test_item_names
-        else:
-            item_names = self.train_item_names
-        for item_name in item_names:
-            ph = self.item2ph[item_name]
-            txt = self.item2txt[item_name]
-            tg_fn = self.item2tgfn.get(item_name)
-            wav_fn = self.item2wavfn[item_name]
-            spk_id = self.item_name2spk_id(item_name)
-            yield item_name, ph, txt, tg_fn, wav_fn, spk_id
-
-    def process(self):
-        os.makedirs(hparams['binary_data_dir'], exist_ok=True)
-        self.spk_map = self.build_spk_map()
-        print("| spk_map: ", self.spk_map)
-        spk_map_fn = f"{hparams['binary_data_dir']}/spk_map.json"
-        json.dump(self.spk_map, open(spk_map_fn, 'w'))
-
-        self.phone_encoder = self._phone_encoder()
-        self.process_data('valid')
-        self.process_data('test')
-        self.process_data('train')
-
-    def process_data(self, prefix):
-        data_dir = hparams['binary_data_dir']
-        args = []
-        builder = IndexedDatasetBuilder(f'{data_dir}/{prefix}')
-        lengths = []
-        f0s = []
-        total_sec = 0
-        if self.binarization_args['with_spk_embed']:
-            voice_encoder = VoiceEncoder().cuda()
-
-        meta_data = list(self.meta_data(prefix))
-        for m in meta_data:
-            args.append(list(m) + [self.phone_encoder, self.binarization_args])
-        num_workers = int(os.getenv('N_PROC', os.cpu_count() // 3))
-        for f_id, (_, item) in enumerate(
-                zip(tqdm(meta_data), chunked_multiprocess_run(self.process_item, args, num_workers=num_workers))):
-            if item is None:
-                continue
-            item['spk_embed'] = voice_encoder.embed_utterance(item['wav']) \
-                if self.binarization_args['with_spk_embed'] else None
-            if not self.binarization_args['with_wav'] and 'wav' in item:
-                #print("del wav")
-                del item['wav']
-            builder.add_item(item)
-            lengths.append(item['len'])
-            total_sec += item['sec']
-            if item.get('f0') is not None:
-                f0s.append(item['f0'])
-        builder.finalize()
-        np.save(f'{data_dir}/{prefix}_lengths.npy', lengths)
-        if len(f0s) > 0:
-            f0s = np.concatenate(f0s, 0)
-            f0s = f0s[f0s != 0]
-            np.save(f'{data_dir}/{prefix}_f0s_mean_std.npy', [np.mean(f0s).item(), np.std(f0s).item()])
-        print(f"| {prefix} total duration: {total_sec:.3f}s")
-
-    @classmethod
-    def process_item(cls, item_name, ph, txt, tg_fn, wav_fn, spk_id, encoder, binarization_args):
-        if hparams['vocoder'] in VOCODERS:
-            wav, mel = VOCODERS[hparams['vocoder']].wav2spec(wav_fn)
-        else:
-            wav, mel = VOCODERS[hparams['vocoder'].split('.')[-1]].wav2spec(wav_fn)
-        res = {
-            'item_name': item_name, 'txt': txt, 'ph': ph, 'mel': mel, 'wav': wav, 'wav_fn': wav_fn,
-            'sec': len(wav) / hparams['audio_sample_rate'], 'len': mel.shape[0], 'spk_id': spk_id
-        }
-        try:
-            if binarization_args['with_f0']:
-                cls.get_pitch(wav, mel, res)
-                if binarization_args['with_f0cwt']:
-                    cls.get_f0cwt(res['f0'], res)
-            if binarization_args['with_txt']:
-                try:
-                    phone_encoded = res['phone'] = encoder.encode(ph)
-                except:
-                    traceback.print_exc()
-                    raise BinarizationError(f"Empty phoneme")
-                if binarization_args['with_align']:
-                    cls.get_align(tg_fn, ph, mel, phone_encoded, res)
-        except BinarizationError as e:
-            print(f"| Skip item ({e}). item_name: {item_name}, wav_fn: {wav_fn}")
-            return None
-        return res
-
-    @staticmethod
-    def get_align(tg_fn, ph, mel, phone_encoded, res):
-        if tg_fn is not None and os.path.exists(tg_fn):
-            mel2ph, dur = get_mel2ph(tg_fn, ph, mel, hparams)
-        else:
-            raise BinarizationError(f"Align not found")
-        if mel2ph.max() - 1 >= len(phone_encoded):
-            raise BinarizationError(
-                f"Align does not match: mel2ph.max() - 1: {mel2ph.max() - 1}, len(phone_encoded): {len(phone_encoded)}")
-        res['mel2ph'] = mel2ph
-        res['dur'] = dur
-
-    @staticmethod
-    def get_pitch(wav, mel, res):
-        f0, pitch_coarse = get_pitch(wav, mel, hparams)
-        if sum(f0) == 0:
-            raise BinarizationError("Empty f0")
-        res['f0'] = f0
-        res['pitch'] = pitch_coarse
-
-    @staticmethod
-    def get_f0cwt(f0, res):
-        from utils.cwt import get_cont_lf0, get_lf0_cwt
-        uv, cont_lf0_lpf = get_cont_lf0(f0)
-        logf0s_mean_org, logf0s_std_org = np.mean(cont_lf0_lpf), np.std(cont_lf0_lpf)
-        cont_lf0_lpf_norm = (cont_lf0_lpf - logf0s_mean_org) / logf0s_std_org
-        Wavelet_lf0, scales = get_lf0_cwt(cont_lf0_lpf_norm)
-        if np.any(np.isnan(Wavelet_lf0)):
-            raise BinarizationError("NaN CWT")
-        res['cwt_spec'] = Wavelet_lf0
-        res['cwt_scales'] = scales
-        res['f0_mean'] = logf0s_mean_org
-        res['f0_std'] = logf0s_std_org
-
-
-if __name__ == "__main__":
-    set_hparams()
-    BaseBinarizer().process()
diff --git a/spaces/kevinwang676/SadTalker/src/gradio_demo.py b/spaces/kevinwang676/SadTalker/src/gradio_demo.py
deleted file mode 100644
index 1e70005831b9f29dc3c7f39642364bc325a4c8a4..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/SadTalker/src/gradio_demo.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import torch, uuid
-import os, sys, shutil
-from src.utils.preprocess import CropAndExtract
-from src.test_audio2coeff import Audio2Coeff  
-from src.facerender.animate import AnimateFromCoeff
-from src.generate_batch import get_data
-from src.generate_facerender_batch import get_facerender_data
-
-from src.utils.init_path import init_path
-
-from pydub import AudioSegment
-
-
-def mp3_to_wav(mp3_filename,wav_filename,frame_rate):
-    mp3_file = AudioSegment.from_file(file=mp3_filename)
-    mp3_file.set_frame_rate(frame_rate).export(wav_filename,format="wav")
-
-
-class SadTalker():
-
-    def __init__(self, checkpoint_path='checkpoints', config_path='src/config', lazy_load=False):
-
-        if torch.cuda.is_available() :
-            device = "cuda"
-        else:
-            device = "cpu"
-        
-        self.device = device
-
-        os.environ['TORCH_HOME']= checkpoint_path
-
-        self.checkpoint_path = checkpoint_path
-        self.config_path = config_path
-      
-
-    def test(self, source_image, driven_audio, preprocess='crop', 
-        still_mode=False,  use_enhancer=False, batch_size=1, size=256, 
-        pose_style = 0, exp_scale=1.0, 
-        use_ref_video = False,
-        ref_video = None,
-        ref_info = None,
-        use_idle_mode = False,
-        length_of_audio = 0, use_blink=True,
-        result_dir='./results/'):
-
-        self.sadtalker_paths = init_path(self.checkpoint_path, self.config_path, size, False, preprocess)
-        print(self.sadtalker_paths)
-            
-        self.audio_to_coeff = Audio2Coeff(self.sadtalker_paths, self.device)
-        self.preprocess_model = CropAndExtract(self.sadtalker_paths, self.device)
-        self.animate_from_coeff = AnimateFromCoeff(self.sadtalker_paths, self.device)
-
-        time_tag = str(uuid.uuid4())
-        save_dir = os.path.join(result_dir, time_tag)
-        os.makedirs(save_dir, exist_ok=True)
-
-        input_dir = os.path.join(save_dir, 'input')
-        os.makedirs(input_dir, exist_ok=True)
-
-        print(source_image)
-        pic_path = os.path.join(input_dir, os.path.basename(source_image)) 
-        shutil.move(source_image, input_dir)
-
-        if driven_audio is not None and os.path.isfile(driven_audio):
-            audio_path = os.path.join(input_dir, os.path.basename(driven_audio))  
-
-            #### mp3 to wav
-            if '.mp3' in audio_path:
-                mp3_to_wav(driven_audio, audio_path.replace('.mp3', '.wav'), 16000)
-                audio_path = audio_path.replace('.mp3', '.wav')
-            else:
-                shutil.move(driven_audio, input_dir)
-
-        elif use_idle_mode:
-            audio_path = os.path.join(input_dir, 'idlemode_'+str(length_of_audio)+'.wav') ## generate audio from this new audio_path
-            from pydub import AudioSegment
-            one_sec_segment = AudioSegment.silent(duration=1000*length_of_audio)  #duration in milliseconds
-            one_sec_segment.export(audio_path, format="wav")
-        else:
-            print(use_ref_video, ref_info)
-            assert use_ref_video == True and ref_info == 'all'
-
-        if use_ref_video and ref_info == 'all': # full ref mode
-            ref_video_videoname = os.path.basename(ref_video)
-            audio_path = os.path.join(save_dir, ref_video_videoname+'.wav')
-            print('new audiopath:',audio_path)
-            # if ref_video contains audio, set the audio from ref_video.
-            cmd = r"ffmpeg -y -hide_banner -loglevel error -i %s %s"%(ref_video, audio_path)
-            os.system(cmd)        
-
-        os.makedirs(save_dir, exist_ok=True)
-        
-        #crop image and extract 3dmm from image
-        first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
-        os.makedirs(first_frame_dir, exist_ok=True)
-        first_coeff_path, crop_pic_path, crop_info = self.preprocess_model.generate(pic_path, first_frame_dir, preprocess, True, size)
-        
-        if first_coeff_path is None:
-            raise AttributeError("No face is detected")
-
-        if use_ref_video:
-            print('using ref video for genreation')
-            ref_video_videoname = os.path.splitext(os.path.split(ref_video)[-1])[0]
-            ref_video_frame_dir = os.path.join(save_dir, ref_video_videoname)
-            os.makedirs(ref_video_frame_dir, exist_ok=True)
-            print('3DMM Extraction for the reference video providing pose')
-            ref_video_coeff_path, _, _ =  self.preprocess_model.generate(ref_video, ref_video_frame_dir, preprocess, source_image_flag=False)
-        else:
-            ref_video_coeff_path = None
-
-        if use_ref_video:
-            if ref_info == 'pose':
-                ref_pose_coeff_path = ref_video_coeff_path
-                ref_eyeblink_coeff_path = None
-            elif ref_info == 'blink':
-                ref_pose_coeff_path = None
-                ref_eyeblink_coeff_path = ref_video_coeff_path
-            elif ref_info == 'pose+blink':
-                ref_pose_coeff_path = ref_video_coeff_path
-                ref_eyeblink_coeff_path = ref_video_coeff_path
-            elif ref_info == 'all':            
-                ref_pose_coeff_path = None
-                ref_eyeblink_coeff_path = None
-            else:
-                raise('error in refinfo')
-        else:
-            ref_pose_coeff_path = None
-            ref_eyeblink_coeff_path = None
-
-        #audio2ceoff
-        if use_ref_video and ref_info == 'all':
-            coeff_path = ref_video_coeff_path # self.audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path)
-        else:
-            batch = get_data(first_coeff_path, audio_path, self.device, ref_eyeblink_coeff_path=ref_eyeblink_coeff_path, still=still_mode, idlemode=use_idle_mode, length_of_audio=length_of_audio, use_blink=use_blink) # longer audio?
-            coeff_path = self.audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path)
-
-        #coeff2video
-        data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode, preprocess=preprocess, size=size, expression_scale = exp_scale)
-        return_path = self.animate_from_coeff.generate(data, save_dir,  pic_path, crop_info, enhancer='gfpgan' if use_enhancer else None, preprocess=preprocess, img_size=size)
-        video_name = data['video_name']
-        print(f'The generated video is named {video_name} in {save_dir}')
-
-        del self.preprocess_model
-        del self.audio_to_coeff
-        del self.animate_from_coeff
-
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
-            
-        import gc; gc.collect()
-        
-        return return_path
-
-    
\ No newline at end of file
diff --git a/spaces/kevinwang676/VALLE/utils/g2p/cleaners.py b/spaces/kevinwang676/VALLE/utils/g2p/cleaners.py
deleted file mode 100644
index 8bb53977673afa893476cab1b946d6a9a00f57ea..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VALLE/utils/g2p/cleaners.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import re
-from utils.g2p.japanese import japanese_to_romaji_with_accent, japanese_to_ipa, japanese_to_ipa2, japanese_to_ipa3
-from utils.g2p.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo, chinese_to_romaji, chinese_to_lazy_ipa, chinese_to_ipa, chinese_to_ipa2
-from utils.g2p.english import english_to_lazy_ipa, english_to_ipa2, english_to_lazy_ipa2
-patterns = [r'\[EN\](.*?)\[EN\]', r'\[ZH\](.*?)\[ZH\]', r'\[JA\](.*?)\[JA\]']
-def japanese_cleaners(text):
-    text = japanese_to_romaji_with_accent(text)
-    text = re.sub(r'([A-Za-z])$', r'\1.', text)
-    return text
-
-def japanese_cleaners2(text):
-    return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…')
-
-def chinese_cleaners(text):
-    '''Pipeline for Chinese text'''
-    text = number_to_chinese(text)
-    text = chinese_to_bopomofo(text)
-    text = latin_to_bopomofo(text)
-    text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text)
-    return text
-
-def cje_cleaners(text):
-    matches = []
-    for pattern in patterns:
-        matches.extend(re.finditer(pattern, text))
-
-    matches.sort(key=lambda x: x.start())  # Sort matches by their start positions
-
-    outputs = ""
-    output_langs = []
-
-    for match in matches:
-        text_segment = text[match.start():match.end()]
-        phon = clean_one(text_segment)
-        if "[EN]" in text_segment:
-            lang = 'en'
-        elif "[ZH]" in text_segment:
-            lang = 'zh'
-        elif "[JA]" in text_segment:
-            lang = 'ja'
-        else:
-            raise ValueError("If you see this error, please report this bug to issues.")
-        outputs += phon
-        output_langs += [lang] * len(phon)
-    assert len(outputs) == len(output_langs)
-    return outputs, output_langs
-
-
-def clean_one(text):
-    if text.find('[ZH]') != -1:
-        text = re.sub(r'\[ZH\](.*?)\[ZH\]',
-                      lambda x: chinese_to_ipa(x.group(1))+' ', text)
-    if text.find('[JA]') != -1:
-        text = re.sub(r'\[JA\](.*?)\[JA\]',
-                      lambda x: japanese_to_ipa2(x.group(1))+' ', text)
-    if text.find('[EN]') != -1:
-        text = re.sub(r'\[EN\](.*?)\[EN\]',
-                      lambda x: english_to_ipa2(x.group(1))+' ', text)
-    text = re.sub(r'\s+$', '', text)
-    text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text)
-    return text
diff --git a/spaces/kevinwang676/Voice-Changer/README.md b/spaces/kevinwang676/Voice-Changer/README.md
deleted file mode 100644
index e92d9ef2dff45520b78329d36bb9f686e7a69d8f..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/Voice-Changer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: VoiceChange
-emoji: 👀
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.28.3
-app_file: app_multi.py
-pinned: false
-license: mit
-duplicated_from: BartPoint/VoiceChange
----
diff --git a/spaces/kevinwang676/VoiceChanger/src/utils/text2speech.py b/spaces/kevinwang676/VoiceChanger/src/utils/text2speech.py
deleted file mode 100644
index 00d165b6cc7774fd200929aafa0ff3b15916111e..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/utils/text2speech.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import os
-import tempfile
-from TTS.api import TTS
-
-
-class TTSTalker():
-    def __init__(self) -> None:
-        model_name = TTS.list_models()[0]
-        self.tts = TTS(model_name)
-
-    def test(self, text, language='en'):
-
-        tempf  = tempfile.NamedTemporaryFile(
-                delete = False,
-                suffix = ('.'+'wav'),
-            )
-
-        self.tts.tts_to_file(text, speaker=self.tts.speakers[0], language=language, file_path=tempf.name)
-
-        return tempf.name
\ No newline at end of file
diff --git a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_hed.py b/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_hed.py
deleted file mode 100644
index b71da04640c23fb906dbae0ec97e0f8a24cbf87b..0000000000000000000000000000000000000000
--- a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_hed.py
+++ /dev/null
@@ -1,181 +0,0 @@
-import gradio as gr
-import torch
-from controlnet_aux import HEDdetector
-from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
-from PIL import Image
-
-from diffusion_webui.utils.model_list import (
-    controlnet_hed_model_list,
-    stable_model_list,
-)
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_LIST,
-    get_scheduler_list,
-)
-
-
-class StableDiffusionControlNetHEDGenerator:
-    def __init__(self):
-        self.pipe = None
-
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-
-            self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
-                pretrained_model_name_or_path=stable_model_path,
-                controlnet=controlnet,
-                safety_checker=None,
-                torch_dtype=torch.float16,
-            )
-
-        self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
-        self.pipe.to("cuda")
-        self.pipe.enable_xformers_memory_efficient_attention()
-
-        return self.pipe
-
-    def controlnet_hed(self, image_path: str):
-        hed = HEDdetector.from_pretrained("lllyasviel/ControlNet")
-        image = Image.open(image_path)
-        image = hed(image)
-
-        return image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_hed_model_path: str,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        sheduler: str,
-        seed_generator: int,
-    ):
-
-        image = self.controlnet_hed(image_path=image_path)
-
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_hed_model_path,
-            scheduler=sheduler,
-        )
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-
-        output = pipe(
-            prompt=prompt,
-            image=image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_hed_image_file = gr.Image(
-                        type="filepath", label="Image"
-                    )
-                    controlnet_hed_prompt = gr.Textbox(
-                        lines=1,
-                        show_label=False,
-                        placeholder="Prompt",
-                    )
-
-                    controlnet_hed_negative_prompt = gr.Textbox(
-                        lines=1,
-                        show_label=False,
-                        placeholder="Negative Prompt",
-                    )
-
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_hed_stable_model_id = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Id",
-                            )
-                            controlnet_hed_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            controlnet_hed_num_inference_step = gr.Slider(
-                                minimum=1,
-                                maximum=100,
-                                step=1,
-                                value=50,
-                                label="Num Inference Step",
-                            )
-
-                            controlnet_hed_num_images_per_prompt = gr.Slider(
-                                minimum=1,
-                                maximum=10,
-                                step=1,
-                                value=1,
-                                label="Number Of Images",
-                            )
-
-                        with gr.Row():
-                            with gr.Column():
-                                controlnet_hed_model_id = gr.Dropdown(
-                                    choices=controlnet_hed_model_list,
-                                    value=controlnet_hed_model_list[0],
-                                    label="ControlNet Model Id",
-                                )
-                                controlnet_hed_scheduler = gr.Dropdown(
-                                    choices=SCHEDULER_LIST,
-                                    value=SCHEDULER_LIST[0],
-                                    label="Scheduler",
-                                )
-
-                                controlnet_hed_seed_generator = gr.Number(
-                                    minimum=0,
-                                    maximum=1000000,
-                                    step=1,
-                                    value=0,
-                                    label="Seed Generator",
-                                )
-
-                    controlnet_hed_predict = gr.Button(value="Generator")
-
-                with gr.Column():
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-            controlnet_hed_predict.click(
-                fn=StableDiffusionControlNetHEDGenerator().generate_image,
-                inputs=[
-                    controlnet_hed_image_file,
-                    controlnet_hed_stable_model_id,
-                    controlnet_hed_model_id,
-                    controlnet_hed_prompt,
-                    controlnet_hed_negative_prompt,
-                    controlnet_hed_num_images_per_prompt,
-                    controlnet_hed_guidance_scale,
-                    controlnet_hed_num_inference_step,
-                    controlnet_hed_scheduler,
-                    controlnet_hed_seed_generator,
-                ],
-                outputs=[output_image],
-            )
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/version.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/version.py
deleted file mode 100644
index 1cce4e50bd692d4002e3cac3c545a3fb2efe95d0..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/version.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-__version__ = '1.3.17'
-
-
-def parse_version_info(version_str: str, length: int = 4) -> tuple:
-    """Parse a version string into a tuple.
-
-    Args:
-        version_str (str): The version string.
-        length (int): The maximum number of version levels. Default: 4.
-
-    Returns:
-        tuple[int | str]: The version info, e.g., "1.3.0" is parsed into
-            (1, 3, 0, 0, 0, 0), and "2.0.0rc1" is parsed into
-            (2, 0, 0, 0, 'rc', 1) (when length is set to 4).
-    """
-    from packaging.version import parse
-    version = parse(version_str)
-    assert version.release, f'failed to parse version {version_str}'
-    release = list(version.release)
-    release = release[:length]
-    if len(release) < length:
-        release = release + [0] * (length - len(release))
-    if version.is_prerelease:
-        release.extend(list(version.pre))
-    elif version.is_postrelease:
-        release.extend(list(version.post))
-    else:
-        release.extend([0, 0])
-    return tuple(release)
-
-
-version_info = tuple(int(x) for x in __version__.split('.')[:3])
-
-__all__ = ['__version__', 'version_info', 'parse_version_info']
diff --git a/spaces/kleinay/qasem-demo/README.md b/spaces/kleinay/qasem-demo/README.md
deleted file mode 100644
index 62205f4fbd0970fcf6ada349de756d4c32d359be..0000000000000000000000000000000000000000
--- a/spaces/kleinay/qasem-demo/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Qasem Demo
-emoji: 🚀
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 3.0.5
-app_file: app.py
-pinned: false
-license: apache-2.0
-python_version: "3.10"
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/kokuma/img-to-music/share_btn.py b/spaces/kokuma/img-to-music/share_btn.py
deleted file mode 100644
index 1a2ac6a6e74b114dbd54c2f24723a87180db51ef..0000000000000000000000000000000000000000
--- a/spaces/kokuma/img-to-music/share_btn.py
+++ /dev/null
@@ -1,100 +0,0 @@
-community_icon_html = """<svg id="share-btn-share-icon" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32">
-    <path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path>
-    <path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path>
-</svg>"""
-
-loading_icon_html = """<svg id="share-btn-loading-icon" style="display:none;" class="animate-spin"
-   style="color: #ffffff; 
-"
-   xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" fill="none" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><circle style="opacity: 0.25;" cx="12" cy="12" r="10" stroke="white" stroke-width="4"></circle><path style="opacity: 0.75;" fill="white" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4zm2 5.291A7.962 7.962 0 014 12H0c0 3.042 1.135 5.824 3 7.938l3-2.647z"></path></svg>"""
-
-share_js = """async () => {
-	async function uploadFile(file){
-		const UPLOAD_URL = 'https://huggingface.co/uploads';
-		const response = await fetch(UPLOAD_URL, {
-			method: 'POST',
-			headers: {
-				'Content-Type': file.type,
-				'X-Requested-With': 'XMLHttpRequest',
-			},
-			body: file, /// <- File inherits from Blob
-		});
-		const url = await response.text();
-		return url;
-	}
-	async function getInputImgFile(imgEl){
-        const res = await fetch(imgEl.src);
-        const blob = await res.blob();
-        const imgId = Date.now() % 200;
-        const isPng = imgEl.src.startsWith(`data:image/png`);
-        if(isPng){
-            const fileName = `sd-perception-${{imgId}}.png`;
-            return new File([blob], fileName, { type: 'image/png' });
-        }else{
-            const fileName = `sd-perception-${{imgId}}.jpg`;
-            return new File([blob], fileName, { type: 'image/jpeg' });
-        }
-	}
-    async function getOutputMusicFile(audioEL){
-        const res = await fetch(audioEL.src);
-        const blob = await res.blob();
-        const audioId = Date.now() % 200;
-        const fileName = `img-to-music-${{audioId}}.wav`;
-        const musicBlob = new File([blob], fileName, { type: 'audio/wav' });
-        console.log(musicBlob);
-        return musicBlob;
-	}
-    
-    async function audioToBase64(audioFile) {
-        return new Promise((resolve, reject) => {
-            let reader = new FileReader();
-            reader.readAsDataURL(audioFile);
-            reader.onload = () => resolve(reader.result);
-            reader.onerror = error => reject(error);
-          
-        });
-      }
-    const gradioEl = document.querySelector('body > gradio-app');
-    // const gradioEl = document.querySelector("gradio-app").shadowRoot;
-    const inputImgEl = gradioEl.querySelector('#input-img img');
-    const outputMusic = gradioEl.querySelector('#music-output audio');
-    const outputMusic_src = gradioEl.querySelector('#music-output audio').src;
-    const outputMusic_name = outputMusic_src.split('/').pop();
-    let titleTxt = outputMusic_name;
-    //if(titleTxt.length > 100){
-    //    titleTxt = titleTxt.slice(0, 100) + ' ...';
-    //}
-    const shareBtnEl = gradioEl.querySelector('#share-btn');
-    const shareIconEl = gradioEl.querySelector('#share-btn-share-icon');
-    const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon');
-    if(!outputMusic){
-        return;
-    };
-    shareBtnEl.style.pointerEvents = 'none';
-    shareIconEl.style.display = 'none';
-    loadingIconEl.style.removeProperty('display');
-    const inputFile = await getInputImgFile(inputImgEl);
-    const urlInputImg = await uploadFile(inputFile);
-    const musicFile = await getOutputMusicFile(outputMusic);
-	const dataOutputMusic = await uploadFile(musicFile);
-    
-    const descriptionMd = `#### Input img:
-<img src='${urlInputImg}' style='max-height: 350px;'>
-
-#### Music:
-
-<audio controls>
-  <source src="${dataOutputMusic}" type="audio/wav">
-Your browser does not support the audio element.
-</audio>
-`;
-    const params = new URLSearchParams({
-        title: titleTxt,
-        description: descriptionMd,
-    });
-	const paramsStr = params.toString();
-	window.open(`https://huggingface.co/spaces/fffiloni/img-to-music/discussions/new?${paramsStr}`, '_blank');
-    shareBtnEl.style.removeProperty('pointer-events');
-    shareIconEl.style.removeProperty('display');
-    loadingIconEl.style.display = 'none';
-}"""
\ No newline at end of file
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/utils.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/utils.py
deleted file mode 100644
index 6f1cc3dd91f71d7d23d23e3872af2631b9a67d6d..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/utils.py
+++ /dev/null
@@ -1,924 +0,0 @@
-""" Handy utility functions. """
-
-from __future__ import annotations
-
-import asyncio
-import copy
-import functools
-import inspect
-import json
-import json.decoder
-import os
-import pkgutil
-import random
-import re
-import sys
-import time
-import typing
-import warnings
-from contextlib import contextmanager
-from enum import Enum
-from io import BytesIO
-from numbers import Number
-from pathlib import Path
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Generator,
-    TypeVar,
-    Union,
-)
-
-import anyio
-import httpx
-import matplotlib
-import requests
-from gradio_client.serializing import Serializable
-from markdown_it import MarkdownIt
-from mdit_py_plugins.dollarmath.index import dollarmath_plugin
-from mdit_py_plugins.footnote.index import footnote_plugin
-from pydantic import BaseModel, parse_obj_as
-
-import gradio
-from gradio.context import Context
-from gradio.strings import en
-
-if TYPE_CHECKING:  # Only import for type checking (is False at runtime).
-    from gradio.blocks import Block, BlockContext
-    from gradio.components import Component
-
-JSON_PATH = os.path.join(os.path.dirname(gradio.__file__), "launches.json")
-GRADIO_VERSION = (
-    (pkgutil.get_data(__name__, "version.txt") or b"").decode("ascii").strip()
-)
-
-T = TypeVar("T")
-
-
-def colab_check() -> bool:
-    """
-    Check if interface is launching from Google Colab
-    :return is_colab (bool): True or False
-    """
-    is_colab = False
-    try:  # Check if running interactively using ipython.
-        from IPython import get_ipython
-
-        from_ipynb = get_ipython()
-        if "google.colab" in str(from_ipynb):
-            is_colab = True
-    except (ImportError, NameError):
-        pass
-    return is_colab
-
-
-def kaggle_check() -> bool:
-    return bool(
-        os.environ.get("KAGGLE_KERNEL_RUN_TYPE") or os.environ.get("GFOOTBALL_DATA_DIR")
-    )
-
-
-def sagemaker_check() -> bool:
-    try:
-        import boto3  # type: ignore
-
-        client = boto3.client("sts")
-        response = client.get_caller_identity()
-        return "sagemaker" in response["Arn"].lower()
-    except Exception:
-        return False
-
-
-def ipython_check() -> bool:
-    """
-    Check if interface is launching from iPython (not colab)
-    :return is_ipython (bool): True or False
-    """
-    is_ipython = False
-    try:  # Check if running interactively using ipython.
-        from IPython import get_ipython
-
-        if get_ipython() is not None:
-            is_ipython = True
-    except (ImportError, NameError):
-        pass
-    return is_ipython
-
-
-def readme_to_html(article: str) -> str:
-    try:
-        response = requests.get(article, timeout=3)
-        if response.status_code == requests.codes.ok:  # pylint: disable=no-member
-            article = response.text
-    except requests.exceptions.RequestException:
-        pass
-    return article
-
-
-def show_tip(interface: gradio.Blocks) -> None:
-    if interface.show_tips and random.random() < 1.5:
-        tip: str = random.choice(en["TIPS"])
-        print(f"Tip: {tip}")
-
-
-def launch_counter() -> None:
-    try:
-        if not os.path.exists(JSON_PATH):
-            launches = {"launches": 1}
-            with open(JSON_PATH, "w+") as j:
-                json.dump(launches, j)
-        else:
-            with open(JSON_PATH) as j:
-                launches = json.load(j)
-            launches["launches"] += 1
-            if launches["launches"] in [25, 50, 150, 500, 1000]:
-                print(en["BETA_INVITE"])
-            with open(JSON_PATH, "w") as j:
-                j.write(json.dumps(launches))
-    except Exception:
-        pass
-
-
-def get_default_args(func: Callable) -> list[Any]:
-    signature = inspect.signature(func)
-    return [
-        v.default if v.default is not inspect.Parameter.empty else None
-        for v in signature.parameters.values()
-    ]
-
-
-def assert_configs_are_equivalent_besides_ids(
-    config1: dict, config2: dict, root_keys: tuple = ("mode",)
-):
-    """Allows you to test if two different Blocks configs produce the same demo.
-
-    Parameters:
-    config1 (dict): nested dict with config from the first Blocks instance
-    config2 (dict): nested dict with config from the second Blocks instance
-    root_keys (Tuple): an interable consisting of which keys to test for equivalence at
-        the root level of the config. By default, only "mode" is tested,
-        so keys like "version" are ignored.
-    """
-    config1 = copy.deepcopy(config1)
-    config2 = copy.deepcopy(config2)
-
-    for key in root_keys:
-        assert config1[key] == config2[key], f"Configs have different: {key}"
-
-    assert len(config1["components"]) == len(
-        config2["components"]
-    ), "# of components are different"
-
-    def assert_same_components(config1_id, config2_id):
-        c1 = list(filter(lambda c: c["id"] == config1_id, config1["components"]))[0]
-        c2 = list(filter(lambda c: c["id"] == config2_id, config2["components"]))[0]
-        c1 = copy.deepcopy(c1)
-        c1.pop("id")
-        c2 = copy.deepcopy(c2)
-        c2.pop("id")
-        assert c1 == c2, f"{c1} does not match {c2}"
-
-    def same_children_recursive(children1, chidren2):
-        for child1, child2 in zip(children1, chidren2):
-            assert_same_components(child1["id"], child2["id"])
-            if "children" in child1 or "children" in child2:
-                same_children_recursive(child1["children"], child2["children"])
-
-    children1 = config1["layout"]["children"]
-    children2 = config2["layout"]["children"]
-    same_children_recursive(children1, children2)
-
-    for d1, d2 in zip(config1["dependencies"], config2["dependencies"]):
-        for t1, t2 in zip(d1.pop("targets"), d2.pop("targets")):
-            assert_same_components(t1, t2)
-        for i1, i2 in zip(d1.pop("inputs"), d2.pop("inputs")):
-            assert_same_components(i1, i2)
-        for o1, o2 in zip(d1.pop("outputs"), d2.pop("outputs")):
-            assert_same_components(o1, o2)
-
-        assert d1 == d2, f"{d1} does not match {d2}"
-
-    return True
-
-
-def format_ner_list(input_string: str, ner_groups: list[dict[str, str | int]]):
-    if len(ner_groups) == 0:
-        return [(input_string, None)]
-
-    output = []
-    end = 0
-    prev_end = 0
-
-    for group in ner_groups:
-        entity, start, end = group["entity_group"], group["start"], group["end"]
-        output.append((input_string[prev_end:start], None))
-        output.append((input_string[start:end], entity))
-        prev_end = end
-
-    output.append((input_string[end:], None))
-    return output
-
-
-def delete_none(_dict: dict, skip_value: bool = False) -> dict:
-    """
-    Delete keys whose values are None from a dictionary
-    """
-    for key, value in list(_dict.items()):
-        if skip_value and key == "value":
-            continue
-        elif value is None:
-            del _dict[key]
-    return _dict
-
-
-def resolve_singleton(_list: list[Any] | Any) -> Any:
-    if len(_list) == 1:
-        return _list[0]
-    else:
-        return _list
-
-
-def component_or_layout_class(cls_name: str) -> type[Component] | type[BlockContext]:
-    """
-    Returns the component, template, or layout class with the given class name, or
-    raises a ValueError if not found.
-
-    Parameters:
-    cls_name (str): lower-case string class name of a component
-    Returns:
-    cls: the component class
-    """
-    import gradio.blocks
-    import gradio.components
-    import gradio.layouts
-    import gradio.templates
-
-    components = [
-        (name, cls)
-        for name, cls in gradio.components.__dict__.items()
-        if isinstance(cls, type)
-    ]
-    templates = [
-        (name, cls)
-        for name, cls in gradio.templates.__dict__.items()
-        if isinstance(cls, type)
-    ]
-    layouts = [
-        (name, cls)
-        for name, cls in gradio.layouts.__dict__.items()
-        if isinstance(cls, type)
-    ]
-    for name, cls in components + templates + layouts:
-        if name.lower() == cls_name.replace("_", "") and (
-            issubclass(cls, gradio.components.Component)
-            or issubclass(cls, gradio.blocks.BlockContext)
-        ):
-            return cls
-    raise ValueError(f"No such component or layout: {cls_name}")
-
-
-def run_coro_in_background(func: Callable, *args, **kwargs):
-    """
-    Runs coroutines in background.
-
-    Warning, be careful to not use this function in other than FastAPI scope, because the event_loop has not started yet.
-    You can use it in any scope reached by FastAPI app.
-
-    correct scope examples: endpoints in routes, Blocks.process_api
-    incorrect scope examples: Blocks.launch
-
-    Use startup_events in routes.py if you need to run a coro in background in Blocks.launch().
-
-
-    Example:
-        utils.run_coro_in_background(fn, *args, **kwargs)
-
-    Args:
-        func:
-        *args:
-        **kwargs:
-
-    Returns:
-
-    """
-    event_loop = asyncio.get_event_loop()
-    return event_loop.create_task(func(*args, **kwargs))
-
-
-def run_sync_iterator_async(iterator):
-    """Helper for yielding StopAsyncIteration from sync iterators."""
-    try:
-        return next(iterator)
-    except StopIteration:
-        # raise a ValueError here because co-routines can't raise StopIteration themselves
-        raise StopAsyncIteration() from None
-
-
-class SyncToAsyncIterator:
-    """Treat a synchronous iterator as async one."""
-
-    def __init__(self, iterator, limiter) -> None:
-        self.iterator = iterator
-        self.limiter = limiter
-
-    def __aiter__(self):
-        return self
-
-    async def __anext__(self):
-        return await anyio.to_thread.run_sync(
-            run_sync_iterator_async, self.iterator, limiter=self.limiter
-        )
-
-
-async def async_iteration(iterator):
-    # anext not introduced until 3.10 :(
-    return await iterator.__anext__()
-
-
-class AsyncRequest:
-    """
-    The AsyncRequest class is a low-level API that allow you to create asynchronous HTTP requests without a context manager.
-    Compared to making calls by using httpx directly, AsyncRequest offers several advantages:
-        (1) Includes response validation functionality both using validation models and functions.
-        (2) Exceptions are handled silently during the request call, which provides the ability to inspect each one
-        request call individually in the case where there are multiple asynchronous request calls and some of them fail.
-        (3) Provides HTTP request types with AsyncRequest.Method Enum class for ease of usage
-
-    AsyncRequest also offers some util functions such as has_exception, is_valid and status to inspect get detailed
-    information about executed request call.
-
-    The basic usage of AsyncRequest is as follows: create a AsyncRequest object with inputs(method, url etc.). Then use it
-    with the "await" statement, and then you can use util functions to do some post request checks depending on your use-case.
-    Finally, call the get_validated_data function to get the response data.
-
-    You can see example usages in test_utils.py.
-    """
-
-    client = httpx.AsyncClient()
-
-    class Method(str, Enum):
-        """
-        Method is an enumeration class that contains possible types of HTTP request methods.
-        """
-
-        ANY = "*"
-        CONNECT = "CONNECT"
-        HEAD = "HEAD"
-        GET = "GET"
-        DELETE = "DELETE"
-        OPTIONS = "OPTIONS"
-        PATCH = "PATCH"
-        POST = "POST"
-        PUT = "PUT"
-        TRACE = "TRACE"
-
-    def __init__(
-        self,
-        method: Method,
-        url: str,
-        *,
-        validation_model: type[BaseModel] | None = None,
-        validation_function: Union[Callable, None] = None,
-        exception_type: type[Exception] = Exception,
-        raise_for_status: bool = False,
-        client: httpx.AsyncClient | None = None,
-        **kwargs,
-    ):
-        """
-        Initialize the Request instance.
-        Args:
-            method(Request.Method) : method of the request
-            url(str): url of the request
-            *
-            validation_model(Type[BaseModel]): a pydantic validation class type to use in validation of the response
-            validation_function(Callable): a callable instance to use in validation of the response
-            exception_class(Type[Exception]): a exception type to throw with its type
-            raise_for_status(bool): a flag that determines to raise httpx.Request.raise_for_status() exceptions.
-        """
-        self._exception: Union[Exception, None] = None
-        self._status = None
-        self._raise_for_status = raise_for_status
-        self._validation_model = validation_model
-        self._validation_function = validation_function
-        self._exception_type = exception_type
-        self._validated_data = None
-        # Create request
-        self._request = self._create_request(method, url, **kwargs)
-        self.client_ = client or self.client
-
-    def __await__(self) -> Generator[None, Any, AsyncRequest]:
-        """
-        Wrap Request's __await__ magic function to create request calls which are executed in one line.
-        """
-        return self.__run().__await__()
-
-    async def __run(self) -> AsyncRequest:
-        """
-        Manage the request call lifecycle.
-        Execute the request by sending it through the client, then check its status.
-        Then parse the request into Json format. And then validate it using the provided validation methods.
-        If a problem occurs in this sequential process,
-        an exception will be raised within the corresponding method, and allowed to be examined.
-        Manage the request call lifecycle.
-
-        Returns:
-            Request
-        """
-        try:
-            # Send the request and get the response.
-            self._response: httpx.Response = await self.client_.send(self._request)
-            # Raise for _status
-            self._status = self._response.status_code
-            if self._raise_for_status:
-                self._response.raise_for_status()
-            # Parse client response data to JSON
-            self._json_response_data = self._response.json()
-            # Validate response data
-            self._validated_data = self._validate_response_data(
-                self._json_response_data
-            )
-        except Exception as exception:
-            # If there is an exception, store it to do further inspections.
-            self._exception = self._exception_type(exception)
-        return self
-
-    @staticmethod
-    def _create_request(method: Method, url: str, **kwargs) -> httpx.Request:
-        """
-        Create a request. This is a httpx request wrapper function.
-        Args:
-            method(Request.Method): request method type
-            url(str): target url of the request
-            **kwargs
-        Returns:
-            Request
-        """
-        request = httpx.Request(method, url, **kwargs)
-        return request
-
-    def _validate_response_data(self, response):
-        """
-        Validate response using given validation methods. If there is a validation method and response is not valid,
-        validation functions will raise an exception for them.
-        Args:
-            response(ResponseJson): response object
-        Returns:
-            ResponseJson: Validated Json object.
-        """
-
-        # We use raw response as a default value if there is no validation method or response is not valid.
-        validated_response = response
-
-        try:
-            # If a validation model is provided, validate response using the validation model.
-            if self._validation_model:
-                validated_response = self._validate_response_by_model(response)
-            # Then, If a validation function is provided, validate response using the validation function.
-            if self._validation_function:
-                validated_response = self._validate_response_by_validation_function(
-                    response
-                )
-        except Exception as exception:
-            # If one of the validation methods does not confirm, raised exception will be silently handled.
-            # We assign this exception to classes instance to do further inspections via is_valid function.
-            self._exception = exception
-
-        return validated_response
-
-    def _validate_response_by_model(self, response) -> BaseModel:
-        """
-        Validate response json using the validation model.
-        Args:
-            response(ResponseJson): response object
-        Returns:
-            ResponseJson: Validated Json object.
-        """
-        validated_data = BaseModel()
-        if self._validation_model:
-            validated_data = parse_obj_as(self._validation_model, response)
-        return validated_data
-
-    def _validate_response_by_validation_function(self, response):
-        """
-        Validate response json using the validation function.
-        Args:
-            response(ResponseJson): response object
-        Returns:
-            ResponseJson: Validated Json object.
-        """
-        validated_data = None
-
-        if self._validation_function:
-            validated_data = self._validation_function(response)
-
-        return validated_data
-
-    def is_valid(self, raise_exceptions: bool = False) -> bool:
-        """
-        Check response object's validity+. Raise exceptions if raise_exceptions flag is True.
-        Args:
-            raise_exceptions(bool) : a flag to raise exceptions in this check
-        Returns:
-            bool: validity of the data
-        """
-        if self.has_exception and self._exception:
-            if raise_exceptions:
-                raise self._exception
-            return False
-        else:
-            # If there is no exception, that means there is no validation error.
-            return True
-
-    def get_validated_data(self):
-        return self._validated_data
-
-    @property
-    def json(self):
-        return self._json_response_data
-
-    @property
-    def exception(self):
-        return self._exception
-
-    @property
-    def has_exception(self):
-        return self.exception is not None
-
-    @property
-    def raise_exceptions(self):
-        if self.has_exception and self._exception:
-            raise self._exception
-
-    @property
-    def status(self):
-        return self._status
-
-
-@contextmanager
-def set_directory(path: Path | str):
-    """Context manager that sets the working directory to the given path."""
-    origin = Path().absolute()
-    try:
-        os.chdir(path)
-        yield
-    finally:
-        os.chdir(origin)
-
-
-def sanitize_value_for_csv(value: str | Number) -> str | Number:
-    """
-    Sanitizes a value that is being written to a CSV file to prevent CSV injection attacks.
-    Reference: https://owasp.org/www-community/attacks/CSV_Injection
-    """
-    if isinstance(value, Number):
-        return value
-    unsafe_prefixes = ["=", "+", "-", "@", "\t", "\n"]
-    unsafe_sequences = [",=", ",+", ",-", ",@", ",\t", ",\n"]
-    if any(value.startswith(prefix) for prefix in unsafe_prefixes) or any(
-        sequence in value for sequence in unsafe_sequences
-    ):
-        value = f"'{value}"
-    return value
-
-
-def sanitize_list_for_csv(values: list[Any]) -> list[Any]:
-    """
-    Sanitizes a list of values (or a list of list of values) that is being written to a
-    CSV file to prevent CSV injection attacks.
-    """
-    sanitized_values = []
-    for value in values:
-        if isinstance(value, list):
-            sanitized_value = [sanitize_value_for_csv(v) for v in value]
-            sanitized_values.append(sanitized_value)
-        else:
-            sanitized_value = sanitize_value_for_csv(value)
-            sanitized_values.append(sanitized_value)
-    return sanitized_values
-
-
-def append_unique_suffix(name: str, list_of_names: list[str]):
-    """Appends a numerical suffix to `name` so that it does not appear in `list_of_names`."""
-    set_of_names: set[str] = set(list_of_names)  # for O(1) lookup
-    if name not in set_of_names:
-        return name
-    else:
-        suffix_counter = 1
-        new_name = f"{name}_{suffix_counter}"
-        while new_name in set_of_names:
-            suffix_counter += 1
-            new_name = f"{name}_{suffix_counter}"
-        return new_name
-
-
-def validate_url(possible_url: str) -> bool:
-    headers = {"User-Agent": "gradio (https://gradio.app/; team@gradio.app)"}
-    try:
-        head_request = requests.head(possible_url, headers=headers)
-        # some URLs, such as AWS S3 presigned URLs, return a 405 or a 403 for HEAD requests
-        if head_request.status_code == 405 or head_request.status_code == 403:
-            return requests.get(possible_url, headers=headers).ok
-        return head_request.ok
-    except Exception:
-        return False
-
-
-def is_update(val):
-    return isinstance(val, dict) and "update" in val.get("__type__", "")
-
-
-def get_continuous_fn(fn: Callable, every: float) -> Callable:
-    def continuous_fn(*args):
-        while True:
-            output = fn(*args)
-            yield output
-            time.sleep(every)
-
-    return continuous_fn
-
-
-async def cancel_tasks(task_ids: set[str]):
-    if sys.version_info < (3, 8):
-        return None
-
-    matching_tasks = [
-        task for task in asyncio.all_tasks() if task.get_name() in task_ids
-    ]
-    for task in matching_tasks:
-        task.cancel()
-    await asyncio.gather(*matching_tasks, return_exceptions=True)
-
-
-def set_task_name(task, session_hash: str, fn_index: int, batch: bool):
-    if sys.version_info >= (3, 8) and not (
-        batch
-    ):  # You shouldn't be able to cancel a task if it's part of a batch
-        task.set_name(f"{session_hash}_{fn_index}")
-
-
-def get_cancel_function(
-    dependencies: list[dict[str, Any]]
-) -> tuple[Callable, list[int]]:
-    fn_to_comp = {}
-    for dep in dependencies:
-        if Context.root_block:
-            fn_index = next(
-                i for i, d in enumerate(Context.root_block.dependencies) if d == dep
-            )
-            fn_to_comp[fn_index] = [
-                Context.root_block.blocks[o] for o in dep["outputs"]
-            ]
-
-    async def cancel(session_hash: str) -> None:
-        task_ids = {f"{session_hash}_{fn}" for fn in fn_to_comp}
-        await cancel_tasks(task_ids)
-
-    return (
-        cancel,
-        list(fn_to_comp.keys()),
-    )
-
-
-def get_type_hints(fn):
-    # Importing gradio with the canonical abbreviation. Used in typing._eval_type.
-    import gradio as gr  # noqa: F401
-    from gradio import Request  # noqa: F401
-
-    if inspect.isfunction(fn) or inspect.ismethod(fn):
-        pass
-    elif callable(fn):
-        fn = fn.__call__
-    else:
-        return {}
-
-    try:
-        return typing.get_type_hints(fn)
-    except TypeError:
-        # On Python 3.9 or earlier, get_type_hints throws a TypeError if the function
-        # has a type annotation that include "|". We resort to parsing the signature
-        # manually using inspect.signature.
-        type_hints = {}
-        sig = inspect.signature(fn)
-        for name, param in sig.parameters.items():
-            if param.annotation is inspect.Parameter.empty:
-                continue
-            if "|" in str(param.annotation):
-                continue
-            # To convert the string annotation to a class, we use the
-            # internal typing._eval_type function. This is not ideal, but
-            # it's the only way to do it without eval-ing the string.
-            # Since the API is internal, it may change in the future.
-            try:
-                type_hints[name] = typing._eval_type(  # type: ignore
-                    typing.ForwardRef(param.annotation), globals(), locals()
-                )
-            except (NameError, TypeError):
-                pass
-        return type_hints
-
-
-def is_special_typed_parameter(name, parameter_types):
-    from gradio.helpers import EventData
-    from gradio.routes import Request
-
-    """Checks if parameter has a type hint designating it as a gr.Request or gr.EventData"""
-    hint = parameter_types.get(name)
-    if not hint:
-        return False
-    is_request = hint == Request
-    is_event_data = inspect.isclass(hint) and issubclass(hint, EventData)
-    return is_request or is_event_data
-
-
-def check_function_inputs_match(fn: Callable, inputs: list, inputs_as_dict: bool):
-    """
-    Checks if the input component set matches the function
-    Returns: None if valid, a string error message if mismatch
-    """
-
-    signature = inspect.signature(fn)
-    parameter_types = get_type_hints(fn)
-    min_args = 0
-    max_args = 0
-    infinity = -1
-    for name, param in signature.parameters.items():
-        has_default = param.default != param.empty
-        if param.kind in [param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD]:
-            if not is_special_typed_parameter(name, parameter_types):
-                if not has_default:
-                    min_args += 1
-                max_args += 1
-        elif param.kind == param.VAR_POSITIONAL:
-            max_args = infinity
-        elif param.kind == param.KEYWORD_ONLY and not has_default:
-            return f"Keyword-only args must have default values for function {fn}"
-    arg_count = 1 if inputs_as_dict else len(inputs)
-    if min_args == max_args and max_args != arg_count:
-        warnings.warn(
-            f"Expected {max_args} arguments for function {fn}, received {arg_count}."
-        )
-    if arg_count < min_args:
-        warnings.warn(
-            f"Expected at least {min_args} arguments for function {fn}, received {arg_count}."
-        )
-    if max_args != infinity and arg_count > max_args:
-        warnings.warn(
-            f"Expected maximum {max_args} arguments for function {fn}, received {arg_count}."
-        )
-
-
-class TupleNoPrint(tuple):
-    # To remove printing function return in notebook
-    def __repr__(self):
-        return ""
-
-    def __str__(self):
-        return ""
-
-
-class MatplotlibBackendMananger:
-    def __enter__(self):
-        self._original_backend = matplotlib.get_backend()
-        matplotlib.use("agg")
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        matplotlib.use(self._original_backend)
-
-
-def tex2svg(formula, *args):
-    with MatplotlibBackendMananger():
-        import matplotlib.pyplot as plt
-
-        fontsize = 20
-        dpi = 300
-        plt.rc("mathtext", fontset="cm")
-        fig = plt.figure(figsize=(0.01, 0.01))
-        fig.text(0, 0, rf"${formula}$", fontsize=fontsize)
-        output = BytesIO()
-        fig.savefig(
-            output,
-            dpi=dpi,
-            transparent=True,
-            format="svg",
-            bbox_inches="tight",
-            pad_inches=0.0,
-        )
-        plt.close(fig)
-        output.seek(0)
-        xml_code = output.read().decode("utf-8")
-        svg_start = xml_code.index("<svg ")
-        svg_code = xml_code[svg_start:]
-        svg_code = re.sub(r"<metadata>.*<\/metadata>", "", svg_code, flags=re.DOTALL)
-        svg_code = re.sub(r' width="[^"]+"', "", svg_code)
-        height_match = re.search(r'height="([\d.]+)pt"', svg_code)
-        if height_match:
-            height = float(height_match.group(1))
-            new_height = height / fontsize  # conversion from pt to em
-            svg_code = re.sub(
-                r'height="[\d.]+pt"', f'height="{new_height}em"', svg_code
-            )
-        copy_code = f"<span style='font-size: 0px'>{formula}</span>"
-    return f"{copy_code}{svg_code}"
-
-
-def abspath(path: str | Path) -> Path:
-    """Returns absolute path of a str or Path path, but does not resolve symlinks."""
-    path = Path(path)
-
-    if path.is_absolute():
-        return path
-
-    # recursively check if there is a symlink within the path
-    is_symlink = path.is_symlink() or any(
-        parent.is_symlink() for parent in path.parents
-    )
-
-    if is_symlink or path == path.resolve():  # in case path couldn't be resolved
-        return Path.cwd() / path
-    else:
-        return path.resolve()
-
-
-def is_in_or_equal(path_1: str | Path, path_2: str | Path):
-    """
-    True if path_1 is a descendant (i.e. located within) path_2 or if the paths are the
-    same, returns False otherwise.
-    Parameters:
-        path_1: str or Path (should be a file)
-        path_2: str or Path (can be a file or directory)
-    """
-    path_1, path_2 = abspath(path_1), abspath(path_2)
-    try:
-        if str(path_1.relative_to(path_2)).startswith(".."):  # prevent path traversal
-            return False
-    except ValueError:
-        return False
-    return True
-
-
-def get_serializer_name(block: Block) -> str | None:
-    if not hasattr(block, "serialize"):
-        return None
-
-    def get_class_that_defined_method(meth: Callable):
-        # Adapted from: https://stackoverflow.com/a/25959545/5209347
-        if isinstance(meth, functools.partial):
-            return get_class_that_defined_method(meth.func)
-        if inspect.ismethod(meth) or (
-            inspect.isbuiltin(meth)
-            and getattr(meth, "__self__", None) is not None
-            and getattr(meth.__self__, "__class__", None)
-        ):
-            for cls in inspect.getmro(meth.__self__.__class__):
-                # Find the first serializer defined in gradio_client that
-                if issubclass(cls, Serializable) and "gradio_client" in cls.__module__:
-                    return cls
-                if meth.__name__ in cls.__dict__:
-                    return cls
-            meth = getattr(meth, "__func__", meth)  # fallback to __qualname__ parsing
-        if inspect.isfunction(meth):
-            cls = getattr(
-                inspect.getmodule(meth),
-                meth.__qualname__.split(".<locals>", 1)[0].rsplit(".", 1)[0],
-                None,
-            )
-            if isinstance(cls, type):
-                return cls
-        return getattr(meth, "__objclass__", None)
-
-    cls = get_class_that_defined_method(block.serialize)  # type: ignore
-    if cls:
-        return cls.__name__
-
-
-def get_markdown_parser() -> MarkdownIt:
-    md = (
-        MarkdownIt(
-            "js-default",
-            {
-                "linkify": True,
-                "typographer": True,
-                "html": True,
-            },
-        )
-        .use(dollarmath_plugin, renderer=tex2svg, allow_digits=False)
-        .use(footnote_plugin)
-        .enable("table")
-    )
-
-    # Add target="_blank" to all links. Taken from MarkdownIt docs: https://github.com/executablebooks/markdown-it-py/blob/master/docs/architecture.md
-    def render_blank_link(self, tokens, idx, options, env):
-        tokens[idx].attrSet("target", "_blank")
-        return self.renderToken(tokens, idx, options, env)
-
-    md.add_render_rule("link_open", render_blank_link)
-
-    return md
-
-
-HTML_TAG_RE = re.compile("<.*?>")
-
-
-def remove_html_tags(raw_html: str | None) -> str:
-    return re.sub(HTML_TAG_RE, "", raw_html or "")
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_paths.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_paths.py
deleted file mode 100644
index 0a994bf5e93fa773148dbe0941fcdb5532fbc15a..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_paths.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# coding=utf-8
-# Copyright 2022-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Contains utilities to handle paths in Huggingface Hub."""
-from fnmatch import fnmatch
-from pathlib import Path
-from typing import Callable, Generator, Iterable, List, Optional, TypeVar, Union
-
-
-T = TypeVar("T")
-
-IGNORE_GIT_FOLDER_PATTERNS = [".git", ".git/*", "*/.git", "**/.git/**"]
-
-
-def filter_repo_objects(
-    items: Iterable[T],
-    *,
-    allow_patterns: Optional[Union[List[str], str]] = None,
-    ignore_patterns: Optional[Union[List[str], str]] = None,
-    key: Optional[Callable[[T], str]] = None,
-) -> Generator[T, None, None]:
-    """Filter repo objects based on an allowlist and a denylist.
-
-    Input must be a list of paths (`str` or `Path`) or a list of arbitrary objects.
-    In the later case, `key` must be provided and specifies a function of one argument
-    that is used to extract a path from each element in iterable.
-
-    Patterns are Unix shell-style wildcards which are NOT regular expressions. See
-    https://docs.python.org/3/library/fnmatch.html for more details.
-
-    Args:
-        items (`Iterable`):
-            List of items to filter.
-        allow_patterns (`str` or `List[str]`, *optional*):
-            Patterns constituting the allowlist. If provided, item paths must match at
-            least one pattern from the allowlist.
-        ignore_patterns (`str` or `List[str]`, *optional*):
-            Patterns constituting the denylist. If provided, item paths must not match
-            any patterns from the denylist.
-        key (`Callable[[T], str]`, *optional*):
-            Single-argument function to extract a path from each item. If not provided,
-            the `items` must already be `str` or `Path`.
-
-    Returns:
-        Filtered list of objects, as a generator.
-
-    Raises:
-        :class:`ValueError`:
-            If `key` is not provided and items are not `str` or `Path`.
-
-    Example usage with paths:
-    ```python
-    >>> # Filter only PDFs that are not hidden.
-    >>> list(filter_repo_objects(
-    ...     ["aaa.PDF", "bbb.jpg", ".ccc.pdf", ".ddd.png"],
-    ...     allow_patterns=["*.pdf"],
-    ...     ignore_patterns=[".*"],
-    ... ))
-    ["aaa.pdf"]
-    ```
-
-    Example usage with objects:
-    ```python
-    >>> list(filter_repo_objects(
-    ... [
-    ...     CommitOperationAdd(path_or_fileobj="/tmp/aaa.pdf", path_in_repo="aaa.pdf")
-    ...     CommitOperationAdd(path_or_fileobj="/tmp/bbb.jpg", path_in_repo="bbb.jpg")
-    ...     CommitOperationAdd(path_or_fileobj="/tmp/.ccc.pdf", path_in_repo=".ccc.pdf")
-    ...     CommitOperationAdd(path_or_fileobj="/tmp/.ddd.png", path_in_repo=".ddd.png")
-    ... ],
-    ... allow_patterns=["*.pdf"],
-    ... ignore_patterns=[".*"],
-    ... key=lambda x: x.repo_in_path
-    ... ))
-    [CommitOperationAdd(path_or_fileobj="/tmp/aaa.pdf", path_in_repo="aaa.pdf")]
-    ```
-    """
-    if isinstance(allow_patterns, str):
-        allow_patterns = [allow_patterns]
-
-    if isinstance(ignore_patterns, str):
-        ignore_patterns = [ignore_patterns]
-
-    if key is None:
-
-        def _identity(item: T) -> str:
-            if isinstance(item, str):
-                return item
-            if isinstance(item, Path):
-                return str(item)
-            raise ValueError(f"Please provide `key` argument in `filter_repo_objects`: `{item}` is not a string.")
-
-        key = _identity  # Items must be `str` or `Path`, otherwise raise ValueError
-
-    for item in items:
-        path = key(item)
-
-        # Skip if there's an allowlist and path doesn't match any
-        if allow_patterns is not None and not any(fnmatch(path, r) for r in allow_patterns):
-            continue
-
-        # Skip if there's a denylist and path matches any
-        if ignore_patterns is not None and any(fnmatch(path, r) for r in ignore_patterns):
-            continue
-
-        yield item
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/mpl-data/plot_directive/plot_directive.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/mpl-data/plot_directive/plot_directive.css
deleted file mode 100644
index d45593c93c95d3718fb9e53a780c8022371e619c..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/mpl-data/plot_directive/plot_directive.css
+++ /dev/null
@@ -1,16 +0,0 @@
-/*
- * plot_directive.css
- * ~~~~~~~~~~~~
- *
- * Stylesheet controlling images created using the `plot` directive within
- * Sphinx.
- *
- * :copyright: Copyright 2020-* by the Matplotlib development team.
- * :license: Matplotlib, see LICENSE for details.
- *
- */
-
-img.plot-directive {
-    border: 0;
-    max-width: 100%;
-}
diff --git a/spaces/lcipolina/Print_Gallery/glide_text2im/model_creation.py b/spaces/lcipolina/Print_Gallery/glide_text2im/model_creation.py
deleted file mode 100644
index 54c37c24546fe0c8e4b22ea903c7039b21da4f4f..0000000000000000000000000000000000000000
--- a/spaces/lcipolina/Print_Gallery/glide_text2im/model_creation.py
+++ /dev/null
@@ -1,195 +0,0 @@
-from glide_text2im.gaussian_diffusion import get_named_beta_schedule
-from glide_text2im.respace import SpacedDiffusion, space_timesteps
-from glide_text2im.text2im_model import (
-    InpaintText2ImUNet,
-    SuperResInpaintText2ImUnet,
-    SuperResText2ImUNet,
-    Text2ImUNet,
-)
-from glide_text2im.tokenizer.bpe import get_encoder
-
-
-def model_and_diffusion_defaults():
-    return dict(
-        image_size=64,
-        num_channels=192,
-        num_res_blocks=3,
-        channel_mult="",
-        num_heads=1,
-        num_head_channels=64,
-        num_heads_upsample=-1,
-        attention_resolutions="32,16,8",
-        dropout=0.1,
-        text_ctx=128,
-        xf_width=512,
-        xf_layers=16,
-        xf_heads=8,
-        xf_final_ln=True,
-        xf_padding=True,
-        diffusion_steps=1000,
-        noise_schedule="squaredcos_cap_v2",
-        timestep_respacing="",
-        use_scale_shift_norm=True,
-        resblock_updown=True,
-        use_fp16=True,
-        cache_text_emb=False,
-        inpaint=False,
-        super_res=False,
-    )
-
-
-def model_and_diffusion_defaults_upsampler():
-    result = model_and_diffusion_defaults()
-    result.update(
-        dict(
-            image_size=256,
-            num_res_blocks=2,
-            noise_schedule="linear",
-            super_res=True,
-        )
-    )
-    return result
-
-
-def create_model_and_diffusion(
-    image_size,
-    num_channels,
-    num_res_blocks,
-    channel_mult,
-    num_heads,
-    num_head_channels,
-    num_heads_upsample,
-    attention_resolutions,
-    dropout,
-    text_ctx,
-    xf_width,
-    xf_layers,
-    xf_heads,
-    xf_final_ln,
-    xf_padding,
-    diffusion_steps,
-    noise_schedule,
-    timestep_respacing,
-    use_scale_shift_norm,
-    resblock_updown,
-    use_fp16,
-    cache_text_emb,
-    inpaint,
-    super_res,
-):
-    model = create_model(
-        image_size,
-        num_channels,
-        num_res_blocks,
-        channel_mult=channel_mult,
-        attention_resolutions=attention_resolutions,
-        num_heads=num_heads,
-        num_head_channels=num_head_channels,
-        num_heads_upsample=num_heads_upsample,
-        use_scale_shift_norm=use_scale_shift_norm,
-        dropout=dropout,
-        text_ctx=text_ctx,
-        xf_width=xf_width,
-        xf_layers=xf_layers,
-        xf_heads=xf_heads,
-        xf_final_ln=xf_final_ln,
-        xf_padding=xf_padding,
-        resblock_updown=resblock_updown,
-        use_fp16=use_fp16,
-        cache_text_emb=cache_text_emb,
-        inpaint=inpaint,
-        super_res=super_res,
-    )
-    diffusion = create_gaussian_diffusion(
-        steps=diffusion_steps,
-        noise_schedule=noise_schedule,
-        timestep_respacing=timestep_respacing,
-    )
-    return model, diffusion
-
-
-def create_model(
-    image_size,
-    num_channels,
-    num_res_blocks,
-    channel_mult,
-    attention_resolutions,
-    num_heads,
-    num_head_channels,
-    num_heads_upsample,
-    use_scale_shift_norm,
-    dropout,
-    text_ctx,
-    xf_width,
-    xf_layers,
-    xf_heads,
-    xf_final_ln,
-    xf_padding,
-    resblock_updown,
-    use_fp16,
-    cache_text_emb,
-    inpaint,
-    super_res,
-):
-    if channel_mult == "":
-        if image_size == 256:
-            channel_mult = (1, 1, 2, 2, 4, 4)
-        elif image_size == 128:
-            channel_mult = (1, 1, 2, 3, 4)
-        elif image_size == 64:
-            channel_mult = (1, 2, 3, 4)
-        else:
-            raise ValueError(f"unsupported image size: {image_size}")
-    else:
-        channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(","))
-        assert 2 ** (len(channel_mult) + 2) == image_size
-
-    attention_ds = []
-    for res in attention_resolutions.split(","):
-        attention_ds.append(image_size // int(res))
-
-    if inpaint and super_res:
-        model_cls = SuperResInpaintText2ImUnet
-    elif inpaint:
-        model_cls = InpaintText2ImUNet
-    elif super_res:
-        model_cls = SuperResText2ImUNet
-    else:
-        model_cls = Text2ImUNet
-    return model_cls(
-        text_ctx=text_ctx,
-        xf_width=xf_width,
-        xf_layers=xf_layers,
-        xf_heads=xf_heads,
-        xf_final_ln=xf_final_ln,
-        tokenizer=get_encoder(),
-        xf_padding=xf_padding,
-        in_channels=3,
-        model_channels=num_channels,
-        out_channels=6,
-        num_res_blocks=num_res_blocks,
-        attention_resolutions=tuple(attention_ds),
-        dropout=dropout,
-        channel_mult=channel_mult,
-        use_fp16=use_fp16,
-        num_heads=num_heads,
-        num_head_channels=num_head_channels,
-        num_heads_upsample=num_heads_upsample,
-        use_scale_shift_norm=use_scale_shift_norm,
-        resblock_updown=resblock_updown,
-        cache_text_emb=cache_text_emb,
-    )
-
-
-def create_gaussian_diffusion(
-    steps,
-    noise_schedule,
-    timestep_respacing,
-):
-    betas = get_named_beta_schedule(noise_schedule, steps)
-    if not timestep_respacing:
-        timestep_respacing = [steps]
-    return SpacedDiffusion(
-        use_timesteps=space_timesteps(steps, timestep_respacing),
-        betas=betas,
-    )
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/BlueSoleil 6.4.275.0WithMobile Serial Number.md b/spaces/lincquiQcaudo/Top-20-Diffusion/BlueSoleil 6.4.275.0WithMobile Serial Number.md
deleted file mode 100644
index 72b1ffea24e6115add124ff7c796d5de49ad3e38..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/BlueSoleil 6.4.275.0WithMobile Serial Number.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>BlueSoleil 6.4.275.0WithMobile serial number</h2><br /><p><b><b>DOWNLOAD</b> &#9733; <a href="https://bytlly.com/2uGxMl">https://bytlly.com/2uGxMl</a></b></p><br /><br />
-
-WHY install delphi 10 up1? ...installation needed slip, or serial? ... wizard will also appear if you were using a trial edition of Delphi for PHP and your trial serial number has expired. ... BlueSoleil 6.4.275.0WithMobile serial 13 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Chak De India Full Movie Hd 1080p Download Kickass Movie.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Chak De India Full Movie Hd 1080p Download Kickass Movie.md
deleted file mode 100644
index 22e6569fdd5905187e25eb0245823a6f265f3da2..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Chak De India Full Movie Hd 1080p Download Kickass Movie.md	
+++ /dev/null
@@ -1,12 +0,0 @@
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diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/House Party License Key !LINK!.md b/spaces/lincquiQcaudo/Top-20-Diffusion/House Party License Key !LINK!.md
deleted file mode 100644
index 97461754cf05e16e98e37738d53c57bbb5e2c0ff..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/House Party License Key !LINK!.md	
+++ /dev/null
@@ -1,44 +0,0 @@
-<h2>House Party License Key</h2><br /><p><b><b>Download</b> &bull;&bull;&bull; <a href="https://bytlly.com/2uGxJp">https://bytlly.com/2uGxJp</a></b></p><br /><br />
-
-for 24 hours at $5.
-
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-
-REGISTRATION
-
-At the link above (or if you have purchased this program in the past), you will be prompted to download the program in a pdf file. Follow the instructions on the screen and you should receive the pdf file.
-
-The payment page does not work, so instead enter your credit card number and then make sure to enter your billing address. Note that this address will be the one on the bill as you will need it to validate the license.
-
-If you don't have your credit card number and billing address on hand, please e-mail or call for further instructions.
-
-On the registration page, enter a password, and then download the file.
-
-Install the program by double-clicking on the "WIPE" folder.
-
-Please note, this program is 100% educational and it is perfectly safe to use. However, the video files contain copyrighted music and are not for commercial use. As such, the licensor will issue a warning message when you start the program.
-
-SCREENSHOTS
-
-INSTALLATION
-
-Follow the instructions on the screen to download and install the program.
-
-When prompted to restart, please do so.
-
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-
-When you first start the program, there will be a pop-up notification asking for your permission. Click "OK" to accept the terms of use, or "Cancel" to decline.
-
-For any time you log into the program, be sure to provide the program with a valid key. For example, to log in from Monday to Friday, the key should read "MJDN" or something similar. If you do not provide a valid key, you will be unable to log in.
-
-A pop-up window will appear whenever you attempt to log in. This window shows the contents of the system clipboard.
-
-The help window contains a collection of useful information about the program and how to use it..
-
-Alexa, Amazon Echo, Dot, Fire tablets and Amazon Fire TV are trademarks of Amazon.com, Inc. or its affiliates. Roku, Roku TV, Android TV, Google Chromecast and Apple TV are trademarks of Roku, Inc. Other trademarks are the property of their respective owners.
-
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diff --git a/spaces/linfanluntan/Grounded-SAM/GroundingDINO/groundingdino/models/__init__.py b/spaces/linfanluntan/Grounded-SAM/GroundingDINO/groundingdino/models/__init__.py
deleted file mode 100644
index e3413961d1d184b99835eb1e919b052d70298bc6..0000000000000000000000000000000000000000
--- a/spaces/linfanluntan/Grounded-SAM/GroundingDINO/groundingdino/models/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# ------------------------------------------------------------------------
-# Grounding DINO
-# url: https://github.com/IDEA-Research/GroundingDINO
-# Copyright (c) 2023 IDEA. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .GroundingDINO import build_groundingdino
-
-
-def build_model(args):
-    # we use register to maintain models from catdet6 on.
-    from .registry import MODULE_BUILD_FUNCS
-
-    assert args.modelname in MODULE_BUILD_FUNCS._module_dict
-    build_func = MODULE_BUILD_FUNCS.get(args.modelname)
-    model = build_func(args)
-    return model
diff --git a/spaces/ljrmary/UT_Hackathon2/README.md b/spaces/ljrmary/UT_Hackathon2/README.md
deleted file mode 100644
index 41cca3b6f7b81b76b6389d0fdd3d33cf43acefaf..0000000000000000000000000000000000000000
--- a/spaces/ljrmary/UT_Hackathon2/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: UT Hackathon - Level 1
-emoji: 🐢
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-duplicated_from: psalama/UT_Hackathon2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/ll0z0y/bingoa/README.md b/spaces/ll0z0y/bingoa/README.md
deleted file mode 100644
index 5d6936218874c647b5d22e13ad4be7edb8936f92..0000000000000000000000000000000000000000
--- a/spaces/ll0z0y/bingoa/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
----
-title: bingo
-emoji: 😊
-colorFrom: red
-colorTo: red
-sdk: docker
-license: mit
-duplicated_from: hf4all/bingo
----
-
-<div align="center">
-
-# Bingo 
-
-Bingo，一个让你呼吸顺畅 New Bing。
-
-高度还原 New Bing 网页版的主要操作，国内可用，兼容绝大多数微软 Bing AI 的功能，可自行部署使用。
-
-![Github stars](https://badgen.net/github/stars/weaigc/bingo?icon=github&label=stars)
-![Gthub issues](https://img.shields.io/github/issues/weaigc/bingo)
-[![docker build](https://github.com/weaigc/bingo/actions/workflows/docker.yml/badge.svg)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![docker hub](https://badgen.net/docker/size/weaigc/bingo?icon=docker&label=image%20size)](https://hub.docker.com/repository/docker/weaigc/bingo/)
-[![MIT License](https://img.shields.io/badge/license-MIT-97c50f)](https://github.com/weaigc/bingo/blob/main/license)
-
-问题反馈请前往 https://github.com/weaigc/bingo/issues
-</div>
-
-
diff --git a/spaces/lsmyrtaj/cse6242-dataminers/plots.py b/spaces/lsmyrtaj/cse6242-dataminers/plots.py
deleted file mode 100644
index 711b058d02f007f0625f1bde963a929172d6373a..0000000000000000000000000000000000000000
--- a/spaces/lsmyrtaj/cse6242-dataminers/plots.py
+++ /dev/null
@@ -1,220 +0,0 @@
-import pandas as pd
-import seaborn as sns
-import streamlit as st
-import matplotlib.pyplot as plt
-import numpy as np
-import altair as alt
-import plotly.express as px
-
-
-def beta(stock_df, choices):
-    symbols, weights, benchmark, investing_style, rf, A_coef  = choices.values()
-    tickers = symbols
-    tickers.append(benchmark)
-    #print(tickers)
-    quantity = weights
-    selected_stocks = stock_df[tickers]
-    # calculating daily return
-    # loops through each stocks
-    # loops through each row belonging to the stock
-    # calculates the percentage change from previous day
-    # sets the value of first row to zero since there is no previous value
-    df_stocks = selected_stocks.copy()
-
-    for i in selected_stocks.columns[1:]:
-        for j in range(1, len(selected_stocks)):
-            df_stocks[i][j] = ((selected_stocks[i][j] - selected_stocks[i][j - 1]) / selected_stocks[i][j - 1]) * 100
-        df_stocks[i][0] = 0
-    # calculate Beta and alpha for a single stock
-    # used sp500 as a benchmark
-    # used polyfit to calculate beta
-    beta_list = []
-    alpha_list = []
-    stocks_daily_return = df_stocks
-    for i in stocks_daily_return.columns:
-        if i != 'Date' and i != benchmark:
-            # stocks_daily_return.plot(kind = 'scatter', x = 'A', y = i)
-            b, a = np.polyfit(stocks_daily_return[benchmark], stocks_daily_return[i], 1)
-            # plt.plot(stocks_daily_return['sp500'], b * stocks_daily_return['sp500'] + a, '-', color = 'r')
-            beta_list.append(round(b, 2))
-            alpha_list.append(round(a, 2))
-            # plt.show()
-    # Formats the results
-    symbols.remove(benchmark)
-    beta = {'Assets': symbols, 'Beta': beta_list}
-    alpha = {'Assets': symbols, 'Alpha': alpha_list}
-    # Creates a header for streamlit
-    st.subheader('Beta and Alpha of Assets Compared to S&P500 index')
-    col1, col2 = st.columns(2)
-
-    with col1:
-        st.dataframe(beta)
-    with col2:
-        st.dataframe(alpha)
-
-
-def ER(stock_df, choices):
-    symbols, weights, benchmark, investing_style, rf, A_coef  = choices.values()
-    symbols_ =symbols.copy()
-    tickers = symbols
-    tickers.append(benchmark)
-    #print(tickers)
-    quantity = weights
-    selected_stocks = stock_df[tickers]
-    # calculating daily return
-    # loops through each stocks
-    # loops through each row belonging to the stock
-    # calculates the percentage change from previous day
-    # sets the value of first row to zero since there is no previous value
-    df_stocks = selected_stocks.copy()
-
-    for i in selected_stocks.columns[1:]:
-        for j in range(1, len(selected_stocks)):
-            df_stocks[i][j] = ((selected_stocks[i][j] - selected_stocks[i][j - 1]) / selected_stocks[i][j - 1]) * 100
-        df_stocks[i][0] = 0
-    beta = {}
-    alpha = {}
-    stocks_daily_return = df_stocks
-    # print(df_stocks)
-
-    for i in stocks_daily_return.columns:
-        if i != 'Date' and i != benchmark:
-            # stocks_daily_return.plot(kind = 'scatter', x = 'A', y = i)
-            b, a = np.polyfit(stocks_daily_return[benchmark], stocks_daily_return[i], 1)
-            # plt.plot(stocks_daily_return['sp500'], b * stocks_daily_return['sp500'] + a, '-', color = 'r')
-            beta[i] = round(b, 2)
-            alpha[i] = round(a, 2)
-            # plt.show()
-
-    # calculating camp for a stock
-    keys = list(beta.keys())
-    ER_ = []
-    # rf = 0 assuming risk-free rate of 0
-    rf = 0
-    # rm - annualize retun
-    rm = stocks_daily_return[benchmark].mean() * 252
-    for i in keys:
-        ER_.append( round(rf + (beta[i] * (rm - rf)), 2))
-
-    #for i in keys:
-    #    print('Expected Return based on CAPM for {} is {}%'.format(i, ER_[i]))
-    #print(ER)
-    symbols.remove(benchmark)
-    #st.subheader('Expected Annual Return Based on CAPM Model')
-
-    Expected_return = {'Assets': symbols_, 'Expected Annual Return': ER_}
-    # Creates a header for streamlit
-    #st.dataframe(Expected_return)
-
-    
-    # calculate expected return for the portfolio
-    # portfolio weights assume equal
-    portfolio_weights = []
-    current_cash_value = 0
-    total_portfolio_value = 0
-    cash_value_stocks =[]
-    for i in range(len(tickers) ):
-        stocks_name = tickers[i]
-        current_cash_value = selected_stocks[stocks_name].iloc[-1]
-        stocks_quantity = quantity[i]
-        cash_value = stocks_quantity * current_cash_value
-        cash_value_stocks.append(cash_value)
-        total_portfolio_value += cash_value
-        portfolio_weights.append(cash_value)
-    #print(portfolio_weights)
-    portfolio_weights = (portfolio_weights / total_portfolio_value)*100
-    ER_portfolio= []
-    ER_portfolio = sum(list(ER_) * portfolio_weights)/100
-    #print(ER_portfolio)
-
-    #st.subheader('Expected Portfolio Return Based on CAPM Model')
-    # Creates a header for streamlit
-    #st.write('Expected Portfolio Return is:', ER_portfolio)
-    Bar_output = Expected_return.copy()
-    Bar_output['Assets'].append('Portfolio')
-    Bar_output['Expected Annual Return'].append(ER_portfolio)
-    fig = px.bar(Bar_output, x='Assets', y="Expected Annual Return",color='Assets') 
-    #fig.update_layout(title_text = 'Annual Expected Return of the Assets and Portfolio',title_x=0.458)
-    st.subheader('Annual Expected Return of the Assets and Portfolio')
-    st.plotly_chart(fig, use_container_width=True)
-    
-    return beta, cash_value_stocks
-
-def basic_portfolio(stock_df):
-    """Uses the stock dataframe to graph the normalized historical cumulative returns of each asset.
-    """
-    # Calculates the daily returns of the inputted dataframe
-    daily_return = stock_df.dropna().pct_change()
-    # Calculates the cumulative return of the previously calculated daily return
-    cumulative_return = (1 + daily_return).cumprod()
-
-    
-    # Graphs the cumulative returns
-    st.line_chart(cumulative_return)
-
-
-def display_heat_map(stock_df,choices):
-    symbols, weights, benchmark, investing_style, rf, A_coef  = choices.values()
-    selected_stocks = stock_df[symbols]
-    # Calcuilates the correlation of the assets in the portfolio
-    price_correlation = selected_stocks.corr()
-
-    
-    # Generates a figure for the heatmap
-    fig, ax = plt.subplots()
-    fig = px.imshow(price_correlation,text_auto=True, aspect="auto")
-    # Displays the heatmap on streamlit
-    st.write(fig)
-   
-
-#def display_portfolio_return(stock_df, choices):
-    """Uses the stock dataframe and the chosen weights from choices to calculate and graph the historical cumulative portfolio return.
-    """
-#    symbols, weights, investment = choices.values()
-
-    # Calculates the daily percentage returns of the 
-#    daily_returns = stock_df.pct_change().dropna()
-    # Applies the weights of each asset to the portfolio
-#    portfolio_returns = daily_returns.dot(weights)
-    # Calculates the cumulative weighted portfolio return
-#    cumulative_returns = (1 + portfolio_returns).cumprod()
-    # Calculates the cumulative profit using the cumulative portfolio return
-#    cumulative_profit = investment * cumulative_returns
-
-    # Graphs the result, and displays it with a header on streamlit
-#    st.subheader('Portfolio Historical Cumulative Returns Based On Inputs!')
-#    st.line_chart(cumulative_profit)
-def buble_interactive(stock_df,choices):
-    symbols, weights, benchmark, investing_style, rf, A_coef  = choices.values()
-    beta,cash_value_weights  = ER(stock_df,choices)
-    my_list = []
-    my_colors = []
-    for i in beta.values():
-        my_list.append(i)
-        if i < 0.3:
-            my_colors.append("Conservative")
-        if i >= 0.3 and i <= 1.1:
-            my_colors.append("Moderate Risk")
-        if i > 1.1:
-            my_colors.append("Risky")
-    
-    df_final =pd.DataFrame()
-    df_final['ticker'] = symbols
-    df_final['quantities'] = weights
-    df_final['cash_value']  =cash_value_weights
-    df_final['Beta'] = my_list
-    df_final['Risk'] = my_colors
-   
-    fig = px.scatter(
-    df_final,
-    x="quantities",
-    y="Beta",
-    size="cash_value",
-    color="Risk",
-    hover_name="ticker",
-    log_x=True,
-    size_max=60,
-    )
-    fig.update_layout(title= benchmark +"Benchmark - Beta of Stock Ticker to Quantity")
-    # -- Input the Plotly chart to the Streamlit interface
-    st.plotly_chart(fig, use_container_width=True)   
\ No newline at end of file
diff --git a/spaces/ma-xu/LIVE/pybind11/tests/local_bindings.h b/spaces/ma-xu/LIVE/pybind11/tests/local_bindings.h
deleted file mode 100644
index b6afb808664de1fdbde011a9bf7c38d3a8794127..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/tests/local_bindings.h
+++ /dev/null
@@ -1,64 +0,0 @@
-#pragma once
-#include "pybind11_tests.h"
-
-/// Simple class used to test py::local:
-template <int> class LocalBase {
-public:
-    LocalBase(int i) : i(i) { }
-    int i = -1;
-};
-
-/// Registered with py::module_local in both main and secondary modules:
-using LocalType = LocalBase<0>;
-/// Registered without py::module_local in both modules:
-using NonLocalType = LocalBase<1>;
-/// A second non-local type (for stl_bind tests):
-using NonLocal2 = LocalBase<2>;
-/// Tests within-module, different-compilation-unit local definition conflict:
-using LocalExternal = LocalBase<3>;
-/// Mixed: registered local first, then global
-using MixedLocalGlobal = LocalBase<4>;
-/// Mixed: global first, then local
-using MixedGlobalLocal = LocalBase<5>;
-
-/// Registered with py::module_local only in the secondary module:
-using ExternalType1 = LocalBase<6>;
-using ExternalType2 = LocalBase<7>;
-
-using LocalVec = std::vector<LocalType>;
-using LocalVec2 = std::vector<NonLocal2>;
-using LocalMap = std::unordered_map<std::string, LocalType>;
-using NonLocalVec = std::vector<NonLocalType>;
-using NonLocalVec2 = std::vector<NonLocal2>;
-using NonLocalMap = std::unordered_map<std::string, NonLocalType>;
-using NonLocalMap2 = std::unordered_map<std::string, uint8_t>;
-
-PYBIND11_MAKE_OPAQUE(LocalVec);
-PYBIND11_MAKE_OPAQUE(LocalVec2);
-PYBIND11_MAKE_OPAQUE(LocalMap);
-PYBIND11_MAKE_OPAQUE(NonLocalVec);
-//PYBIND11_MAKE_OPAQUE(NonLocalVec2); // same type as LocalVec2
-PYBIND11_MAKE_OPAQUE(NonLocalMap);
-PYBIND11_MAKE_OPAQUE(NonLocalMap2);
-
-
-// Simple bindings (used with the above):
-template <typename T, int Adjust = 0, typename... Args>
-py::class_<T> bind_local(Args && ...args) {
-    return py::class_<T>(std::forward<Args>(args)...)
-        .def(py::init<int>())
-        .def("get", [](T &i) { return i.i + Adjust; });
-};
-
-// Simulate a foreign library base class (to match the example in the docs):
-namespace pets {
-class Pet {
-public:
-    Pet(std::string name) : name_(name) {}
-    std::string name_;
-    const std::string &name() { return name_; }
-};
-}
-
-struct MixGL { int i; MixGL(int i) : i{i} {} };
-struct MixGL2 { int i; MixGL2(int i) : i{i} {} };
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/replace.h b/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/replace.h
deleted file mode 100644
index d8fb5746f1ced28be6571c8535ce0d8615863234..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/system/detail/adl/replace.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the replace.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch replace
-
-#include <thrust/system/detail/sequential/replace.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/replace.h>
-#include <thrust/system/cuda/detail/replace.h>
-#include <thrust/system/omp/detail/replace.h>
-#include <thrust/system/tbb/detail/replace.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_REPLACE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/replace.h>
-#include __THRUST_HOST_SYSTEM_REPLACE_HEADER
-#undef __THRUST_HOST_SYSTEM_REPLACE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_REPLACE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/replace.h>
-#include __THRUST_DEVICE_SYSTEM_REPLACE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_REPLACE_HEADER
-
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/face_util.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/face_util.py
deleted file mode 100644
index e6f2552ed459e7c15929c30b503347d7d8b9cbdb..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/utils/face_util.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import cv2
-import numpy as np
-import os
-import torch
-from skimage import transform as trans
-
-from basicsr.utils import imwrite
-
-try:
-    import dlib
-except ImportError:
-    print('Please install dlib before testing face restoration.' 'Reference:　https://github.com/davisking/dlib')
-
-
-class FaceRestorationHelper(object):
-    """Helper for the face restoration pipeline."""
-
-    def __init__(self, upscale_factor, face_size=512):
-        self.upscale_factor = upscale_factor
-        self.face_size = (face_size, face_size)
-
-        # standard 5 landmarks for FFHQ faces with 1024 x 1024
-        self.face_template = np.array([[686.77227723, 488.62376238], [586.77227723, 493.59405941],
-                                       [337.91089109, 488.38613861], [437.95049505, 493.51485149],
-                                       [513.58415842, 678.5049505]])
-        self.face_template = self.face_template / (1024 // face_size)
-        # for estimation the 2D similarity transformation
-        self.similarity_trans = trans.SimilarityTransform()
-
-        self.all_landmarks_5 = []
-        self.all_landmarks_68 = []
-        self.affine_matrices = []
-        self.inverse_affine_matrices = []
-        self.cropped_faces = []
-        self.restored_faces = []
-        self.save_png = True
-
-    def init_dlib(self, detection_path, landmark5_path, landmark68_path):
-        """Initialize the dlib detectors and predictors."""
-        self.face_detector = dlib.cnn_face_detection_model_v1(detection_path)
-        self.shape_predictor_5 = dlib.shape_predictor(landmark5_path)
-        self.shape_predictor_68 = dlib.shape_predictor(landmark68_path)
-
-    def free_dlib_gpu_memory(self):
-        del self.face_detector
-        del self.shape_predictor_5
-        del self.shape_predictor_68
-
-    def read_input_image(self, img_path):
-        # self.input_img is Numpy array, (h, w, c) with RGB order
-        self.input_img = dlib.load_rgb_image(img_path)
-
-    def detect_faces(self, img_path, upsample_num_times=1, only_keep_largest=False):
-        """
-        Args:
-            img_path (str): Image path.
-            upsample_num_times (int): Upsamples the image before running the
-                face detector
-
-        Returns:
-            int: Number of detected faces.
-        """
-        self.read_input_image(img_path)
-        det_faces = self.face_detector(self.input_img, upsample_num_times)
-        if len(det_faces) == 0:
-            print('No face detected. Try to increase upsample_num_times.')
-        else:
-            if only_keep_largest:
-                print('Detect several faces and only keep the largest.')
-                face_areas = []
-                for i in range(len(det_faces)):
-                    face_area = (det_faces[i].rect.right() - det_faces[i].rect.left()) * (
-                        det_faces[i].rect.bottom() - det_faces[i].rect.top())
-                    face_areas.append(face_area)
-                largest_idx = face_areas.index(max(face_areas))
-                self.det_faces = [det_faces[largest_idx]]
-            else:
-                self.det_faces = det_faces
-        return len(self.det_faces)
-
-    def get_face_landmarks_5(self):
-        for face in self.det_faces:
-            shape = self.shape_predictor_5(self.input_img, face.rect)
-            landmark = np.array([[part.x, part.y] for part in shape.parts()])
-            self.all_landmarks_5.append(landmark)
-        return len(self.all_landmarks_5)
-
-    def get_face_landmarks_68(self):
-        """Get 68 densemarks for cropped images.
-
-        Should only have one face at most in the cropped image.
-        """
-        num_detected_face = 0
-        for idx, face in enumerate(self.cropped_faces):
-            # face detection
-            det_face = self.face_detector(face, 1)  # TODO: can we remove it?
-            if len(det_face) == 0:
-                print(f'Cannot find faces in cropped image with index {idx}.')
-                self.all_landmarks_68.append(None)
-            else:
-                if len(det_face) > 1:
-                    print('Detect several faces in the cropped face. Use the '
-                          ' largest one. Note that it will also cause overlap '
-                          'during paste_faces_to_input_image.')
-                    face_areas = []
-                    for i in range(len(det_face)):
-                        face_area = (det_face[i].rect.right() - det_face[i].rect.left()) * (
-                            det_face[i].rect.bottom() - det_face[i].rect.top())
-                        face_areas.append(face_area)
-                    largest_idx = face_areas.index(max(face_areas))
-                    face_rect = det_face[largest_idx].rect
-                else:
-                    face_rect = det_face[0].rect
-                shape = self.shape_predictor_68(face, face_rect)
-                landmark = np.array([[part.x, part.y] for part in shape.parts()])
-                self.all_landmarks_68.append(landmark)
-                num_detected_face += 1
-
-        return num_detected_face
-
-    def warp_crop_faces(self, save_cropped_path=None, save_inverse_affine_path=None):
-        """Get affine matrix, warp and cropped faces.
-
-        Also get inverse affine matrix for post-processing.
-        """
-        for idx, landmark in enumerate(self.all_landmarks_5):
-            # use 5 landmarks to get affine matrix
-            self.similarity_trans.estimate(landmark, self.face_template)
-            affine_matrix = self.similarity_trans.params[0:2, :]
-            self.affine_matrices.append(affine_matrix)
-            # warp and crop faces
-            cropped_face = cv2.warpAffine(self.input_img, affine_matrix, self.face_size)
-            self.cropped_faces.append(cropped_face)
-            # save the cropped face
-            if save_cropped_path is not None:
-                path, ext = os.path.splitext(save_cropped_path)
-                if self.save_png:
-                    save_path = f'{path}_{idx:02d}.png'
-                else:
-                    save_path = f'{path}_{idx:02d}{ext}'
-
-                imwrite(cv2.cvtColor(cropped_face, cv2.COLOR_RGB2BGR), save_path)
-
-            # get inverse affine matrix
-            self.similarity_trans.estimate(self.face_template, landmark * self.upscale_factor)
-            inverse_affine = self.similarity_trans.params[0:2, :]
-            self.inverse_affine_matrices.append(inverse_affine)
-            # save inverse affine matrices
-            if save_inverse_affine_path is not None:
-                path, _ = os.path.splitext(save_inverse_affine_path)
-                save_path = f'{path}_{idx:02d}.pth'
-                torch.save(inverse_affine, save_path)
-
-    def add_restored_face(self, face):
-        self.restored_faces.append(face)
-
-    def paste_faces_to_input_image(self, save_path):
-        # operate in the BGR order
-        input_img = cv2.cvtColor(self.input_img, cv2.COLOR_RGB2BGR)
-        h, w, _ = input_img.shape
-        h_up, w_up = h * self.upscale_factor, w * self.upscale_factor
-        # simply resize the background
-        upsample_img = cv2.resize(input_img, (w_up, h_up))
-        assert len(self.restored_faces) == len(
-            self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.')
-        for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices):
-            inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up))
-            mask = np.ones((*self.face_size, 3), dtype=np.float32)
-            inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up))
-            # remove the black borders
-            inv_mask_erosion = cv2.erode(inv_mask, np.ones((2 * self.upscale_factor, 2 * self.upscale_factor),
-                                                           np.uint8))
-            inv_restored_remove_border = inv_mask_erosion * inv_restored
-            total_face_area = np.sum(inv_mask_erosion) // 3
-            # compute the fusion edge based on the area of face
-            w_edge = int(total_face_area**0.5) // 20
-            erosion_radius = w_edge * 2
-            inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8))
-            blur_size = w_edge * 2
-            inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0)
-            upsample_img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * upsample_img
-        if self.save_png:
-            save_path = save_path.replace('.jpg', '.png').replace('.jpeg', '.png')
-        imwrite(upsample_img.astype(np.uint8), save_path)
-
-    def clean_all(self):
-        self.all_landmarks_5 = []
-        self.all_landmarks_68 = []
-        self.restored_faces = []
-        self.affine_matrices = []
-        self.cropped_faces = []
-        self.inverse_affine_matrices = []
diff --git a/spaces/manishjaiswal/07-GraphViz-PyDeck-Map-AIUIUX-Demo/app.py b/spaces/manishjaiswal/07-GraphViz-PyDeck-Map-AIUIUX-Demo/app.py
deleted file mode 100644
index 2269db8d939a14ea82be9ecbb76908861c9106cf..0000000000000000000000000000000000000000
--- a/spaces/manishjaiswal/07-GraphViz-PyDeck-Map-AIUIUX-Demo/app.py
+++ /dev/null
@@ -1,509 +0,0 @@
-import streamlit as st
-import graphviz as graphviz
-import pandas as pd
-import numpy as np
-
-st.title('Graphviz Gallery: https://graphviz.org/gallery/')
-
-# Using code:
-
-# Create a graphlib graph object
-graph = graphviz.Digraph()
-graph.edge('Grandpa', 'Ancestors')
-graph.edge('Grandma', 'Ancestors')
-graph.edge('Uncle', 'Grandma')
-graph.edge('Aunt', 'Grandma')
-graph.edge('Mom', 'Grandma')
-graph.edge('Cousin Bob', 'Aunt')
-graph.edge('Cousin Sue', 'Aunt')
-graph.edge('Brother', 'Mom')
-graph.edge('Sister', 'Mom')
-st.graphviz_chart(graph)
-
-
-st.graphviz_chart(''' 
-digraph G2 {
-    node [shape=plaintext];
-    struct1 [label=<<TABLE>
-      <TR><TD><IMG SRC="1.png"></IMG></TD></TR>
-      <TR><TD>caption</TD></TR>
-    </TABLE>>];
-}
-''')
-
-
-
-st.title('Graphviz Dot Language: https://graphviz.org/doc/info/lang.html')
-
-# Using graph language:
-st.graphviz_chart(''' 
-digraph G {
-  rankdir=LR
-  node [shape=plaintext]
-  a [
-     label=<
-<TABLE BORDER="0" CELLBORDER="1" CELLSPACING="0">
-  <TR><TD ROWSPAN="3" BGCOLOR="yellow">class</TD></TR>
-  <TR><TD PORT="here" BGCOLOR="lightblue">qualifier</TD></TR>
-</TABLE>>
-  ]
-  b [shape=ellipse style=filled
-     label=<
-<TABLE BGCOLOR="bisque">
-  <TR>
-      <TD COLSPAN="3">elephant</TD> 
-      <TD ROWSPAN="2" BGCOLOR="chartreuse" 
-          VALIGN="bottom" ALIGN="right">two</TD>
-  </TR>
-  <TR>
-    <TD COLSPAN="2" ROWSPAN="2">
-      <TABLE BGCOLOR="grey">
-        <TR><TD>corn</TD></TR> 
-        <TR><TD BGCOLOR="yellow">c</TD></TR> 
-        <TR><TD>f</TD></TR> 
-      </TABLE>
-    </TD>
-    <TD BGCOLOR="white">penguin</TD> 
-  </TR> 
-  <TR>
-    <TD COLSPAN="2" BORDER="4" ALIGN="right" PORT="there">4</TD>
-  </TR>
-</TABLE>>
-  ]
-  c [ 
-  label=<long line 1<BR/>line 2<BR ALIGN="LEFT"/>line 3<BR ALIGN="RIGHT"/>>
-  ]
-  subgraph { rank=same b c }
-  a:here -> b:there [dir=both arrowtail=diamond]
-  c -> b
-  d [shape=triangle]
-  d -> c [label=<
-<TABLE>
-  <TR>
-    <TD BGCOLOR="red" WIDTH="10"> </TD>
-    <TD>Edge labels<BR/>also</TD>
-    <TD BGCOLOR="blue" WIDTH="10"> </TD>
-  </TR>
-</TABLE>>
-  ]
-}
-''')
-
-st.graphviz_chart(''' 
-digraph R {
-  rankdir=LR
-  node [style=rounded]
-  node1 [shape=box]
-  node2 [fillcolor=yellow, style="rounded,filled", shape=diamond]
-  node3 [shape=record, label="{ a | b | c }"]
-  node1 -> node2 -> node3
-}
-''')
-
-st.title('Vega Lite Example: https://docs.streamlit.io/library/api-reference/charts/st.vega_lite_chart ')
-df = pd.DataFrame(
-     np.random.randn(200, 3),
-     columns=['a', 'b', 'c'])
-
-st.vega_lite_chart(df, {
-     'mark': {'type': 'circle', 'tooltip': True},
-     'encoding': {
-         'x': {'field': 'a', 'type': 'quantitative'},
-         'y': {'field': 'b', 'type': 'quantitative'},
-         'size': {'field': 'c', 'type': 'quantitative'},
-         'color': {'field': 'c', 'type': 'quantitative'},
-     },
- })
-
-# More graph examples
-
-st.graphviz_chart(''' 
-digraph structs {
-    node [shape=record];
-    struct1 [label="<f0> left|<f1> mid&#92; dle|<f2> right"];
-    struct2 [label="<f0> one|<f1> two"];
-    struct3 [label="hello&#92;nworld |{ b |{c|<here> d|e}| f}| g | h"];
-    struct1:f1 -> struct2:f0;
-    struct1:f2 -> struct3:here;
-}
-''')
-
-st.graphviz_chart(''' 
-graph G {
-  fontname="Helvetica,Arial,sans-serif"
-  node [fontname="Helvetica,Arial,sans-serif"]
-  edge [fontname="Helvetica,Arial,sans-serif"]
-  layout=fdp
-  e
-  subgraph clusterA {
-    a -- b;
-    subgraph clusterC {
-      C -- D;
-    }
-  }
-  subgraph clusterB {
-    d -- f
-  }
-  d -- D
-  e -- clusterB
-  clusterC -- clusterB
-}
-''')
-
-st.graphviz_chart(''' 
-graph Transparency {
-	layout=neato
-	start=11 // empiric value to set orientation
-	bgcolor="#0000ff11"
-	node [shape=circle width=2.22 label="" style=filled]
-	5 [color="#0000ff80"]
-	6 [color="#ee00ee80"]
-	1 [color="#ff000080"]
-	2 [color="#eeee0080"]
-	3 [color="#00ff0080"]
-	4 [color="#00eeee80"]
-	1 -- 2 -- 3 -- 4 -- 5 -- 6 -- 1
-	}
-''')
-
-st.graphviz_chart('''
-digraph UML_Class_diagram {
-	fontname="Helvetica,Arial,sans-serif"
-	node [fontname="Helvetica,Arial,sans-serif"]
-	edge [fontname="Helvetica,Arial,sans-serif"]
-	labelloc="t"
-	label="UML Class diagram demo"
-	graph [splines=false]
-	node [shape=record style=filled fillcolor=gray95]
-	edge [arrowhead=vee style=dashed]
-	Client -> Interface1 [xlabel=dependency]
-	Client -> Interface2
-	edge [dir=back arrowtail=empty style=""]
-	Interface1 -> Class1 [xlabel=inheritance]
-	Interface2 -> Class1 [dir=none]
-	Interface2 [label="" xlabel="Simple\ninterface" shape=circle]
-	Interface1[label = <{<b>«interface» I/O</b> | + property<br align="left"/>...<br align="left"/>|+ method<br align="left"/>...<br align="left"/>}>]
-	Class1[label = <{<b>I/O class</b> | + property<br align="left"/>...<br align="left"/>|+ method<br align="left"/>...<br align="left"/>}>]
-	edge [dir=back arrowtail=empty style=dashed]
-	Class1 -> System_1 [xlabel=implementation]
-	System_1 [label = <{<b>System</b> | + property<br align="left"/>...<br align="left"/>|+ method<br align="left"/>...<br align="left"/>}>]
-	"Shared resource" [label = <{<b>Shared resource</b> | + property<br align="left"/>...<br align="left"/>|+ method<br align="left"/>...<br align="left"/>}>]
-	edge [dir=back arrowtail=diamond]
-	"System_1" -> Subsystem_1 [xlabel="composition"]
-	Subsystem_1[label = <{<b>Subsystem 1</b> | + property<br align="left"/>...<br align="left"/>|+ method<br align="left"/>...<br align="left"/>}>]
-	Subsystem_2[label = <{<b>Subsystem 2</b> | + property<br align="left"/>...<br align="left"/>|+ method<br align="left"/>...<br align="left"/>}>]
-	Subsystem_3[label = <{<b>Subsystem 3</b> | + property<br align="left"/>...<br align="left"/>|+ method<br align="left"/>...<br align="left"/>}>]
-	"System_1" -> Subsystem_2
-	"System_1" -> Subsystem_3
-	edge [xdir=back arrowtail=odiamond]
-	Subsystem_1 -> "Shared resource" [xlabel=aggregation]
-	{Subsystem_2 Subsystem_3 } -> "Shared resource"
-}
-''')
-
-
-
-st.graphviz_chart('''
-digraph G {
-	fontname="Helvetica,Arial,sans-serif"
-	node [fontname="Helvetica,Arial,sans-serif"]
-	edge [fontname="Helvetica,Arial,sans-serif"]
-	subgraph cluster_1 {
-		node [ style=filled,shape="box",fillcolor="antiquewhite:aquamarine" ]n5;
-		node [ shape="ellipse",fillcolor="bisque4:blue2" ]n4;
-		node [ shape="circle",fillcolor="cadetblue1:chocolate1" ]n3;
-		node [ shape="diamond",fillcolor="crimson:cyan4" ]n2;
-		node [ shape="triangle",fillcolor="deepskyblue2:firebrick" ]n1;
-		node [ shape="pentagon",fillcolor="gray24:gray88" ]n0;
-		label = "X11 Colors";
-	}
-	subgraph cluster_2 {
-		node [ style=filled,shape="box",fillcolor="bisque:brown" ]n11;
-		node [ shape="ellipse",fillcolor="green:darkorchid" ]n10;
-		node [ shape="circle",fillcolor="deepskyblue:gold" ]n9;
-		node [ shape="diamond",fillcolor="lightseagreen:orangered" ]n8;
-		node [ shape="triangle",fillcolor="turquoise:salmon" ]n7;
-		node [ shape="pentagon",fillcolor="snow:black" ]n6;
-		label = "SVG Colors";
-	}
-	subgraph cluster_3 {
-		node [ style=filled,shape="box",fillcolor="/accent3/1:/accent3/3" ]n17;
-		node [ shape="ellipse",fillcolor="/accent4/1:/accent4/4" ]n16;
-		node [ shape="circle",fillcolor="/accent5/1:/accent5/5" ]n15;
-		node [ shape="diamond",fillcolor="/accent6/1:/accent6/6" ]n14;
-		node [ shape="triangle",fillcolor="/accent7/1:/accent7/7" ]n13;
-		node [ shape="pentagon",fillcolor="/accent8/1:/accent8/8" ]n12;
-		label = "Brewer - accent";
-	}
-	subgraph cluster_4 {
-		node [ style=filled,shape="box",fillcolor="/blues3/1:/blues3/2" ]n23;
-		node [ shape="ellipse",fillcolor="/blues4/1:/blues4/3" ]n22;
-		node [ shape="circle",fillcolor="/blues5/1:/blues5/4" ]n21;
-		node [ shape="diamond",fillcolor="/blues6/1:/blues6/5" ]n20;
-		node [ shape="triangle",fillcolor="/blues7/1:/blues7/6" ]n19;
-		node [ shape="pentagon",fillcolor="/blues8/1:/blues8/7" ]n18;
-		label = "Brewer - blues";
-	}
-n3 -> n9 -> n15 -> n21;
-}
-''')
-
-st.graphviz_chart('''
-digraph G {bgcolor="#0000FF44:#FF000044" gradientangle=90
-	fontname="Helvetica,Arial,sans-serif"
-	node [fontname="Helvetica,Arial,sans-serif"]
-	edge [fontname="Helvetica,Arial,sans-serif"]
-	subgraph cluster_0 {
-		style=filled;
-		color=lightgrey;
-		fillcolor="darkgray:gold";
-		gradientangle=0
-		node [fillcolor="yellow:green" style=filled gradientangle=270] a0;
-		node [fillcolor="lightgreen:red"] a1;
-		node [fillcolor="lightskyblue:darkcyan"] a2;
-		node [fillcolor="cyan:lightslateblue"] a3;
-		a0 -> a1 -> a2 -> a3;
-		label = "process #1";
-	}
-	subgraph cluster_1 {
-		node [fillcolor="yellow:magenta" 
-			 style=filled gradientangle=270] b0;
-		node [fillcolor="violet:darkcyan"] b1;
-		node [fillcolor="peachpuff:red"] b2;
-		node [fillcolor="mediumpurple:purple"] b3;
-		b0 -> b1 -> b2 -> b3;
-		label = "process #2";
-		color=blue
-		fillcolor="darkgray:gold";
-		gradientangle=0
-		style=filled;
-	}
-	start -> a0;
-	start -> b0;
-	a1 -> b3;
-	b2 -> a3;
-	a3 -> a0;
-	a3 -> end;
-	b3 -> end;
-	start [shape=Mdiamond ,
-		fillcolor="pink:red",
-		gradientangle=90,
-		style=radial];
-	end [shape=Msquare,
-		fillcolor="lightyellow:orange",
-		style=radial,
-		gradientangle=90];
-}
-''')
-
-st.graphviz_chart('''
-graph Color_wheel {
-	graph [
-		layout = neato
-		label = "Color wheel, 33 colors.\nNeato layout"
-		labelloc = b
-		fontname = "Helvetica,Arial,sans-serif"
-		start = regular
-		normalize = 0
-	]
-	node [
-		shape = circle
-		style = filled
-		color = "#00000088"
-		fontname = "Helvetica,Arial,sans-serif"
-	]
-	edge [
-		len = 2.7
-		color = "#00000088"
-		fontname = "Helvetica,Arial,sans-serif"
-	]
-	subgraph Dark {
-		node [fontcolor = white width = 1.4]
-		center [width = 1 style = invis shape = point]
-		center -- darkred [label = "0°/360°"]
-		darkred [fillcolor = darkred]
-		brown [fillcolor = brown]
-		brown -- center [label = "30°"]
-		olive [fillcolor = olive]
-		olive -- center [label = "60°"]
-		darkolivegreen [fillcolor = darkolivegreen fontsize = 10]
-		darkolivegreen -- center [label = "90°"]
-		darkgreen [fillcolor = darkgreen]
-		darkgreen -- center [label = "120°"]
-		"dark hue 0.416" [color = ".416 1 .6" fontcolor = white]
-		"dark hue 0.416" -- center [label = "150°"]
-		darkcyan [fillcolor = darkcyan]
-		darkcyan -- center [label = "180°"]
-		"dark hue 0.583" [color = ".583 1 .6" fontcolor = white]
-		"dark hue 0.583" -- center [label = "210°"]
-		darkblue [fillcolor = darkblue]
-		darkblue -- center [label = "240°"]
-		"dark hue 0.750" [color = ".750 1 .6"]
-		"dark hue 0.750" -- center [label = "270°"]
-		darkmagenta [fillcolor = darkmagenta]
-		darkmagenta -- center [label = "300°"]
-		"dark hue 0.916" [color = ".916 1 .6"]
-		"dark hue 0.916" -- center [label = "330°"]
-	}
-	subgraph Tue {
-		node [width = 1.3]
-		"hue 0.083" -- brown
-		"hue 0.083" [color = ".083 1 1"]
-		"hue 0.125" [color = ".125 1 1"]
-		"hue 0.166" -- olive
-		"hue 0.166" [color = ".166 1 1"]
-		"hue 0.208" [color = ".208 1 1"]
-		"hue 0.250" -- darkolivegreen
-		"hue 0.250" [color = ".250 1 1"]
-		"hue 0.291" [color = ".291 1 1"]
-		"hue 0.333" -- darkgreen
-		"hue 0.333" [color = ".333 1 1"]
-		"hue 0.375" [color = ".375 1 1"]
-		"hue 0.416" -- "dark hue 0.416"
-		"hue 0.416" [color = ".416 1 1"]
-		"hue 0.458" [color = ".458 1 1"]
-		"hue 0.500" -- darkcyan
-		"hue 0.500" [color = ".500 1 1"]
-		"hue 0.541" [color = ".541 1 1"]
-		node [fontcolor = white]
-		"hue 0.000" [color = ".000 1 1"]
-		"hue 0.000" -- darkred
-		"hue 0.041" [color = ".041 1 1"]
-		"hue 0.583" -- "dark hue 0.583"
-		"hue 0.583" [color = ".583 1 1"]
-		"hue 0.625" [color = ".625 1 1"]
-		"hue 0.666" -- darkblue
-		"hue 0.666" [color = ".666 1 1"]
-		"hue 0.708" [color = ".708 1 1"]
-		"hue 0.750" -- "dark hue 0.750"
-		"hue 0.750" [color = ".750 1 1"]
-		"hue 0.791" [color = ".791 1 1"]
-		"hue 0.833" -- darkmagenta
-		"hue 0.833" [color = ".833 1 1"]
-		"hue 0.875" [color = ".875 1 1"]
-		"hue 0.916" -- "dark hue 0.916"
-		"hue 0.916" [color = ".916 1 1"]
-		"hue 0.958" [color = ".958 1 1"]
-		edge [len = 1]
-		"hue 0.000" -- "hue 0.041" -- "hue 0.083" -- "hue 0.125" -- "hue 0.166" -- "hue 0.208"
-		"hue 0.208" -- "hue 0.250" -- "hue 0.291" -- "hue 0.333" -- "hue 0.375" -- "hue 0.416"
-		"hue 0.416" -- "hue 0.458" -- "hue 0.500" --"hue 0.541" -- "hue 0.583" -- "hue 0.625"
-		"hue 0.625" -- "hue 0.666" -- "hue 0.708" -- "hue 0.750" -- "hue 0.791" -- "hue 0.833"
-		"hue 0.833" -- "hue 0.875" -- "hue 0.916" -- "hue 0.958" -- "hue 0.000"
-	}
-	subgraph Main_colors {
-		node [width = 2 fontsize = 20]
-		red [fillcolor = red fontcolor = white]
-		orangered [fillcolor = orangered]
-		orange [fillcolor = orange]
-		gold [fillcolor = gold]
-		yellow [fillcolor = yellow]
-		yellowgreen [fillcolor = yellowgreen]
-		deeppink [fillcolor = deeppink fontcolor = white]
-		fuchsia [label = "fuchsia\nmagenta" fillcolor = fuchsia fontcolor = white]
-		purple [fillcolor = purple fontcolor = white]
-		blue [fillcolor = blue fontcolor = white]
-		cornflowerblue [fillcolor = cornflowerblue]
-		deepskyblue [fillcolor = deepskyblue]
-		aqua [fillcolor = aqua label = "aqua\ncyan"]
-		springgreen [fillcolor = springgreen]
-		green [fillcolor = green]
-		purple -- fuchsia -- deeppink -- red
-		cornflowerblue -- blue -- purple
-		cornflowerblue -- deepskyblue -- aqua [len = 1.7]
-		aqua -- springgreen -- green -- yellowgreen -- yellow
-		yellow -- gold -- orange -- orangered -- red [len = 1.6]
-		orange -- "hue 0.083"
-		deeppink -- "hue 0.916"
-		deeppink -- "hue 0.875"
-		red -- "hue 0.000"
-		yellowgreen -- "hue 0.250"
-		blue -- "hue 0.666"
-		yellow -- "hue 0.166"
-		gold -- "hue 0.125"
-		green -- "hue 0.333"
-		springgreen -- "hue 0.416"
-		aqua -- "hue 0.500"
-		cornflowerblue -- "hue 0.583"
-		deepskyblue -- "hue 0.541"
-		purple -- "hue 0.791"
-		purple -- "hue 0.750"
-		fuchsia -- "hue 0.833"
-	}
-	subgraph Light_colors {
-		node [width = 2 fontsize = 20]
-		node [shape = circle width = 1.8]
-		edge [len = 2.1]
-		pink [fillcolor = pink]
-		pink -- red
-		lightyellow [fillcolor = lightyellow]
-		lightyellow -- yellow
-		mediumpurple [fillcolor = mediumpurple]
-		mediumpurple -- purple
-		violet [fillcolor = violet]
-		violet -- fuchsia
-		hotpink [fillcolor = hotpink]
-		hotpink -- deeppink
-		"light hue 0.250" [color = ".250 .2 1"]
-		"light hue 0.250" -- yellowgreen
-		lightcyan [fillcolor = lightcyan]
-		lightcyan -- aqua
-		lightslateblue [fillcolor = lightslateblue]
-		lightslateblue -- blue
-		lightgreen [fillcolor = lightgreen]
-		lightgreen -- green
-		lightskyblue [fillcolor = lightskyblue]
-		lightskyblue -- deepskyblue
-		peachpuff [fillcolor = peachpuff]
-		peachpuff -- orange
-		"light hue 0.416" [color = ".416 .2 1"]
-		"light hue 0.416" -- springgreen
-	}
-	subgraph Tints {
-		node [width = 1]
-		edge [len = 2.4]
-		"hue 0 tint" -- pink
-		"hue 0 tint" [color = "0 .1 1"]
-		"hue 0.041 tint" [color = ".041 .1 1"]
-		"hue 0.083 tint" -- peachpuff
-		"hue 0.083 tint" [color = ".083 .1 1"]
-		"hue 0.125 tint" [color = ".125 .1 1"]
-		"hue 0.166 tint" -- lightyellow
-		"hue 0.166 tint" [color = ".166 .1 1"]
-		"hue 0.208 tint" [color = ".208 .1 1"]
-		"hue 0.250 tint" -- "light hue 0.250"
-		"hue 0.250 tint" [color = ".250 .1 1"]
-		"hue 0.291 tint" [color = ".291 .1 1"]
-		"hue 0.333 tint" -- lightgreen
-		"hue 0.333 tint" [color = ".333 .1 1"]
-		"hue 0.375 tint" [color = ".375 .1 1"]
-		"hue 0.416 tint" -- "light hue 0.416"
-		"hue 0.416 tint" [color = ".416 .1 1"]
-		"hue 0.458 tint" [color = ".458 .1 1"]
-		"hue 0.5 tint" -- lightcyan
-		"hue 0.5 tint" [color = ".5 .1 1"]
-		"hue 0.541 tint" -- lightskyblue
-		"hue 0.541 tint" [color = ".541 .1 1"]
-		"hue 0.583 tint" [color = ".583 .1 1"]
-		"hue 0.625 tint" [color = ".625 .1 1"]
-		"hue 0.666 tint" -- lightslateblue
-		"hue 0.666 tint" [color = ".666 .1 1"]
-		"hue 0.708 tint" [color = ".708 .1 1"]
-		"hue 0.750 tint" -- mediumpurple
-		"hue 0.750 tint" [color = ".750 .1 1"]
-		"hue 0.791 tint" [color = ".791 .1 1"]
-		"hue 0.833 tint" -- violet
-		"hue 0.833 tint" [color = ".833 .1 1"]
-		"hue 0.875 tint" [color = ".875 .1 1"]
-		"hue 0.916 tint" -- hotpink
-		"hue 0.916 tint" [color = ".916 .1 1"]
-		"hue 0.958 tint" [color = ".958 .1 1"]
-		edge [len = 2]
-		"hue 0 tint" -- "hue 0.041 tint" -- "hue 0.083 tint" -- "hue 0.125 tint" -- "hue 0.166 tint" -- "hue 0.208 tint"
-		"hue 0.208 tint" -- "hue 0.250 tint" -- "hue 0.291 tint" -- "hue 0.333 tint" -- "hue 0.375 tint" -- "hue 0.416 tint"
-		"hue 0.416 tint" -- "hue 0.458 tint" -- "hue 0.5 tint" --"hue 0.541 tint" -- "hue 0.583 tint" -- "hue 0.625 tint"
-		"hue 0.625 tint" -- "hue 0.666 tint" -- "hue 0.708 tint" -- "hue 0.750 tint" -- "hue 0.791 tint" -- "hue 0.833 tint"
-		"hue 0.833 tint" -- "hue 0.875 tint" -- "hue 0.916 tint" -- "hue 0.958 tint" -- "hue 0 tint"
-	}
-	}
-''')
\ No newline at end of file
diff --git a/spaces/marioboy/neil-breen/toolbox/__init__.py b/spaces/marioboy/neil-breen/toolbox/__init__.py
deleted file mode 100644
index 531d6adef076007afd6116eb6472485f540e80de..0000000000000000000000000000000000000000
--- a/spaces/marioboy/neil-breen/toolbox/__init__.py
+++ /dev/null
@@ -1,357 +0,0 @@
-from toolbox.ui import UI
-from encoder import inference as encoder
-from synthesizer.inference import Synthesizer
-from vocoder import inference as vocoder
-from pathlib import Path
-from time import perf_counter as timer
-from toolbox.utterance import Utterance
-import numpy as np
-import traceback
-import sys
-import torch
-import librosa
-from audioread.exceptions import NoBackendError
-
-# Use this directory structure for your datasets, or modify it to fit your needs
-recognized_datasets = [
-    "LibriSpeech/dev-clean",
-    "LibriSpeech/dev-other",
-    "LibriSpeech/test-clean",
-    "LibriSpeech/test-other",
-    "LibriSpeech/train-clean-100",
-    "LibriSpeech/train-clean-360",
-    "LibriSpeech/train-other-500",
-    "LibriTTS/dev-clean",
-    "LibriTTS/dev-other",
-    "LibriTTS/test-clean",
-    "LibriTTS/test-other",
-    "LibriTTS/train-clean-100",
-    "LibriTTS/train-clean-360",
-    "LibriTTS/train-other-500",
-    "LJSpeech-1.1",
-    "VoxCeleb1/wav",
-    "VoxCeleb1/test_wav",
-    "VoxCeleb2/dev/aac",
-    "VoxCeleb2/test/aac",
-    "VCTK-Corpus/wav48",
-]
-
-#Maximum of generated wavs to keep on memory
-MAX_WAVES = 15
-
-class Toolbox:
-    def __init__(self, datasets_root, enc_models_dir, syn_models_dir, voc_models_dir, seed, no_mp3_support):
-        if not no_mp3_support:
-            try:
-                librosa.load("samples/6829_00000.mp3")
-            except NoBackendError:
-                print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
-                  "Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
-                exit(-1)
-        self.no_mp3_support = no_mp3_support
-        sys.excepthook = self.excepthook
-        self.datasets_root = datasets_root
-        self.utterances = set()
-        self.current_generated = (None, None, None, None) # speaker_name, spec, breaks, wav
-        
-        self.synthesizer = None # type: Synthesizer
-        self.current_wav = None
-        self.waves_list = []
-        self.waves_count = 0
-        self.waves_namelist = []
-
-        # Check for webrtcvad (enables removal of silences in vocoder output)
-        try:
-            import webrtcvad
-            self.trim_silences = True
-        except:
-            self.trim_silences = False
-
-        # Initialize the events and the interface
-        self.ui = UI()
-        self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir, seed)
-        self.setup_events()
-        self.ui.start()
-
-    def excepthook(self, exc_type, exc_value, exc_tb):
-        traceback.print_exception(exc_type, exc_value, exc_tb)
-        self.ui.log("Exception: %s" % exc_value)
-        
-    def setup_events(self):
-        # Dataset, speaker and utterance selection
-        self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
-        random_func = lambda level: lambda: self.ui.populate_browser(self.datasets_root,
-                                                                     recognized_datasets,
-                                                                     level)
-        self.ui.random_dataset_button.clicked.connect(random_func(0))
-        self.ui.random_speaker_button.clicked.connect(random_func(1))
-        self.ui.random_utterance_button.clicked.connect(random_func(2))
-        self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
-        self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
-        
-        # Model selection
-        self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
-        def func(): 
-            self.synthesizer = None
-        self.ui.synthesizer_box.currentIndexChanged.connect(func)
-        self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
-        
-        # Utterance selection
-        func = lambda: self.load_from_browser(self.ui.browse_file())
-        self.ui.browser_browse_button.clicked.connect(func)
-        func = lambda: self.ui.draw_utterance(self.ui.selected_utterance, "current")
-        self.ui.utterance_history.currentIndexChanged.connect(func)
-        func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer.sample_rate)
-        self.ui.play_button.clicked.connect(func)
-        self.ui.stop_button.clicked.connect(self.ui.stop)
-        self.ui.record_button.clicked.connect(self.record)
-
-        #Audio
-        self.ui.setup_audio_devices(Synthesizer.sample_rate)
-
-        #Wav playback & save
-        func = lambda: self.replay_last_wav()
-        self.ui.replay_wav_button.clicked.connect(func)
-        func = lambda: self.export_current_wave()
-        self.ui.export_wav_button.clicked.connect(func)
-        self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
-
-        # Generation
-        func = lambda: self.synthesize() or self.vocode()
-        self.ui.generate_button.clicked.connect(func)
-        self.ui.synthesize_button.clicked.connect(self.synthesize)
-        self.ui.vocode_button.clicked.connect(self.vocode)
-        self.ui.random_seed_checkbox.clicked.connect(self.update_seed_textbox)
-
-        # UMAP legend
-        self.ui.clear_button.clicked.connect(self.clear_utterances)
-
-    def set_current_wav(self, index):
-        self.current_wav = self.waves_list[index]
-
-    def export_current_wave(self):
-        self.ui.save_audio_file(self.current_wav, Synthesizer.sample_rate)
-
-    def replay_last_wav(self):
-        self.ui.play(self.current_wav, Synthesizer.sample_rate)
-
-    def reset_ui(self, encoder_models_dir, synthesizer_models_dir, vocoder_models_dir, seed):
-        self.ui.populate_browser(self.datasets_root, recognized_datasets, 0, True)
-        self.ui.populate_models(encoder_models_dir, synthesizer_models_dir, vocoder_models_dir)
-        self.ui.populate_gen_options(seed, self.trim_silences)
-        
-    def load_from_browser(self, fpath=None):
-        if fpath is None:
-            fpath = Path(self.datasets_root,
-                         self.ui.current_dataset_name,
-                         self.ui.current_speaker_name,
-                         self.ui.current_utterance_name)
-            name = str(fpath.relative_to(self.datasets_root))
-            speaker_name = self.ui.current_dataset_name + '_' + self.ui.current_speaker_name
-            
-            # Select the next utterance
-            if self.ui.auto_next_checkbox.isChecked():
-                self.ui.browser_select_next()
-        elif fpath == "":
-            return 
-        else:
-            name = fpath.name
-            speaker_name = fpath.parent.name
-
-        if fpath.suffix.lower() == ".mp3" and self.no_mp3_support:
-                self.ui.log("Error: No mp3 file argument was passed but an mp3 file was used")
-                return
-
-        # Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
-        # playback, so as to have a fair comparison with the generated audio
-        wav = Synthesizer.load_preprocess_wav(fpath)
-        self.ui.log("Loaded %s" % name)
-
-        self.add_real_utterance(wav, name, speaker_name)
-        
-    def record(self):
-        wav = self.ui.record_one(encoder.sampling_rate, 5)
-        if wav is None:
-            return 
-        self.ui.play(wav, encoder.sampling_rate)
-
-        speaker_name = "user01"
-        name = speaker_name + "_rec_%05d" % np.random.randint(100000)
-        self.add_real_utterance(wav, name, speaker_name)
-        
-    def add_real_utterance(self, wav, name, speaker_name):
-        # Compute the mel spectrogram
-        spec = Synthesizer.make_spectrogram(wav)
-        self.ui.draw_spec(spec, "current")
-
-        # Compute the embedding
-        if not encoder.is_loaded():
-            self.init_encoder()
-        encoder_wav = encoder.preprocess_wav(wav)
-        embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
-
-        # Add the utterance
-        utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, False)
-        self.utterances.add(utterance)
-        self.ui.register_utterance(utterance)
-
-        # Plot it
-        self.ui.draw_embed(embed, name, "current")
-        self.ui.draw_umap_projections(self.utterances)
-        
-    def clear_utterances(self):
-        self.utterances.clear()
-        self.ui.draw_umap_projections(self.utterances)
-        
-    def synthesize(self):
-        self.ui.log("Generating the mel spectrogram...")
-        self.ui.set_loading(1)
-        
-        # Update the synthesizer random seed
-        if self.ui.random_seed_checkbox.isChecked():
-            seed = int(self.ui.seed_textbox.text())
-            self.ui.populate_gen_options(seed, self.trim_silences)
-        else:
-            seed = None
-
-        if seed is not None:
-            torch.manual_seed(seed)
-
-        # Synthesize the spectrogram
-        if self.synthesizer is None or seed is not None:
-            self.init_synthesizer()
-
-        texts = self.ui.text_prompt.toPlainText().split("\n")
-        embed = self.ui.selected_utterance.embed
-        embeds = [embed] * len(texts)
-        specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
-        breaks = [spec.shape[1] for spec in specs]
-        spec = np.concatenate(specs, axis=1)
-        
-        self.ui.draw_spec(spec, "generated")
-        self.current_generated = (self.ui.selected_utterance.speaker_name, spec, breaks, None)
-        self.ui.set_loading(0)
-
-    def vocode(self):
-        speaker_name, spec, breaks, _ = self.current_generated
-        assert spec is not None
-
-        # Initialize the vocoder model and make it determinstic, if user provides a seed
-        if self.ui.random_seed_checkbox.isChecked():
-            seed = int(self.ui.seed_textbox.text())
-            self.ui.populate_gen_options(seed, self.trim_silences)
-        else:
-            seed = None
-
-        if seed is not None:
-            torch.manual_seed(seed)
-
-        # Synthesize the waveform
-        if not vocoder.is_loaded() or seed is not None:
-            self.init_vocoder()
-
-        def vocoder_progress(i, seq_len, b_size, gen_rate):
-            real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
-            line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
-                   % (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
-            self.ui.log(line, "overwrite")
-            self.ui.set_loading(i, seq_len)
-        if self.ui.current_vocoder_fpath is not None:
-            self.ui.log("")
-            wav = vocoder.infer_waveform(spec, progress_callback=vocoder_progress)
-        else:
-            self.ui.log("Waveform generation with Griffin-Lim... ")
-            wav = Synthesizer.griffin_lim(spec)
-        self.ui.set_loading(0)
-        self.ui.log(" Done!", "append")
-        
-        # Add breaks
-        b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
-        b_starts = np.concatenate(([0], b_ends[:-1]))
-        wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
-        breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
-        wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
-
-        # Trim excessive silences
-        if self.ui.trim_silences_checkbox.isChecked():
-            wav = encoder.preprocess_wav(wav)
-
-        # Play it
-        wav = wav / np.abs(wav).max() * 0.97
-        self.ui.play(wav, Synthesizer.sample_rate)
-
-        # Name it (history displayed in combobox)
-        # TODO better naming for the combobox items?
-        wav_name = str(self.waves_count + 1)
-
-        #Update waves combobox
-        self.waves_count += 1
-        if self.waves_count > MAX_WAVES:
-          self.waves_list.pop()
-          self.waves_namelist.pop()
-        self.waves_list.insert(0, wav)
-        self.waves_namelist.insert(0, wav_name)
-
-        self.ui.waves_cb.disconnect()
-        self.ui.waves_cb_model.setStringList(self.waves_namelist)
-        self.ui.waves_cb.setCurrentIndex(0)
-        self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
-
-        # Update current wav
-        self.set_current_wav(0)
-        
-        #Enable replay and save buttons:
-        self.ui.replay_wav_button.setDisabled(False)
-        self.ui.export_wav_button.setDisabled(False)
-
-        # Compute the embedding
-        # TODO: this is problematic with different sampling rates, gotta fix it
-        if not encoder.is_loaded():
-            self.init_encoder()
-        encoder_wav = encoder.preprocess_wav(wav)
-        embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
-        
-        # Add the utterance
-        name = speaker_name + "_gen_%05d" % np.random.randint(100000)
-        utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, True)
-        self.utterances.add(utterance)
-        
-        # Plot it
-        self.ui.draw_embed(embed, name, "generated")
-        self.ui.draw_umap_projections(self.utterances)
-        
-    def init_encoder(self):
-        model_fpath = self.ui.current_encoder_fpath
-        
-        self.ui.log("Loading the encoder %s... " % model_fpath)
-        self.ui.set_loading(1)
-        start = timer()
-        encoder.load_model(model_fpath)
-        self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
-        self.ui.set_loading(0)
-
-    def init_synthesizer(self):
-        model_fpath = self.ui.current_synthesizer_fpath
-
-        self.ui.log("Loading the synthesizer %s... " % model_fpath)
-        self.ui.set_loading(1)
-        start = timer()
-        self.synthesizer = Synthesizer(model_fpath)
-        self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
-        self.ui.set_loading(0)
-           
-    def init_vocoder(self):
-        model_fpath = self.ui.current_vocoder_fpath
-        # Case of Griffin-lim
-        if model_fpath is None:
-            return 
-    
-        self.ui.log("Loading the vocoder %s... " % model_fpath)
-        self.ui.set_loading(1)
-        start = timer()
-        vocoder.load_model(model_fpath)
-        self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
-        self.ui.set_loading(0)
-
-    def update_seed_textbox(self):
-       self.ui.update_seed_textbox() 
diff --git a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/resemblyer_util/speaker_emb.py b/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/resemblyer_util/speaker_emb.py
deleted file mode 100644
index df3593c34c4527e90ddc51283193323d94653647..0000000000000000000000000000000000000000
--- a/spaces/marlenezw/audio-driven-animations/MakeItTalk/thirdparty/resemblyer_util/speaker_emb.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from resemblyzer import preprocess_wav, VoiceEncoder
-import numpy as np
-import torch
-
-
-def get_spk_emb(audio_file_dir, segment_len=960000):
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    resemblyzer_encoder = VoiceEncoder(device=device)
-
-    wav = preprocess_wav(audio_file_dir)
-    l = len(wav) // segment_len # segment_len = 16000 * 60
-    l = np.max([1, l])
-    all_embeds = []
-    for i in range(l):
-        mean_embeds, cont_embeds, wav_splits = resemblyzer_encoder.embed_utterance(
-            wav[segment_len * i:segment_len* (i + 1)], return_partials=True, rate=2)
-        all_embeds.append(mean_embeds)
-    all_embeds = np.array(all_embeds)
-    mean_embed = np.mean(all_embeds, axis=0)
-
-    return mean_embed, all_embeds
-
-
-
-if __name__ == '__main__':
-    m, a = get_spk_emb(r'E:\Dataset\TalkingToon\Obama\test_wav_files\obama_example.wav')
-    print('Speaker embedding:', m)
-    print(m.shape)
\ No newline at end of file
diff --git a/spaces/matthoffner/starchat-ui/utils/app/conversation.ts b/spaces/matthoffner/starchat-ui/utils/app/conversation.ts
deleted file mode 100644
index 3fdfbcf2368802a8d867a99cf40ade44d11efba7..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/starchat-ui/utils/app/conversation.ts
+++ /dev/null
@@ -1,30 +0,0 @@
-import { Conversation } from '@/types/chat';
-
-export const updateConversation = (
-  updatedConversation: Conversation,
-  allConversations: Conversation[],
-) => {
-  const updatedConversations = allConversations.map((c) => {
-    if (c.id === updatedConversation.id) {
-      return updatedConversation;
-    }
-
-    return c;
-  });
-
-  saveConversation(updatedConversation);
-  saveConversations(updatedConversations);
-
-  return {
-    single: updatedConversation,
-    all: updatedConversations,
-  };
-};
-
-export const saveConversation = (conversation: Conversation) => {
-  localStorage.setItem('selectedConversation', JSON.stringify(conversation));
-};
-
-export const saveConversations = (conversations: Conversation[]) => {
-  localStorage.setItem('conversationHistory', JSON.stringify(conversations));
-};
diff --git a/spaces/mboth/klassifizierungDatenpunkte/app.py b/spaces/mboth/klassifizierungDatenpunkte/app.py
deleted file mode 100644
index 48841ee26e7ee74189c6f79634dae690ecd00178..0000000000000000000000000000000000000000
--- a/spaces/mboth/klassifizierungDatenpunkte/app.py
+++ /dev/null
@@ -1,303 +0,0 @@
-import gradio as gr
-import math
-import transformers
-from transformers import pipeline
-
-model = pipeline("text-classification", model = 'mboth/klassifizierungGrundfunktionenKH')
-klassifikation_waerme_versorgen = pipeline("text-classification", model = 'mboth/klassifizierungWaermeVersorgen')
-klassifikation_waerme_verteilen = pipeline("text-classification", model = 'mboth/klassifizierungWaermeVerteilen')
-klassifikation_waerme_erzeugen = pipeline("text-classification", model = 'mboth/klassifizierungErzeugen')
-klassifikation_luft_versorgen = pipeline("text-classification", model = 'mboth/klassifizierungLuftVersorgen')
-klassifikation_luft_verteilen = pipeline("text-classification", model = 'mboth/klassifizierungLuftVerteilen')
-klassifikation_kälte_versorgen = pipeline("text-classification", model = 'mboth/klassifizierungKaelteVersorgen')
-klassifikation_medien_versorgen = pipeline("text-classification", model = 'mboth/klassifizierungMedienVersorgen')
-klassifikation_sichern = pipeline("text-classification", model = 'mboth/klassifizierungSichern')
-klassifikation_luft_bereitstellen = pipeline("text-classification", model = 'mboth/klassifizierungLuftBereitstellen')
-klassifikation_datenpunkte_großes_modell = pipeline("zero-shot-classification", model = 'mboth/klassifizierungDatenpunkteNLI')
-
-def predict(description, name):
-
-    hypothese = 'Der Datenpunkt beschreibt: {}.'
-
-    label_beziehen = ['Alarmmeldung', 'Anforderung', 'Messwert Außentemperatur', 'Betriebsstunden Pumpe', 'Grenzwert Vorlauftemperatur Sekundär', 'Sollwert Maximale Vorlauftemperatur', 'Sollwert Minimale Vorlauftemperatur', 'Offset Vorlauftemperatur',
-            'Regler', 'Messwert Rücklauftemperatur Primär', 'Messwert Rücklauftemperatur Sekundär', 'Rückmeldung Handschaltung Ventil', 'Rückmeldung Handschaltung Klappe', 'Rückmeldung Betriebsart', 'Rückmeldung Ventil Rücklauf',
-            'Sollwert Außentemperatur Maximal Tag', 'Sollwert Nachlaufzeit Pumpe', 'Schaltbefehl Anlage', 'Schaltbefehl Pumpe', 'Sollwert Vorlauftemperatur', 'Stellbefehl Ventil', 'Störmeldung', 'Messwert Vorlauftemperatur Sekundär',
-            'Zähler', 'Messwert Vorlauftemperatur Primär', 'Grenzwert Rücklauftemperatur Sekundär', 'Rückmeldung Handschaltung Fernwärme', 'Rückmeldung Stellsignal']
-    label_speichern = ['Externe Vorrangschaltung Aktiv', 'Rückmeldung Zeitplan','Sollwert Maximale Hysterese Speichertemperatur','Sollwert Speichertemperatur','Sollwert Speichertemperatur Unten','Messwert Speichertemperatur','Messwert Speichertemperatur Mitte'
-            'Messwert Speichertemperatur Oben','Messwert Speichertemperatur Unten','Störmeldung','Rückmeldung Zeitplan']
-
-    label_kessel = ['Anforderung', 'Anzahl Schaltungen', 'Betriebsstunden','Messwert Druck','Freigabe','Messwert Temperatur','Regler','Reset Betriebsstunden','Rückmeldung Betrieb','Rückmeldung Klappe','Rückmeldung Stellsignal'
-              'Rückmeldung Handschaltung','Rückmeldung Leistung','Rückmeldung Not Aus','Schaltbefehl Not Aus','Sollwert Temperatur','Sollwert Wartezeit','Sollwert Leistung','Schaltbefehl Anlage','Stellbefehl Anlage','Störmeldung','Überhöhung Kesselanlage',
-              'Wartungsmeldung','Sollwert Einschaltverzögerung','Sollwert Abschaltung','Schaltbefehl Klappe']
-    label_bhkw = ['Betriebsstunden','Laufzeit Nächste Wartung','Messwert Abgastemperatur','Messwert Gasverbrauch','Messwert Spannung','Messwert Strom','Messwert Temperatur Generator','Rückmeldung Ölnachspeisung Aktiv'
-              'Rückmeldung Start', 'Rückmeldung Handschaltung','Rückmeldung Batterie','Schaltbefehl Anlage','Störmeldung','Warnmeldung','Wartungsmeldung','Rückmeldung Betrieb']
-    label_wärmepumpe = ['Rückmeldung-Handschaltung-Brunnenpumpe','Rückmeldung-Betriebsbereit','Rückmeldung-Betriebsbereit','Rückmeldung-Blockierschutz-Umwälzpumpe','Rückmeldung-Blockierschutz-Brunnenpumpe','Rückmeldung-Umschaltventil-Zu',
-              'Störmeldung','Zähler-Volumenstrom-Förderbrunnen']
-    label_pelletkessel = ['Rückmeldung Schnecke Leer','Rückmeldung Betrieb','Messwert Außentemperatur','Messwert Primärluft','Restsauerstoff','Messwert Temperatur Einschubrohr']
-
-    label_pumpe = ['Anzahl-Schaltungen','Betriebsstunden','Messwert Durchfluss','Messwert Energieverbrauch','Messwert Leistungsaufnahme','Messwert Stromaufnahme','Messwert Drehzahl','Reset Betriebsstunden','Rückmeldung Handschaltung Pumpe',
-                    'Rückmeldung Betrieb','Schaltbefehl','Schaltbefehl Blockierschutz','Sollwert Frostschutz','Sollwert Laufzeit Blockierschutz','Sollwert Nacht','Sollwert Nachlaufzeit','Sollwert Tag','Status Übersteuern Ein','Störmeldung','Wartungsintervall']
-    label_ventil = ['Handschaltung','Laufzeit 3 Punkt Antrieb','Rückmeldung Handschaltung','Rückmeldung Stellsignal','Sollwert Stellsignal Min','Sollwert Stellsignal Max','Stellbefehl','Störmeldung','Stellbefehl Max','Stellbefehl Min',
-                    'Sollwert Mischventil','Laufzeit Ventil']
-    label_raum = ['Freigabe Heizung','Freigabe Raumkorrektur','Freigabe Stützbetrieb Nacht Ventil','Freigabe Stützbetrieb','Freigabe Stützbetrieb Tag Ventil','Freigabe Stellantrieb','Freigabe Zeitprogramm','Messwert Raumtemperatur',
-                  'Rückmeldung Normalbetrieb','Rückmeldung Ventil Handschaltung','Schaltbefehl Raumkorrektur','Sollwert Raumkorrektur','Sollwert Raumtemperatur Tag','Sollwert Raumtemperatur Nacht','Sollwert minimale Raumtemperatur',
-                  'Sollwertverschiebung','Sollwert Stützbetrieb Tag','Sollwert Stützbetrieb Nacht','Sollwert Aufheizbetrieb','Stellbefehl','Aktivierung Raumoptimierung','Rückmeldung Aufheizbetrieb','Rückmeldung Absenkbetrieb']
-    label_vorlauf = ['Anhebung Vorlauftemperatur','Messwert Vorlauftemperatur','Sollwert- orlauftemperatur','Sollwert Maximale Vorlauftemperatur','Sollwert Minimale Vorlauftemperatur','Sollwert Nachtabsenkung Vorlauftemperatur',
-                      'Störmeldung','Rückmeldung Sollwertabweichung Vorlauftemperatur','Sollwertkorrektur Vorlauftemperatur']
-    label_rücklauf = ['Messwert Rücklauftemperatur','Sollwert Maximale Rücklauftemperatur','Sollwert Minimale Rücklauftemperatur','Sollwert Rücklauftemperatur','Rohrheizung']
-    label_heizkreis_allgemein = ['Alarmmeldung','Aktuelle Leistung','Grenzwert Frost','Heizkurve','Messwert Außentemperatur','Regler','Rückmeldung Nutzzeitverlängerung','Schaltbefehl Anlage','Schaltbefehl Nutzzeitverlängerung',
-                                  'Schaltbefehl Nachtabsenkung','Sollwert Maximale Aufheizzeit','Sollwert Aufheizzeit','Sollwert Nachtabsenkung','Sollwert Nutzzeitverlängerung','Sollwert Speicherfähigkeit','Sollwert Stützbetrieb Tag','Sollwert Überhöhung Hydraulische Weiche',
-                                  'Stützbetrieb Nacht Erreicht','Warmwasserbereitung','Parallelverschiebung','Rückmeldung Tagbetrieb','Störmeldung','Wärmemengenzähler','Sollwert Minimale Außentemperatur','Sollwert Außentemperatur','Rückmeldung Betriebsart',
-                                  'Rückmeldung Zeitplan','Rückmeldung Regelabweichung','Sollwert Abschalten Stützbetrieb','Schaltbefehl Start Stop Optimierung','Schaltbefehl Gleitendes Schalten','Schaltbefehl Zeitprogramm']
-
-    label_abluft_allgemein = ['Alarmmeldung','Befehlsausführkontrolle','Messwert Druck','Messwert Feuchte','Messwert Temperatur','Messwert Luftqualität','Messwert Volumenstrom','Rückmeldung Handschaltung','Rückmeldung Nutzzeitverlängerung',
-                              'Rückmeldung Ventil','Rückmeldung Zeitplan','Schaltbefehl Anlage','Sollwert Druck','Sollwert Feuchte','Sollwert Temperatur','Sollwert CO2 Konzentration','Sollwert Volumenstrom','Störmeldung',
-                              'Warnmeldung CO2 Hoch','Warnmeldung Feuchte','Warnmeldung Temperatur Hoch','Warnmeldung Temperatur Niedrig','Wartungsmeldung']
-    label_abluftventilator = ['Alarmmeldung','Anzahl Schaltungen','Befehlausführungskontrolle','Betriebsstunden','Messwert Differenzdruck','Messwert Volumenstrom','Reset Betriebsstunden','Rückmeldung Nutzzeitverlängerung',
-                              'Rückmeldung Handschaltung','Rückmeldung Reperaturschalter','Rückmeldung Betrieb','Rückmeldung Zeitplan','Schaltbefehl','Sollwert Laufzeit','Sollwert FU','Stellbefehl','Störmeldung']
-    label_zuluftventilator = ['Alarmmeldung', 'Anzahl Schaltungen', 'Befehlausführungskontrolle', 'Betriebsstunden', 'Messwert Differenzdruck', 'Messwert Volumenstrom', 'Reset Betriebsstunden', 'Rückmeldung Drehzahl',
-              'Rückmeldung Nutzzeitverlängerung', 'Rückmeldung Handschaltung', 'Rückmeldung Reperaturschalter', 'Rückmeldung Stellsignal', 'Rückmeldung Betrieb', 'Rückmeldung Zeitplan', 'Rückmeldung Laufüberwachung',
-              'Schaltbefehl', 'Sollwert Laufzeit', 'Sollwert Stellsignal', 'Stellbefehl', 'Störmeldung', 'Wartungsmeldung']
-    label_zuluft_allgemein = ['Alarmmeldung Frostschutz', 'Alarmmeldung', 'Befehlsausführkontrolle', 'Messwert Druck', 'Messwert Feuchte', 'Messwert Temperatur', 'Messwert Luftqualität', 'Messwert Volumenstrom', 'Rückmeldung Handschaltung',
-            'Rückmeldung Nutzzeitverlängerung','Rückmeldung Ventil', 'Rückmeldung Zeitplan', 'Rückmeldung Grenzwert Soll Ist Abweichung Temperatur', 'Sollwert Grenzwert Soll Ist Abweichung Temperatur', 'Schaltbefehl Anlage', 
-            'Sollwert Druck', 'Sollwert Feuchte', 'Sollwert Feuchte Max', 'Sollwert Feuchte Min', 'Sollwert Frostschutz', 'Sollwert Temperatur', 'Sollwert Temperatur Min', 'Sollwert Temperatur Max', 'Sollwert CO2-Konzentration', 
-            'Sollwert CO2-Konzentration Max', 'Sollwert Volumenstrom', 'Sollwert Volumenstrom Max', 'Sollwert Volumenstrom Min', 'Störmeldung', 'Warnmeldung CO2 Hoch', 'Warnmeldung Feuchte', 'Warnmeldung Temperatur Hoch', 
-            'Warnmeldung Temperatur Niedrig', 'Wartungsmeldung']
-    label_klappe = ['Alarmmeldung', 'Befehlsausführkontrolle','Rückmeldung Betrieb', 'Rückmeldung Klappe Auf', 'Rückmeldung Klappe Zu', 'Rückmeldung Handschaltung', 'Rückmeldung Stellsignal', 'Schaltbefehl', 'Störmeldung', 'Stellbefehl', 'Sollwert Stellsignal']
-    label_befeuchter = ['Rückmeldung Betrieb','Betriebsstunden','Sollwert Befeuchten','Stellbefehl','Störmeldung','Schaltbefehl']
-    label_entrauchung = ['Rückmeldung Betrieb','Wartungsmeldung','Störmeldung','Schaltbefehl']
-    label_erhitzer = ['Alarmmeldung','Anzahl Schaltungen','Betriebsstunden','Messwert Durchfluss','Messwert Energieverbrauch','Messwert Leistungsaufnahme','Messwert Rücklauftemperatur','Messwert Stromaufnahme',
-                      'Messwert Vorlauftemperatur','Messwert Drehzahl','Reset Betriebsstunden','Rückmeldung Handschaltung Pumpe','Rückmeldung Handschaltung Ventil','Rückmeldung Betrieb','Rückmeldung Stellsignal','Schaltbefehl',
-                      'Schaltbefehl Blockierschutz','Schaltbefehl Frostschutz','Sollwert Frostschutz','Sollwert Laufzeit Blockierschutz','Sollwert Nacht','Sollwert Nachlaufzeit','Sollwert Dauerfreigabe','Sollwert Tag',
-                      'Status Übersteuern-Ein','Stellbefehl Ventil','Störmeldung','Wartungsintervall','Wartungsmeldung','Grenzwert Rücklauftemperatur']
-    label_filter = ['Messwert Druck','Wartungsmeldun -Abluft','Wartungsmeldung Zuluft','Wartungsmeldung Fortluft','Wartungsmeldung Außenluft','Wartungsmeldung Filter','Störmeldung']
-    label_gerät_allgemein = ['Alarmmeldung','Anforderung Tableau','Messwert Außentemperatur','Sollwert Kühlbedarf','Schaltbefehl Anlage','Übersteuert','Rückmeldung Anfahrbetrieb','Rückmeldung Batterie',
-                            'Rückmeldung Betrieb','Rückmeldung Handschaltung','Rückmeldung Quittierung','Rückmeldung Freie Nachtkühlung','Rückmeldung Ferienprogramm','Rückmeldung Nutzzeitverlängerung','Rückmeldung Restlaufzeit Nutzzeitverlängerung',
-                            'Rückmeldung Spülen','Schaltbefehl Nachtkühlung','Schaltbefehl Optimierte Luftqualität','Schaltbefehl Tagesprogramm','Schaltbefehl Nutzzeitverlängerung','Sollwert Feuchte','Sollwert Spülzeit','Sollwert Freie-Nachtkühlung',
-                            'Sollwert Nutzzeitverlängerung','Sollwert Wärmebedarf','Sollwert Maximale Einschaltverzögerung','Störmeldung','Rückmeldung Anlage Fern','Schaltbefehl Anlage Fern']
-    label_kühler = ['Alarm Frostschutz','Anzahl Schaltungen','Betriebsstunden','Messwert Rücklauftemperatur','Messwert Vorlauftemperatur','Rückmeldung Klappe Auf','Rückmeldung Betrieb','Rückmeldung Stellsignal',
-                    'Sollwert Kühlbedarf','Stellbefehl Ventil','Zählwert Kühlwasser','Zählwert Kältemenge']
-    label_raum_rlt = ['Alarmmeldung','Alarme Zurück Gestellt','Betriebsmeldung Präsenzmelder','Messwert Feuchte','Messwert CO2','Messwert Raumtemperatur','Rückmeldung Betrieb','Rückmeldung Ventil','Rückmeldung Klappe Auf',
-                    'Rückmeldung Kommunikation','Sollwert Ausschaltverzögerung','Sollwert Einschaltverzögerung','Sollwert CO2','Sollwert CO2 Max','Sollwert Feuchte','Sollwert Raumtemperatur','Störmeldung','Warnmeldung CO2 Hoch',
-                    'Warnmeldung Feuchte','Warnmeldung Temperatur Hoch','Warnmeldung Temperatur Niedrig']
-    label_umluft = ['Rückmeldung Klappe Auf','Rückmeldung Klappe Zu','Schaltbefehl','Stellbefehl','Rückmeldung Betrieb','Rückmeldung Handschaltung']
-    label_vsr = ['Schaltbefehl','Rückmeldung Stellsignal','Stellbefehl','Rückmeldung Handschaltung']
-    label_wrg = ['Alarmmeldung','Messwert Temperatur Austritt Zuluft','Messwert Temperatur Eintritt Zuluft','Messwert Temperatur Eintritt Abluft','Messwert Temperatur Austritt Abluft','Messwert Vorlauftemperatur',
-                'Pumpe','Rückmeldung Betrieb','Rückmeldung Handschaltung','Rückmeldung Stellsignal','Schaltbefehl','Sollwert Frostschutz','Sollwert Stellsignal','Sollwert Stellsignal Min','Sollwert Stellsignal Max','Stellbefehl',
-                'Stellbefehl WRG Bypass','Störmeldung']
-
-    #completion = model(prompt)[0]["generated_text"]
-    datapoint = 'Beschreibung: ' + description + '; Name: ' + name
-    #print(datapoint)
-    klass_grundfunktion = model(datapoint)
-    #print(completion[0]['label'])
-    #return completion[0]['label']
-    score = round((klass_grundfunktion[0]['score'] * 100), 2)
-    grundfunktion = 'Die Grundfunktion des Datenpunktes lautet: '+ str(klass_grundfunktion[0]['label'])+ ' mit einem Score von: '+ str(score) + ' %'
-
-    if klass_grundfunktion[0]['label'] == 'WaermeVersorgen':
-      klasse = klassifikation_waerme_versorgen(datapoint)
-      score_klasse = round((klasse[0]['score'] * 100), 2)
-      klasse_string = 'Die zweite Grundfunktion des Datenpunktes lautet: ' + str(klasse[0]['label']) + ' mit einem Score von: '+ str(score_klasse) + ' %'
-      if klasse[0]['label'] == 'Verteilen':
-        klasse_komponente = klassifikation_waerme_verteilen(datapoint)
-        score_komponente = round((klasse_komponente[0]['score'] * 100), 2)
-        klasse_komponente_string = 'Die Komponente des Datenpunktes lautet: ' + str(klasse_komponente[0]['label']) + ' mit einem Score von: '+ str(score_komponente) + ' %'
-        if klasse_komponente[0]['label'] == 'Pumpe':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_pumpe)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Ventil':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_ventil)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Raum':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_raum)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Vorlauf':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_vorlauf)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Ruecklauf':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_rücklauf)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'HeizkreisAllgemein':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_heizkreis_allgemein)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-
-      elif klasse[0]['label'] == 'Erzeugen':
-        klasse_komponente = klassifikation_waerme_erzeugen(datapoint)
-        score_komponente = round((klasse_komponente[0]['score'] * 100), 2)
-        klasse_komponente_string = 'Die Komponente des Datenpunktes lautet: ' + str(klasse_komponente[0]['label']) + ' mit einem Score von: '+ str(score_komponente) + ' %'
-        if klasse_komponente[0]['label'] == 'Kessel':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_kessel)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'BHKW':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_bhkw)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Wärmepumpe':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_wärmepumpe)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Pelletkessel':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_pelletkessel)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'WaermeversorgerAllgemein':
-          datenpunkt_string = 'Noch nicht ausgeprägt'
-
-      elif klasse[0]['label'] == 'Beziehen':
-        datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_beziehen)
-        score_dp = round((datenpunkt['scores'][0] * 100), 2)
-        klasse_komponente_string = 'Die Komponente des Datenpunktes lautet: Fernwärme'
-        datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-      elif klasse[0]['label'] == 'Speichern':
-        datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_speichern)
-        score_dp = round((datenpunkt['scores'][0] * 100), 2)
-        klasse_komponente_string = 'Die Komponente des Datenpunktes lautet: Speicher'
-        datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-
-    elif klass_grundfunktion[0]['label'] == 'LuftVersorgen':
-      klasse = klassifikation_luft_versorgen(datapoint)
-      score_klasse = round((klasse[0]['score'] * 100), 2)
-      klasse_string = 'Die zweite Grundfunktion des Datenpunktes lautet: ' + str(klasse[0]['label']) + ' mit einem Score von: '+ str(score_klasse) + ' %'
-
-      if klasse[0]['label'] == 'LuftVerteilen':
-        klasse_komponente = klassifikation_luft_verteilen(datapoint)
-        score_komponente = round((klasse_komponente[0]['score'] * 100), 2)
-        klasse_komponente_string = 'Die Komponente des Datenpunktes lautet: ' + str(klasse_komponente[0]['label']) + ' mit einem Score von: '+ str(score_komponente) + ' %'
-        print(klasse_komponente_string)
-        if klasse_komponente[0]['label'] == 'Raum':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_raum_rlt)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'VolumenstromreglerAbluft':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_vsr)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'VolumenstromreglerZuluft':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_vsr)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-
-      elif klasse[0]['label'] == 'LuftBereitstellen':
-        klasse_komponente = klassifikation_luft_bereitstellen(datapoint)
-        score_komponente = round((klasse_komponente[0]['score'] * 100), 2)
-        klasse_komponente_string = 'Die Komponente des Datenpunktes lautet: ' + str(klasse_komponente[0]['label']) + ' mit einem Score von: '+ str(score_komponente) + ' %'
-        print(klasse_komponente_string)
-        if klasse_komponente[0]['label'] == 'ZuluftAllgemein':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_zuluft_allgemein)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'AbluftAllgemein':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_abluft_allgemein)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Abluftventilator':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_abluftventilator)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Zuluftventilator':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_zuluftventilator)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Fortluftklappe':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_klappe)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Außenluftklappe':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_klappe)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Abluftklappe':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_klappe)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Zuluftklappe':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_klappe)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Befeuchter':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_befeuchter)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Entrauchung':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_entrauchung)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Erhitzer':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_erhitzer)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Filter':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_filter)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'GerätAllgemein':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_gerät_allgemein)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Kühler':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_kühler)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Umluft':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_umluft)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-        elif klasse_komponente[0]['label'] == 'Wärmerückgewinnung':
-          datenpunkt = klassifikation_datenpunkte_großes_modell(sequences = datapoint, hypothesis_template = hypothese, candidate_labels = label_wrg)
-          score_dp = round((datenpunkt['scores'][0] * 100), 2)
-          datenpunkt_string = 'Der Datenpunkt lautet: ' + str(datenpunkt['labels'][0]) + ' mit einem Score von: '+ str(score_dp) + ' %'
-
-        elif klasse_komponente[0]['label'] == 'Regler':
-          datenpunkt_string = 'Noch nicht ausgeprägt'
-
-    elif klass_grundfunktion[0]['label'] == 'MedienVersorgen':
-      klasse = klassifikation_medien_versorgen(datapoint)
-      score_klasse = round((klasse[0]['score'] * 100), 2)
-      klasse_string = 'Die zweite Grundfunktion des Datenpunktes lautet: ' + str(klasse[0]['label']) + ' mit einem Score von: '+ str(score_klasse) + ' %'
-      klasse_komponente_string = 'Noch nicht ausgeprägt'
-      datenpunkt_string = 'Noch nicht ausgeprägt'
-
-    elif klass_grundfunktion[0]['label'] == 'Sichern':
-      klasse = klassifikation_sichern(datapoint)
-      score_klasse = round((klasse[0]['score'] * 100), 2)
-      klasse_string = 'Die zweite Grundfunktion des Datenpunktes lautet: ' + str(klasse[0]['label']) + ' mit einem Score von: '+ str(score_klasse) + ' %'
-      klasse_komponente_string = 'Noch nicht ausgeprägt'
-      datenpunkt_string = 'Noch nicht ausgeprägt'
-
-    elif klass_grundfunktion[0]['label'] == 'KaelteVersorgen':
-      klasse = klassifikation_kälte_versorgen(datapoint)
-      score_klasse = round((klasse[0]['score'] * 100), 2)
-      klasse_string = 'Die zweite Grundfunktion des Datenpunktes lautet: ' + str(klasse[0]['label']) + ' mit einem Score von: '+ str(score_klasse) + ' %'
-      klasse_komponente_string = 'Noch nicht ausgeprägt'
-      datenpunkt_string = 'Noch nicht ausgeprägt'
-
-    elif klass_grundfunktion[0]['label'] == 'StromVersorgen':
-      klasse_string = 'Noch nicht ausgeprägt'
-      klasse_komponente_string = 'Noch nicht ausgeprägt'
-      datenpunkt_string = 'Noch nicht ausgeprägt'
-
-    elif klass_grundfunktion[0]['label'] == 'AndereAnlagen':
-      klasse_string = 'Noch nicht ausgeprägt'
-      klasse_komponente_string = 'Noch nicht ausgeprägt'
-      datenpunkt_string = 'Noch nicht ausgeprägt'
-
-    elif klass_grundfunktion[0]['label'] == 'Befoerdern':
-      klasse_string = 'Noch nicht ausgeprägt'
-      klasse_komponente_string = 'Noch nicht ausgeprägt'
-      datenpunkt_string = 'Noch nicht ausgeprägt'
-
-    return [grundfunktion, klasse_string, klasse_komponente_string, datenpunkt_string]
-    
-title = 'Klassifizierung Datenpunkte'
-gr.Interface(fn = predict, inputs = [gr.Textbox(label="Description:", placeholder="z.B. Schaltbefehl Pumpe", lines=1), gr.Textbox(label="Name:", placeholder="z.B. 101HZG401PU101SB", lines=1)], 
-             title = title, examples=[["Schaltbefehl Zul.-Ventilator", '101RLT204ZU405VE202SB'], ["Vorl. Temp.", '101HZG204VL405MW']], theme = 'huggingface',
-             outputs = [gr.Textbox(label = 'Grundfunktion'),gr.Textbox(label = 'Funktion Ebene 2'), gr.Textbox(label = 'Komponente'),gr.Textbox(label = 'Datenpunkt')]).launch()
-   
\ No newline at end of file
diff --git a/spaces/megatron7/bert-base-chinese/app.py b/spaces/megatron7/bert-base-chinese/app.py
deleted file mode 100644
index 3d9f36ff130c71e3850b7c5662c5967b767ef0ab..0000000000000000000000000000000000000000
--- a/spaces/megatron7/bert-base-chinese/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/bert-base-chinese").launch()
\ No newline at end of file
diff --git a/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_modules.py b/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_modules.py
deleted file mode 100644
index 6cf676ce37c1eaf8428c4094e749f862182cb0c3..0000000000000000000000000000000000000000
--- a/spaces/meraih/English-Japanese-Anime-TTS/ONNXVITS_modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from ONNXVITS_transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        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, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        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, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        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, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/merve/anonymization/source/measuring-diversity/image-layout.js b/spaces/merve/anonymization/source/measuring-diversity/image-layout.js
deleted file mode 100644
index 7a06cc4399043f317e81c28da4139599a84f58da..0000000000000000000000000000000000000000
--- a/spaces/merve/anonymization/source/measuring-diversity/image-layout.js
+++ /dev/null
@@ -1,73 +0,0 @@
-
-
-var lURLs = `
-img/green_doctor.png
-img/blue_doctor.jpg
-img/green0.png
-img/bright_blue.png
-img/blue0.png
-img/blue1.png
-`.trim().split('\n')
-
-
-var rURLs = `
-img/white0.png
-img/white1.png
-img/white2.png
-img/white3.png
-img/white4.png
-img/white5.png
-`.trim().split('\n')
-
-
-var constructionSel = d3.select('#construction')
-  .html('')
-
-// constructionSel.append('div.top').each(function(){
-//   var metrics = [{str: 'Male', key: 'Male', target: .5}]
-//   var active ={ percents: {Male: .5}}
-//   addMetrics(metrics, {topSel: d3.select(this).append('div.top'), active, isSmall: true})()
-// })
-
-constructionSel.append('img')
-  .at({src: 'img/construction.jpg', width: 900})
-
-constructionSel.append('div')
-  .st({fontWeight: 500, fontSize: 14})
-  .text('Stock “construction worker” images')
-
-
-
-
-var width = 400
-var coatDivs = d3.select('#coat-v-gender').html('').st({marginBottom: 40})
-  .appendMany('div', [lURLs, rURLs])
-  .st({width: width, display: 'inline-block', marginRight: 20})
-
-
-coatDivs.each(function(d, i){
-  var metrics = [
-    {str: 'Blue', key: 'Blue', target: .5},
-    {str: 'Male', key: 'Male', target: .5},
-  ]
-
-  var active = !i ? {percents: {Blue: .5, Male: 1}} : {percents: {Blue: 0, Male: .5}}
-
-  addMetrics(metrics, {topSel: d3.select(this).append('div.top'), active, isSmall: true})()
-})
-
-coatDivs
-  .st({fontWeight: 500, fontSize: 14})
-  .appendMany('div', d => d.slice(0, 6))
-  .st({backgroundImage: d => 'url(' + d + ')', width: width/3 - 10, height: 100, display: 'inline-block'})
-  .st({marginRight: 8, outline: '1px solid #000'})
-
-coatDivs
-  .append('div')
-  .text((d, i) => d == lURLs ? 'Male-presenting doctors wearing different colored clothes' : 'Doctor of different genders wearing white clothes')
-
-
-
-
-
-// https://t3.gstatic.com/images?q=tbn:ANd9GcRziJdedqu58HeAlI9xtWhrVtCjVo6xO_uSHdQkxAI0q41XozLWT3xKd36S1NbuSoIOVvV4Huw26zAvdM_374qKuN9J88E
\ No newline at end of file
diff --git a/spaces/merve/data-leak/public/style.css b/spaces/merve/data-leak/public/style.css
deleted file mode 100644
index ad619bacc7b5b7f61788de06850a80ccc7561b83..0000000000000000000000000000000000000000
--- a/spaces/merve/data-leak/public/style.css
+++ /dev/null
@@ -1,434 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-
-html{
-	background-color: #fff;
-	font-weight: normal;
-}
-
-
-body{
-	max-width: 850px;
-	margin: 0px auto;
-	font-family: 'Roboto Slab', serif;
-  font-family: 'Roboto', Helvetica, sans-serif;
-  font-weight: 300;
-	line-height: 1.55em;
-	font-size: 16px;
-	margin-top: 5px;
-	margin-bottom: 80px;
-  color: #3C4043;
-  font-smoothing: antialiased;
-}
-
-@media (max-width: 760px){
-	body{
-		padding: 5px;
-	}
-}
-
-p{
-	line-height: 1.55em;
-  font-size: 16px;
-  /*line-height: 28px;*/
-  color: #3C4043;
-  letter-spacing: 0.1px;
-}
-
-a{
-	color: black;
-}
-
-.header{
-  position: relative;
-  color: black;
-  font-size: 16px;
-  height: 24px;
-  overflow: visible;
-  font-family: 'Google Sans', sans-serif;
-  font-weight: 100;
-  font-size: 20px;
-  margin: 0px auto;
-  margin-top: 15px;
-  padding-left: 20px;
-}
-.header-left{
-  vertical-align: middle;
-  font-size: 20px;
-  margin: 0px auto;
-  width: 300px;
-}
-.header-left img{
-  width: 100px;
-  opacity: 1;
-  top: 0px;
-  position: relative;
-}
-.header-left a:first-child{
-  float: left;
-}
-.header-left a:last-child{
-  position: relative;
-  top: 8px;
-  margin-left: 20px;
-  float: left;
-}
-.header-left a{
-  line-height: 20px;
-  -webkit-font-smoothing: antialiased;
-  letter-spacing: 0.1px;
-  font-size: 20px;
-  text-transform: uppercase;
-  font-family: "Google Sans";
-  text-align: right;
-  -webkit-tap-highlight-color: rgba(255,255,255,0);
-  font-weight: 300;
-  text-decoration: none;
-  /*margin: 50px 0 0 50px;*/
-  display: inline-block;
-  color: #00695C !important;
-}
-.header-left a:hover{
-  color: #ff4081 !important;
-}
-
-@media (max-width: 750px){
-  .header-right span{
-    opacity: 0;
-  }
-}
-.header a{
-	/*opacity: .5;*/
-	text-decoration: none;
-}
-.header a:hover{
-	opacity: 1
-}
-
-
-p{
-  max-width: 750px;
-  margin: 0px auto;
-  margin-block-start: 1em;
-  margin-block-end: 1em;
-}
-
-/*TODO mobile padding?*/
-
-h3{
-  max-width: 750px;
-  margin: 0px auto;
-  font-weight: 100;
-  line-height: 1.3em;
-}
-
-h1,h2,h3,h4,h5{
-	font-family: 'Google Sans', sans-serif;
-  font-weight: 100;
-	margin-top: 1.5em;
-	margin-bottom: .5em;
-}
-h1{
-	font-weight: 100;
-	font-size: 34px;
-	margin-bottom: .5em;
-  line-height: 1.3em;
-  margin-top: 1.4em;
-  text-align: center;
-  font-family: "Google Sans";
-  /*color: #00695C;*/
-}
-h2,h3,h4,h5{
-	font-size: 22px;
-}
-
-/*wp classes*/
-img.aligncenter {
-  display: block;
-  margin: auto;
-  max-width: 750px;
-}
-
-
-
-html{
-  overflow-x: hidden;
-}
-
-.full-width{
-  width: 100vw;
-  position: relative;
-  left: 50%;
-  right: 50%;
-  margin-left: -50vw;
-  margin-right: -50vw;
-  overflow: hidden;
-}
-
-.full-width img{
-  max-width: 100%;
-  display: block;
-  margin: 0 auto;
-}
-
-.full-width.px980 img, .full-width.px980 div{
-  max-width: 980px;
-}
-.full-width > div, .full-width > div > div{
-  margin: 0px auto;
-}
-.full-width.px750 img, .full-width.px750 div{
-  max-width: 750px;
-}
-
-draft{
-  display: none;
-  /*visibility: collapse;*/
-}
-
-
-h1, .post-summary{
-  max-width: 750px;
-  margin: 0px auto;
-}
-.post-summary{
-  font-size: 19px;
-  margin-bottom: 65px;
-  line-height: 1.5em;
-}
-
-h1{
-  margin-bottom: 40px;
-  margin-top: 50px;
-}
-
-.post-tags{
-  line-height: 1.55em;
-  font-style: italic;
-}
-
-.thumbnail-caption{
-  font-style: italic;
-}
-
-
-
-
-
-
-/*graph scroll stuff*/
-
-#container{
-  position: relative;
-  width: 900px;
-  margin-left: -25px;
-}
-
-#container h3{
-  line-height: 1.3em;
-}
-
-
-
-
-
-
-.tooltip {
-  top: -1000px;
-  position: fixed;
-  padding: 10px;
-  background: rgba(255, 255, 255, .90);
-  border: 1px solid lightgray;
-  pointer-events: none;
-  width: 300px;
-}
-.tooltip-hidden{
-  opacity: 0;
-  transition: all .3s;
-  transition-delay: .1s;
-}
-
-@media (max-width: 590px){
-  div.tooltip{
-    bottom: -1px;
-    width: calc(100%);
-    left: -1px !important;
-    right: -1px !important;
-    top: auto !important;
-    width: auto !important;
-  }
-}
-
-
-
-
-.footend{
-  margin-left: -9px;
-  width: 10px;
-}
-
-
-.footstart, .footend{
-  text-decoration: none;
-}
-
-.footstart:hover, .footend:hover{
-  text-decoration: underline;
-}
-
-
-
-
-#recirc{
-}
-
-#recirc .img{
-  outline: 1px solid #ccc;
-}
-
-#recirc .post:hover .img{
-  outline: 1px solid #333;
-}
-
-#recirc .title{
-  /*color: #00695C;*/
-  font-size: 18px;
-  font-weight: 500;
-  margin-bottom: -10px;
-  /*height: 10px !important;*/
-  /*opacity: 0;*/
-}
-
-#recirc .post:hover .title{
-  text-decoration: underline !important;
-}
-
-#recirc .post{
-  margin-bottom: 30px;
-}
-
-
-
-
-
-
-
-
-
-
-
-
-
-/*Nav Style*/
-#nav-container{
-  width: 100vw;
-  margin-left: calc(50% - 50vw);
-  display: inline-block;
-  /*display: none;*/
-}
-#navigation {
-  margin: 0 auto;
-  max-width: 1260px;
-  -webkit-font-smoothing: antialiased;
-  font-family: 'Open Sans', Helvetica, sans-serif;
-  font-weight: 300;
-  letter-spacing: 0.1px;
-
-
-  color: rgba(0,0,0,.87);
-  font-size: 14px;
-  line-height: 20px;
-  -webkit-font-smoothing: antialiased;
-  font-family: 'Open Sans', Helvetica, sans-serif;
-  font-weight: 300;
-  letter-spacing: 0.1px;
-  display: flex;
-  flex-flow: row wrap;
-  align-items: stretch;
-  padding: 8px;
-  margin: 0 auto;
-  max-width: 1260px;
-}
-.mdl-grid {
-  display: -webkit-flex;
-  display: -ms-flexbox;
-  display: flex;
-  -webkit-flex-flow: row wrap;
-  -ms-flex-flow: row wrap;
-  flex-flow: row wrap;
-  margin: 0 auto;
-  -webkit-align-items: stretch;
-  -ms-flex-align: stretch;
-  align-items: stretch;
-}
-
-.mdl-cell {
-  box-sizing: border-box;
-}
-
-.nav-links {
-  font-size: 20px;
-  text-transform: uppercase;
-  font-family: "Google Sans";
-  color: #4a4a4a;
-  text-align: right;
-}
-
-.nav-logo-small {
-  width: 110px;
-  margin: 42px 0 0 0;
-}
-.nav-links .selected {
-  color: #00695C !important;
-}
-/*.nav-links a:visited {
-  color: #4a4a4a;
-}
-a:visited {
-  color: #7B1FA2;
-}
-*/
-.nav-links a {
-  color: inherit;
-  text-decoration: none;
-  margin: 50px 0 0 50px;
-  display: inline-block;
-}
-
-
-@media screen and (max-width: 1035px){
-  .nav-links {
-    font-size: 16px;
-  }
-}
-
-.nav-links{
-  line-height: 20px;
-  -webkit-font-smoothing: antialiased;
-  font-weight: 300;
-  letter-spacing: 0.1px;
-  box-sizing: border-box;
-  margin: 8px;
-  width: calc(66.6666666667% - 16px);
-  font-size: 20px;
-  text-transform: uppercase;
-  font-family: "Google Sans";
-  color: #4a4a4a;
-  text-align: right;
-}
-
diff --git a/spaces/merve/fill-in-the-blank/source/measuring-fairness/students.js b/spaces/merve/fill-in-the-blank/source/measuring-fairness/students.js
deleted file mode 100644
index 4af55cba8cc763d96aa478be96a785048d9edc42..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/measuring-fairness/students.js
+++ /dev/null
@@ -1,90 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-window.makeStudents = function(){
-  var seed = new Math.seedrandom('he4a15')
-  var rand = d3.randomUniform.source(seed)(0, 1)
-  var letters = 'abcdefgijlmnopqrsuvwxyz'
-  letters = (letters + letters.toUpperCase()).split('')
-
-  var nSickCols = 6
-  var mSickCols = 8
-  var fSickCols = nSickCols*2 - mSickCols
-
-  var students = d3.range(nCols*nCols).map(i => {
-    var letter = letters[~~d3.randomUniform.source(seed)(0, letters.length)()]
-
-    var isMale = i % 2 == 0
-    var isSick = i < (isMale ? mSickCols : fSickCols)*nCols
-    var grade = isSick*.5 + rand()
-    var pos = {}
-
-    return {letter, isSick, isMale, grade, pos}
-  })
-
-  students = _.sortBy(students, d => -d.grade)
-  d3.nestBy(students, d => d.isSick).forEach(group => {
-    var isSick = group[0].isSick
-
-    var sickCols = nSickCols
-    var cols = isSick ? sickCols : nCols - sickCols
-    var xOffset = isSick ? 0 : sickCols
-
-    group.forEach((d, i) => {
-      d.pos.allIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols)]
-      var spreadIJ = d.pos.allIJ.slice()
-      if (!d.isSick) spreadIJ[0] += .1
-      d.pos.all = spreadIJ.map(d => d*c.width/10)
-    })
-  })
-
-  d3.nestBy(students, d => d.isSick + '-' + d.isMale).forEach(group => {
-    var isSick = group[0].isSick
-    var isMale = group[0].isMale
-
-    var sickCols = isMale ? mSickCols : fSickCols
-    var cols = isSick ? sickCols : nCols - sickCols
-    var xOffset = isSick ? 0 : sickCols
-    var yOffset = isMale ? nCols/2 + 2 : 0
-
-    group.forEach((d, i) => {
-      d.pos.sexIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols) + yOffset]
-      d.pos.sexGroupIJ = [cols - 1 - (i % cols) + xOffset, ~~(i/cols)]
-      var spreadIJ = d.pos.sexIJ.slice()
-      if (!d.isSick) spreadIJ[0] += .1
-      d.pos.sex = spreadIJ.map(d => d*c.width/10)
-    })
-  })
-
-  students.maleOffsetJ = nCols/2 + 2
-  students.maleOffsetPx= students.maleOffsetJ*c.width/10
-
-  students.fSickCols = fSickCols
-  students.mSickCols = mSickCols
-
-  students.colWidth = c.width/10
-
-  students.rand = rand
-  return students
-}
-
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/merve/measuring-fairness/source/uncertainty-calibration/init.js b/spaces/merve/measuring-fairness/source/uncertainty-calibration/init.js
deleted file mode 100644
index d23a4fecea1bfa4fae6557043d8053dc3acc29ce..0000000000000000000000000000000000000000
--- a/spaces/merve/measuring-fairness/source/uncertainty-calibration/init.js
+++ /dev/null
@@ -1,36 +0,0 @@
-window.thresholds = [0, 0.2, 0.4, 0.6, 0.8, 1];  
-window.emojis = ['☀️','🌧️'];
-window.constant_score = 0.5;
-
-window.ttSel = d3.select('body').selectAppend('div.tooltip.tooltip-hidden')
-
-
-window.init = function(){
-
-    var graphSel = d3.select('#graph')
-    var width = height = graphSel.node().offsetWidth
-    if (innerWidth <= 925){
-        width = innerWidth
-        height = innerHeight*.65
-        window.isMobile = true
-    }
-    fig_height = height/2
-    fig_width = width
-
-
-    window.util = window.initUtil()
-    window.weatherGraph = window.drawWeatherGraph(graphSel, fig_height, fig_width);
-    window.calibrationCurve = window.drawCalibrationCurve(graphSel, fig_height, fig_width);
-    // window.calibrationSlider = window.drawCalibrationSlider(weatherGraph, calibrationCurve, fig_width/2)
-    // window.modelRemapper = window.drawModelRemapping(fig_width/2);
-
-
-    window.slides = window.drawSlides()
-    weatherGraph.renderThresholds()
-
-}
-
-window.init()
-
-
-
diff --git a/spaces/merve/uncertainty-calibration/source/measuring-fairness/gs.js b/spaces/merve/uncertainty-calibration/source/measuring-fairness/gs.js
deleted file mode 100644
index f3f72c87ecdb3e28fb4f4d198d70900b431151c2..0000000000000000000000000000000000000000
--- a/spaces/merve/uncertainty-calibration/source/measuring-fairness/gs.js
+++ /dev/null
@@ -1,106 +0,0 @@
-/* Copyright 2020 Google LLC. 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.
-==============================================================================*/
-
-
-
-
-window.makeGS = function(){
-  var gs = {}
-
-  var bodySel = d3.select('body')
-
-  var prevSlideIndex = -1
-  function updateSlide(i){
-    var slide = slides[i]
-    if (!slide) return
-
-    gs.prevSlide = gs.curSlide
-    gs.curSlide = slide
-
-    var dur = gs.prevSlide ? 500*1 : 0
-
-    sel.personSel.transition().duration(dur)
-      .translate(d => d.pos[slide.pos])
-
-    sel.textSel.transition().duration(dur)
-      .at({fill: slide.textFill})
-
-
-    sel.rectSel.transition('opacity').duration(dur)
-      .at({opacity: slide.rectOpacity})
-
-    if (!slide.animateThreshold){
-      sel.rectSel.transition('fill').duration(dur)
-        .at({fill: slide.rectFill})
-
-      sel.textSel.transition('stroke').duration(dur)
-        .st({strokeWidth: slide.textStroke})
-
-      slider.setSlider(slide.threshold, true)
-      bodySel.transition('gs-tween')
-    } else {
-      sel.rectSel.transition('fill').duration(dur)
-      sel.textSel.transition('stroke').duration(dur)
-
-      bodySel.transition('gs-tween').duration(dur*2)
-        .attrTween('gs-tween', () => {
-          var i = d3.interpolate(slider.threshold, slide.threshold)
-
-          return t => {
-            slider.setSlider(i(t))
-          }
-        })
-    }
-
-
-    sel.truthAxis.transition().duration(dur)
-      .st({opacity: slide.truthAxisOpacity})
-
-    sel.mlAxis.transition().duration(dur)
-      .st({opacity: slide.mlAxisOpacity})
-
-    sel.fpAxis.transition().duration(dur)
-      .st({opacity: slide.fpAxisOpacity})
-
-    sel.sexAxis.transition().duration(dur)
-      .st({opacity: slide.sexAxisOpacity})
-
-    sel.brAxis.transition().duration(dur)
-      .st({opacity: slide.brAxisOpacity})
-
-    sel.botAxis.transition().duration(dur)
-      .translate(slide.botAxisY, 1)
-
-
-    prevSlideIndex = i
-    slides.curSlide = slide
-  }
-
-  gs.graphScroll = d3.graphScroll()
-    .container(d3.select('.container-1'))
-    .graph(d3.selectAll('container-1 #graph'))
-    .eventId('uniqueId1')
-    .sections(d3.selectAll('.container-1 #sections > div'))
-    .offset(innerWidth < 900 ? 300 : 520)
-    .on('active', updateSlide)
-
-  return gs
-}
-
-
-
-
-
-if (window.init) window.init()
diff --git a/spaces/michael135/dontalk/Dockerfile b/spaces/michael135/dontalk/Dockerfile
deleted file mode 100644
index 95dd8620e127dfa3471d2fc93e86f0918c56ee24..0000000000000000000000000000000000000000
--- a/spaces/michael135/dontalk/Dockerfile
+++ /dev/null
@@ -1,124 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-
-ENV DEBIAN_FRONTEND noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PIP_DISABLE_PIP_VERSION_CHECK=1
-ENV PIP_NO_CACHE_DIR=1
-
-# OS setup
-RUN apt-get update -y \
-    && apt-get upgrade -y \
-    && apt-get install -y \
-        libgl1 \
-        libglib2.0-0 \
-        curl \
-        vim \
-        wget \
-        git \
-        git-lfs \
-        tzdata \
-        bash \
-        ca-certificates \
-        libreadline8 \
-        bzip2 \
-        psmisc \
-        procps \
-        netbase \
-        openssh-client \
-        libsqlite3-dev \
-        python3-pip \
-        python3-venv \
-        python-is-python3 \
-        build-essential \
-        libssl-dev \
-        libffi-dev \
-        aria2 \
-    \
-    && pip3 install --upgrade pip \
-    \
-    && git lfs install \
-    \
-    && apt-get clean autoclean \
-    && apt-get autoremove --yes \
-    && rm -rf /var/lib/apt/lists/*
-
-# OS timezone setting (UTC)
-RUN echo "UTC" > /etc/timezone
-ENV TZ=UTC
-
-# Poetry for Python packages
-RUN curl -sSL https://install.python-poetry.org | POETRY_HOME=/usr/local/poetry python3 - --yes \
-    && ln -s /usr/local/poetry/bin/poetry /usr/bin/poetry \
-    \
-    && poetry config virtualenvs.create false \
-    && poetry config virtualenvs.in-project false
-
-# Create non-root user
-ENV ENV="/etc/profile"
-RUN adduser --disabled-password --gecos '' user && \
-    mkdir -p /app && \
-    chown -R user:user /app && \
-    printf "\n. /etc/profile\n" >> /home/user/.profile \
-    printf "\n. /etc/profile\n" >> /home/user/.bashrc
-
-# Sets up virtualenv for dependencies
-ENV VIRTUAL_ENV="/opt/venv"
-ENV VIRTUAL_ENV_DISABLE_PROMPT=1
-ENV POETRY_ACTIVE=1
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-RUN echo "export PATH=$PATH" >> /home/user/.bashrc \
-    && python3 -m venv $VIRTUAL_ENV \
-    && /opt/venv/bin/pip install --upgrade --no-cache-dir pip \
-    && chown -R user:user /opt/venv
-
-# Run as non-root user
-USER user
-WORKDIR /app
-
-# Installation of basic Python dependencies specified in pyproject.toml
-COPY --chown=user:user pyproject.toml poetry.lock /app/
-RUN poetry install
-
-# AUTOMATIC1111' WebUI
-RUN git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui /app/stable-diffusion-webui \
-    && (cd /app/stable-diffusion-webui && git checkout a9fed7c364061ae6efb37f797b6b522cb3cf7aa2)
-
-# Deforum extension
-RUN git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui \
-    && (cd /app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui && git checkout 2366bfdb47c226df0d14e712445414e459febad3)
-
-# Images Browser WebUI extension
-RUN git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser \
-    && (cd /app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser && git checkout a42c7a30181636a05815e62426d5eff4d3340529)
-
-# CiviTAI Browser WebUI extension
-RUN git clone https://github.com/Vetchems/sd-civitai-browser /app/stable-diffusion-webui/extensions/sd-civitai-browser \
-    && (cd /app/stable-diffusion-webui/extensions/sd-civitai-browser && git checkout b25a5daf7df3f6340d3e243d533228d8ade5288d)
-
-# Additional Networks WebUI extension
-RUN git clone https://github.com/kohya-ss/sd-webui-additional-networks /app/stable-diffusion-webui/extensions/sd-webui-additional-networks \
-    && (cd /app/stable-diffusion-webui/extensions/sd-webui-additional-networks && git checkout d2758b6c8e2e8e956865a87b31fd74d3d7c010cb) \
-    && mkdir -p /app/stable-diffusion-webui/extensions/sd-webui-additional-networks/models/LoRA
-
-# ControlNet WebUI extension
-RUN git clone https://github.com/Mikubill/sd-webui-controlnet /app/stable-diffusion-webui/extensions/sd-webui-controlnet \
-    && (cd /app/stable-diffusion-webui/extensions/sd-webui-controlnet && git checkout 274dd5df217a03e059e9cf052447aece81bbd1cf) \
-    && mkdir -p /app/stable-diffusion-webui/models/ControlNet
-
-# Prepare WebUI environment
-WORKDIR /app/stable-diffusion-webui
-RUN /opt/venv/bin/python launch.py --exit --skip-torch-cuda-test --xformers
-
-# Patch WebUI
-RUN sed -i -e 's/                show_progress=False,/                show_progress=True,/g' modules/ui.py
-RUN sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' webui.py
-RUN sed -i -e 's/ outputs=\[/queue=False, &/g' modules/ui.py
-RUN sed -i -e 's/               queue=False,  /                /g' modules/ui.py
-
-# Copy startup scripts
-COPY --chown=user:user run.py on_start.sh config.json ui-config.json shared-config.json shared-ui-config.json header_patch.py /app/stable-diffusion-webui/
-RUN chmod +x on_start.sh
-
-EXPOSE 7860
-
-CMD ["/opt/venv/bin/python", "run.py", "--listen", "--ui-config-file", "ui-config.json", "--ui-settings-file", "config.json", "--disable-console-progressbars", "--cors-allow-origins", "huggingface.co,hf.space", "--no-progressbar-hiding", "--enable-console-prompts", "--no-download-sd-model", "--api", "--skip-version-check"]
diff --git a/spaces/microsoft/unicl-img-recog-demo/model/text_encoder/registry.py b/spaces/microsoft/unicl-img-recog-demo/model/text_encoder/registry.py
deleted file mode 100644
index 8991272a6e2294ea86eee338cf61d87e4123f724..0000000000000000000000000000000000000000
--- a/spaces/microsoft/unicl-img-recog-demo/model/text_encoder/registry.py
+++ /dev/null
@@ -1,18 +0,0 @@
-_lang_encoders = {}
-
-
-def register_lang_encoder(fn):
-    module_name_split = fn.__module__.split('.')
-    model_name = module_name_split[-1]
-
-    _lang_encoders[model_name] = fn
-
-    return fn
-
-
-def lang_encoders(model_name):
-    return _lang_encoders[model_name]
-
-
-def is_lang_encoder(model_name):
-    return model_name in _lang_encoders
diff --git a/spaces/mikeee/chinese-llama-2-7b-ggml-q4/run-app.sh b/spaces/mikeee/chinese-llama-2-7b-ggml-q4/run-app.sh
deleted file mode 100644
index a63a8e06f941fc702fd223ea3d4de2e28692824e..0000000000000000000000000000000000000000
--- a/spaces/mikeee/chinese-llama-2-7b-ggml-q4/run-app.sh
+++ /dev/null
@@ -1 +0,0 @@
-nodemon -w app.py -x python app.py
diff --git a/spaces/mikeee/s3nh-garage-bAInd-Stable-Platypus2-13B-GGML/README.md b/spaces/mikeee/s3nh-garage-bAInd-Stable-Platypus2-13B-GGML/README.md
deleted file mode 100644
index 6f379174304f8c161e32c131abf157b72f5d5d60..0000000000000000000000000000000000000000
--- a/spaces/mikeee/s3nh-garage-bAInd-Stable-Platypus2-13B-GGML/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: S3nh Garage BAInd Stable Platypus2 13B GGML
-emoji: ⚡
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/miyaaa666/bingo/src/components/providers.tsx b/spaces/miyaaa666/bingo/src/components/providers.tsx
deleted file mode 100644
index 892226412d80fe0b05211911b9e245cd22876460..0000000000000000000000000000000000000000
--- a/spaces/miyaaa666/bingo/src/components/providers.tsx
+++ /dev/null
@@ -1,15 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { ThemeProvider as NextThemesProvider } from 'next-themes'
-import { ThemeProviderProps } from 'next-themes/dist/types'
-
-import { TooltipProvider } from '@/components/ui/tooltip'
-
-export function Providers({ children, ...props }: ThemeProviderProps) {
-  return (
-    <NextThemesProvider {...props}>
-      <TooltipProvider>{children}</TooltipProvider>
-    </NextThemesProvider>
-  )
-}
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/roberta/README.md b/spaces/mshukor/UnIVAL/fairseq/examples/roberta/README.md
deleted file mode 100644
index ed4d5df52ccea01216276054a1f253d0d16c0409..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/roberta/README.md
+++ /dev/null
@@ -1,296 +0,0 @@
-# RoBERTa: A Robustly Optimized BERT Pretraining Approach
-
-https://arxiv.org/abs/1907.11692
-
-## Introduction
-
-RoBERTa iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data. See the associated paper for more details.
-
-### What's New:
-
-- December 2020: German model (GottBERT) is available: [GottBERT](https://github.com/pytorch/fairseq/tree/main/examples/gottbert).
-- January 2020: Italian model (UmBERTo) is available from Musixmatch Research: [UmBERTo](https://github.com/musixmatchresearch/umberto).
-- November 2019: French model (CamemBERT) is available: [CamemBERT](https://github.com/pytorch/fairseq/tree/main/examples/camembert).
-- November 2019: Multilingual encoder (XLM-RoBERTa) is available: [XLM-R](https://github.com/pytorch/fairseq/tree/main/examples/xlmr).
-- September 2019: TensorFlow and TPU support via the [transformers library](https://github.com/huggingface/transformers).
-- August 2019: RoBERTa is now supported in the [pytorch-transformers library](https://github.com/huggingface/pytorch-transformers).
-- August 2019: Added [tutorial for finetuning on WinoGrande](https://github.com/pytorch/fairseq/tree/main/examples/roberta/wsc#roberta-training-on-winogrande-dataset).
-- August 2019: Added [tutorial for pretraining RoBERTa using your own data](README.pretraining.md).
-
-## Pre-trained models
-
-Model | Description | # params | Download
----|---|---|---
-`roberta.base` | RoBERTa using the BERT-base architecture | 125M | [roberta.base.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.base.tar.gz)
-`roberta.large` | RoBERTa using the BERT-large architecture | 355M | [roberta.large.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz)
-`roberta.large.mnli` | `roberta.large` finetuned on [MNLI](http://www.nyu.edu/projects/bowman/multinli) | 355M | [roberta.large.mnli.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.mnli.tar.gz)
-`roberta.large.wsc` | `roberta.large` finetuned on [WSC](wsc/README.md) | 355M | [roberta.large.wsc.tar.gz](https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.wsc.tar.gz)
-
-## Results
-
-**[GLUE (Wang et al., 2019)](https://gluebenchmark.com/)**
-_(dev set, single model, single-task finetuning)_
-
-Model | MNLI | QNLI | QQP | RTE | SST-2 | MRPC | CoLA | STS-B
----|---|---|---|---|---|---|---|---
-`roberta.base` | 87.6 | 92.8 | 91.9 | 78.7 | 94.8 | 90.2 | 63.6 | 91.2
-`roberta.large` | 90.2 | 94.7 | 92.2 | 86.6 | 96.4 | 90.9 | 68.0 | 92.4
-`roberta.large.mnli` | 90.2 | - | - | - | - | - | - | -
-
-**[SuperGLUE (Wang et al., 2019)](https://super.gluebenchmark.com/)**
-_(dev set, single model, single-task finetuning)_
-
-Model | BoolQ | CB | COPA | MultiRC | RTE | WiC | WSC
----|---|---|---|---|---|---|---
-`roberta.large` | 86.9 | 98.2 | 94.0 | 85.7 | 89.5 | 75.6 | -
-`roberta.large.wsc` | - | - | - | - | - | - | 91.3
-
-**[SQuAD (Rajpurkar et al., 2018)](https://rajpurkar.github.io/SQuAD-explorer/)**
-_(dev set, no additional data used)_
-
-Model | SQuAD 1.1 EM/F1 | SQuAD 2.0 EM/F1
----|---|---
-`roberta.large` | 88.9/94.6 | 86.5/89.4
-
-**[RACE (Lai et al., 2017)](http://www.qizhexie.com/data/RACE_leaderboard.html)**
-_(test set)_
-
-Model | Accuracy | Middle | High
----|---|---|---
-`roberta.large` | 83.2 | 86.5 | 81.3
-
-**[HellaSwag (Zellers et al., 2019)](https://rowanzellers.com/hellaswag/)**
-_(test set)_
-
-Model | Overall | In-domain | Zero-shot | ActivityNet | WikiHow
----|---|---|---|---|---
-`roberta.large` | 85.2 | 87.3 | 83.1 | 74.6 | 90.9
-
-**[Commonsense QA (Talmor et al., 2019)](https://www.tau-nlp.org/commonsenseqa)**
-_(test set)_
-
-Model | Accuracy
----|---
-`roberta.large` (single model) | 72.1
-`roberta.large` (ensemble) | 72.5
-
-**[Winogrande (Sakaguchi et al., 2019)](https://arxiv.org/abs/1907.10641)**
-_(test set)_
-
-Model | Accuracy
----|---
-`roberta.large` | 78.1
-
-**[XNLI (Conneau et al., 2018)](https://arxiv.org/abs/1809.05053)**
-_(TRANSLATE-TEST)_
-
-Model | en | fr | es | de | el | bg | ru | tr | ar | vi | th | zh | hi | sw | ur
----|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
-`roberta.large.mnli` | 91.3 | 82.91 | 84.27 | 81.24 | 81.74 | 83.13 | 78.28 | 76.79 | 76.64 | 74.17 | 74.05 | 77.5 | 70.9 | 66.65 | 66.81
-
-## Example usage
-
-##### Load RoBERTa from torch.hub (PyTorch >= 1.1):
-```python
-import torch
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large')
-roberta.eval()  # disable dropout (or leave in train mode to finetune)
-```
-
-##### Load RoBERTa (for PyTorch 1.0 or custom models):
-```python
-# Download roberta.large model
-wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
-tar -xzvf roberta.large.tar.gz
-
-# Load the model in fairseq
-from fairseq.models.roberta import RobertaModel
-roberta = RobertaModel.from_pretrained('/path/to/roberta.large', checkpoint_file='model.pt')
-roberta.eval()  # disable dropout (or leave in train mode to finetune)
-```
-
-##### Apply Byte-Pair Encoding (BPE) to input text:
-```python
-tokens = roberta.encode('Hello world!')
-assert tokens.tolist() == [0, 31414, 232, 328, 2]
-roberta.decode(tokens)  # 'Hello world!'
-```
-
-##### Extract features from RoBERTa:
-```python
-# Extract the last layer's features
-last_layer_features = roberta.extract_features(tokens)
-assert last_layer_features.size() == torch.Size([1, 5, 1024])
-
-# Extract all layer's features (layer 0 is the embedding layer)
-all_layers = roberta.extract_features(tokens, return_all_hiddens=True)
-assert len(all_layers) == 25
-assert torch.all(all_layers[-1] == last_layer_features)
-```
-
-##### Use RoBERTa for sentence-pair classification tasks:
-```python
-# Download RoBERTa already finetuned for MNLI
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
-roberta.eval()  # disable dropout for evaluation
-
-# Encode a pair of sentences and make a prediction
-tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.')
-roberta.predict('mnli', tokens).argmax()  # 0: contradiction
-
-# Encode another pair of sentences
-tokens = roberta.encode('Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.')
-roberta.predict('mnli', tokens).argmax()  # 2: entailment
-```
-
-##### Register a new (randomly initialized) classification head:
-```python
-roberta.register_classification_head('new_task', num_classes=3)
-logprobs = roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], grad_fn=<LogSoftmaxBackward>)
-```
-
-##### Batched prediction:
-```python
-import torch
-from fairseq.data.data_utils import collate_tokens
-
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.mnli')
-roberta.eval()
-
-batch_of_pairs = [
-    ['Roberta is a heavily optimized version of BERT.', 'Roberta is not very optimized.'],
-    ['Roberta is a heavily optimized version of BERT.', 'Roberta is based on BERT.'],
-    ['potatoes are awesome.', 'I like to run.'],
-    ['Mars is very far from earth.', 'Mars is very close.'],
-]
-
-batch = collate_tokens(
-    [roberta.encode(pair[0], pair[1]) for pair in batch_of_pairs], pad_idx=1
-)
-
-logprobs = roberta.predict('mnli', batch)
-print(logprobs.argmax(dim=1))
-# tensor([0, 2, 1, 0])
-```
-
-##### Using the GPU:
-```python
-roberta.cuda()
-roberta.predict('new_task', tokens)  # tensor([[-1.1050, -1.0672, -1.1245]], device='cuda:0', grad_fn=<LogSoftmaxBackward>)
-```
-
-## Advanced usage
-
-#### Filling masks:
-
-RoBERTa can be used to fill `<mask>` tokens in the input. Some examples from the
-[Natural Questions dataset](https://ai.google.com/research/NaturalQuestions/):
-```python
-roberta.fill_mask('The first Star wars movie came out in <mask>', topk=3)
-# [('The first Star wars movie came out in 1977', 0.9504708051681519, ' 1977'), ('The first Star wars movie came out in 1978', 0.009986862540245056, ' 1978'), ('The first Star wars movie came out in 1979', 0.009574787691235542, ' 1979')]
-
-roberta.fill_mask('Vikram samvat calender is official in <mask>', topk=3)
-# [('Vikram samvat calender is official in India', 0.21878819167613983, ' India'), ('Vikram samvat calender is official in Delhi', 0.08547237515449524, ' Delhi'), ('Vikram samvat calender is official in Gujarat', 0.07556215673685074, ' Gujarat')]
-
-roberta.fill_mask('<mask> is the common currency of the European Union', topk=3)
-# [('Euro is the common currency of the European Union', 0.9456493854522705, 'Euro'), ('euro is the common currency of the European Union', 0.025748178362846375, 'euro'), ('€ is the common currency of the European Union', 0.011183084920048714, '€')]
-```
-
-#### Pronoun disambiguation (Winograd Schema Challenge):
-
-RoBERTa can be used to disambiguate pronouns. First install spaCy and download the English-language model:
-```bash
-pip install spacy
-python -m spacy download en_core_web_lg
-```
-
-Next load the `roberta.large.wsc` model and call the `disambiguate_pronoun`
-function. The pronoun should be surrounded by square brackets (`[]`) and the
-query referent surrounded by underscores (`_`), or left blank to return the
-predicted candidate text directly:
-```python
-roberta = torch.hub.load('pytorch/fairseq', 'roberta.large.wsc', user_dir='examples/roberta/wsc')
-roberta.cuda()  # use the GPU (optional)
-
-roberta.disambiguate_pronoun('The _trophy_ would not fit in the brown suitcase because [it] was too big.')
-# True
-roberta.disambiguate_pronoun('The trophy would not fit in the brown _suitcase_ because [it] was too big.')
-# False
-
-roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] feared violence.')
-# 'The city councilmen'
-roberta.disambiguate_pronoun('The city councilmen refused the demonstrators a permit because [they] advocated violence.')
-# 'demonstrators'
-```
-
-See the [RoBERTA Winograd Schema Challenge (WSC) README](wsc/README.md) for more details on how to train this model.
-
-#### Extract features aligned to words:
-
-By default RoBERTa outputs one feature vector per BPE token. You can instead
-realign the features to match [spaCy's word-level tokenization](https://spacy.io/usage/linguistic-features#tokenization)
-with the `extract_features_aligned_to_words` method. This will compute a
-weighted average of the BPE-level features for each word and expose them in
-spaCy's `Token.vector` attribute:
-```python
-doc = roberta.extract_features_aligned_to_words('I said, "hello RoBERTa."')
-assert len(doc) == 10
-for tok in doc:
-    print('{:10}{} (...)'.format(str(tok), tok.vector[:5]))
-# <s>       tensor([-0.1316, -0.0386, -0.0832, -0.0477,  0.1943], grad_fn=<SliceBackward>) (...)
-# I         tensor([ 0.0559,  0.1541, -0.4832,  0.0880,  0.0120], grad_fn=<SliceBackward>) (...)
-# said      tensor([-0.1565, -0.0069, -0.8915,  0.0501, -0.0647], grad_fn=<SliceBackward>) (...)
-# ,         tensor([-0.1318, -0.0387, -0.0834, -0.0477,  0.1944], grad_fn=<SliceBackward>) (...)
-# "         tensor([-0.0486,  0.1818, -0.3946, -0.0553,  0.0981], grad_fn=<SliceBackward>) (...)
-# hello     tensor([ 0.0079,  0.1799, -0.6204, -0.0777, -0.0923], grad_fn=<SliceBackward>) (...)
-# RoBERTa   tensor([-0.2339, -0.1184, -0.7343, -0.0492,  0.5829], grad_fn=<SliceBackward>) (...)
-# .         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
-# "         tensor([-0.1341, -0.1203, -0.1012, -0.0621,  0.1892], grad_fn=<SliceBackward>) (...)
-# </s>      tensor([-0.0930, -0.0392, -0.0821,  0.0158,  0.0649], grad_fn=<SliceBackward>) (...)
-```
-
-#### Evaluating the `roberta.large.mnli` model:
-
-Example python code snippet to evaluate accuracy on the MNLI `dev_matched` set.
-```python
-label_map = {0: 'contradiction', 1: 'neutral', 2: 'entailment'}
-ncorrect, nsamples = 0, 0
-roberta.cuda()
-roberta.eval()
-with open('glue_data/MNLI/dev_matched.tsv') as fin:
-    fin.readline()
-    for index, line in enumerate(fin):
-        tokens = line.strip().split('\t')
-        sent1, sent2, target = tokens[8], tokens[9], tokens[-1]
-        tokens = roberta.encode(sent1, sent2)
-        prediction = roberta.predict('mnli', tokens).argmax().item()
-        prediction_label = label_map[prediction]
-        ncorrect += int(prediction_label == target)
-        nsamples += 1
-print('| Accuracy: ', float(ncorrect)/float(nsamples))
-# Expected output: 0.9060
-```
-
-## Finetuning
-
-- [Finetuning on GLUE](README.glue.md)
-- [Finetuning on custom classification tasks (e.g., IMDB)](README.custom_classification.md)
-- [Finetuning on Winograd Schema Challenge (WSC)](wsc/README.md)
-- [Finetuning on Commonsense QA (CQA)](commonsense_qa/README.md)
-
-## Pretraining using your own data
-
-See the [tutorial for pretraining RoBERTa using your own data](README.pretraining.md).
-
-## Citation
-
-```bibtex
-@article{liu2019roberta,
-    title = {RoBERTa: A Robustly Optimized BERT Pretraining Approach},
-    author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and
-              Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and
-              Luke Zettlemoyer and Veselin Stoyanov},
-    journal={arXiv preprint arXiv:1907.11692},
-    year = {2019},
-}
-```
diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py b/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py
deleted file mode 100644
index 7b9414b0eb3b30c935478cd5b8a894168bd8cc98..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/examples/simultaneous_translation/models/transformer_monotonic_attention.py
+++ /dev/null
@@ -1,302 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from typing import Dict, List, NamedTuple, Optional
-
-import torch
-import torch.nn as nn
-from examples.simultaneous_translation.modules.monotonic_transformer_layer import (
-    TransformerMonotonicDecoderLayer,
-    TransformerMonotonicEncoderLayer,
-)
-from fairseq.models import (
-    register_model,
-    register_model_architecture,
-)
-from fairseq.models.transformer import (
-    TransformerModel,
-    TransformerEncoder,
-    TransformerDecoder,
-    base_architecture,
-    transformer_iwslt_de_en,
-    transformer_vaswani_wmt_en_de_big,
-    tiny_architecture
-)
-from torch import Tensor
-
-DEFAULT_MAX_SOURCE_POSITIONS = 1024
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-READ_ACTION = 0
-WRITE_ACTION = 1
-
-TransformerMonotonicDecoderOut = NamedTuple(
-    "TransformerMonotonicDecoderOut",
-    [
-        ("action", int),
-        ("p_choose", Optional[Tensor]),
-        ("attn_list", Optional[List[Optional[Dict[str, Tensor]]]]),
-        ("encoder_out", Optional[Dict[str, List[Tensor]]]),
-        ("encoder_padding_mask", Optional[Tensor]),
-    ],
-)
-
-
-@register_model("transformer_unidirectional")
-class TransformerUnidirectionalModel(TransformerModel):
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return TransformerMonotonicEncoder(args, src_dict, embed_tokens)
-
-
-@register_model("transformer_monotonic")
-class TransformerModelSimulTrans(TransformerModel):
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return TransformerMonotonicEncoder(args, src_dict, embed_tokens)
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        return TransformerMonotonicDecoder(args, tgt_dict, embed_tokens)
-
-
-class TransformerMonotonicEncoder(TransformerEncoder):
-    def __init__(self, args, dictionary, embed_tokens):
-        super().__init__(args, dictionary, embed_tokens)
-
-        self.dictionary = dictionary
-        self.layers = nn.ModuleList([])
-        self.layers.extend(
-            [
-                TransformerMonotonicEncoderLayer(args)
-                for i in range(args.encoder_layers)
-            ]
-        )
-
-
-class TransformerMonotonicDecoder(TransformerDecoder):
-    """
-    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
-    is a :class:`TransformerDecoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): decoding dictionary
-        embed_tokens (torch.nn.Embedding): output embedding
-        no_encoder_attn (bool, optional): whether to attend to encoder outputs
-            (default: False).
-    """
-
-    def __init__(self, args, dictionary, embed_tokens, no_encoder_attn=False):
-        super().__init__(args, dictionary, embed_tokens, no_encoder_attn=False)
-
-        self.dictionary = dictionary
-        self.layers = nn.ModuleList([])
-        self.layers.extend(
-            [
-                TransformerMonotonicDecoderLayer(args)
-                for _ in range(args.decoder_layers)
-            ]
-        )
-        self.policy_criterion = getattr(args, "policy_criterion", "any")
-        self.num_updates = None
-
-    def set_num_updates(self, num_updates):
-        self.num_updates = num_updates
-
-    def pre_attention(
-        self,
-        prev_output_tokens,
-        encoder_out_dict: Dict[str, List[Tensor]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-    ):
-        positions = (
-            self.embed_positions(
-                prev_output_tokens,
-                incremental_state=incremental_state,
-            )
-            if self.embed_positions is not None
-            else None
-        )
-
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-            if positions is not None:
-                positions = positions[:, -1:]
-        # embed tokens and positions
-        x = self.embed_scale * self.embed_tokens(prev_output_tokens)
-
-        if self.project_in_dim is not None:
-            x = self.project_in_dim(x)
-
-        if positions is not None:
-            x += positions
-
-        x = self.dropout_module(x)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        encoder_out = encoder_out_dict["encoder_out"][0]
-
-        if "encoder_padding_mask" in encoder_out_dict:
-            encoder_padding_mask = (
-                encoder_out_dict["encoder_padding_mask"][0]
-                if encoder_out_dict["encoder_padding_mask"]
-                and len(encoder_out_dict["encoder_padding_mask"]) > 0
-                else None
-            )
-        else:
-            encoder_padding_mask = None
-
-        return x, encoder_out, encoder_padding_mask
-
-    def post_attention(self, x):
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        if self.project_out_dim is not None:
-            x = self.project_out_dim(x)
-
-        return x
-
-    def clean_cache(
-        self,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]],
-        end_id: Optional[int] = None,
-    ):
-        """
-        Clean cache in the monotonic layers.
-        The cache is generated because of a forward pass of decoder has run but no prediction,
-        so that the self attention key value in decoder is written in the incremental state.
-        end_id is the last idx of the layers
-        """
-        if end_id is None:
-            end_id = len(self.layers)
-
-        for index, layer in enumerate(self.layers):
-            if index < end_id:
-                layer.prune_incremental_state(incremental_state)
-
-    def extract_features(
-        self,
-        prev_output_tokens,
-        encoder_out: Optional[Dict[str, List[Tensor]]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,  # unused
-        alignment_layer: Optional[int] = None,  # unused
-        alignment_heads: Optional[int] = None,  # unsed
-    ):
-        """
-        Similar to *forward* but only return features.
-
-        Returns:
-            tuple:
-                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
-                - a dictionary with any model-specific outputs
-        """
-        # incremental_state = None
-        assert encoder_out is not None
-        (x, encoder_outs, encoder_padding_mask) = self.pre_attention(
-            prev_output_tokens, encoder_out, incremental_state
-        )
-        attn = None
-        inner_states = [x]
-        attn_list: List[Optional[Dict[str, Tensor]]] = []
-
-        p_choose = torch.tensor([1.0])
-
-        for i, layer in enumerate(self.layers):
-
-            x, attn, _ = layer(
-                x=x,
-                encoder_out=encoder_outs,
-                encoder_padding_mask=encoder_padding_mask,
-                incremental_state=incremental_state,
-                self_attn_mask=self.buffered_future_mask(x)
-                if incremental_state is None
-                else None,
-            )
-
-            inner_states.append(x)
-            attn_list.append(attn)
-
-            if incremental_state is not None:
-                if_online = incremental_state["online"]["only"]
-                assert if_online is not None
-                if if_online.to(torch.bool):
-                    # Online indicates that the encoder states are still changing
-                    assert attn is not None
-                    if self.policy_criterion == "any":
-                        # Any head decide to read than read
-                        head_read = layer.encoder_attn._get_monotonic_buffer(incremental_state)["head_read"]
-                        assert head_read is not None
-                        if head_read.any():
-                            # We need to prune the last self_attn saved_state
-                            # if model decide not to read
-                            # otherwise there will be duplicated saved_state
-                            self.clean_cache(incremental_state, i + 1)
-
-                            return x, TransformerMonotonicDecoderOut(
-                                action=0,
-                                p_choose=p_choose,
-                                attn_list=None,
-                                encoder_out=None,
-                                encoder_padding_mask=None,
-                            )
-
-        x = self.post_attention(x)
-
-        return x, TransformerMonotonicDecoderOut(
-            action=1,
-            p_choose=p_choose,
-            attn_list=attn_list,
-            encoder_out=encoder_out,
-            encoder_padding_mask=encoder_padding_mask,
-        )
-
-
-@register_model_architecture("transformer_monotonic", "transformer_monotonic")
-def base_monotonic_architecture(args):
-    base_architecture(args)
-    args.encoder_unidirectional = getattr(args, "encoder_unidirectional", False)
-
-
-@register_model_architecture(
-    "transformer_monotonic", "transformer_monotonic_iwslt_de_en"
-)
-def transformer_monotonic_iwslt_de_en(args):
-    transformer_iwslt_de_en(args)
-    base_monotonic_architecture(args)
-
-
-# parameters used in the "Attention Is All You Need" paper (Vaswani et al., 2017)
-@register_model_architecture(
-    "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_de_big"
-)
-def transformer_monotonic_vaswani_wmt_en_de_big(args):
-    transformer_vaswani_wmt_en_de_big(args)
-
-
-@register_model_architecture(
-    "transformer_monotonic", "transformer_monotonic_vaswani_wmt_en_fr_big"
-)
-def transformer_monotonic_vaswani_wmt_en_fr_big(args):
-    transformer_monotonic_vaswani_wmt_en_fr_big(args)
-
-
-@register_model_architecture(
-    "transformer_unidirectional", "transformer_unidirectional_iwslt_de_en"
-)
-def transformer_unidirectional_iwslt_de_en(args):
-    transformer_iwslt_de_en(args)
-
-
-@register_model_architecture("transformer_monotonic", "transformer_monotonic_tiny")
-def monotonic_tiny_architecture(args):
-    tiny_architecture(args)
-    base_monotonic_architecture(args)
diff --git a/spaces/mshukor/UnIVAL/models/unival/unify_transformer.py b/spaces/mshukor/UnIVAL/models/unival/unify_transformer.py
deleted file mode 100644
index f26c52cde9fe3cbe84b76c54d22419e40e061afc..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/models/unival/unify_transformer.py
+++ /dev/null
@@ -1,2334 +0,0 @@
-# Copyright 2022 The OFA-Sys Team. 
-# All rights reserved.
-# This source code is licensed under the Apache 2.0 license 
-# found in the LICENSE file in the root directory.
- 
-import math
-import random
-from typing import Any, Dict, List, Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq import utils
-from fairseq.distributed import fsdp_wrap
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderDecoderModel,
-    FairseqIncrementalDecoder,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.modules import (
-    AdaptiveSoftmax,
-    BaseLayer,
-    FairseqDropout,
-    LayerDropModuleList,
-    LayerNorm,
-    SinusoidalPositionalEmbedding,
-)
-from fairseq.modules.checkpoint_activations import checkpoint_wrapper
-from fairseq.modules.quant_noise import quant_noise as apply_quant_noise_
-from torch import Tensor
-
-from .unify_transformer_layer import TransformerEncoderLayer, TransformerDecoderLayer
-from .resnet import ResNet
-from .frozen_bn import FrozenBatchNorm2d
-
-
-# image 
-from .encoders.timm_resnet import resnet101, resnet152, resnet50
-# video 
-from .encoders.resnext3d import ResNeXt3D, ResNeXtBottleneck
-
-# audio
-from .encoders.pann import create_pann_model
-from data.audio_utils import  AUDIO_CFG, dotdict
-
-DEFAULT_MAX_SOURCE_POSITIONS = 1024
-DEFAULT_MAX_TARGET_POSITIONS = 1024
-
-
-DEFAULT_MIN_PARAMS_TO_WRAP = int(1e8)
-import math 
-
-def BatchNorm2d(out_chan, momentum=0.1, eps=1e-3):
-    return nn.SyncBatchNorm.convert_sync_batchnorm(
-        nn.BatchNorm2d(out_chan, momentum=momentum, eps=eps)
-    )
-
-
-def make_token_bucket_position(bucket_size, max_position=DEFAULT_MAX_SOURCE_POSITIONS):
-    context_pos = torch.arange(max_position, dtype=torch.long)[:, None]
-    memory_pos = torch.arange(max_position, dtype=torch.long)[None, :]
-    relative_pos = context_pos - memory_pos
-    sign = torch.sign(relative_pos)
-    mid = bucket_size // 2
-    abs_pos = torch.where((relative_pos<mid) & (relative_pos > -mid), mid-1, torch.abs(relative_pos))
-    log_pos = torch.ceil(torch.log(abs_pos/mid)/math.log((max_position-1)/mid) * (mid-1)) + mid
-    log_pos = log_pos.int()
-    bucket_pos = torch.where(abs_pos.le(mid), relative_pos, log_pos*sign).long()
-    return bucket_pos + bucket_size - 1
-
-
-def make_image_bucket_position(bucket_size, num_relative_distance):
-    coords_h = torch.arange(bucket_size)
-    coords_w = torch.arange(bucket_size)
-    coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij'))  # 2, Wh, Ww
-    coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-    relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-    relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-    relative_coords[:, :, 0] += bucket_size - 1  # shift to start from 0
-    relative_coords[:, :, 1] += bucket_size - 1
-    relative_coords[:, :, 0] *= 2 * bucket_size - 1
-    relative_position_index = torch.zeros(size=(bucket_size * bucket_size + 1,) * 2, dtype=relative_coords.dtype)
-    relative_position_index[1:, 1:] = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-    relative_position_index[0, 0:] = num_relative_distance - 3
-    relative_position_index[0:, 0] = num_relative_distance - 2
-    relative_position_index[0, 0] = num_relative_distance - 1
-    return relative_position_index
-
-
-class PromptEncoder(torch.nn.Module):
-    r"""
-    Prompt encoder to generate prompts, including prompt, prefix, instance and instruction
-    """
-
-    def __init__(
-            self,
-            type,
-            length,
-            projection,
-            embed_dim,
-            proj_dim,
-            layers,
-            vocab_size):
-        super().__init__()
-        self.prefix_projection = projection
-
-        if type == "prefix":
-            layers = layers
-            prompt_vocab_size = length
-
-        if self.prefix_projection:
-            self.embedding = torch.nn.Embedding(prompt_vocab_size, embed_dim)
-            self.trans = torch.nn.Sequential(
-                torch.nn.Linear(embed_dim, proj_dim),
-                torch.nn.ReLU(),
-                torch.nn.Linear(proj_dim, layers * 2 * embed_dim)
-            )
-        else:
-            if type == "prefix":
-                self.embedding = torch.nn.Embedding(
-                    prompt_vocab_size, layers * 2 * embed_dim)
-
-    def forward(self, prefix: torch.Tensor):
-        if self.prefix_projection:
-            prefix_tokens = self.embedding(prefix)
-            past_key_values = self.trans(prefix_tokens)
-        else:
-            past_key_values = self.embedding(prefix)
-        return past_key_values
-
-
-@register_model("unify_transformer")
-class TransformerModel(FairseqEncoderDecoderModel):
-    """
-    Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
-    <https://arxiv.org/abs/1706.03762>`_.
-
-    Args:
-        encoder (TransformerEncoder): the encoder
-        decoder (TransformerDecoder): the decoder
-
-    The Transformer model provides the following named architectures and
-    command-line arguments:
-
-    .. argparse::
-        :ref: fairseq.models.transformer_parser
-        :prog:
-    """
-
-    def __init__(self, args, encoder, decoder):
-        super().__init__(encoder, decoder)
-        self.args = args
-        self.supports_align_args = True
-
-        
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        # fmt: off
-        parser.add_argument('--activation-fn',
-                            choices=utils.get_available_activation_fns(),
-                            help='activation function to use')
-        parser.add_argument('--dropout', type=float, metavar='D',
-                            help='dropout probability')
-        parser.add_argument('--attention-dropout', type=float, metavar='D',
-                            help='dropout probability for attention weights')
-        parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
-                            help='dropout probability after activation in FFN.')
-        parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
-                            help='path to pre-trained encoder embedding')
-        parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
-                            help='encoder embedding dimension')
-        parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
-                            help='encoder embedding dimension for FFN')
-        parser.add_argument('--encoder-layers', type=int, metavar='N',
-                            help='num encoder layers')
-        parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
-                            help='num encoder attention heads')
-        parser.add_argument('--encoder-normalize-before', action='store_true',
-                            help='apply layernorm before each encoder block')
-        parser.add_argument('--encoder-learned-pos', action='store_true',
-                            help='use learned positional embeddings in the encoder')
-        parser.add_argument('--bitfit', default=False, action='store_true',
-                            help='use bitfit in the transformer')
-        parser.add_argument('--freeze-encoder', action='store_true',
-                            help='freeze the parameters in the encoder')
-
-
-        parser.add_argument('--adapter', action='store_true',
-                            help='use adapter in the model')
-        parser.add_argument('--adapter-dim', type=int, metavar='N',
-                            help='adapter-down-dim')
-        ### vl ADAPTER
-        parser.add_argument('--adapter-type', type=str, metavar='STR',
-                            help='adapter-type')
-        parser.add_argument('--unfreeze', action='store_true',
-                            help='unfreeze model when using adapters/prompts')
-
-        parser.add_argument('--encoder-prompt', action='store_true',
-                            help='use prompt tuning in the encoder')
-        parser.add_argument('--encoder-prompt-type', type=str, metavar='STR',
-                            choices=['prefix'],
-                            help='the type of prompt tuning')
-        parser.add_argument('--encoder-prompt-projection', action='store_true',
-                            help='use prompt projection')
-        parser.add_argument('--encoder-prompt-length', type=int, metavar='N',
-                            help='use prompt tuning in the decoder')
-        parser.add_argument('--encoder-prompt-dim', type=int, metavar='N',
-                            help='encoder prompt dimension if use encoder prompt projection')
-        parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
-                            help='path to pre-trained decoder embedding')
-        parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
-                            help='decoder embedding dimension')
-        parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
-                            help='decoder embedding dimension for FFN')
-        parser.add_argument('--decoder-layers', type=int, metavar='N',
-                            help='num decoder layers')
-        parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
-                            help='num decoder attention heads')
-        parser.add_argument('--decoder-learned-pos', action='store_true',
-                            help='use learned positional embeddings in the decoder')
-        parser.add_argument('--decoder-normalize-before', action='store_true',
-                            help='apply layernorm before each decoder block')
-        parser.add_argument('--decoder-output-dim', type=int, metavar='N',
-                            help='decoder output dimension (extra linear layer '
-                                 'if different from decoder embed dim')
-        parser.add_argument('--freeze-decoder', action='store_true',
-                            help='freeze the parameters in the decoder')
-
-        parser.add_argument('--decoder-prompt', action='store_true',
-                            help='use prompt tuning in the decoder')
-        parser.add_argument('--decoder-prompt-type', type=str, metavar='STR',
-                            choices=['prefix'],
-                            help='the type of prompt tuning')
-        parser.add_argument('--decoder-prompt-length', type=int, metavar='N',
-                            help='use prompt tuning in the decoder')
-        parser.add_argument('--decoder-prompt-projection', action='store_true',
-                            help='use prompt projection')
-        parser.add_argument('--decoder-prompt-dim', type=int, metavar='N',
-                            help='decoder prompt dimension if use decoder prompt projection')
-        parser.add_argument('--share-decoder-input-output-embed', action='store_true',
-                            help='share decoder input and output embeddings')
-        parser.add_argument('--share-all-embeddings', action='store_true',
-                            help='share encoder, decoder and output embeddings'
-                                 ' (requires shared dictionary and embed dim)')
-        parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
-                            help='if set, disables positional embeddings (outside self attention)')
-        parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
-                            help='comma separated list of adaptive softmax cutoff points. '
-                                 'Must be used with adaptive_loss criterion'),
-        parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
-                            help='sets adaptive softmax dropout for the tail projections')
-        parser.add_argument('--layernorm-embedding', action='store_true',
-                            help='add layernorm to embedding')
-        parser.add_argument('--no-scale-embedding', action='store_true',
-                            help='if True, dont scale embeddings')
-        parser.add_argument('--checkpoint-activations', action='store_true',
-                            help='checkpoint activations at each layer, which saves GPU '
-                                 'memory usage at the cost of some additional compute')
-        parser.add_argument('--offload-activations', action='store_true',
-                            help='checkpoint activations at each layer, then save to gpu. Sets --checkpoint-activations.')
-        # args for "Cross+Self-Attention for Transformer Models" (Peitz et al., 2019)
-        parser.add_argument('--no-cross-attention', default=False, action='store_true',
-                            help='do not perform cross-attention')
-        parser.add_argument('--cross-self-attention', default=False, action='store_true',
-                            help='perform cross+self-attention')
-        # args for "Reducing Transformer Depth on Demand with Structured Dropout" (Fan et al., 2019)
-        parser.add_argument('--encoder-layerdrop', type=float, metavar='D', default=0,
-                            help='LayerDrop probability for encoder')
-        parser.add_argument('--decoder-layerdrop', type=float, metavar='D', default=0,
-                            help='LayerDrop probability for decoder')
-        parser.add_argument('--encoder-layers-to-keep', default=None,
-                            help='which layers to *keep* when pruning as a comma-separated list')
-        parser.add_argument('--decoder-layers-to-keep', default=None,
-                            help='which layers to *keep* when pruning as a comma-separated list')
-        # args for Training with Quantization Noise for Extreme Model Compression ({Fan*, Stock*} et al., 2020)
-        parser.add_argument('--quant-noise-pq', type=float, metavar='D', default=0,
-                            help='iterative PQ quantization noise at training time')
-        parser.add_argument('--quant-noise-pq-block-size', type=int, metavar='D', default=8,
-                            help='block size of quantization noise at training time')
-        parser.add_argument('--quant-noise-scalar', type=float, metavar='D', default=0,
-                            help='scalar quantization noise and scalar quantization at training time')
-        # args for Fully Sharded Data Parallel (FSDP) training
-        parser.add_argument(
-            '--min-params-to-wrap', type=int, metavar='D', default=DEFAULT_MIN_PARAMS_TO_WRAP,
-            help=(
-                'minimum number of params for a layer to be wrapped with FSDP() when '
-                'training with --ddp-backend=fully_sharded. Smaller values will '
-                'improve memory efficiency, but may make torch.distributed '
-                'communication less efficient due to smaller input sizes. This option '
-                'is set to 0 (i.e., always wrap) when --checkpoint-activations or '
-                '--offload-activations are passed.'
-            )
-        )
-
-        parser.add_argument('--resnet-drop-path-rate', type=float,
-                            help='resnet drop path rate')
-        parser.add_argument('--encoder-drop-path-rate', type=float,
-                            help='encoder drop path rate')
-        parser.add_argument('--decoder-drop-path-rate', type=float,
-                            help='encoder drop path rate')
-
-        parser.add_argument('--token-bucket-size', type=int,
-                            help='token bucket size')
-        parser.add_argument('--image-bucket-size', type=int,
-                            help='image bucket size')
-
-        parser.add_argument('--attn-scale-factor', type=float,
-                            help='attention scale factor')
-        parser.add_argument('--freeze-resnet', action='store_true',
-                            help='freeze resnet')
-        parser.add_argument('--freeze-encoder-embedding', action='store_true',
-                            help='freeze encoder token embedding')
-        parser.add_argument('--freeze-decoder-embedding', action='store_true',
-                            help='freeze decoder token embedding')
-        parser.add_argument('--add-type-embedding', action='store_true',
-                            help='add source/region/patch type embedding')
-        parser.add_argument('--add-mm-type-embedding', action='store_true',
-                            help='add source/region/patch type embedding')
-        
-        parser.add_argument('--interpolate-position', action='store_true',
-                            help='interpolate position')
-
-        parser.add_argument('--resnet-type', choices=['resnet50', 'resnet101', 'resnet152'],
-                            help='resnet type')
-        parser.add_argument('--resnet-model-path', type=str, metavar='STR',
-                            help='path to load resnet')
-        parser.add_argument('--code-image-size', type=int,
-                            help='code image size')
-        parser.add_argument('--patch-layernorm-embedding', action='store_true',
-                            help='add layernorm to patch embedding')
-        parser.add_argument('--code-layernorm-embedding', action='store_true',
-                            help='add layernorm to code embedding')
-        parser.add_argument('--entangle-position-embedding', action='store_true',
-                            help='entangle position embedding')
-        parser.add_argument('--disable-entangle', action='store_true',
-                            help='disable entangle')
-        parser.add_argument('--sync-bn', action='store_true',
-                            help='sync batchnorm')
-
-        parser.add_argument('--scale-attn', action='store_true',
-                            help='scale attn')
-        parser.add_argument('--scale-fc', action='store_true',
-                            help='scale fc')
-        parser.add_argument('--scale-heads', action='store_true',
-                            help='scale heads')
-        parser.add_argument('--scale-resids', action='store_true',
-                            help='scale resids')
-        # fmt: on
-
-        # image encoder
-        parser.add_argument('--image-encoder-name', type=str, metavar='STR', default='resnet',
-                            help='image_encoder_name')
-        parser.add_argument('--return-hidden-state-vision', action='store_true',
-                            help='return_hidden_state_vision')
-
-        parser.add_argument('--freeze-image-encoder', action='store_true',
-                            help='freeze_image_encoder')    
-
-        parser.add_argument('--nograd', action='store_true',
-                            help='nograd for vis encoder')   
-
-        parser.add_argument('--encoder-eval', action='store_true',
-                            help='vision encoder.eval()')  
-        # video
-        parser.add_argument('--video-encoder-name', type=str, default=None,
-                    help='video_encoder_name')              
-
-        parser.add_argument('--video-model-path', type=str, default=None,
-                    help='video_model_path')    
-        parser.add_argument('--freeze-video-encoder', action='store_true',
-                            help='freeze_video_encoder')   
-
-        parser.add_argument('--sample-patch-num', type=int, default=None,
-                            help='num of tokens selected at random')
-        parser.add_argument('--with-cls', action='store_true',
-                            help='with_cls')  
-
-        parser.add_argument('--sample-video-patch-num', type=int, default=None,
-                            help='num of tokens selected at random')
-        # audio 
-        parser.add_argument('--audio-encoder-name', type=str, default=None,
-                    help='audio_encoder_name')                 
-        parser.add_argument('--audio-model-path', type=str, default=None,
-                    help='audio_model_path')    
-        parser.add_argument('--freeze-audio-encoder', action='store_true',
-                            help='freeze_audio_encoder')
-        parser.add_argument('--fusion-type', type=str, default=None,
-                    help='fusion_type') 
-        parser.add_argument('--enable-fusion', action='store_true',
-                            help='enable_fusion')
-        parser.add_argument('--mel-bins', type=int, default=64,
-                            help='mel_bins')
-        parser.add_argument('--hop-size', type=int, default=480,
-                            help='hop_size')
-        parser.add_argument('--sample-audio-patch-num', type=int, default=None,
-                            help='num of tokens selected at random')
-        parser.add_argument('--fstride', type=int, default=10,
-                            help='fstride')
-        parser.add_argument('--tstride', type=int, default=10,
-                            help='tstride')
-        parser.add_argument('--input-tdim', type=int, default=1024,
-                        help='input_tdim')
-
-
-        # progressive training 
-
-        parser.add_argument('--progressive', action='store_true',
-                            help='progressive')
-        parser.add_argument('--unfreeze-epoch-encoder', type=int, default=0,
-                            help='unfreeze_epoch_encoder')
-        parser.add_argument('--unfreeze-epoch-decoder', type=int, default=0,
-                            help='unfreeze_epoch_decoder')
-
-        parser.add_argument('--unfreeze-epoch-image', type=int, default=0,
-                            help='unfreeze_epoch_image')
-        parser.add_argument('--unfreeze-epoch-video', type=int, default=0,
-                        help='unfreeze_epoch_video')                  
-        parser.add_argument('--unfreeze-epoch-audio', type=int, default=0,
-                            help='unfreeze_epoch_audio')
-
-
-        ## only linear 
-        parser.add_argument('--only-linear-proj', action='store_true',
-                            help='only_linear_proj')
-        parser.add_argument('--unfreeze-epoch', type=int, default=0,
-                            help='unfreeze_epoch')
-        
-        parser.add_argument('--freeze-perception', action='store_true',
-                            help='freeze_perception')
-        ## 
-        parser.add_argument('--qk-norm', action='store_true',
-                            help='qk_norm')
-
-        
-        parser.add_argument('--layernorm-image-embedding', action='store_true',
-                            help='layernorm_image_embedding')
-        
-        parser.add_argument('--layernorm-video-embedding', action='store_true',
-                            help='layernorm_video_embedding')
-        
-        parser.add_argument('--layernorm-audio-embedding', action='store_true',
-                            help='layernorm_audio_embedding')
-        
-        parser.add_argument('--freeze-batchnorm', action='store_true',
-                            help='freeze_batchnorm')
-        
-        parser.add_argument('--freeze-resnet-video', action='store_true',
-                            help='freeze resnet video')
-        
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present in older models
-        base_architecture(args)
-
-        if args.encoder_layers_to_keep:
-            args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
-        if args.decoder_layers_to_keep:
-            args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
-
-        if getattr(args, "max_source_positions", None) is None:
-            args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
-        if getattr(args, "max_target_positions", None) is None:
-            args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
-
-        src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
-
-        if args.share_all_embeddings:
-            if src_dict != tgt_dict:
-                raise ValueError("--share-all-embeddings requires a joined dictionary")
-            if args.encoder_embed_dim != args.decoder_embed_dim:
-                raise ValueError(
-                    "--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
-                )
-            if args.decoder_embed_path and (
-                args.decoder_embed_path != args.encoder_embed_path
-            ):
-                raise ValueError(
-                    "--share-all-embeddings not compatible with --decoder-embed-path"
-                )
-            encoder_embed_tokens = cls.build_embedding(
-                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
-            )
-            decoder_embed_tokens = encoder_embed_tokens
-            args.share_decoder_input_output_embed = True
-        else:
-            encoder_embed_tokens = cls.build_embedding(
-                args, src_dict, args.encoder_embed_dim, args.encoder_embed_path
-            )
-            decoder_embed_tokens = cls.build_embedding(
-                args, tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
-            )
-        if getattr(args, "freeze_encoder_embedding", False) or getattr(
-                args, "encoder_prompt", False) or getattr(args, "decoder_prompt", False) or getattr(args, "adapter", False):    
-            encoder_embed_tokens.weight.requires_grad = False
-        if getattr(args, "freeze_decoder_embedding", False) or getattr(
-                args, "encoder_prompt", False) or getattr(args, "decoder_prompt", False) or getattr(args, "adapter", False):    
-            decoder_embed_tokens.weight.requires_grad = False
-        if getattr(args, "offload_activations", False):
-            args.checkpoint_activations = True  # offloading implies checkpointing
-        encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens)
-        decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
-
-        if getattr(args, "freeze_encoder", False):
-            encoder.requires_grad_(False)
-
-
-            encoder.embed_images.requires_grad_(True)
-            encoder.image_proj.requires_grad_(True)
-            encoder.embed_image_positions.requires_grad_(True)
-
-            if encoder.layernorm_image_embedding is not None:
-                encoder.layernorm_image_embedding.requires_grad_(True)
-                print("freeze image LN")
-
-            if hasattr(encoder, 'embed_videos'):
-                encoder.embed_videos.requires_grad_(True)
-                encoder.video_proj.requires_grad_(True)
-                encoder.embed_video_positions.requires_grad_(True)
-
-                if encoder.layernorm_video_embedding is not None:
-                    encoder.layernorm_video_embedding.requires_grad_(True)
-                    print("freeze audio LN")
-
-            if getattr(args, "audio_encoder_name", False):
-                encoder.embed_audios.requires_grad_(True)
-                encoder.audio_proj.requires_grad_(True)
-                encoder.embed_audio_positions.requires_grad_(True)
-                if encoder.layernorm_audio_embedding is not None:
-                    encoder.layernorm_audio_embedding.requires_grad_(True)
-                    print("freeze audio LN")
-
-
-
-        if getattr(args, "freeze_decoder", False):
-            decoder.requires_grad_(False)
-
-
-        if getattr(args, "encoder_prompt", False) or getattr(
-                args, "decoder_prompt", False) or getattr(
-                args, "adapter", False):
-            if not getattr(args, "unfreeze", False):
-                encoder.requires_grad_(False)
-                decoder.requires_grad_(False)
-            if getattr(args, "encoder_prompt", False):
-                encoder.encoder_prompt_encoder.requires_grad_(True)
-            if getattr(args, "decoder_prompt", False):
-                decoder.decoder_prompt_encoder.requires_grad_(True)
-            if getattr(args, "adapter", False):
-                for idx, layer in enumerate(encoder.layers):
-                    layer.adapter.requires_grad_(True)
-                for idx, layer in enumerate(decoder.layers):
-                    layer.adapter.requires_grad_(True)     
-        if getattr(args, "freeze_image_encoder", False):
-            encoder.embed_images.requires_grad_(False)
-
-        if getattr(args, "freeze_video_encoder", False) and hasattr(encoder, 'embed_videos'):
-            encoder.embed_videos.requires_grad_(False)
-        
-        if getattr(args, "freeze_audio_encoder", False) and hasattr(encoder, 'embed_audios'):
-            encoder.embed_audios.requires_grad_(False)
-
-        if getattr(args, "freeze_batchnorm", False):
-
-            for n, p in encoder.named_parameters():
-                if 'bn' in n:
-                    p.requires_grad = False
-                    print("freeze:", n)
-                
-        if hasattr(encoder, 'embed_audios'):
-            if hasattr(encoder.embed_audios, 'logmel_extractor'):
-                for param in encoder.embed_audios.logmel_extractor.parameters():
-                    param.requires_grad = False
-
-        if getattr(args, "encoder_eval", False):
-            print("Set encoders.eval()")
-            encoder.embed_images.eval()
-
-            if getattr(args, "video_encoder_name", None):
-                encoder.embed_videos.eval()
-
-
-        if not args.share_all_embeddings:
-            min_params_to_wrap = getattr(
-                args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP
-            )
-            # fsdp_wrap is a no-op when --ddp-backend != fully_sharded
-            encoder = fsdp_wrap(encoder, min_num_params=min_params_to_wrap)
-            decoder = fsdp_wrap(decoder, min_num_params=min_params_to_wrap)
-        return cls(args, encoder, decoder)
-
-    @classmethod
-    def build_embedding(cls, args, dictionary, embed_dim, path=None):
-        num_embeddings = len(dictionary)
-        padding_idx = dictionary.pad()
-
-        args.vocab_size = num_embeddings
-        emb = Embedding(num_embeddings, embed_dim, padding_idx)
-        # if provided, load from preloaded dictionaries
-        if path:
-            embed_dict = utils.parse_embedding(path)
-            utils.load_embedding(embed_dict, dictionary, emb)
-        return emb
-
-    @classmethod
-    def build_encoder(cls, args, src_dict, embed_tokens):
-        return TransformerEncoder(args, src_dict, embed_tokens)
-
-    @classmethod
-    def build_decoder(cls, args, tgt_dict, embed_tokens):
-        return TransformerDecoder(
-            args,
-            tgt_dict,
-            embed_tokens,
-            no_encoder_attn=getattr(args, "no_cross_attention", False),
-        )
-
-    # TorchScript doesn't support optional arguments with variable length (**kwargs).
-    # Current workaround is to add union of all arguments in child classes.
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        prev_output_tokens,
-        return_all_hiddens: bool = True,
-        features_only: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        """
-        Run the forward pass for an encoder-decoder model.
-
-        Copied from the base class, but without ``**kwargs``,
-        which are not supported by TorchScript.
-        """
-        encoder_out = self.encoder(
-            src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens
-        )
-        decoder_out = self.decoder(
-            prev_output_tokens,
-            encoder_out=encoder_out,
-            features_only=features_only,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-            src_lengths=src_lengths,
-            return_all_hiddens=return_all_hiddens,
-        )
-        return decoder_out
-
-    # Since get_normalized_probs is in the Fairseq Model which is not scriptable,
-    # I rewrite the get_normalized_probs from Base Class to call the
-    # helper function in the Base Class.
-    @torch.jit.export
-    def get_normalized_probs(
-        self,
-        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
-        log_probs: bool,
-        sample: Optional[Dict[str, Tensor]] = None,
-    ):
-        """Get normalized probabilities (or log probs) from a net's output."""
-        return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
-
-
-class TransformerEncoder(FairseqEncoder):
-    """
-    Transformer encoder consisting of *args.encoder_layers* layers. Each layer
-    is a :class:`TransformerEncoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): encoding dictionary
-        embed_tokens (torch.nn.Embedding): input embedding
-    """
-
-    def __init__(self, args, dictionary, embed_tokens):
-        self.args = args
-        super().__init__(dictionary)
-        self.register_buffer("version", torch.Tensor([3]))
-  
-
-        self.freeze_image_encoder = getattr(args, "freeze_image_encoder", False)
-        self.freeze_video_encoder = getattr(args, "freeze_video_encoder", False)
-        self.nograd = getattr(args, "nograd", False)
-        self.num_frames = getattr(args, 'num_frames', 4)
-
-        self.sample_patch_num = getattr(args, "sample_patch_num", 196)
-
-        self.sample_audio_patch_num = getattr(args, "sample_audio_patch_num", self.sample_patch_num)
-        self.sample_video_patch_num = getattr(args, "sample_video_patch_num", self.sample_patch_num)
-
-        self.with_cls = getattr(args, "with_cls", False)
-        print("self.sample_patch_num", self.sample_patch_num)
-        print("self.sample_audio_patch_num", self.sample_audio_patch_num)
-        print("self.sample_video_patch_num", self.sample_video_patch_num)
-        print("self.with_cls", self.with_cls)
-
-        if getattr(args, "encoder_prompt", False):
-            self.encoder_prompt_encoder = PromptEncoder(
-                type=args.encoder_prompt_type,
-                length=args.encoder_prompt_length,
-                projection=args.encoder_prompt_projection,
-                embed_dim=args.encoder_embed_dim,
-                proj_dim=args.encoder_prompt_dim,
-                layers=args.encoder_layers,
-                vocab_size=args.vocab_size)
-        self.encoder_dropout = nn.Dropout(p=0.2)
-        
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.encoder_layerdrop = args.encoder_layerdrop
-
-        embed_dim = embed_tokens.embedding_dim
-        self.padding_idx = embed_tokens.padding_idx
-        self.max_source_positions = args.max_source_positions
-        self.num_attention_heads = args.encoder_attention_heads
-
-        self.embed_tokens = embed_tokens
-
-        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
-
-        if getattr(args, "layernorm_embedding", False):
-            self.layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_embedding = None
-
-        self.mm_type_embedding = False 
-        if getattr(args, "add_mm_type_embedding", False):
-            self.type_embedding = Embedding(4, embed_dim, padding_idx=None)
-            self.mm_type_embedding = True
-        elif getattr(args, "add_type_embedding", False):
-            self.type_embedding = Embedding(2, embed_dim, padding_idx=None)
-        else:
-            self.type_embedding = None
-
-        norm_layer = None
-        norm_layer_video = None
-        if getattr(args, "sync_bn", False):
-            norm_layer = BatchNorm2d
-        else:
-            if getattr(args, "freeze_resnet", False): # or getattr(args, "freeze_batchnorm", False)
-                norm_layer = FrozenBatchNorm2d
-                print("Frozen image bn", norm_layer)
-            if getattr(args, "freeze_resnet_video", False):
-                norm_layer_video = FrozenBatchNorm2d
-                print("Frozen video bn", norm_layer_video)
-
- 
-        
-        if getattr(args, "image_encoder_name", None):
-
-            if 'timm_resnet' in args.image_encoder_name:
-                if args.resnet_type == 'resnet101':
-                    self.embed_images = resnet101(norm_layer=norm_layer)
-                elif args.resnet_type == 'resnet152':
-                    self.embed_images = resnet152(norm_layer=norm_layer)
-                elif args.resnet_type == 'resnet50':
-                    self.embed_images = resnet50(norm_layer=norm_layer)
-                else:
-                    raise NotImplementedError
-                self.image_proj = Linear(2048, embed_dim)
-            else: 
-                if args.resnet_type == 'resnet101':
-                    self.embed_images = ResNet([3, 4, 23], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-                elif args.resnet_type == 'resnet152':
-                    self.embed_images = ResNet([3, 8, 36], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-                elif args.resnet_type == 'resnet50':
-                    self.embed_images = ResNet([3, 4, 6], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-                else:
-                    raise NotImplementedError
-                self.image_proj = Linear(1024, embed_dim)
-        else: 
-            if args.resnet_type == 'resnet101':
-                self.embed_images = ResNet([3, 4, 23], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-            elif args.resnet_type == 'resnet152':
-                self.embed_images = ResNet([3, 8, 36], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-            elif args.resnet_type == 'resnet50':
-                self.embed_images = ResNet([3, 4, 6], norm_layer=norm_layer, drop_path_rate=args.resnet_drop_path_rate)
-            else:
-                raise NotImplementedError
-            self.image_proj = Linear(1024, embed_dim)
-
-        if getattr(args, "layernorm_image_embedding", False):
-            self.layernorm_image_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_image_embedding = None
-
-
-        ## video 
-        if getattr(args, "video_encoder_name", None):
-            print("Loading: ", args.video_encoder_name)
-            patch_frame_size = getattr(args, 'patch_frame_size', 224)
-            num_frames = getattr(args, 'num_frames', 4)
-            pretrained_model = getattr(args, "video_model_path", None)
-
-            if  'resnext' in args.video_encoder_name:
-
-                if 'all' in args.video_encoder_name:
-                    if 'resnext101' in args.video_encoder_name :
-                        self.embed_videos = ResNeXt3D(ResNeXtBottleneck, [3, 4, 23, 3], norm_layer=norm_layer_video)
-                    elif 'resnext152' in args.video_encoder_name:
-                        self.embed_videos = ResNeXt3D(ResNeXtBottleneck, [3, 8, 36, 3], norm_layer=norm_layer_video)
-                    elif 'resnext50' in args.video_encoder_name:
-                        self.embed_videos = ResNeXt3D(ResNeXtBottleneck, [3, 4, 6, 3], norm_layer=norm_layer_video)
-                    else:
-                        raise NotImplementedError
-                    vis_dim = 2048
-                else:
-                    if args.video_encoder_name == 'resnext101':
-                        self.embed_videos = ResNeXt3D(ResNeXtBottleneck, [3, 4, 23])
-                    elif args.video_encoder_name == 'resnext152':
-                        self.embed_videos = ResNeXt3D(ResNeXtBottleneck, [3, 8, 36])
-                    elif args.video_encoder_name == 'resnext50':
-                        self.embed_videos = ResNeXt3D(ResNeXtBottleneck, [3, 4, 6])
-                    else:
-                        raise NotImplementedError
-                    vis_dim = 1024
-
-                self.embed_video_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-
-                if pretrained_model:
-                    print("load pretrained_model {}".format(pretrained_model))
-                    state_dict = torch.load(pretrained_model)['state_dict']
-                    if 'module' in list(state_dict.keys())[0]:
-                        from collections import OrderedDict
-                        new_state_dict = OrderedDict()
-                        for k, v in state_dict.items():
-                            name = k[7:] # remove 'module.' of dataparallel
-                            new_state_dict[name]=v
-                        state_dict = new_state_dict
-    
-                    msg = self.embed_videos.load_state_dict(state_dict, strict=False)
-                    print(msg)
-
-            else:
-                raise NotImplemented
-
-
-            self.video_proj = Linear(vis_dim, embed_dim)
-
-
-        if getattr(args, "layernorm_video_embedding", False):
-            self.layernorm_video_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_video_embedding = None
-
-        ## video 
-        if getattr(args, "audio_encoder_name", None):
-            print("Loading: ", args.audio_encoder_name)
-
-
-
-            pretrained_audio_model = getattr(args, "audio_model_path", None)
-            audio_cfg = getattr(args, "audio_cfg", None)
-            audio_cfg = audio_cfg if audio_cfg is not None else AUDIO_CFG
-            
-            audio_cfg = dotdict(audio_cfg)
-            enable_fusion = getattr(args, "enable_fusion", False)
-            fusion_type = getattr(args, "fusion_type", None)
-            
-            audio_cfg['mel_bins'] = getattr(args, "mel_bins", 64)
-            audio_cfg['hop_size'] = getattr(args, "hop_size", 480)
-
-            if 'pann' in args.audio_encoder_name:
-
-                if 'cnn6' in args.audio_encoder_name:
-                    audio_cfg['model_name'] = 'Cnn6'
-                    audio_dim = 512
-                elif 'cnn10' in args.audio_encoder_name:
-                    audio_cfg['model_name'] = 'Cnn10'
-                    audio_dim = 1024
-                elif 'cnn14' in args.audio_encoder_name:
-                    audio_cfg['model_name'] = 'Cnn14'
-                    # audio_dim = 512
-                    audio_dim = 2048
-                else:
-                    raise NotImplementedError
-
-                self.embed_audios = create_pann_model(audio_cfg, enable_fusion, fusion_type)
-        
-            
-                self.embed_audio_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-
-
-
-                if pretrained_audio_model:
-                    print("load pretrained_model {}".format(pretrained_audio_model))
-                    state_dict = torch.load(pretrained_audio_model)
-                    if 'model' in state_dict:
-                        state_dict = state_dict['model']
-
-                    if 'module' in list(state_dict.keys())[0]:
-                        from collections import OrderedDict
-                        new_state_dict = OrderedDict()
-                        for k, v in state_dict.items():
-                            name = k.replace('module.', '') # remove 'module.' of dataparallel
-                            new_state_dict[name]=v
-                        state_dict = new_state_dict
-
-                    if 'sed_model.' in list(state_dict.keys())[0] or 'module.' in list(state_dict.keys())[0]:
-                        from collections import OrderedDict
-                        new_state_dict = OrderedDict()
-                        for k, v in state_dict.items():
-                            name = k.replace('sed_model.', '').replace('module.', '').replace('audio_branch.', '') # remove 'module.' of dataparallel
-                            new_state_dict[name]=v
-                        state_dict = new_state_dict
-
-                    if audio_cfg['mel_bins'] != 64:
-
-                        del_keys = []
-                        for k, v in state_dict.items():
-                            if 'logmel_extractor' in k or 'bn0' in k:
-                                del_keys.append(k)
-
-                        for k in del_keys:
-                            del state_dict[k]
-
-                    msg = self.embed_audios.load_state_dict(state_dict, strict=False)
-                    print(msg)
-
-            else:
-                raise NotImplementedError
-
-            self.audio_proj = Linear(audio_dim, embed_dim)
-           
-
-        if getattr(args, "layernorm_audio_embedding", False):
-            self.layernorm_audio_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_audio_embedding = None
-
-
-        if getattr(args, "resnet_model_path", None):
-            print("load resnet {}".format(args.resnet_model_path))
-            resnet_state_dict = torch.load(self.args.resnet_model_path)
-            msg = self.embed_images.load_state_dict(resnet_state_dict, strict=False)
-            print(msg)
-
-        if getattr(args, "patch_layernorm_embedding", False):
-            self.patch_layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.patch_layernorm_embedding = None
-
-        self.embed_positions = Embedding(args.max_source_positions + 2, embed_dim)
-        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-        self.pos_ln = LayerNorm(embed_dim)
-        self.image_pos_ln = LayerNorm(embed_dim)
-        self.pos_scaling = float(embed_dim / args.encoder_attention_heads * args.attn_scale_factor) ** -0.5
-        self.pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.pos_k_linear = nn.Linear(embed_dim, embed_dim)
-
-        if not args.adaptive_input and args.quant_noise_pq > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(embed_dim, embed_dim, bias=False),
-                args.quant_noise_pq,
-                args.quant_noise_pq_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        if self.encoder_layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.encoder_layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-
-        dpr = [x.item() for x in torch.linspace(0, args.encoder_drop_path_rate, args.encoder_layers)]
-        self.layers.extend(
-            [self.build_encoder_layer(args, drop_path_rate=dpr[i]) for i in range(args.encoder_layers)]
-        )
-        self.num_layers = len(self.layers)
-
-        if args.encoder_normalize_before:
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-        token_bucket_size = args.token_bucket_size
-        token_num_rel_dis = 2 * token_bucket_size - 1
-        token_rp_bucket = make_token_bucket_position(token_bucket_size)
-        self.token_rel_pos_table_list = nn.ModuleList(
-            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
-        )
-
-        image_bucket_size = args.image_bucket_size
-        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
-        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
-        self.image_rel_pos_table_list = nn.ModuleList(
-            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.encoder_layers)]
-        )
-
-        self.patch_image_size = args.patch_image_size
-        self.orig_patch_image_size = args.orig_patch_image_size
-
-        self.register_buffer("token_rp_bucket", token_rp_bucket)
-        self.register_buffer("image_rp_bucket", image_rp_bucket)
-        self.entangle_position_embedding = args.entangle_position_embedding
-
-
-    def build_encoder_layer(self, args, drop_path_rate=0.0):
-        layer = TransformerEncoderLayer(args, drop_path_rate=drop_path_rate, \
-            use_adapter=getattr(args, "adapter", False), adapter_dim=getattr(args, "adapter_dim", 200), 
-            adapter_type=getattr(args, "adapter_type", 'UN'))
-        checkpoint = getattr(args, "checkpoint_activations", False)
-        if checkpoint:
-            offload_to_cpu = getattr(args, "offload_activations", False)
-            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
-        # if we are checkpointing, enforce that FSDP always wraps the
-        # checkpointed layer, regardless of layer size
-        min_params_to_wrap = (
-            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
-            if not checkpoint else 0
-        )
-        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
-        return layer
-
-    def get_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
-        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
-        values = values.unsqueeze(0).expand(x.size(0), -1, -1, -1)
-        values = values.permute([0, 3, 1, 2])
-        return values.contiguous()
-
-    def get_image_rel_pos_bias(self, image_position_ids, idx):
-        bsz, seq_len = image_position_ids.shape
-        rp_bucket_size = self.image_rp_bucket.size(1)
-
-        rp_bucket = self.image_rp_bucket.unsqueeze(0).expand(
-            bsz, rp_bucket_size, rp_bucket_size
-        ).gather(1, image_position_ids[:, :, None].expand(bsz, seq_len, rp_bucket_size)
-        ).gather(2, image_position_ids[:, None, :].expand(bsz, seq_len, seq_len))
-        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
-        values = values.permute(0, 3, 1, 2)
-        return values
-
-    def get_patch_audios_info(self, patch_images, sample_patch_num, device):
-
-        if self.nograd and self.freeze_audio_encoder:
-            with torch.no_grad():
-                image_embed = self.embed_audios(patch_images)
-        else:
-            image_embed = self.embed_audios(patch_images)
-
-        # in case of cnn (bs, c, h, w)
-        h, w = image_embed.shape[-2:]
-        image_num_patches = h * w 
-        sh = int(math.ceil(math.sqrt(image_num_patches))) # to keep within image_bucket_size
-        sw = sh
-        image_embed = image_embed.flatten(2).transpose(1, 2) # (bs, c, hlw) -> (bs, hlw, c)
-
-        image_padding_mask = patch_images.new_zeros((patch_images.size(0), image_num_patches)).bool()
-        
-        image_position_idx = torch.arange(sw).unsqueeze(0).expand(sh, sw) + \
-                             torch.arange(sh).unsqueeze(1) * self.args.image_bucket_size + 1
-        image_position_idx = image_position_idx.reshape(-1).to(device)[:image_num_patches]
-        image_position_ids = image_position_idx[None, :].expand(patch_images.size(0), image_num_patches)
-
-        if sample_patch_num is not None and sample_patch_num < image_num_patches:
-            patch_orders = [
-                random.sample(range(image_num_patches), k=sample_patch_num)
-                for _ in range(patch_images.size(0))
-            ]
-            patch_orders = torch.LongTensor(patch_orders).to(device)
-            image_embed = image_embed.gather(
-                1, patch_orders.unsqueeze(2).expand(-1, -1, image_embed.size(2))
-            )
-            image_num_patches = sample_patch_num
-            image_padding_mask = image_padding_mask.gather(1, patch_orders)
-            image_position_ids = image_position_ids.gather(1, patch_orders)
-
-        image_pos_embed = self.embed_audio_positions(image_position_ids)
-
-
-        image_embed = self.audio_proj(image_embed.type(self.audio_proj.weight.dtype))
-
-        if self.layernorm_audio_embedding is not None:
-            image_embed = self.layernorm_audio_embedding(image_embed)
-
-        return image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed
-
-
-    def get_patch_videos_info(self, patch_images, sample_patch_num, device):
-        if self.nograd and self.freeze_video_encoder:
-            with torch.no_grad():
-                _, image_embed = self.embed_videos(patch_images)
-        else:
-            _, image_embed = self.embed_videos(patch_images)
-        
-        l, h, w = image_embed.shape[-3:]
-        image_num_patches = h * w * l
-        image_embed = image_embed.flatten(2).transpose(1, 2) # (bs, c, hlw) -> (bs, hlw, c)
-        numframes = l
-
-        image_padding_mask = patch_images.new_zeros((patch_images.size(0), image_num_patches)).bool()
-        
-        image_position_idx = torch.arange(w).unsqueeze(0).expand(h, w) + \
-                             torch.arange(h).unsqueeze(1) * self.args.image_bucket_size + 1
-        image_position_idx = image_position_idx.unsqueeze(0).expand(numframes, -1, -1)  
-        image_position_idx = image_position_idx.reshape(-1).to(device)
-        image_position_ids = image_position_idx[None, :].expand(patch_images.size(0), image_num_patches)
-
-        if sample_patch_num is not None and sample_patch_num < image_num_patches:
-            patch_orders = [
-                random.sample(range(image_num_patches), k=sample_patch_num)
-                for _ in range(patch_images.size(0))
-            ]
-            patch_orders = torch.LongTensor(patch_orders).to(device)
-            image_embed = image_embed.gather(
-                1, patch_orders.unsqueeze(2).expand(-1, -1, image_embed.size(2))
-            )
-            image_num_patches = sample_patch_num
-            image_padding_mask = image_padding_mask.gather(1, patch_orders)
-            image_position_ids = image_position_ids.gather(1, patch_orders)
-
-        image_pos_embed = self.embed_video_positions(image_position_ids)
-
-
-        image_embed = self.video_proj(image_embed)
-
-        if self.layernorm_video_embedding is not None:
-            image_embed = self.layernorm_video_embedding(image_embed)
-        
-        return image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed
-
-    def get_patch_images_info(self, patch_images, sample_patch_num, device):
-        if self.nograd and self.freeze_image_encoder:
-            with torch.no_grad():
-                image_embed = self.embed_images(patch_images)
-        else:
-            image_embed = self.embed_images(patch_images)
-
-        if isinstance(image_embed, tuple):
-            _, image_embed = image_embed
-
-        h, w = image_embed.shape[-2:]
-        image_num_patches = h * w
-        image_embed = image_embed.flatten(2).transpose(1, 2)
-        image_padding_mask = patch_images.new_zeros((patch_images.size(0), image_num_patches)).bool()
-        image_position_idx = torch.arange(w).unsqueeze(0).expand(h, w) + \
-                             torch.arange(h).unsqueeze(1) * self.args.image_bucket_size + 1
-        image_position_idx = image_position_idx.view(-1).to(device)
-        image_position_ids = image_position_idx[None, :].expand(patch_images.size(0), image_num_patches)
-
-        if sample_patch_num is not None and sample_patch_num < image_num_patches:
-            patch_orders = [
-                random.sample(range(image_num_patches), k=sample_patch_num)
-                for _ in range(patch_images.size(0))
-            ]
-            patch_orders = torch.LongTensor(patch_orders).to(device)
-            image_embed = image_embed.gather(
-                1, patch_orders.unsqueeze(2).expand(-1, -1, image_embed.size(2))
-            )
-            image_num_patches = sample_patch_num
-            image_padding_mask = image_padding_mask.gather(1, patch_orders)
-            image_position_ids = image_position_ids.gather(1, patch_orders)
-        orig_num_patches = (self.orig_patch_image_size // 16) ** 2
-        orig_hw= self.orig_patch_image_size // 16
-        if getattr(self.args, "interpolate_position", False) and image_num_patches > orig_num_patches:
-            old_image_position_ids = torch.arange(orig_hw).unsqueeze(0).expand(orig_hw, orig_hw) + \
-                                     torch.arange(orig_hw).unsqueeze(1) * self.args.image_bucket_size + 1
-            old_image_position_ids = old_image_position_ids.to(device)
-            old_image_pos_embed = self.embed_image_positions(old_image_position_ids)
-            old_image_pos_embed = old_image_pos_embed.reshape(1, orig_hw, orig_hw, -1).permute(0, 3, 1, 2)
-            image_pos_embed = F.interpolate(old_image_pos_embed, size=(h, w), mode='bilinear')
-            image_pos_embed = image_pos_embed.permute(0, 2, 3, 1).reshape(1, image_num_patches, -1)
-            image_pos_embed = image_pos_embed.expand(patch_images.size(0), -1, -1)
-        else:
-            image_pos_embed = self.embed_image_positions(image_position_ids)
-
-
-
-        image_embed = self.image_proj(image_embed)
-        if self.layernorm_image_embedding is not None:
-            image_embed = self.layernorm_image_embedding(image_embed)
-
-        return image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed
-
-    def get_encoder_prompt(self, prompt_tokens):
-        past_key_values = self.encoder_prompt_encoder(prompt_tokens)
-        bsz, seqlen, _ = past_key_values.shape
-        past_key_values = past_key_values.view(
-            bsz,
-            seqlen,
-            (self.args.encoder_layers) * 2,
-            self.args.encoder_attention_heads,
-            self.args.encoder_embed_dim // self.args.encoder_attention_heads,
-        )
-        past_key_values = self.encoder_dropout(past_key_values)
-        past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(2)
-        return past_key_values
-    
-    def forward_embedding(
-        self,
-        src_tokens,
-        image_embed: Optional[torch.Tensor] = None,
-        image_embed_2: Optional[torch.Tensor] = None,
-        token_embedding: Optional[torch.Tensor] = None,
-        pos_embed: Optional[torch.Tensor] = None,
-        image_pos_embed: Optional[torch.Tensor] = None,
-        image_pos_embed_2: Optional[torch.Tensor] = None,
-        patch_types: Optional[torch.Tensor] = None,
-    ):
-        # embed tokens and positions
-        if token_embedding is None:
-            token_embedding = self.embed_tokens(src_tokens)
-        x = embed = self.embed_scale * token_embedding
-        if self.entangle_position_embedding and pos_embed is not None:
-            x += pos_embed
-        if self.type_embedding is not None:
-            x += self.type_embedding(src_tokens.new_zeros(x.size()[:2]))
-        if self.layernorm_embedding is not None:
-            x = self.layernorm_embedding(x)
-        x = self.dropout_module(x)
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-
-        # embed raw images
-        if image_embed is not None:
-            # image_embed = self.image_proj(image_embed)
-            image_x = image_embed = self.embed_scale * image_embed
-            if self.entangle_position_embedding and image_pos_embed is not None:
-                image_x += image_pos_embed[:, -image_x.shape[1]:, :] # account for cls token
-            if self.type_embedding is not None:
-                if self.mm_type_embedding:
-                    mm_type = patch_types.unsqueeze(1).to(src_tokens.device) + 1 # 0 for text
-                    image_x += self.type_embedding(mm_type)
-                else:
-                    image_x += self.type_embedding(src_tokens.new_ones(image_x.size()[:2]))
-            if self.patch_layernorm_embedding is not None:
-                image_x = self.patch_layernorm_embedding(image_x)
-            image_x = self.dropout_module(image_x)
-            if self.quant_noise is not None:
-                image_x = self.quant_noise(image_x)
-            x = torch.cat([image_x, x], dim=1)
-            embed = torch.cat([image_embed, embed], dim=1)
-
-        if image_embed_2 is not None:
-            assert self.type_embedding is not None
-            # image_embed_2 = self.image_proj(image_embed_2)
-            image_x_2 = image_embed_2 = self.embed_scale * image_embed_2
-            if self.entangle_position_embedding and image_pos_embed_2 is not None:
-                image_x_2 += image_pos_embed_2[:, -image_x_2.shape[1]:, :]
-            if self.type_embedding is not None:
-                image_x_2 += self.type_embedding(src_tokens.new_full(image_x_2.size()[:2], fill_value=2))
-            if self.patch_layernorm_embedding is not None:
-                image_x_2 = self.patch_layernorm_embedding(image_x_2)
-            image_x_2 = self.dropout_module(image_x_2)
-            if self.quant_noise is not None:
-                image_x_2 = self.quant_noise(image_x_2)
-            x = torch.cat([image_x_2, x], dim=1)
-            embed = torch.cat([image_embed_2, embed], dim=1)
-
-        return x, embed
-
-    def forward(
-        self,
-        src_tokens,
-        src_lengths,
-        patch_images: Optional[torch.Tensor] = None,
-        patch_images_2: Optional[torch.Tensor] = None,
-        patch_masks: Optional[torch.Tensor] = None,
-        code_masks: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-        sample_patch_num: Optional[int] = None,
-        patch_videos: Optional[int] = None,
-        patch_types: Optional[torch.Tensor] = None,
-        patch_audios: Optional[torch.Tensor] = None,
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        return self.forward_scriptable(src_tokens,
-                                       src_lengths,
-                                       patch_images,
-                                       patch_images_2,
-                                       patch_masks,
-                                       return_all_hiddens,
-                                       token_embeddings,
-                                       self.sample_patch_num,
-                                       patch_videos=patch_videos,
-                                       patch_types=patch_types,
-                                       patch_audios=patch_audios,
-                                       sample_audio_patch_num=self.sample_audio_patch_num,
-                                       sample_video_patch_num=self.sample_video_patch_num)
-
-    # TorchScript doesn't support super() method so that the scriptable Subclass
-    # can't access the base class model in Torchscript.
-    # Current workaround is to add a helper function with different name and
-    # call the helper function from scriptable Subclass.
-    def forward_scriptable(
-        self,
-        src_tokens,
-        src_lengths,
-        patch_images: Optional[torch.Tensor] = None,
-        patch_images_2: Optional[torch.Tensor] = None,
-        patch_masks: Optional[torch.Tensor] = None,
-        return_all_hiddens: bool = False,
-        token_embeddings: Optional[torch.Tensor] = None,
-        sample_patch_num: Optional[int] = None,
-        patch_videos: Optional[int] = None,
-        patch_types: Optional[torch.Tensor] = None,
-        patch_audios:  Optional[int] = None,
-        sample_audio_patch_num: Optional[int] = None,
-        sample_video_patch_num: Optional[int] = None,
-    ):
-        """
-        Args:
-            src_tokens (LongTensor): tokens in the source language of shape
-                `(batch, src_len)`
-            src_lengths (torch.LongTensor): lengths of each source sentence of
-                shape `(batch)`
-            return_all_hiddens (bool, optional): also return all of the
-                intermediate hidden states (default: False).
-            token_embeddings (torch.Tensor, optional): precomputed embeddings
-                default `None` will recompute embeddings
-
-        Returns:
-            dict:
-                - **encoder_out** (Tensor): the last encoder layer's output of
-                  shape `(src_len, batch, embed_dim)`
-                - **encoder_padding_mask** (ByteTensor): the positions of
-                  padding elements of shape `(batch, src_len)`
-                - **encoder_embedding** (Tensor): the (scaled) embedding lookup
-                  of shape `(batch, src_len, embed_dim)`
-                - **encoder_states** (List[Tensor]): all intermediate
-                  hidden states of shape `(src_len, batch, embed_dim)`.
-                  Only populated if *return_all_hiddens* is True.
-        """
-        prompt_tokens = None
-        prompt_padding_mask = None
-        prompt_kv_list = None
-        if self.args.encoder_prompt:
-            bsz, seq_len = src_tokens.shape[0], src_tokens.shape[1]
-            if self.args.encoder_prompt_type in ("prefix"):
-                prompt_tokens = torch.arange(
-                    0, self.args.encoder_prompt_length).to(
-                    src_tokens.device)
-                prompt_tokens = prompt_tokens.unsqueeze(0).expand(bsz, -1)
-                prompt_padding_mask = torch.zeros_like(prompt_tokens).to(prompt_tokens.device)
-            prompt_kv_list = self.get_encoder_prompt(prompt_tokens)
-        image_embed = None
-        image_embed_2 = None
-        image_pos_embed = None
-        image_pos_embed_2 = None
-        num_image_tokens = None
-
-        if sample_audio_patch_num is None:
-            sample_audio_patch_num = sample_patch_num
-
-        if sample_video_patch_num is None:
-            sample_video_patch_num = sample_patch_num
-
-        if patch_images is not None:
-
-            if patch_types is not None:
-                video_idx = patch_types==1
-                image_idx = patch_types==0
-                audio_idx = patch_types==2
-
-                image_idx_ = (patch_types==0).nonzero()[:, 0]
-                video_idx_ = (patch_types==1).nonzero()[:, 0]
-                audio_idx_ = (patch_types==2).nonzero()[:, 0]
-
-                image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = None,None,None,None,None
-                # print(image_idx_, video_idx_, audio_idx_)
-                if torch.any(image_idx).item():
-                    image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = \
-                        self.get_patch_images_info(patch_images, sample_patch_num, src_tokens.device)
-
-                    ids_merge = image_idx_
-
-                if torch.any(video_idx).item():
-                    video_embed, image_num_patches, video_padding_mask, video_position_ids, video_pos_embed = \
-                        self.get_patch_videos_info(patch_videos[video_idx], sample_video_patch_num, src_tokens.device)
-
-                    if image_embed is not None:
-                        ids_merge = torch.cat((ids_merge, video_idx_), dim=0).long()
-
-                        image_embed = torch.cat((image_embed, video_embed), dim=0)
-                        bs, L, D = image_embed.shape
-                        image_padding_mask = torch.cat((image_padding_mask, video_padding_mask), dim=0)
-                        image_position_ids = torch.cat((image_position_ids, video_position_ids), dim=0)
-                        image_pos_embed = torch.cat((image_pos_embed, video_pos_embed), dim=0)
-                        
-                        image_embed = torch.gather(image_embed, dim=0, index=ids_merge[:, None, None].repeat(1, L, D))
-                        image_padding_mask = torch.gather(image_padding_mask, dim=0, index=ids_merge[:, None].repeat(1, image_padding_mask.shape[1]))
-                        image_position_ids = torch.gather(image_position_ids, dim=0, index=ids_merge[:, None].repeat(1, image_position_ids.shape[1]))
-                        image_pos_embed = torch.gather(image_pos_embed, dim=0, index=ids_merge[:, None, None].repeat(1, image_pos_embed.shape[1], D))
-
-                    else:
-                        image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = \
-                            video_embed, image_num_patches, video_padding_mask, video_position_ids, video_pos_embed
-                        ids_merge = video_idx_
-
-                if torch.any(audio_idx).item() : # or image_embed is None
-                    audio_embed, image_num_patches, audio_padding_mask, audio_position_ids, audio_pos_embed = \
-                        self.get_patch_audios_info(patch_audios[audio_idx], sample_audio_patch_num, src_tokens.device)
-
-                    if image_embed is not None:
-
-                        ids_merge = torch.cat((ids_merge, audio_idx_), dim=0).long()
-
-
-                        image_embed = torch.cat((image_embed, audio_embed), dim=0)
-                        bs, L, D = image_embed.shape
-                        image_padding_mask = torch.cat((image_padding_mask, audio_padding_mask), dim=0)
-                        image_position_ids = torch.cat((image_position_ids, audio_position_ids), dim=0)
-                        image_pos_embed = torch.cat((image_pos_embed, audio_pos_embed), dim=0)
-                        
-                        image_embed = torch.gather(image_embed, dim=0, index=ids_merge[:, None, None].repeat(1, L, D))
-                        image_padding_mask = torch.gather(image_padding_mask, dim=0, index=ids_merge[:, None].repeat(1, image_padding_mask.shape[1]))
-                        image_position_ids = torch.gather(image_position_ids, dim=0, index=ids_merge[:, None].repeat(1, image_position_ids.shape[1]))
-                        image_pos_embed = torch.gather(image_pos_embed, dim=0, index=ids_merge[:, None, None].repeat(1, image_pos_embed.shape[1], D))
-
-                    else:
-                        image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = \
-                            audio_embed, image_num_patches, audio_padding_mask, audio_position_ids, audio_pos_embed
-
-            else:
-                image_embed, image_num_patches, image_padding_mask, image_position_ids, image_pos_embed = \
-            self.get_patch_images_info(patch_images, sample_patch_num, src_tokens.device)
-
-            image_padding_mask[~patch_masks] = True
-
-            num_image_tokens=image_num_patches
-        if patch_images_2 is not None:
-            image_embed_2, image_num_patches_2, image_padding_mask_2, image_position_ids_2, image_pos_embed_2 = \
-                self.get_patch_images_info(patch_images_2, sample_patch_num, src_tokens.device)
-            image_padding_mask_2[~patch_masks] = True
-            num_image_tokens+=image_num_patches_2
-
-        encoder_padding_mask = src_tokens.eq(self.padding_idx)
-        if patch_images is not None:
-            encoder_padding_mask = torch.cat([image_padding_mask, encoder_padding_mask], dim=1)
-        if patch_images_2 is not None:
-            encoder_padding_mask = torch.cat([image_padding_mask_2, encoder_padding_mask], dim=1)
-        has_pads = (src_tokens.device.type == "xla" or encoder_padding_mask.any())
-
-        pos_embed = self.embed_positions(utils.new_arange(src_tokens))
-        x, encoder_embedding = self.forward_embedding(
-            src_tokens, image_embed, image_embed_2, token_embeddings,
-            pos_embed, image_pos_embed, image_pos_embed_2, patch_types=patch_types
-        )
-
-        # account for padding while computing the representation
-        if has_pads:
-            x = x * (1 - encoder_padding_mask.unsqueeze(-1).type_as(x))
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        pos_embed = self.pos_ln(pos_embed)
-        if patch_images is not None:
-            image_pos_embed = self.image_pos_ln(image_pos_embed)
-            pos_embed = torch.cat([image_pos_embed, pos_embed], dim=1)
-        if patch_images_2 is not None:
-            image_pos_embed_2 = self.image_pos_ln(image_pos_embed_2)
-            pos_embed = torch.cat([image_pos_embed_2, pos_embed], dim=1)
-
-        pos_q = self.pos_q_linear(pos_embed).view(
-            pos_embed.size(0), pos_embed.size(1), self.num_attention_heads, -1
-        ).transpose(1, 2) * self.pos_scaling
-        pos_k = self.pos_k_linear(pos_embed).view(
-            pos_embed.size(0), pos_embed.size(1), self.num_attention_heads, -1
-        ).transpose(1, 2)
-        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))
-
-        encoder_states = []
-
-        if return_all_hiddens:
-            encoder_states.append(x)
-
-        if prompt_padding_mask is not None:
-            encoder_padding_mask = torch.cat([prompt_padding_mask, encoder_padding_mask], dim=1)
-        # encoder layers
-        if self.with_cls:
-            offset_rel_pos = 1
-            abs_pos_bias = F.pad(abs_pos_bias, (1, 0, 1, 0), "constant", 0)
-        else:
-            offset_rel_pos = 0
-            
-        for idx, layer in enumerate(self.layers):
-            self_attn_bias = abs_pos_bias.clone()
-            self_attn_bias[:, :, -src_tokens.size(1):, -src_tokens.size(1):] += self.get_rel_pos_bias(src_tokens, idx)
-            if patch_images_2 is not None:
-                self_attn_bias[:, :, offset_rel_pos:image_num_patches_2, offset_rel_pos:image_num_patches_2] += \
-                    self.get_image_rel_pos_bias(image_position_ids_2, idx)
-                self_attn_bias[:, :, offset_rel_pos+image_num_patches_2:image_num_patches_2+image_num_patches, offset_rel_pos+image_num_patches_2:image_num_patches_2+image_num_patches] += \
-                    self.get_image_rel_pos_bias(image_position_ids, idx)
-            elif patch_images is not None:
-                self_attn_bias[:, :, offset_rel_pos:x.size(0) - src_tokens.size(1), offset_rel_pos:x.size(0) - src_tokens.size(1)] += \
-                    self.get_image_rel_pos_bias(image_position_ids, idx)
-
-            self_attn_bias = self_attn_bias.reshape(-1, self_attn_bias.size(2), self_attn_bias.size(2))
-
-            if self.args.encoder_prompt:
-                if self.args.encoder_prompt_type != "prompt":
-                    prompt_kv = prompt_kv_list[idx]
-                else:
-                    if idx == 0:
-                        prompt_kv = prompt_kv_list[idx]
-                    else:
-                        prompt_kv = None
-            else:
-                prompt_kv = None 
-            x = layer(x, encoder_padding_mask=encoder_padding_mask if has_pads else None, \
-                    self_attn_bias=self_attn_bias, prompt_kv=prompt_kv, num_image_tokens=num_image_tokens)
-            if return_all_hiddens:
-                assert encoder_states is not None
-                encoder_states.append(x)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-        if self.args.encoder_prompt:
-            encoder_padding_mask = encoder_padding_mask[:, prompt_tokens.size(1):]
-        # The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
-        # `forward` so we use a dictionary instead.
-        # TorchScript does not support mixed values so the values are all lists.
-        # The empty list is equivalent to None.
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [encoder_padding_mask],  # B x T
-            "encoder_embedding": [],  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [],
-            "position_embeddings": [pos_embed],  # B x T x C
-        }
-
-    @torch.jit.export
-    def reorder_encoder_out(self, encoder_out: Dict[str, List[Tensor]], new_order):
-        """
-        Reorder encoder output according to *new_order*.
-
-        Args:
-            encoder_out: output from the ``forward()`` method
-            new_order (LongTensor): desired order
-
-        Returns:
-            *encoder_out* rearranged according to *new_order*
-        """
-        if len(encoder_out["encoder_out"]) == 0:
-            new_encoder_out = []
-        else:
-            new_encoder_out = [encoder_out["encoder_out"][0].index_select(1, new_order)]
-        if len(encoder_out["encoder_padding_mask"]) == 0:
-            new_encoder_padding_mask = []
-        else:
-            new_encoder_padding_mask = [
-                encoder_out["encoder_padding_mask"][0].index_select(0, new_order)
-            ]
-        if len(encoder_out["encoder_embedding"]) == 0:
-            new_encoder_embedding = []
-        else:
-            new_encoder_embedding = [
-                encoder_out["encoder_embedding"][0].index_select(0, new_order)
-            ]
-
-        if len(encoder_out["src_tokens"]) == 0:
-            new_src_tokens = []
-        else:
-            new_src_tokens = [(encoder_out["src_tokens"][0]).index_select(0, new_order)]
-
-        if len(encoder_out["src_lengths"]) == 0:
-            new_src_lengths = []
-        else:
-            new_src_lengths = [(encoder_out["src_lengths"][0]).index_select(0, new_order)]
-
-        if len(encoder_out["position_embeddings"]) == 0:
-            new_position_embeddings = []
-        else:
-            new_position_embeddings = [(encoder_out["position_embeddings"][0]).index_select(0, new_order)]
-
-        encoder_states = encoder_out["encoder_states"]
-        if len(encoder_states) > 0:
-            for idx, state in enumerate(encoder_states):
-                encoder_states[idx] = state.index_select(1, new_order)
-
-        return {
-            "encoder_out": new_encoder_out,  # T x B x C
-            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
-            "encoder_embedding": new_encoder_embedding,  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": new_src_tokens,  # B x T
-            "src_lengths": new_src_lengths,  # B x 1
-            "position_embeddings": new_position_embeddings,  # B x T x C
-        }
-
-    def max_positions(self):
-        """Maximum input length supported by the encoder."""
-        if self.embed_positions is None:
-            return self.max_source_positions
-        return self.max_source_positions
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
-            weights_key = "{}.embed_positions.weights".format(name)
-            if weights_key in state_dict:
-                print("deleting {0}".format(weights_key))
-                del state_dict[weights_key]
-            state_dict[
-                "{}.embed_positions._float_tensor".format(name)
-            ] = torch.FloatTensor(1)
-        for i in range(self.num_layers):
-            # update layer norms
-            self.layers[i].upgrade_state_dict_named(
-                state_dict, "{}.layers.{}".format(name, i)
-            )
-
-        prefix = name + "." if name != "" else ""
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict:
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
-
-        if len(state_dict["encoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
-            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["encoder.embed_image_positions.weight"])
-            embed_dim = state_dict["encoder.embed_image_positions.weight"].size(1)
-            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
-            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_pos_embed_to_add = new_pos_embed_to_add.to(
-                dtype=state_dict["encoder.embed_image_positions.weight"].dtype,
-            )
-            state_dict["encoder.embed_image_positions.weight"] = torch.cat(
-                [state_dict["encoder.embed_image_positions.weight"], new_pos_embed_to_add]
-            )
-        return state_dict
-
-
-class TransformerDecoder(FairseqIncrementalDecoder):
-    """
-    Transformer decoder consisting of *args.decoder_layers* layers. Each layer
-    is a :class:`TransformerDecoderLayer`.
-
-    Args:
-        args (argparse.Namespace): parsed command-line arguments
-        dictionary (~fairseq.data.Dictionary): decoding dictionary
-        embed_tokens (torch.nn.Embedding): output embedding
-        no_encoder_attn (bool, optional): whether to attend to encoder outputs
-            (default: False).
-    """
-
-    def __init__(
-        self,
-        args,
-        dictionary,
-        embed_tokens,
-        no_encoder_attn=False,
-        output_projection=None,
-    ):
-        self.args = args
-        super().__init__(dictionary)
-        self.register_buffer("version", torch.Tensor([3]))
-        self._future_mask = torch.empty(0)
-        self.with_cls = getattr(args, "with_cls", False)
-
-        if getattr(args, "decoder_prompt", False):
-            self.decoder_prompt_encoder = PromptEncoder(
-                type=args.decoder_prompt_type,
-                length=args.decoder_prompt_length,
-                projection=args.decoder_prompt_projection,
-                embed_dim=args.decoder_embed_dim,
-                proj_dim=args.decoder_prompt_dim,
-                layers=args.decoder_layers,
-                vocab_size=args.vocab_size)
-            self.decoder_dropout = nn.Dropout(p=0.2)
-
-        self.dropout_module = FairseqDropout(
-            args.dropout, module_name=self.__class__.__name__
-        )
-        self.decoder_layerdrop = args.decoder_layerdrop
-        self.share_input_output_embed = args.share_decoder_input_output_embed
-        self.num_attention_heads = args.decoder_attention_heads
-
-        input_embed_dim = embed_tokens.embedding_dim
-        embed_dim = args.decoder_embed_dim
-        self.embed_dim = embed_dim
-        self.output_embed_dim = args.decoder_output_dim
-
-        self.padding_idx = embed_tokens.padding_idx
-        self.max_target_positions = args.max_target_positions
-
-        self.embed_tokens = embed_tokens
-
-        self.embed_scale = 1.0 if args.no_scale_embedding else math.sqrt(embed_dim)
-
-        if not args.adaptive_input and args.quant_noise_pq > 0:
-            self.quant_noise = apply_quant_noise_(
-                nn.Linear(embed_dim, embed_dim, bias=False),
-                args.quant_noise_pq,
-                args.quant_noise_pq_block_size,
-            )
-        else:
-            self.quant_noise = None
-
-        self.project_in_dim = (
-            Linear(input_embed_dim, embed_dim, bias=False)
-            if embed_dim != input_embed_dim
-            else None
-        )
-
-        if getattr(args, "layernorm_embedding", False):
-            self.layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.layernorm_embedding = None
-
-        self.window_size = args.code_image_size // 8
-
-        self.embed_positions = Embedding(args.max_target_positions + 2, embed_dim)
-        self.embed_image_positions = Embedding(args.image_bucket_size ** 2 + 1, embed_dim)
-        self.pos_ln = LayerNorm(embed_dim)
-        self.image_pos_ln = LayerNorm(embed_dim)
-        self.pos_scaling = float(embed_dim / self.num_attention_heads * args.attn_scale_factor) ** -0.5
-        self.self_pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.self_pos_k_linear = nn.Linear(embed_dim, embed_dim)
-        self.cross_pos_q_linear = nn.Linear(embed_dim, embed_dim)
-        self.cross_pos_k_linear = nn.Linear(embed_dim, embed_dim)
-
-        if getattr(args, "code_layernorm_embedding", False):
-            self.code_layernorm_embedding = LayerNorm(embed_dim)
-        else:
-            self.code_layernorm_embedding = None
-
-        self.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-        if self.decoder_layerdrop > 0.0:
-            self.layers = LayerDropModuleList(p=self.decoder_layerdrop)
-        else:
-            self.layers = nn.ModuleList([])
-
-        dpr = [x.item() for x in torch.linspace(0, args.decoder_drop_path_rate, args.decoder_layers)]
-        self.layers.extend(
-            [
-                self.build_decoder_layer(args, no_encoder_attn, drop_path_rate=dpr[i])
-                for i in range(args.decoder_layers)
-            ]
-        )
-        self.num_layers = len(self.layers)
-
-        if args.decoder_normalize_before:
-            self.layer_norm = LayerNorm(embed_dim)
-        else:
-            self.layer_norm = None
-
-        self.project_out_dim = (
-            Linear(embed_dim, self.output_embed_dim, bias=False)
-            if embed_dim != self.output_embed_dim and not args.tie_adaptive_weights
-            else None
-        )
-
-        self.adaptive_softmax = None
-        self.output_projection = output_projection
-        if self.output_projection is None:
-            self.build_output_projection(args, dictionary, embed_tokens)
-
-        token_bucket_size = args.token_bucket_size
-        token_num_rel_dis = 2 * token_bucket_size - 1
-        token_rp_bucket = make_token_bucket_position(token_bucket_size)
-        self.token_rel_pos_table_list = nn.ModuleList(
-            [Embedding(token_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
-        )
-
-        image_bucket_size = args.image_bucket_size
-        image_num_rel_dis = (2 * image_bucket_size - 1) * (2 * image_bucket_size - 1) + 3
-        image_rp_bucket = make_image_bucket_position(image_bucket_size, image_num_rel_dis)
-        image_position_idx = torch.arange(self.window_size).unsqueeze(0).expand(self.window_size, self.window_size) + \
-                             torch.arange(self.window_size).unsqueeze(1) * image_bucket_size + 1
-        image_position_idx = torch.cat([torch.tensor([0]), image_position_idx.view(-1)])
-        image_position_idx = torch.cat([image_position_idx, torch.tensor([1024] * 769)])
-        self.image_rel_pos_table_list = nn.ModuleList(
-            [Embedding(image_num_rel_dis, self.num_attention_heads, zero_init=True) for _ in range(args.decoder_layers)]
-        )
-
-        self.register_buffer("token_rp_bucket", token_rp_bucket)
-        self.register_buffer("image_rp_bucket", image_rp_bucket)
-        self.register_buffer("image_position_idx", image_position_idx)
-        self.entangle_position_embedding = args.entangle_position_embedding
-
-    def get_decoder_prompt(self, prompt_tokens):
-        past_key_values = self.decoder_prompt_encoder(prompt_tokens)
-        bsz, seqlen, _ = past_key_values.shape
-        past_key_values = past_key_values.view(
-            bsz,
-            seqlen,
-            self.args.decoder_layers * 2,
-            self.args.decoder_attention_heads,
-            self.args.decoder_embed_dim // self.args.decoder_attention_heads,
-        )
-        past_key_values = self.decoder_dropout(past_key_values)
-        past_key_values = past_key_values.permute([2, 0, 3, 1, 4]).split(2)
-        return past_key_values
-
-    def build_output_projection(self, args, dictionary, embed_tokens):
-        if args.adaptive_softmax_cutoff is not None:
-            self.adaptive_softmax = AdaptiveSoftmax(
-                len(dictionary),
-                self.output_embed_dim,
-                utils.eval_str_list(args.adaptive_softmax_cutoff, type=int),
-                dropout=args.adaptive_softmax_dropout,
-                adaptive_inputs=embed_tokens if args.tie_adaptive_weights else None,
-                factor=args.adaptive_softmax_factor,
-                tie_proj=args.tie_adaptive_proj,
-            )
-        elif self.share_input_output_embed:
-            self.output_projection = nn.Linear(
-                self.embed_tokens.weight.shape[1],
-                self.embed_tokens.weight.shape[0],
-                bias=False,
-            )
-            self.output_projection.weight = self.embed_tokens.weight
-        else:
-            self.output_projection = nn.Linear(
-                self.output_embed_dim, len(dictionary), bias=False
-            )
-            nn.init.normal_(
-                self.output_projection.weight, mean=0, std=self.output_embed_dim ** -0.5
-            )
-        num_base_layers = getattr(args, "base_layers", 0)
-        for i in range(num_base_layers):
-            self.layers.insert(((i+1) * args.decoder_layers) // (num_base_layers + 1), BaseLayer(args))
-
-    def build_decoder_layer(self, args, no_encoder_attn=False, drop_path_rate=0.0):
-        layer = TransformerDecoderLayer(args, no_encoder_attn, drop_path_rate= \
-            drop_path_rate, use_adapter=getattr(args, "adapter", False), adapter_dim=getattr(args, "adapter_dim", 200))
-        checkpoint = getattr(args, "checkpoint_activations", False)
-        if checkpoint:
-            offload_to_cpu = getattr(args, "offload_activations", False)
-            layer = checkpoint_wrapper(layer, offload_to_cpu=offload_to_cpu)
-        # if we are checkpointing, enforce that FSDP always wraps the
-        # checkpointed layer, regardless of layer size
-        min_params_to_wrap = (
-            getattr(args, "min_params_to_wrap", DEFAULT_MIN_PARAMS_TO_WRAP)
-            if not checkpoint else 0
-        )
-        layer = fsdp_wrap(layer, min_num_params=min_params_to_wrap)
-        return layer
-
-    def get_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        rp_bucket = self.token_rp_bucket[:seq_len, :seq_len]
-        values = F.embedding(rp_bucket, self.token_rel_pos_table_list[idx].weight)
-        values = values.permute([2, 0, 1])
-        return values.contiguous()
-
-    def get_image_rel_pos_bias(self, x, idx):
-        seq_len = x.size(1)
-        image_position_idx = self.image_position_idx[:seq_len]
-        rp_bucket = self.image_rp_bucket[image_position_idx][:, image_position_idx]
-        values = F.embedding(rp_bucket, self.image_rel_pos_table_list[idx].weight)
-        values = values.permute(2, 0, 1)
-        return values
-
-    def get_pos_info(self, tokens, tgt_pos_embed, src_pos_embed=None, use_image=False):
-        batch_size = tokens.size(0)
-        tgt_len = tokens.size(1)
-        tgt_pos_embed = self.image_pos_ln(tgt_pos_embed) if use_image else self.pos_ln(tgt_pos_embed)
-        if src_pos_embed is not None:
-            src_len = src_pos_embed.size(1)
-            pos_q = self.cross_pos_q_linear(tgt_pos_embed).view(
-                batch_size, tgt_len, self.num_attention_heads, -1
-            ).transpose(1, 2) * self.pos_scaling
-            pos_k = self.cross_pos_k_linear(src_pos_embed).view(
-                batch_size, src_len, self.num_attention_heads, -1
-            ).transpose(1, 2)
-        else:
-            src_len = tgt_pos_embed.size(1)
-            pos_q = self.self_pos_q_linear(tgt_pos_embed).view(
-                batch_size, tgt_len, self.num_attention_heads, -1
-            ).transpose(1, 2) * self.pos_scaling
-            pos_k = self.self_pos_k_linear(tgt_pos_embed).view(
-                batch_size, src_len, self.num_attention_heads, -1
-            ).transpose(1, 2)
-        abs_pos_bias = torch.matmul(pos_q, pos_k.transpose(2, 3))
-        return abs_pos_bias
-
-    def forward(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor] = None,
-        encoder_out: Optional[Dict[str, List[Tensor]]] = None,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        features_only: bool = False,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-        src_lengths: Optional[Any] = None,
-        return_all_hiddens: bool = False,
-    ):
-        """
-        Args:
-            prev_output_tokens (LongTensor): previous decoder outputs of shape
-                `(batch, tgt_len)`, for teacher forcing
-            encoder_out (optional): output from the encoder, used for
-                encoder-side attention, should be of size T x B x C
-            incremental_state (dict): dictionary used for storing state during
-                :ref:`Incremental decoding`
-            features_only (bool, optional): only return features without
-                applying output layer (default: False).
-            full_context_alignment (bool, optional): don't apply
-                auto-regressive mask to self-attention (default: False).
-
-        Returns:
-            tuple:
-                - the decoder's output of shape `(batch, tgt_len, vocab)`
-                - a dictionary with any model-specific outputs
-        """
-        # print(self.training, "encoder_out", encoder_out['encoder_out'][0][0])
-        x, extra = self.extract_features(
-            prev_output_tokens,
-            code_masks=code_masks,
-            encoder_out=encoder_out,
-            incremental_state=incremental_state,
-            full_context_alignment=full_context_alignment,
-            alignment_layer=alignment_layer,
-            alignment_heads=alignment_heads,
-        )
-
-        if not features_only:
-            x = self.output_layer(x)
-        return x, extra
-
-    def extract_features(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor],
-        encoder_out: Optional[Dict[str, List[Tensor]]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        return self.extract_features_scriptable(
-            prev_output_tokens,
-            code_masks,
-            encoder_out,
-            incremental_state,
-            full_context_alignment,
-            alignment_layer,
-            alignment_heads,
-        )
-
-    """
-    A scriptable subclass of this class has an extract_features method and calls
-    super().extract_features, but super() is not supported in torchscript. A copy of
-    this function is made to be used in the subclass instead.
-    """
-
-    def extract_features_scriptable(
-        self,
-        prev_output_tokens,
-        code_masks: Optional[torch.Tensor],
-        encoder_out: Optional[Dict[str, List[Tensor]]],
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        """
-        Similar to *forward* but only return features.
-
-        Includes several features from "Jointly Learning to Align and
-        Translate with Transformer Models" (Garg et al., EMNLP 2019).
-
-        Args:
-            full_context_alignment (bool, optional): don't apply
-                auto-regressive mask to self-attention (default: False).
-            alignment_layer (int, optional): return mean alignment over
-                heads at this layer (default: last layer).
-            alignment_heads (int, optional): only average alignment over
-                this many heads (default: all heads).
-
-        Returns:
-            tuple:
-                - the decoder's features of shape `(batch, tgt_len, embed_dim)`
-                - a dictionary with any model-specific outputs
-        """
-        prompt_tokens = None
-        prompt_padding_mask = None
-        prompt_kv_list = None
-        if self.args.decoder_prompt:
-            bsz, seq_len = prev_output_tokens.shape[0], prev_output_tokens.shape[1]
-            if self.args.decoder_prompt_type in ("prefix"):
-                prompt_tokens = torch.arange(
-                    0, self.args.decoder_prompt_length).to(
-                    prev_output_tokens.device)
-                prompt_tokens = prompt_tokens.unsqueeze(0).expand(bsz, -1)
-                prompt_padding_mask = torch.zeros_like(prompt_tokens).to(prompt_tokens.device)
-            prompt_kv_list = self.get_decoder_prompt(prompt_tokens)
-        bs, slen = prev_output_tokens.size()
-        if alignment_layer is None:
-            alignment_layer = self.num_layers - 1
-
-        enc: Optional[Tensor] = None
-        padding_mask: Optional[Tensor] = None
-        if encoder_out is not None and len(encoder_out["encoder_out"]) > 0:
-            enc = encoder_out["encoder_out"][0]
-            assert (
-                enc.size()[1] == bs
-            ), f"Expected enc.shape == (t, {bs}, c) got {enc.shape}"
-        if encoder_out is not None and len(encoder_out["encoder_padding_mask"]) > 0:
-            padding_mask = encoder_out["encoder_padding_mask"][0]
-
-        bsz, tgt_len = prev_output_tokens.shape
-        token_position_idx = utils.new_arange(prev_output_tokens)
-        tgt_pos_embed = self.embed_positions(token_position_idx)
-        if code_masks is not None and torch.any(code_masks):
-            image_position_idx = self.image_position_idx[:prev_output_tokens.size(1)].unsqueeze(0).expand(bsz, tgt_len)
-            tgt_pos_embed[code_masks] = self.embed_image_positions(image_position_idx)[code_masks]
-
-        # self attn position bias
-        self_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=False)
-        if code_masks is not None and torch.any(code_masks):
-            self_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, use_image=True)
-            self_abs_pos_bias[code_masks] = self_image_abs_pos_bias[code_masks]
-        # cross attn position bias
-        src_pos_embed = encoder_out['position_embeddings'][0]
-        cross_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed)
-
-
-
-        if code_masks is not None and torch.any(code_masks):
-            cross_image_abs_pos_bias = self.get_pos_info(prev_output_tokens, tgt_pos_embed, src_pos_embed=src_pos_embed, use_image=True)
-            cross_abs_pos_bias[code_masks] = cross_image_abs_pos_bias[code_masks]
-        
-        if self.with_cls:
-            cross_abs_pos_bias = F.pad(cross_abs_pos_bias, (enc.shape[0] - cross_abs_pos_bias.shape[-1], 0, prev_output_tokens.shape[1] - cross_abs_pos_bias.shape[2], 0), "constant", 0)
-
-
-        cross_abs_pos_bias = cross_abs_pos_bias.reshape(-1, *cross_abs_pos_bias.size()[-2:])
-
-        all_prev_output_tokens = prev_output_tokens.clone()
-        # print(all_prev_output_tokens.shape, prev_output_tokens.shape, tgt_pos_embed.shape, self_abs_pos_bias.shape)
-        if incremental_state is not None:
-            prev_output_tokens = prev_output_tokens[:, -1:]
-            cross_abs_pos_bias = cross_abs_pos_bias[:, -1:, :]
-            tgt_pos_embed = tgt_pos_embed[:, -1:, :]
-
-        # embed tokens and positions
-        x = self.embed_scale * self.embed_tokens(prev_output_tokens)
-
-        if self.quant_noise is not None:
-            x = self.quant_noise(x)
-
-        if self.project_in_dim is not None:
-            x = self.project_in_dim(x)
-
-        if self.entangle_position_embedding is not None and not self.args.disable_entangle:
-            x += tgt_pos_embed
-
-        if self.layernorm_embedding is not None:
-            if code_masks is None or not code_masks.any() or not getattr(self, "code_layernorm_embedding", False):
-                x = self.layernorm_embedding(x)
-            elif code_masks is not None and code_masks.all():
-                x = self.code_layernorm_embedding(x)
-            else:
-                x[~code_masks] = self.layernorm_embedding(x[~code_masks])
-                x[code_masks] = self.code_layernorm_embedding(x[code_masks])
-
-        x = self.dropout_module(x)
-
-        # B x T x C -> T x B x C
-        x = x.transpose(0, 1)
-
-        self_attn_padding_mask: Optional[Tensor] = None
-        if self.cross_self_attention or prev_output_tokens.eq(self.padding_idx).any():
-            self_attn_padding_mask = prev_output_tokens.eq(self.padding_idx)
-            if prompt_padding_mask is not None:
-                self_attn_padding_mask = torch.cat([prompt_padding_mask, self_attn_padding_mask], dim=1)
-
-        # decoder layers
-        attn: Optional[Tensor] = None
-        inner_states: List[Optional[Tensor]] = [x]
-        for idx, layer in enumerate(self.layers):
-            if incremental_state is None and not full_context_alignment:
-                self_attn_mask = self.buffered_future_mask(x)
-                if self.args.decoder_prompt:
-                    seq_len, prompt_len = x.size(0), prompt_tokens.size(1)
-                    prompt_mask = torch.zeros([seq_len, prompt_len]).to(x.device)
-                    self_attn_mask = torch.cat([prompt_mask, self_attn_mask], dim=1)
-            else:
-                self_attn_mask = None
-
-            self_attn_bias = self_abs_pos_bias.clone()
-            # print(self_attn_bias.shape, 'self_attn_bias')
-            if code_masks is None or not code_masks.any():
-                # try:
-                self_attn_bias += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-                # except:
-                #     print(idx, self_attn_bias.shape, all_prev_output_tokens.shape, self_abs_pos_bias.shape, incremental_state)
-                #     self_attn_bias += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            elif code_masks is not None and code_masks.all():
-                self_attn_bias += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            else:
-                self_attn_bias[~code_masks] += self.get_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-                self_attn_bias[code_masks] += self.get_image_rel_pos_bias(all_prev_output_tokens, idx).unsqueeze(0)
-            self_attn_bias = self_attn_bias.reshape(-1, *self_attn_bias.size()[-2:])
-            if incremental_state is not None:
-                self_attn_bias = self_attn_bias[:, -1:, :]
-
-            if self.args.decoder_prompt:
-                if self.args.decoder_prompt_type != "prompt":
-                    prompt_kv = prompt_kv_list[idx]
-                else:
-                    if idx == 0:
-                        prompt_kv = prompt_kv_list[idx]
-                    else:
-                        prompt_kv = None
-            else:
-                prompt_kv = None
-
-            x, layer_attn, _ = layer(
-                x,
-                enc,
-                padding_mask,
-                incremental_state,
-                self_attn_mask=self_attn_mask,
-                self_attn_padding_mask=self_attn_padding_mask,
-                need_attn=bool((idx == alignment_layer)),
-                need_head_weights=bool((idx == alignment_layer)),
-                self_attn_bias=self_attn_bias,
-                cross_attn_bias=cross_abs_pos_bias,
-                prompt_kv=prompt_kv
-            )
-            inner_states.append(x)
-            if layer_attn is not None and idx == alignment_layer:
-                attn = layer_attn.float().to(x)
-
-        if attn is not None:
-            if alignment_heads is not None:
-                attn = attn[:alignment_heads]
-
-            # average probabilities over heads
-            attn = attn.mean(dim=0)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        # T x B x C -> B x T x C
-        x = x.transpose(0, 1)
-
-        if self.project_out_dim is not None:
-            x = self.project_out_dim(x)
-
-        return x, {"attn": [attn], "inner_states": inner_states}
-
-    def output_layer(self, features):
-        """Project features to the vocabulary size."""
-        if self.adaptive_softmax is None:
-            # project back to size of vocabulary
-            return self.output_projection(features)
-        else:
-            return features
-
-    def max_positions(self):
-        """Maximum output length supported by the decoder."""
-        if self.embed_positions is None:
-            return self.max_target_positions
-        return self.max_target_positions
-
-    def buffered_future_mask(self, tensor):
-        dim = tensor.size(0)
-        if (
-            self._future_mask.size(0) == 0
-            or (not self._future_mask.device == tensor.device)
-            or self._future_mask.size(0) < dim
-        ):
-            self._future_mask = torch.triu(
-                utils.fill_with_neg_inf(torch.zeros([dim, dim])), 1
-            )
-        self._future_mask = self._future_mask.to(tensor)
-        return self._future_mask[:dim, :dim]
-
-    def upgrade_state_dict_named(self, state_dict, name):
-        """Upgrade a (possibly old) state dict for new versions of fairseq."""
-        if isinstance(self.embed_positions, SinusoidalPositionalEmbedding):
-            weights_key = "{}.embed_positions.weights".format(name)
-            if weights_key in state_dict:
-                del state_dict[weights_key]
-            state_dict[
-                "{}.embed_positions._float_tensor".format(name)
-            ] = torch.FloatTensor(1)
-
-        if f"{name}.output_projection.weight" not in state_dict:
-            if self.share_input_output_embed:
-                embed_out_key = f"{name}.embed_tokens.weight"
-            else:
-                embed_out_key = f"{name}.embed_out"
-            if embed_out_key in state_dict:
-                state_dict[f"{name}.output_projection.weight"] = state_dict[
-                    embed_out_key
-                ]
-                if not self.share_input_output_embed:
-                    del state_dict[embed_out_key]
-
-        for i in range(self.num_layers):
-            # update layer norms
-            self.layers[i].upgrade_state_dict_named(
-                state_dict, "{}.layers.{}".format(name, i)
-            )
-
-
-
-        prefix = name + "." if name != "" else ""
-        image_params = ["image_position_idx"]
-        for image_param in image_params:
-            state_dict[prefix + image_param] = self.state_dict()[image_param]
-        for param_name, param_tensor in self.state_dict().items():
-            if (prefix + param_name) not in state_dict:
-                state_dict[prefix + param_name] = self.state_dict()[param_name]
-
-        if len(state_dict["decoder.embed_image_positions.weight"]) < len(self.state_dict()["embed_image_positions.weight"]):
-            num_posids_to_add = len(self.state_dict()["embed_image_positions.weight"]) - len(state_dict["decoder.embed_image_positions.weight"])
-            embed_dim = state_dict["decoder.embed_image_positions.weight"].size(1)
-            new_pos_embed_to_add = torch.zeros(num_posids_to_add, embed_dim)
-            nn.init.normal_(new_pos_embed_to_add, mean=0, std=embed_dim ** -0.5)
-            new_pos_embed_to_add = new_pos_embed_to_add.to(
-                dtype=state_dict["decoder.embed_image_positions.weight"].dtype,
-            )
-            state_dict["decoder.embed_image_positions.weight"] = torch.cat(
-                [state_dict["decoder.embed_image_positions.weight"], new_pos_embed_to_add]
-            )
-        return state_dict
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx=None, zero_init=False):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    if padding_idx is not None:
-        nn.init.constant_(m.weight[padding_idx], 0)
-    if zero_init:
-        nn.init.constant_(m.weight, 0)
-    return m
-
-
-def Linear(in_features, out_features, bias=True):
-    m = nn.Linear(in_features, out_features, bias)
-    nn.init.xavier_uniform_(m.weight)
-    if bias:
-        nn.init.constant_(m.bias, 0.0)
-    return m
-
-
-@register_model_architecture("unify_transformer", "unify_transformer")
-def base_architecture(args):
-    args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
-    args.encoder_learned_pos = getattr(args, "encoder_learned_pos", False)
-    args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", False)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
-    args.attention_dropout = getattr(args, "attention_dropout", 0.0)
-    args.activation_dropout = getattr(args, "activation_dropout", 0.0)
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.no_cross_attention = getattr(args, "no_cross_attention", False)
-    args.cross_self_attention = getattr(args, "cross_self_attention", False)
-
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-
-    args.encoder_prompt = getattr(args, "encoder_prompt", False)
-    args.encoder_prompt_length = getattr(args, "encoder_prompt_length", 100)
-    args.encoder_prompt_type = getattr(args, "encoder_prompt_type", "prefix")
-    args.encoder_prompt_projection = getattr(args, "encoder_prompt_projection", False)
-    args.encoder_prompt_dim = getattr(args, "encoder_prompt_dim", 2 * args.encoder_embed_dim)
-
-    args.decoder_prompt = getattr(args, "decoder_prompt", False)
-    args.decoder_prompt_length = getattr(args, "decoder_prompt_length", 100)
-    args.decoder_prompt_type = getattr(args, "decoder_prompt_type", "prefix")
-    args.decoder_prompt_projection = getattr(args, "decoder_prompt_projection", False)
-    args.decoder_prompt_dim = getattr(args, "decoder_prompt_dim", 2 * args.encoder_embed_dim)
-
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    args.layernorm_embedding = getattr(args, "layernorm_embedding", False)
-    args.tie_adaptive_weights = getattr(args, "tie_adaptive_weights", False)
-    args.checkpoint_activations = getattr(args, "checkpoint_activations", False)
-    args.offload_activations = getattr(args, "offload_activations", False)
-    if args.offload_activations:
-        args.checkpoint_activations = True
-    args.encoder_layers_to_keep = getattr(args, "encoder_layers_to_keep", None)
-    args.decoder_layers_to_keep = getattr(args, "decoder_layers_to_keep", None)
-    args.encoder_layerdrop = getattr(args, "encoder_layerdrop", 0)
-    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0)
-    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-    args.quant_noise_pq_block_size = getattr(args, "quant_noise_pq_block_size", 8)
-    args.quant_noise_scalar = getattr(args, "quant_noise_scalar", 0)
diff --git a/spaces/mshukor/UnIVAL/run_scripts/image_gen/eval_utils/dataset.py b/spaces/mshukor/UnIVAL/run_scripts/image_gen/eval_utils/dataset.py
deleted file mode 100644
index ed7a86f13b359a79d22c89681a8af32792e47c95..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/run_scripts/image_gen/eval_utils/dataset.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from torch.utils import data
-from PIL import Image
-import os
-
-
-class Dataset(data.Dataset):
-    'Characterizes a dataset for PyTorch'
-
-    def __init__(self, path, transform=None):
-        'Initialization'
-        self.file_names = self.get_filenames(path)
-        self.transform = transform
-
-    def __len__(self):
-        'Denotes the total number of samples'
-        return len(self.file_names)
-
-    def __getitem__(self, index):
-        'Generates one sample of data'
-        img = Image.open(self.file_names[index]).convert('RGB')
-        # Convert image and label to torch tensors
-        if self.transform is not None:
-            img = self.transform(img)
-        return img
-
-    def get_filenames(self, data_path):
-        images = []
-        for path, subdirs, files in os.walk(data_path):
-            for name in files:
-                if name.rfind('jpg') != -1 or name.rfind('png') != -1:
-                    filename = os.path.join(path, name)
-                    if os.path.isfile(filename):
-                        images.append(filename)
-        return images
diff --git a/spaces/mygyasir/genious_bgremover/carvekit/ml/wrap/__init__.py b/spaces/mygyasir/genious_bgremover/carvekit/ml/wrap/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/utils/__init__.py b/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/utils/__init__.py
deleted file mode 100644
index da53a4d25419f5de3252af664a7aca5551950f3a..0000000000000000000000000000000000000000
--- a/spaces/nakamura196/yolov5-kunshujo/ultralytics/yolov5/utils/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-utils/initialization
-"""
-
-
-def notebook_init(verbose=True):
-    # Check system software and hardware
-    print('Checking setup...')
-
-    import os
-    import shutil
-
-    from utils.general import check_requirements, emojis, is_colab
-    from utils.torch_utils import select_device  # imports
-
-    check_requirements(('psutil', 'IPython'))
-    import psutil
-    from IPython import display  # to display images and clear console output
-
-    if is_colab():
-        shutil.rmtree('/content/sample_data', ignore_errors=True)  # remove colab /sample_data directory
-
-    # System info
-    if verbose:
-        gb = 1 << 30  # bytes to GiB (1024 ** 3)
-        ram = psutil.virtual_memory().total
-        total, used, free = shutil.disk_usage("/")
-        display.clear_output()
-        s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)'
-    else:
-        s = ''
-
-    select_device(newline=False)
-    print(emojis(f'Setup complete ✅ {s}'))
-    return display
diff --git a/spaces/nakas/MusicGenDemucs/audiocraft/modules/codebooks_patterns.py b/spaces/nakas/MusicGenDemucs/audiocraft/modules/codebooks_patterns.py
deleted file mode 100644
index c5b35cbea8cff84aa56116dbdd860fc72a913a13..0000000000000000000000000000000000000000
--- a/spaces/nakas/MusicGenDemucs/audiocraft/modules/codebooks_patterns.py
+++ /dev/null
@@ -1,539 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import namedtuple
-from dataclasses import dataclass
-from functools import lru_cache
-import logging
-import typing as tp
-
-from abc import ABC, abstractmethod
-import torch
-
-LayoutCoord = namedtuple('LayoutCoord', ['t', 'q'])  # (timestep, codebook index)
-PatternLayout = tp.List[tp.List[LayoutCoord]]  # Sequence of coordinates
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class Pattern:
-    """Base implementation of a pattern over a sequence with multiple codebooks.
-
-    The codebook pattern consists in a layout, defining for each sequence step
-    the list of coordinates of each codebook timestep in the resulting interleaved sequence.
-    The first item of the pattern is always an empty list in order to properly insert a special token
-    to start with. For convenience, we also keep track of ``n_q`` the number of codebooks used for the pattern
-    and ``timesteps`` the number of timesteps corresponding to the original sequence.
-
-    The pattern provides convenient methods to build and revert interleaved sequences from it:
-    ``build_pattern_sequence`` maps a given a dense input tensor of multi-codebook sequence from [B, K, T]
-        to the interleaved sequence of shape [B, K, S] applying the pattern, with S being the batch size,
-        K being the number of codebooks, T the number of original timesteps and S the number of sequence steps
-        for the output sequence. The unfilled positions are replaced with a special token and the built sequence
-        is returned along with a mask indicating valid tokens.
-    ``revert_pattern_sequence`` maps back an interleaved sequence of shape [B, K, S] to the original alignment
-        of codebooks across timesteps to an output tensor of shape [B, K, T], using again a special token and a mask
-        to fill and specify invalid positions if needed.
-    See the dedicated methods for more details.
-    """
-    # Pattern layout, for each sequence step, we have a list of coordinates
-    # corresponding to the original codebook timestep and position.
-    # The first list is always an empty list in order to properly insert
-    # a special token to start with.
-    layout: PatternLayout
-    timesteps: int
-    n_q: int
-
-    def __post_init__(self):
-        assert len(self.layout) > 0
-        assert self.layout[0] == []
-        self._validate_layout()
-        self._build_reverted_sequence_scatter_indexes = lru_cache(100)(self._build_reverted_sequence_scatter_indexes)
-        self._build_pattern_sequence_scatter_indexes = lru_cache(100)(self._build_pattern_sequence_scatter_indexes)
-        logger.info("New pattern, time steps: %d, sequence steps: %d", self.timesteps, len(self.layout))
-
-    def _validate_layout(self):
-        """Runs checks on the layout to ensure a valid pattern is defined.
-        A pattern is considered invalid if:
-            - Multiple timesteps for a same codebook are defined in the same sequence step
-            - The timesteps for a given codebook are not in ascending order as we advance in the sequence
-              (this would mean that we have future timesteps before past timesteps).
-        """
-        q_timesteps = {q: 0 for q in range(self.n_q)}
-        for s, seq_coords in enumerate(self.layout):
-            if len(seq_coords) > 0:
-                qs = set()
-                for coord in seq_coords:
-                    qs.add(coord.q)
-                    last_q_timestep = q_timesteps[coord.q]
-                    assert coord.t >= last_q_timestep, \
-                        f"Past timesteps are found in the sequence for codebook = {coord.q} at step {s}"
-                    q_timesteps[coord.q] = coord.t
-                # each sequence step contains at max 1 coordinate per codebook
-                assert len(qs) == len(seq_coords), \
-                    f"Multiple entries for a same codebook are found at step {s}"
-
-    @property
-    def num_sequence_steps(self):
-        return len(self.layout) - 1
-
-    @property
-    def max_delay(self):
-        max_t_in_seq_coords = 0
-        for seq_coords in self.layout[1:]:
-            for coords in seq_coords:
-                max_t_in_seq_coords = max(max_t_in_seq_coords, coords.t + 1)
-        return max_t_in_seq_coords - self.timesteps
-
-    @property
-    def valid_layout(self):
-        valid_step = len(self.layout) - self.max_delay
-        return self.layout[:valid_step]
-
-    def get_sequence_coords_with_timestep(self, t: int, q: tp.Optional[int] = None):
-        """Get codebook coordinates in the layout that corresponds to the specified timestep t
-        and optionally to the codebook q. Coordinates are returned as a tuple with the sequence step
-        and the actual codebook coordinates.
-        """
-        assert t <= self.timesteps, "provided timesteps is greater than the pattern's number of timesteps"
-        if q is not None:
-            assert q <= self.n_q, "provided number of codebooks is greater than the pattern's number of codebooks"
-        coords = []
-        for s, seq_codes in enumerate(self.layout):
-            for code in seq_codes:
-                if code.t == t and (q is None or code.q == q):
-                    coords.append((s, code))
-        return coords
-
-    def get_steps_with_timestep(self, t: int, q: tp.Optional[int] = None) -> tp.List[int]:
-        return [step for step, coords in self.get_sequence_coords_with_timestep(t, q)]
-
-    def get_first_step_with_timesteps(self, t: int, q: tp.Optional[int] = None) -> tp.Optional[int]:
-        steps_with_timesteps = self.get_steps_with_timestep(t, q)
-        return steps_with_timesteps[0] if len(steps_with_timesteps) > 0 else None
-
-    def _build_pattern_sequence_scatter_indexes(self, timesteps: int, n_q: int, keep_only_valid_steps: bool,
-                                                device: tp.Union[torch.device, str] = 'cpu'):
-        """Build scatter indexes corresponding to the pattern, up to the provided sequence_steps.
-
-        Args:
-            timesteps (int): Maximum number of timesteps steps to consider.
-            keep_only_valid_steps (bool): Restrict the pattern layout to match only valid steps.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            indexes (torch.Tensor): Indexes corresponding to the sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes, of shape [K, S].
-        """
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert timesteps <= self.timesteps, "invalid number of timesteps used to build the sequence from the pattern"
-        # use the proper layout based on whether we limit ourselves to valid steps only or not,
-        # note that using the valid_layout will result in a truncated sequence up to the valid steps
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, len(ref_layout), dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, len(ref_layout), dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        # the last value is n_q * timesteps as we have flattened z and append special token as the last token
-        # which will correspond to the index: n_q * timesteps
-        indexes[:] = n_q * timesteps
-        # iterate over the pattern and fill scattered indexes and mask
-        for s, sequence_coords in enumerate(ref_layout):
-            for coords in sequence_coords:
-                if coords.t < timesteps:
-                    indexes[coords.q, s] = coords.t + coords.q * timesteps
-                    mask[coords.q, s] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def build_pattern_sequence(self, z: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Build sequence corresponding to the pattern from the input tensor z.
-        The sequence is built using up to sequence_steps if specified, and non-pattern
-        coordinates are filled with the special token.
-
-        Args:
-            z (torch.Tensor): Input tensor of multi-codebooks sequence, of shape [B, K, T].
-            special_token (int): Special token used to fill non-pattern coordinates in the new sequence.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, S] with S
-                corresponding either to the sequence_steps if provided, otherwise to the length of the pattern.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, S].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, S].
-        """
-        B, K, T = z.shape
-        indexes, mask = self._build_pattern_sequence_scatter_indexes(
-            T, K, keep_only_valid_steps=keep_only_valid_steps, device=str(z.device)
-        )
-        z = z.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        z = torch.cat([z, torch.zeros_like(z[:, :1]) + special_token], dim=1)
-        values = z[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def _build_reverted_sequence_scatter_indexes(self, sequence_steps: int, n_q: int,
-                                                 keep_only_valid_steps: bool = False,
-                                                 is_model_output: bool = False,
-                                                 device: tp.Union[torch.device, str] = 'cpu'):
-        """Builds scatter indexes required to retrieve the original multi-codebook sequence
-        from interleaving pattern.
-
-        Args:
-            sequence_steps (int): Sequence steps.
-            n_q (int): Number of codebooks.
-            keep_only_valid_steps (bool): Build a sequence from the pattern up to valid (= fully defined) steps.
-                Steps that are beyond valid steps will be replaced by the special_token in that case.
-            is_model_output (bool): Whether to keep the sequence item corresponding to initial special token or not.
-            device (Union[torch.device, str]): Device for created tensors.
-        Returns:
-            torch.Tensor: Indexes for reconstructing the output, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        ref_layout = self.valid_layout if keep_only_valid_steps else self.layout
-        # TODO(jade): Do we want to further truncate to only valid timesteps here as well?
-        timesteps = self.timesteps
-        assert n_q == self.n_q, f"invalid number of codebooks for the sequence and the pattern: {n_q} != {self.n_q}"
-        assert sequence_steps <= len(ref_layout), \
-            f"sequence to revert is longer than the defined pattern: {sequence_steps} > {len(ref_layout)}"
-
-        # ensure we take the appropriate indexes to keep the model output from the first special token as well
-        if is_model_output:
-            ref_layout = ref_layout[1:]
-
-        # single item indexing being super slow with pytorch vs. numpy, so we use numpy here
-        indexes = torch.zeros(n_q, timesteps, dtype=torch.long).numpy()
-        mask = torch.zeros(n_q, timesteps, dtype=torch.bool).numpy()
-        # fill indexes with last sequence step value that will correspond to our special token
-        indexes[:] = n_q * sequence_steps
-        for s, sequence_codes in enumerate(ref_layout):
-            if s < sequence_steps:
-                for code in sequence_codes:
-                    if code.t < timesteps:
-                        indexes[code.q, code.t] = s + code.q * sequence_steps
-                        mask[code.q, code.t] = 1
-        indexes = torch.from_numpy(indexes).to(device)
-        mask = torch.from_numpy(mask).to(device)
-        return indexes, mask
-
-    def revert_pattern_sequence(self, s: torch.Tensor, special_token: int, keep_only_valid_steps: bool = False):
-        """Revert a sequence built from the pattern back to the original multi-codebook sequence without interleaving.
-        The sequence is reverted using up to timesteps if specified, and non-pattern coordinates
-        are filled with the special token.
-
-        Args:
-            s (torch.Tensor): Interleaved sequence tensor obtained from the pattern, of shape [B, K, S].
-            special_token (int or float): Special token used to fill non-pattern coordinates in the new sequence.
-        Returns:
-            values (torch.Tensor): Interleaved sequence matching the pattern, of shape [B, K, T] with T
-                corresponding either to the timesteps if provided, or the total timesteps in pattern otherwise.
-            indexes (torch.Tensor): Indexes corresponding to the interleaved sequence, of shape [K, T].
-            mask (torch.Tensor): Mask corresponding to indexes that matches valid indexes of shape [K, T].
-        """
-        B, K, S = s.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=False, device=str(s.device)
-        )
-        s = s.view(B, -1)
-        # we append the special token as the last index of our flattened z tensor
-        s = torch.cat([s, torch.zeros_like(s[:, :1]) + special_token], dim=1)
-        values = s[:, indexes.view(-1)]
-        values = values.view(B, K, indexes.shape[-1])
-        return values, indexes, mask
-
-    def revert_pattern_logits(self, logits: torch.Tensor, special_token: float, keep_only_valid_steps: bool = False):
-        """Revert model logits obtained on a sequence built from the pattern
-        back to a tensor matching the original sequence.
-
-        This method is similar to ``revert_pattern_sequence`` with the following specificities:
-        1. It is designed to work with the extra cardinality dimension
-        2. We return the logits for the first sequence item that matches the special_token and
-        which matching target in the original sequence is the first item of the sequence,
-        while we skip the last logits as there is no matching target
-        """
-        B, card, K, S = logits.shape
-        indexes, mask = self._build_reverted_sequence_scatter_indexes(
-            S, K, keep_only_valid_steps, is_model_output=True, device=logits.device
-        )
-        logits = logits.reshape(B, card, -1)
-        # we append the special token as the last index of our flattened z tensor
-        logits = torch.cat([logits, torch.zeros_like(logits[:, :, :1]) + special_token], dim=-1)  # [B, card, K x S]
-        values = logits[:, :, indexes.view(-1)]
-        values = values.view(B, card, K, indexes.shape[-1])
-        return values, indexes, mask
-
-
-class CodebooksPatternProvider(ABC):
-    """Abstraction around providing pattern for interleaving codebooks.
-
-    The CodebooksPatternProvider abstraction allows to implement various strategies to
-    define interleaving pattern of sequences composed of multiple codebooks. For a given
-    number of codebooks `n_q`, the pattern provider can generate a specified pattern
-    corresponding to a sequence of `T` timesteps with `n_q` parallel codebooks. This pattern
-    can be used to construct a new sequence from the original codes respecting the specified
-    pattern. The pattern is defined as a list of list of code coordinates, code coordinate
-    being a tuple with the original timestep and codebook to build the new sequence.
-    Note that all patterns must start with an empty list that is then used to insert a first
-    sequence step of special tokens in the newly generated sequence.
-
-    Args:
-        n_q (int): number of codebooks.
-        cached (bool): if True, patterns for a given length are cached. In general
-            that should be true for efficiency reason to avoid synchronization points.
-    """
-    def __init__(self, n_q: int, cached: bool = True):
-        assert n_q > 0
-        self.n_q = n_q
-        self.get_pattern = lru_cache(100)(self.get_pattern)  # type: ignore
-
-    @abstractmethod
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern with specific interleaving between codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        raise NotImplementedError()
-
-
-class DelayedPatternProvider(CodebooksPatternProvider):
-    """Provider for delayed pattern across delayed codebooks.
-    Codebooks are delayed in the sequence and sequence steps will contain codebooks
-    from different timesteps.
-
-    Example:
-        Taking timesteps=4 and n_q=3, delays=None, the multi-codebook sequence:
-        [[1, 2, 3, 4],
-        [1, 2, 3, 4],
-        [1, 2, 3, 4]]
-        The resulting sequence obtained from the returned pattern is:
-        [[S, 1, 2, 3, 4],
-        [S, S, 1, 2, 3],
-        [S, S, S, 1, 2]]
-        (with S being a special token)
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-        flatten_first (int): Flatten the first N timesteps.
-        empty_initial (int): Prepend with N empty list of coordinates.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None,
-                 flatten_first: int = 0, empty_initial: int = 0):
-        super().__init__(n_q)
-        if delays is None:
-            delays = list(range(n_q))
-        self.delays = delays
-        self.flatten_first = flatten_first
-        self.empty_initial = empty_initial
-        assert len(self.delays) == self.n_q
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        max_delay = max(self.delays)
-        if self.empty_initial:
-            out += [[] for _ in range(self.empty_initial)]
-        if self.flatten_first:
-            for t in range(min(timesteps, self.flatten_first)):
-                for q in range(self.n_q):
-                    out.append([LayoutCoord(t, q)])
-        for t in range(self.flatten_first, timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= self.flatten_first:
-                    v.append(LayoutCoord(t_for_q, q))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class ParallelPatternProvider(DelayedPatternProvider):
-    """Provider for parallel pattern across codebooks.
-    This pattern provider is a special case of the delayed pattern with actually no delay,
-    hence delays=repeat(0, n_q).
-
-    Args:
-        n_q (int): Number of codebooks.
-    """
-    def __init__(self, n_q: int):
-        super().__init__(n_q, [0] * n_q)
-
-
-class UnrolledPatternProvider(CodebooksPatternProvider):
-    """Provider for unrolling codebooks pattern.
-    This pattern provider enables to represent the codebook flattened completely or only to some extend
-    while also specifying a given delay between the flattened codebooks representation, allowing to
-    unroll the codebooks in the sequence.
-
-    Example:
-        1. Flattening of the codebooks.
-        By default, the pattern provider will fully flatten the codebooks such as flattening=range(n_q),
-        taking n_q = 3 and timesteps = 4:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, 1, S, S, 2, S, S, 3, S, S, 4],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        2. Partial flattening of the codebooks. The ``flattening`` parameter allows to specify the inner step
-        for each of the codebook, allowing to define which codebook to flatten (or keep in parallel), for example
-        taking n_q = 3, timesteps = 4 and flattening = [0, 1, 1]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [S, 1, S, S, 2, S, S, 3, S, S, 4, S],
-         [1, S, S, 2, S, S, 3, S, S, 4, S, S]]
-        3. Flattening with delay. The ``delay`` parameter allows to further unroll the sequence of codebooks
-        allowing to specify the delay per codebook. Note that the delay between codebooks flattened to the
-        same inner timestep should be coherent. For example, taking n_q = 3, timesteps = 4, flattening = [0, 1, 1]
-        and delays = [0, 3, 3]:
-        [[1, 2, 3, 4],
-         [1, 2, 3, 4],
-         [1, 2, 3, 4]]
-        will result into:
-        [[S, S, S, 1, S, 2, S, 3, S, 4],
-         [S, S, S, 1, S, 2, S, 3, S, 4],
-         [1, 2, 3, S, 4, S, 5, S, 6, S]]
-
-    Args:
-        n_q (int): Number of codebooks.
-        flattening (Optional[List[int]]): Flattening schema over the codebooks. If not defined,
-            the codebooks will be flattened to 1 codebook per step, meaning that the sequence will
-            have n_q extra steps for each timestep.
-        delays (Optional[List[int]]): Delay for each of the codebooks. If not defined,
-            no delay is added and therefore will default to [0] * ``n_q``.
-            Note that two codebooks that will be flattened to the same inner step
-            should have the same delay, otherwise the pattern is considered as invalid.
-    """
-    FlattenedCodebook = namedtuple('FlattenedCodebook', ['codebooks', 'delay'])
-
-    def __init__(self, n_q: int, flattening: tp.Optional[tp.List[int]] = None,
-                 delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if flattening is None:
-            flattening = list(range(n_q))
-        if delays is None:
-            delays = [0] * n_q
-        assert len(flattening) == n_q
-        assert len(delays) == n_q
-        assert sorted(flattening) == flattening
-        assert sorted(delays) == delays
-        self._flattened_codebooks = self._build_flattened_codebooks(delays, flattening)
-        self.max_delay = max(delays)
-
-    def _build_flattened_codebooks(self, delays: tp.List[int], flattening: tp.List[int]):
-        """Build a flattened codebooks representation as a dictionary of inner step
-        and the actual codebook indices corresponding to the flattened codebook. For convenience, we
-        also store the delay associated to the flattened codebook to avoid maintaining an extra mapping.
-        """
-        flattened_codebooks: dict = {}
-        for q, (inner_step, delay) in enumerate(zip(flattening, delays)):
-            if inner_step not in flattened_codebooks:
-                flat_codebook = UnrolledPatternProvider.FlattenedCodebook(codebooks=[q], delay=delay)
-            else:
-                flat_codebook = flattened_codebooks[inner_step]
-                assert flat_codebook.delay == delay, (
-                    "Delay and flattening between codebooks is inconsistent: ",
-                    "two codebooks flattened to the same position should have the same delay."
-                )
-                flat_codebook.codebooks.append(q)
-            flattened_codebooks[inner_step] = flat_codebook
-        return flattened_codebooks
-
-    @property
-    def _num_inner_steps(self):
-        """Number of inner steps to unroll between timesteps in order to flatten the codebooks.
-        """
-        return max([inner_step for inner_step in self._flattened_codebooks.keys()]) + 1
-
-    def num_virtual_steps(self, timesteps: int) -> int:
-        return timesteps * self._num_inner_steps + 1
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        """Builds pattern for delay across codebooks.
-
-        Args:
-            timesteps (int): Total numer of timesteps.
-        """
-        # the PatternLayout is built as a tuple of sequence position and list of coordinates
-        # so that it can be reordered properly given the required delay between codebooks of given timesteps
-        indexed_out: list = [(-1, [])]
-        max_timesteps = timesteps + self.max_delay
-        for t in range(max_timesteps):
-            # for each timestep, we unroll the flattened codebooks,
-            # emitting the sequence step with the corresponding delay
-            for step in range(self._num_inner_steps):
-                if step in self._flattened_codebooks:
-                    # we have codebooks at this virtual step to emit
-                    step_codebooks = self._flattened_codebooks[step]
-                    t_for_q = t + step_codebooks.delay
-                    coords = [LayoutCoord(t, q) for q in step_codebooks.codebooks]
-                    if t_for_q < max_timesteps and t < max_timesteps:
-                        indexed_out.append((t_for_q, coords))
-                else:
-                    # there is no codebook in this virtual step so we emit an empty list
-                    indexed_out.append((t, []))
-        out = [coords for _, coords in sorted(indexed_out)]
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class VALLEPattern(CodebooksPatternProvider):
-    """Almost VALL-E style pattern. We futher allow some delays for the
-    codebooks other than the first one.
-
-    Args:
-        n_q (int): Number of codebooks.
-        delays (Optional[List[int]]): Delay for each of the codebooks.
-            If delays not defined, each codebook is delayed by 1 compared to the previous one.
-    """
-    def __init__(self, n_q: int, delays: tp.Optional[tp.List[int]] = None):
-        super().__init__(n_q)
-        if delays is None:
-            delays = [0] * (n_q - 1)
-        self.delays = delays
-        assert len(self.delays) == self.n_q - 1
-        assert sorted(self.delays) == self.delays
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for t in range(timesteps):
-            out.append([LayoutCoord(t, 0)])
-        max_delay = max(self.delays)
-        for t in range(timesteps + max_delay):
-            v = []
-            for q, delay in enumerate(self.delays):
-                t_for_q = t - delay
-                if t_for_q >= 0:
-                    v.append(LayoutCoord(t_for_q, q + 1))
-            out.append(v)
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
-
-
-class MusicLMPattern(CodebooksPatternProvider):
-    """Almost MusicLM style pattern. This is equivalent to full flattening
-    but in a different order.
-
-    Args:
-        n_q (int): Number of codebooks.
-        group_by (int): Number of codebooks to group together.
-    """
-    def __init__(self, n_q: int, group_by: int = 2):
-        super().__init__(n_q)
-        self.group_by = group_by
-
-    def get_pattern(self, timesteps: int) -> Pattern:
-        out: PatternLayout = [[]]
-        for offset in range(0, self.n_q, self.group_by):
-            for t in range(timesteps):
-                for q in range(offset, offset + self.group_by):
-                    out.append([LayoutCoord(t, q)])
-        return Pattern(out, n_q=self.n_q, timesteps=timesteps)
diff --git a/spaces/nathanTQ/ChatDev/chatdev/roster.py b/spaces/nathanTQ/ChatDev/chatdev/roster.py
deleted file mode 100644
index eab9c091a5c8aa309d86880b3f20416d8a9cb19b..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/chatdev/roster.py
+++ /dev/null
@@ -1,20 +0,0 @@
-class Roster():
-    def __init__(self) -> None:
-        self.agents = list()
-
-    def _recruit(self, agent_name: str):
-        self.agents.append(agent_name)
-
-    def _exist_employee(self, agent_name: str):
-        names = self.agents + [agent_name]
-        names = [name.lower().strip() for name in names]
-        names = [name.replace(" ", "").replace("_", "") for name in names]
-        agent_name = names[-1]
-        if agent_name in names[:-1]:
-            return True
-        return False
-
-    def _print_employees(self):
-        names = self.agents
-        names = [name.lower().strip() for name in names]
-        print("Employees: {}".format(names))
diff --git a/spaces/nathanTQ/ChatDev/online_log/static/js/main.js b/spaces/nathanTQ/ChatDev/online_log/static/js/main.js
deleted file mode 100644
index 3776dae87524b5fea0ca2f2e5d40b2bf3e3cd0ca..0000000000000000000000000000000000000000
--- a/spaces/nathanTQ/ChatDev/online_log/static/js/main.js
+++ /dev/null
@@ -1,111 +0,0 @@
-function scrollToBottom() {
-  var scrollContainer = document.getElementById('chat-box');
-  scrollContainer.scrollTop = scrollContainer.scrollHeight;
-}
-
-function append_message(role, text, avatarUrl) {
-  
-  var message_container = $("<div></div>").addClass("message-container");
-  var avatar_element = $("<span></span>").addClass("avatar");
-  var role_element = $("<p></p>").addClass("role").text(role);
-
-  if (avatarUrl) {
-    avatar_element.css("background-image", `url(${avatarUrl})`);
-  } else {
-    avatar_element.css("background-color", "green");
-  }
-
-  message_container.append(role_element);
-  message_container.append(avatar_element);
-
-  var parsedText = role === 'System' ? parseSystemMessage(text) : parseCodeBlocks(text, role);
-
-  message_container.append(parsedText);
-
-  $("#chat-box").append(message_container);
-  scrollToBottom();
-}
-
-function parseCodeBlocks(text, role) {
-  var parts = text.split(/(```[\s\S]*?```)/g);
-  var parsedText = $("<div></div>").addClass("message-text");
-  parts.forEach(part => {
-    if (part.startsWith("```") && role != "System") {
-      var trimmedBlock = part.trim();
-      var language = trimmedBlock.match(/^```(\w+)/);
-      if (language) {
-        language = language[1];
-        var codeContent = trimmedBlock.replace(/^```(\w+)/, '').replace(/```$/, '');
-        var codeBlockHTML = `
-          <div class="code-block">
-            <div class="code-block-header">${role} - ${language}</div>
-            <pre class="language-${language} dark line-numbers" data-line><code>${hljs.highlightAuto(codeContent, [language]).value}</code></pre>
-          </div>
-        `;
-        parsedText.append(codeBlockHTML);
-      }
-    } else {
-      parsedText.append(marked(_.escape(part), {breaks: true}));
-    }
-  });
-  return parsedText;
-}
-
-
-function get_new_messages() {
-
-  $.getJSON("/get_messages", function (data) {
-    var lastDisplayedMessageIndex = $("#chat-box .message-container").length;
-
-    for (var i = lastDisplayedMessageIndex; i < data.length; i++) {
-      var role = data[i].role;
-      var text = data[i].text;
-      var avatarUrl = data[i].avatarUrl;
-
-      append_message(role, text, avatarUrl);
-
-    }
-  });
-}
-
-function parseSystemMessage(text) {
-  var message = $("<div></div>").addClass("message-text").addClass("system-message");
-  var firstLine = text.split('\n')[0];
-  var collapsed = true;
-
-  var messageContent = $("<div></div>").html(marked(firstLine, { breaks: true })).addClass("original-markdown");
-  var originalMarkdown = $("<div></div>").html(marked(text, { breaks: true })).addClass("original-markdown");
-
-  var expandButton = $("<button></button>")
-    .addClass("expand-button")
-    .text("Expand")
-    .click(function () {
-      if (collapsed) {
-        messageContent.hide();
-        originalMarkdown.show();
-        expandButton.text("Collapse");
-      } else {
-        messageContent.show();
-        originalMarkdown.hide();
-        expandButton.text("Expand");
-      }
-      collapsed = !collapsed;
-    });
-
-  message.append(messageContent);
-  message.append(originalMarkdown);
-  message.append(expandButton);
-
-  originalMarkdown.hide();
-
-  return message;
-}
-
-
-$(document).ready(function () {
-  get_new_messages();
-  setInterval(function () {
-    get_new_messages();
-  }, 1000);
-});
-
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key !FULL!.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key !FULL!.md
deleted file mode 100644
index 8bf923b50067b1d7540f48aa6b6a8547ef50abdd..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key !FULL!.md	
+++ /dev/null
@@ -1,26 +0,0 @@
-<br />
-Here is a possible title and article with SEO optimization and HTML formatting for the keyword "FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key":
-
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-<li><b>Easy to use interface:</b> FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key has a user-friendly interface that makes it easy to navigate and access the effects and presets. You can also drag and drop the effects onto your timeline and adjust the parameters with simple sliders and buttons.</li>
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-<h2>Benefits of FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key</h2>
-<p>FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key has many benefits that can make your video editing experience more enjoyable and productive. Some of the benefits are:</p>
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-<li><b>Enhance your videos with professional effects:</b> With FXhome Ignite Pro 4.4.7730.53585 Pre-Cracked Serial Key, you can add stunning effects to your videos that can make them stand out from the crowd. You can create realistic simulations of fire, smoke, water, lightning, explosions, and more; add stylized looks like retro, glitch, film grain, vignette, and more; apply cinematic flares, lens dirt, light leaks, and more; adjust the color, contrast, brightness, saturation, and more; and much more.</li>
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-</ul>
-<h2>Drawbacks of FXhome Ignite Pro</p> 7196e7f11a<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Guru Geethaya Sinhala Film 36.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Guru Geethaya Sinhala Film 36.md
deleted file mode 100644
index c49a443668928f8794eda8551ed8c30417ca87f5..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Guru Geethaya Sinhala Film 36.md	
+++ /dev/null
@@ -1,15 +0,0 @@
-
-Here is a possible title and article with html formatting for the keyword "Guru Geethaya Sinhala Film 36":
-
-<h1>Guru Geethaya: A Sinhala Film Based on a Novel by Chingiz Aitmatov</h1>
-<p>Guru Geethaya is a 2016 Sinhala film directed by Upali Gamlath and based on the novel The First Teacher by Chingiz Aitmatov. The film tells the story of a young teacher who arrives in a remote village in Sri Lanka in the 1940s and tries to educate the children and transform the society. The film stars Roshan Ravindra as the teacher, Kalpani Jayasinghe as his love interest, and Gamini Hettiarachchi as the village headman.</p>
-<h2>Guru Geethaya Sinhala Film 36</h2><br /><p><b><b>Download</b> &#127379; <a href="https://urlcod.com/2uIaUa">https://urlcod.com/2uIaUa</a></b></p><br /><br />
-<p>The film was praised for its cinematography, music, and performances, and won several awards at the Sarasaviya Film Festival and the SIGNIS Awards. The film also received positive reviews from critics and audiences, who appreciated its faithful adaptation of the novel and its portrayal of rural life and culture. The film was screened at several international film festivals, such as the Moscow International Film Festival and the Kerala International Film Festival.</p>
-<p>Guru Geethaya is considered to be one of the best Sinhala films of recent times and a tribute to the legacy of Chingiz Aitmatov, who was a renowned Kyrgyz writer and diplomat. The film is also a testament to the universal appeal of his stories, which transcend boundaries of language, culture, and time.</p>Here are a few more paragraphs with html formatting for the article:
-
-<p>The novel The First Teacher was written by Chingiz Aitmatov in 1962 and was based on his own experience as a teacher in a remote village in Kyrgyzstan. The novel explores the themes of education, social change, cultural conflict, love, and sacrifice. The novel was translated into many languages and became a bestseller in the Soviet Union and abroad. It was also adapted into a film by Andrei Konchalovsky in 1965, which won several awards and acclaim.</p>
-<p>The novel and the film are both set in the 1920s, when Kyrgyzstan was part of the Soviet Union and was undergoing a process of modernization and collectivization. The novel depicts the clash between the traditional way of life of the nomadic Kirghiz people and the new socialist ideology and values. The novel also shows the difficulties and challenges faced by the first teachers who tried to bring literacy and enlightenment to the rural areas. The novel portrays the teacher Duishen as a hero who sacrifices his personal happiness and even his life for the sake of his students and his mission.</p>
-<p></p>
-<p>The novel and the film have been praised for their realistic and sympathetic portrayal of the Kirghiz culture and history. They have also been criticized for their ideological bias and propaganda. Some critics have argued that the novel and the film present a distorted and romanticized image of the Soviet intervention in Kyrgyzstan and ignore the negative aspects of collectivization, such as famine, repression, and violence. Others have defended the novel and the film as artistic expressions of Aitmatov's humanism and vision of social justice.</p> 7196e7f11a<br />
-<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Requiem For A Dream Soundtrack Download Zippy.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Requiem For A Dream Soundtrack Download Zippy.md
deleted file mode 100644
index abf90f3a37aead426645095d4dee9458c478ca71..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Requiem For A Dream Soundtrack Download Zippy.md	
+++ /dev/null
@@ -1,24 +0,0 @@
-<br />
-<h1>How to Download the Requiem for a Dream Soundtrack for Free</h1>
-<p>Requiem for a Dream is a 2000 film directed by Darren Aronofsky that explores the lives of four people addicted to different substances. The film is known for its powerful and disturbing imagery, as well as its haunting soundtrack composed by Clint Mansell and performed by the Kronos Quartet.</p>
-<p>The soundtrack consists of 33 tracks that range from atmospheric and melancholic to intense and frenetic. The most famous track is "Lux Aeterna", which has been used in many trailers and commercials for other films and shows. The soundtrack was released by Nonesuch Records on October 3, 2000[^1^].</p>
-<h2>Requiem For A Dream Soundtrack Download Zippy</h2><br /><p><b><b>Download Zip</b> ->>->>->> <a href="https://urlcod.com/2uIbD8">https://urlcod.com/2uIbD8</a></b></p><br /><br />
-<p>If you are a fan of the film or the music, you might want to download the soundtrack for free. There are several ways to do that, but one of the easiest and fastest is to use a website called Zippyshare. Zippyshare is a file-sharing service that allows users to upload and download files without any registration or limits. Here are the steps to download the Requiem for a Dream soundtrack from Zippyshare:</p>
-<ol>
-<li>Go to <a href="https://www.zippyshare.com/">Zippyshare.com</a> and type "Requiem for a Dream soundtrack" in the search box.</li>
-<li>You will see a list of results with different file names and sizes. Choose the one that matches your preferences. For example, you can choose "Requiem For A Dream Original Song.mp3" if you want to download only the main theme[^2^], or "Requiem for a Dream (Soundtrack from the Motion Picture).zip" if you want to download the whole album[^3^]. You can also check the file size and duration before downloading.</li>
-<li>Click on the file name and you will be redirected to a new page with a download button. Click on the download button and wait for a few seconds until a countdown appears. After the countdown ends, click on the download button again and save the file to your device.</li>
-<li>Enjoy listening to the Requiem for a Dream soundtrack!</li>
-</ol>
-<p>Note: Zippyshare is not an official source for downloading music, so you might encounter some ads or pop-ups while using it. Also, downloading music without paying for it might be illegal in some countries, so use it at your own risk.</p>
-
-<p>If you want to learn more about the Requiem for a Dream soundtrack, you can also check out some of the online sources that offer more information and reviews. For example, you can visit the following websites:</p>
-<ul>
-<li><a href="https://music.apple.com/us/album/requiem-for-a-dream-soundtrack-from-the-motion-picture/41228773">Apple Music</a>: Here you can listen to the soundtrack online, buy it for $9.99, or add it to your library if you have an Apple Music subscription. You can also read some user reviews and ratings.</li>
-<li><a href="https://archive.org/details/RequiemForADreamOriginalSong">Internet Archive</a>: Here you can download the original song "Requiem for a Dream" for free, as well as other related files. You can also stream it online or embed it on your website. The Internet Archive is a non-profit organization that preserves digital content for future generations.</li>
-<li><a href="https://music.youtube.com/playlist?list=OLAK5uy_nHCi9vUh-UtWGO3DfZTBSMjFIwMaulPuI">Youtube Music</a>: Here you can listen to the soundtrack online, buy it for $9.49, or add it to your library if you have a Youtube Music subscription. You can also watch some videos related to the film and the music.</li>
-</ul>
-<p>The Requiem for a Dream soundtrack is a masterpiece of film music that captures the mood and themes of the film. It is a must-listen for anyone who appreciates cinematic music and emotional storytelling. Whether you download it from Zippyshare or buy it from an official source, you will not regret listening to this soundtrack.</p>
-<p></p> 81aa517590<br />
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\ No newline at end of file
diff --git a/spaces/nightfury/dreamlike-art-dreamlike-diffusion-1.0/README.md b/spaces/nightfury/dreamlike-art-dreamlike-diffusion-1.0/README.md
deleted file mode 100644
index 6cb2d86dec677dc7f461ca5f609301d5636ee0a6..0000000000000000000000000000000000000000
--- a/spaces/nightfury/dreamlike-art-dreamlike-diffusion-1.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Dreamlike Art Dreamlike Diffusion 1.0
-emoji: 📈
-colorFrom: gray
-colorTo: red
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/nightfury/img2audio_video_prompt_tags/app.py b/spaces/nightfury/img2audio_video_prompt_tags/app.py
deleted file mode 100644
index 255dfb0733364d56aa1d98dccb4bf925883c9e0d..0000000000000000000000000000000000000000
--- a/spaces/nightfury/img2audio_video_prompt_tags/app.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import time
-
-import gradio as gr
-from sentence_transformers import SentenceTransformer
-
-import httpx
-import json
-
-from utils import get_tags_for_prompts, get_mubert_tags_embeddings, get_pat
-#import subprocess
-import os
-import uuid
-from tempfile import gettempdir
-from PIL import Image
-import cv2
-from pprint import pprint
-
-minilm = SentenceTransformer('all-MiniLM-L6-v2')
-mubert_tags_embeddings = get_mubert_tags_embeddings(minilm)
-
-# image_to_text = gr.Interface.load("spaces/doevent/image_to_text", api_key=os.environ['HF_TOKEN'])
-image_to_text = gr.Blocks.load(name="spaces/pharma/CLIP-Interrogator")
-def center_crop(img, dim: tuple = (512, 512)):
-    
-    """Returns center cropped image
-	Args:
-	img: image to be center cropped
-	dim: dimensions (width, height) to be cropped
-    """
-    
-    width, height = img.shape[1], img.shape[0]
-
-    # process crop width and height for max available dimension
-    crop_width = dim[0] if dim[0]<img.shape[1] else img.shape[1]
-    crop_height = dim[1] if dim[1]<img.shape[0] else img.shape[0] 
-    mid_x, mid_y = int(width/2), int(height/2)
-    cw2, ch2 = int(crop_width/2), int(crop_height/2) 
-    crop_img = img[mid_y-ch2:mid_y+ch2, mid_x-cw2:mid_x+cw2]
-    return crop_img
-
-
-def scale_image(img, factor=1):
-	"""Returns resize image by scale factor.
-	This helps to retain resolution ratio while resizing.
-	Args:
-	img: image to be scaled
-	factor: scale factor to resize
-	"""
-	return cv2.resize(img,(int(img.shape[1]*factor), int(img.shape[0]*factor)))
-	
-	
-def get_track_by_tags(tags, pat, duration, maxit=20, loop=False):
-    if loop:
-        mode = "loop"
-    else:
-        mode = "track"
-    r = httpx.post('https://api-b2b.mubert.com/v2/RecordTrackTTM',
-                   json={
-                       "method": "RecordTrackTTM",
-                       "params": {
-                           "pat": pat,
-                           "duration": duration,
-                           "tags": tags,
-                           "mode": mode
-                       }
-                   })
-    
-    pprint(r.text)
-    rdata = json.loads(r.text)
-    assert rdata['status'] == 1, rdata['error']['text']
-    trackurl = rdata['data']['tasks'][0]['download_link']
-
-    #print('Generating track ', end='')
-    for i in range(maxit):
-        r = httpx.get(trackurl)
-        if r.status_code == 200:
-            return trackurl
-        time.sleep(1)
-
-
-def generate_track_by_prompt(image, email, duration, loop=False):
-    try:
-        # Checking Image Aspect Ratio
-        filename_png = f"{uuid.uuid4().hex}.png"
-        filepath_png = f"{gettempdir()}/{filename_png}"
-        
-        with Image.open(image) as im:
-            # image size
-            ratio_width = im.size[0]
-            ratio_height = im.size[1]
-            im.convert("RGB").save(filepath_png)
-        if ratio_width > 3501 or ratio_height > 3501:
-            raise gr.Error("Image aspect ratio must not exceed width: 1024 px or height: 1024 px.")
-        elif ratio_width > 3500 or ratio_height > 3500:
-            image_g = cv2.imread(image)
-            scale_img = scale_image(image_g, factor=0.2)
-            cv2.imwrite(filepath_png, scale_img)
-        elif ratio_width > 1800 or ratio_height > 1800:
-            image_g = cv2.imread(image)
-            scale_img = scale_image(image_g, factor=0.3)
-            cv2.imwrite(filepath_png, scale_img)
-        elif ratio_width > 900 or ratio_height > 900:
-            image_g = cv2.imread(image)
-            scale_img = scale_image(image_g, factor=0.5)
-            cv2.imwrite(filepath_png, scale_img)
-        
-        # prompt = image_to_text(filepath_png, "Image Captioning", "", "Nucleus sampling")
-        prompt = image_to_text(filepath_png, "ViT-L (best for Stable Diffusion 1.*)", "Fast",  fn_index=1)[0]
-        print(f"PROMPT: {prompt}")
-  
-        pat = get_pat(email)
-        _, tags = get_tags_for_prompts(minilm, mubert_tags_embeddings, [prompt, ])[0]
-        filepath = get_track_by_tags(tags, pat, int(duration), loop=loop)
-           
-        filename_mp3 = filepath.split("/")[-1]
-        filepath_mp3 = f"{gettempdir()}/{filename_mp3}"
-        filename_mp4 = f"{uuid.uuid4().hex}.mp4"
-        filepath_mp4 = f"{gettempdir()}/{filename_mp4}"
-        
-        os.system(f"wget {filepath} -P {gettempdir()}")
-        
-        # waveform
-        with Image.open(filepath_png) as im:
-            width = im.size[0]
-            height = im.size[1]
-        print(f"{width}x{height}")
-        command = f'ffmpeg -hide_banner -loglevel warning -y -i {filepath_mp3} -loop 1 -i {filepath_png} -filter_complex "[0:a]showwaves=s={width}x{height}:colors=0xffffff:mode=cline,format=rgba[v];[1:v][v]overlay[outv]" -map "[outv]" -map 0:a -c:v libx264 -r 15 -c:a copy -pix_fmt yuv420p -shortest {filepath_mp4}'
-        os.system(command)
-        os.remove(filepath_png)
-        os.remove(filepath_mp3)
-        
-        return filepath_mp4, filepath, prompt, tags
-    except Exception as e:
-        raise gr.Error(str(e))
-
-title = r"""
-    <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-        <div style="
-                  display: inline-flex;
-                  align-items: center;
-                  gap: 0.8rem;
-                  font-size: 0.75rem;">
-            <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-                      Image to audio, Video, Prompt, and Tags (🖼️->🎵 🎞️ 🖹 🔖)
-            </h1>
-        </div>
-    </div> 
-"""
-
-description = r"""
-<center>
-    <div class="description">
-         <p style="margin-bottom: 10px; font-size: 94%">
-            Sends an image in to <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator" target="_blank">CLIP Interrogator</a>
-            to generate a text prompt which is then run through 
-            <a href="https://huggingface.co/Mubert" target="_blank">Mubert</a> text-to-music to generate music from the input image!
-         </p>
-    </div>
-</center>
-"""
-
-# r"""           <p style="margin-bottom: 10px; font-size: 94%">
-#              Sends an image in to <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator" target="_blank">CLIP Interrogator</a>
-#              to generate a text prompt which is then run through 
-#               <a href="https://huggingface.co/Mubert" target="_blank">Mubert</a> text-to-music to generate music from the input image!
-#              </p>
-#      </div>
-#"""
-
-
-article = r"""
-<center>
-    <div class="footer">
-        <p> 
-        Gradio Demo by 🤗 <a href="https://huggingface.co/nightfury/" target="_blank">Nightfury</a>
-        </p>
-        <img src='https://visitor-badge.glitch.me/badge?page_id=dj_Image_audio_Video_Prompt_Tags' alt='visitor badge'>
-    </div>
-</center>
-"""
-
-iface = gr.Interface(fn=generate_track_by_prompt, 
-                    inputs=[gr.Image(type="filepath"), 
-                    "text", 
-                    gr.Slider(label="duration (seconds)", value=30, minimum=10, maximum=60)], 
-                    outputs=[gr.Video(label="Video"), 
-                    gr.Audio(label="Audio"),
-                    gr.Text(label="Prompt"),
-                    gr.Text(label="Tags")],
-                    title=title,
-                    description=description,
-                    article=article
-                    )
-iface.queue().launch()
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/config/defaults.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/config/defaults.py
deleted file mode 100644
index bd2a5f6b2de4af2caa1f65c64ab93a5e3ac21780..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/config/defaults.py
+++ /dev/null
@@ -1,650 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .config import CfgNode as CN
-
-# NOTE: given the new config system
-# (https://detectron2.readthedocs.io/en/latest/tutorials/lazyconfigs.html),
-# we will stop adding new functionalities to default CfgNode.
-
-# -----------------------------------------------------------------------------
-# Convention about Training / Test specific parameters
-# -----------------------------------------------------------------------------
-# Whenever an argument can be either used for training or for testing, the
-# corresponding name will be post-fixed by a _TRAIN for a training parameter,
-# or _TEST for a test-specific parameter.
-# For example, the number of images during training will be
-# IMAGES_PER_BATCH_TRAIN, while the number of images for testing will be
-# IMAGES_PER_BATCH_TEST
-
-# -----------------------------------------------------------------------------
-# Config definition
-# -----------------------------------------------------------------------------
-
-_C = CN()
-
-# The version number, to upgrade from old configs to new ones if any
-# changes happen. It's recommended to keep a VERSION in your config file.
-_C.VERSION = 2
-
-_C.MODEL = CN()
-_C.MODEL.LOAD_PROPOSALS = False
-_C.MODEL.MASK_ON = False
-_C.MODEL.KEYPOINT_ON = False
-_C.MODEL.DEVICE = "cuda"
-_C.MODEL.META_ARCHITECTURE = "GeneralizedRCNN"
-
-# Path (a file path, or URL like detectron2://.., https://..) to a checkpoint file
-# to be loaded to the model. You can find available models in the model zoo.
-_C.MODEL.WEIGHTS = ""
-
-# Values to be used for image normalization (BGR order, since INPUT.FORMAT defaults to BGR).
-# To train on images of different number of channels, just set different mean & std.
-# Default values are the mean pixel value from ImageNet: [103.53, 116.28, 123.675]
-_C.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
-# When using pre-trained models in Detectron1 or any MSRA models,
-# std has been absorbed into its conv1 weights, so the std needs to be set 1.
-# Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
-_C.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
-
-
-# -----------------------------------------------------------------------------
-# INPUT
-# -----------------------------------------------------------------------------
-_C.INPUT = CN()
-# By default, {MIN,MAX}_SIZE options are used in transforms.ResizeShortestEdge.
-# Please refer to ResizeShortestEdge for detailed definition.
-# Size of the smallest side of the image during training
-_C.INPUT.MIN_SIZE_TRAIN = (800,)
-# Sample size of smallest side by choice or random selection from range give by
-# INPUT.MIN_SIZE_TRAIN
-_C.INPUT.MIN_SIZE_TRAIN_SAMPLING = "choice"
-# Maximum size of the side of the image during training
-_C.INPUT.MAX_SIZE_TRAIN = 1333
-# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.
-_C.INPUT.MIN_SIZE_TEST = 800
-# Maximum size of the side of the image during testing
-_C.INPUT.MAX_SIZE_TEST = 1333
-# Mode for flipping images used in data augmentation during training
-# choose one of ["horizontal, "vertical", "none"]
-_C.INPUT.RANDOM_FLIP = "horizontal"
-
-# `True` if cropping is used for data augmentation during training
-_C.INPUT.CROP = CN({"ENABLED": False})
-# Cropping type. See documentation of `detectron2.data.transforms.RandomCrop` for explanation.
-_C.INPUT.CROP.TYPE = "relative_range"
-# Size of crop in range (0, 1] if CROP.TYPE is "relative" or "relative_range" and in number of
-# pixels if CROP.TYPE is "absolute"
-_C.INPUT.CROP.SIZE = [0.9, 0.9]
-
-
-# Whether the model needs RGB, YUV, HSV etc.
-# Should be one of the modes defined here, as we use PIL to read the image:
-# https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-modes
-# with BGR being the one exception. One can set image format to BGR, we will
-# internally use RGB for conversion and flip the channels over
-_C.INPUT.FORMAT = "BGR"
-# The ground truth mask format that the model will use.
-# Mask R-CNN supports either "polygon" or "bitmask" as ground truth.
-_C.INPUT.MASK_FORMAT = "polygon"  # alternative: "bitmask"
-
-
-# -----------------------------------------------------------------------------
-# Dataset
-# -----------------------------------------------------------------------------
-_C.DATASETS = CN()
-# List of the dataset names for training. Must be registered in DatasetCatalog
-# Samples from these datasets will be merged and used as one dataset.
-_C.DATASETS.TRAIN = ()
-# List of the pre-computed proposal files for training, which must be consistent
-# with datasets listed in DATASETS.TRAIN.
-_C.DATASETS.PROPOSAL_FILES_TRAIN = ()
-# Number of top scoring precomputed proposals to keep for training
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN = 2000
-# List of the dataset names for testing. Must be registered in DatasetCatalog
-_C.DATASETS.TEST = ()
-# List of the pre-computed proposal files for test, which must be consistent
-# with datasets listed in DATASETS.TEST.
-_C.DATASETS.PROPOSAL_FILES_TEST = ()
-# Number of top scoring precomputed proposals to keep for test
-_C.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST = 1000
-
-# -----------------------------------------------------------------------------
-# DataLoader
-# -----------------------------------------------------------------------------
-_C.DATALOADER = CN()
-# Number of data loading threads
-_C.DATALOADER.NUM_WORKERS = 4
-# If True, each batch should contain only images for which the aspect ratio
-# is compatible. This groups portrait images together, and landscape images
-# are not batched with portrait images.
-_C.DATALOADER.ASPECT_RATIO_GROUPING = True
-# Options: TrainingSampler, RepeatFactorTrainingSampler
-_C.DATALOADER.SAMPLER_TRAIN = "TrainingSampler"
-# Repeat threshold for RepeatFactorTrainingSampler
-_C.DATALOADER.REPEAT_THRESHOLD = 0.0
-# Tf True, when working on datasets that have instance annotations, the
-# training dataloader will filter out images without associated annotations
-_C.DATALOADER.FILTER_EMPTY_ANNOTATIONS = True
-
-# ---------------------------------------------------------------------------- #
-# Backbone options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.BACKBONE = CN()
-
-_C.MODEL.BACKBONE.NAME = "build_resnet_backbone"
-# Freeze the first several stages so they are not trained.
-# There are 5 stages in ResNet. The first is a convolution, and the following
-# stages are each group of residual blocks.
-_C.MODEL.BACKBONE.FREEZE_AT = 2
-
-
-# ---------------------------------------------------------------------------- #
-# FPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.FPN = CN()
-# Names of the input feature maps to be used by FPN
-# They must have contiguous power of 2 strides
-# e.g., ["res2", "res3", "res4", "res5"]
-_C.MODEL.FPN.IN_FEATURES = []
-_C.MODEL.FPN.OUT_CHANNELS = 256
-
-# Options: "" (no norm), "GN"
-_C.MODEL.FPN.NORM = ""
-
-# Types for fusing the FPN top-down and lateral features. Can be either "sum" or "avg"
-_C.MODEL.FPN.FUSE_TYPE = "sum"
-
-
-# ---------------------------------------------------------------------------- #
-# Proposal generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.PROPOSAL_GENERATOR = CN()
-# Current proposal generators include "RPN", "RRPN" and "PrecomputedProposals"
-_C.MODEL.PROPOSAL_GENERATOR.NAME = "RPN"
-# Proposal height and width both need to be greater than MIN_SIZE
-# (a the scale used during training or inference)
-_C.MODEL.PROPOSAL_GENERATOR.MIN_SIZE = 0
-
-
-# ---------------------------------------------------------------------------- #
-# Anchor generator options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ANCHOR_GENERATOR = CN()
-# The generator can be any name in the ANCHOR_GENERATOR registry
-_C.MODEL.ANCHOR_GENERATOR.NAME = "DefaultAnchorGenerator"
-# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
-# Format: list[list[float]]. SIZES[i] specifies the list of sizes to use for
-# IN_FEATURES[i]; len(SIZES) must be equal to len(IN_FEATURES) or 1.
-# When len(SIZES) == 1, SIZES[0] is used for all IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.SIZES = [[32, 64, 128, 256, 512]]
-# Anchor aspect ratios. For each area given in `SIZES`, anchors with different aspect
-# ratios are generated by an anchor generator.
-# Format: list[list[float]]. ASPECT_RATIOS[i] specifies the list of aspect ratios (H/W)
-# to use for IN_FEATURES[i]; len(ASPECT_RATIOS) == len(IN_FEATURES) must be true,
-# or len(ASPECT_RATIOS) == 1 is true and aspect ratio list ASPECT_RATIOS[0] is used
-# for all IN_FEATURES.
-_C.MODEL.ANCHOR_GENERATOR.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
-# Anchor angles.
-# list[list[float]], the angle in degrees, for each input feature map.
-# ANGLES[i] specifies the list of angles for IN_FEATURES[i].
-_C.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]
-# Relative offset between the center of the first anchor and the top-left corner of the image
-# Value has to be in [0, 1). Recommend to use 0.5, which means half stride.
-# The value is not expected to affect model accuracy.
-_C.MODEL.ANCHOR_GENERATOR.OFFSET = 0.0
-
-# ---------------------------------------------------------------------------- #
-# RPN options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RPN = CN()
-_C.MODEL.RPN.HEAD_NAME = "StandardRPNHead"  # used by RPN_HEAD_REGISTRY
-
-# Names of the input feature maps to be used by RPN
-# e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
-_C.MODEL.RPN.IN_FEATURES = ["res4"]
-# Remove RPN anchors that go outside the image by BOUNDARY_THRESH pixels
-# Set to -1 or a large value, e.g. 100000, to disable pruning anchors
-_C.MODEL.RPN.BOUNDARY_THRESH = -1
-# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]
-# Minimum overlap required between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD
-# ==> positive RPN example: 1)
-# Maximum overlap allowed between an anchor and ground-truth box for the
-# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD
-# ==> negative RPN example: 0)
-# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)
-# are ignored (-1)
-_C.MODEL.RPN.IOU_THRESHOLDS = [0.3, 0.7]
-_C.MODEL.RPN.IOU_LABELS = [0, -1, 1]
-# Number of regions per image used to train RPN
-_C.MODEL.RPN.BATCH_SIZE_PER_IMAGE = 256
-# Target fraction of foreground (positive) examples per RPN minibatch
-_C.MODEL.RPN.POSITIVE_FRACTION = 0.5
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.RPN.BBOX_REG_LOSS_TYPE = "smooth_l1"
-_C.MODEL.RPN.BBOX_REG_LOSS_WEIGHT = 1.0
-# Weights on (dx, dy, dw, dh) for normalizing RPN anchor regression targets
-_C.MODEL.RPN.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.RPN.SMOOTH_L1_BETA = 0.0
-_C.MODEL.RPN.LOSS_WEIGHT = 1.0
-# Number of top scoring RPN proposals to keep before applying NMS
-# When FPN is used, this is *per FPN level* (not total)
-_C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 12000
-_C.MODEL.RPN.PRE_NMS_TOPK_TEST = 6000
-# Number of top scoring RPN proposals to keep after applying NMS
-# When FPN is used, this limit is applied per level and then again to the union
-# of proposals from all levels
-# NOTE: When FPN is used, the meaning of this config is different from Detectron1.
-# It means per-batch topk in Detectron1, but per-image topk here.
-# See the "find_top_rpn_proposals" function for details.
-_C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 2000
-_C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
-# NMS threshold used on RPN proposals
-_C.MODEL.RPN.NMS_THRESH = 0.7
-# Set this to -1 to use the same number of output channels as input channels.
-_C.MODEL.RPN.CONV_DIMS = [-1]
-
-# ---------------------------------------------------------------------------- #
-# ROI HEADS options
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_HEADS = CN()
-_C.MODEL.ROI_HEADS.NAME = "Res5ROIHeads"
-# Number of foreground classes
-_C.MODEL.ROI_HEADS.NUM_CLASSES = 80
-# Names of the input feature maps to be used by ROI heads
-# Currently all heads (box, mask, ...) use the same input feature map list
-# e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
-_C.MODEL.ROI_HEADS.IN_FEATURES = ["res4"]
-# IOU overlap ratios [IOU_THRESHOLD]
-# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)
-# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)
-_C.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]
-_C.MODEL.ROI_HEADS.IOU_LABELS = [0, 1]
-# RoI minibatch size *per image* (number of regions of interest [ROIs]) during training
-# Total number of RoIs per training minibatch =
-#   ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH
-# E.g., a common configuration is: 512 * 16 = 8192
-_C.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512
-# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)
-_C.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25
-
-# Only used on test mode
-
-# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to
-# balance obtaining high recall with not having too many low precision
-# detections that will slow down inference post processing steps (like NMS)
-# A default threshold of 0.0 increases AP by ~0.2-0.3 but significantly slows down
-# inference.
-_C.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.05
-# Overlap threshold used for non-maximum suppression (suppress boxes with
-# IoU >= this threshold)
-_C.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.5
-# If True, augment proposals with ground-truth boxes before sampling proposals to
-# train ROI heads.
-_C.MODEL.ROI_HEADS.PROPOSAL_APPEND_GT = True
-
-# ---------------------------------------------------------------------------- #
-# Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_HEAD = CN()
-# C4 don't use head name option
-# Options for non-C4 models: FastRCNNConvFCHead,
-_C.MODEL.ROI_BOX_HEAD.NAME = ""
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_TYPE = "smooth_l1"
-# The final scaling coefficient on the box regression loss, used to balance the magnitude of its
-# gradients with other losses in the model. See also `MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT`.
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_LOSS_WEIGHT = 1.0
-# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets
-# These are empirically chosen to approximately lead to unit variance targets
-_C.MODEL.ROI_BOX_HEAD.BBOX_REG_WEIGHTS = (10.0, 10.0, 5.0, 5.0)
-# The transition point from L1 to L2 loss. Set to 0.0 to make the loss simply L1.
-_C.MODEL.ROI_BOX_HEAD.SMOOTH_L1_BETA = 0.0
-_C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO = 0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_BOX_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-_C.MODEL.ROI_BOX_HEAD.NUM_FC = 0
-# Hidden layer dimension for FC layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.FC_DIM = 1024
-_C.MODEL.ROI_BOX_HEAD.NUM_CONV = 0
-# Channel dimension for Conv layers in the RoI box head
-_C.MODEL.ROI_BOX_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_BOX_HEAD.NORM = ""
-# Whether to use class agnostic for bbox regression
-_C.MODEL.ROI_BOX_HEAD.CLS_AGNOSTIC_BBOX_REG = False
-# If true, RoI heads use bounding boxes predicted by the box head rather than proposal boxes.
-_C.MODEL.ROI_BOX_HEAD.TRAIN_ON_PRED_BOXES = False
-
-# Federated loss can be used to improve the training of LVIS
-_C.MODEL.ROI_BOX_HEAD.USE_FED_LOSS = False
-# Sigmoid cross entrophy is used with federated loss
-_C.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE = False
-# The power value applied to image_count when calcualting frequency weight
-_C.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT_POWER = 0.5
-# Number of classes to keep in total
-_C.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CLASSES = 50
-
-# ---------------------------------------------------------------------------- #
-# Cascaded Box Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_BOX_CASCADE_HEAD = CN()
-# The number of cascade stages is implicitly defined by the length of the following two configs.
-_C.MODEL.ROI_BOX_CASCADE_HEAD.BBOX_REG_WEIGHTS = (
-    (10.0, 10.0, 5.0, 5.0),
-    (20.0, 20.0, 10.0, 10.0),
-    (30.0, 30.0, 15.0, 15.0),
-)
-_C.MODEL.ROI_BOX_CASCADE_HEAD.IOUS = (0.5, 0.6, 0.7)
-
-
-# ---------------------------------------------------------------------------- #
-# Mask Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_MASK_HEAD = CN()
-_C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
-_C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_MASK_HEAD.NUM_CONV = 0  # The number of convs in the mask head
-_C.MODEL.ROI_MASK_HEAD.CONV_DIM = 256
-# Normalization method for the convolution layers.
-# Options: "" (no norm), "GN", "SyncBN".
-_C.MODEL.ROI_MASK_HEAD.NORM = ""
-# Whether to use class agnostic for mask prediction
-_C.MODEL.ROI_MASK_HEAD.CLS_AGNOSTIC_MASK = False
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_MASK_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-
-# ---------------------------------------------------------------------------- #
-# Keypoint Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.ROI_KEYPOINT_HEAD = CN()
-_C.MODEL.ROI_KEYPOINT_HEAD.NAME = "KRCNNConvDeconvUpsampleHead"
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_RESOLUTION = 14
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_SAMPLING_RATIO = 0
-_C.MODEL.ROI_KEYPOINT_HEAD.CONV_DIMS = tuple(512 for _ in range(8))
-_C.MODEL.ROI_KEYPOINT_HEAD.NUM_KEYPOINTS = 17  # 17 is the number of keypoints in COCO.
-
-# Images with too few (or no) keypoints are excluded from training.
-_C.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE = 1
-# Normalize by the total number of visible keypoints in the minibatch if True.
-# Otherwise, normalize by the total number of keypoints that could ever exist
-# in the minibatch.
-# The keypoint softmax loss is only calculated on visible keypoints.
-# Since the number of visible keypoints can vary significantly between
-# minibatches, this has the effect of up-weighting the importance of
-# minibatches with few visible keypoints. (Imagine the extreme case of
-# only one visible keypoint versus N: in the case of N, each one
-# contributes 1/N to the gradient compared to the single keypoint
-# determining the gradient direction). Instead, we can normalize the
-# loss by the total number of keypoints, if it were the case that all
-# keypoints were visible in a full minibatch. (Returning to the example,
-# this means that the one visible keypoint contributes as much as each
-# of the N keypoints.)
-_C.MODEL.ROI_KEYPOINT_HEAD.NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS = True
-# Multi-task loss weight to use for keypoints
-# Recommended values:
-#   - use 1.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is True
-#   - use 4.0 if NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS is False
-_C.MODEL.ROI_KEYPOINT_HEAD.LOSS_WEIGHT = 1.0
-# Type of pooling operation applied to the incoming feature map for each RoI
-_C.MODEL.ROI_KEYPOINT_HEAD.POOLER_TYPE = "ROIAlignV2"
-
-# ---------------------------------------------------------------------------- #
-# Semantic Segmentation Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.SEM_SEG_HEAD = CN()
-_C.MODEL.SEM_SEG_HEAD.NAME = "SemSegFPNHead"
-_C.MODEL.SEM_SEG_HEAD.IN_FEATURES = ["p2", "p3", "p4", "p5"]
-# Label in the semantic segmentation ground truth that is ignored, i.e., no loss is calculated for
-# the correposnding pixel.
-_C.MODEL.SEM_SEG_HEAD.IGNORE_VALUE = 255
-# Number of classes in the semantic segmentation head
-_C.MODEL.SEM_SEG_HEAD.NUM_CLASSES = 54
-# Number of channels in the 3x3 convs inside semantic-FPN heads.
-_C.MODEL.SEM_SEG_HEAD.CONVS_DIM = 128
-# Outputs from semantic-FPN heads are up-scaled to the COMMON_STRIDE stride.
-_C.MODEL.SEM_SEG_HEAD.COMMON_STRIDE = 4
-# Normalization method for the convolution layers. Options: "" (no norm), "GN".
-_C.MODEL.SEM_SEG_HEAD.NORM = "GN"
-_C.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT = 1.0
-
-_C.MODEL.PANOPTIC_FPN = CN()
-# Scaling of all losses from instance detection / segmentation head.
-_C.MODEL.PANOPTIC_FPN.INSTANCE_LOSS_WEIGHT = 1.0
-
-# options when combining instance & semantic segmentation outputs
-_C.MODEL.PANOPTIC_FPN.COMBINE = CN({"ENABLED": True})  # "COMBINE.ENABLED" is deprecated & not used
-_C.MODEL.PANOPTIC_FPN.COMBINE.OVERLAP_THRESH = 0.5
-_C.MODEL.PANOPTIC_FPN.COMBINE.STUFF_AREA_LIMIT = 4096
-_C.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = 0.5
-
-
-# ---------------------------------------------------------------------------- #
-# RetinaNet Head
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RETINANET = CN()
-
-# This is the number of foreground classes.
-_C.MODEL.RETINANET.NUM_CLASSES = 80
-
-_C.MODEL.RETINANET.IN_FEATURES = ["p3", "p4", "p5", "p6", "p7"]
-
-# Convolutions to use in the cls and bbox tower
-# NOTE: this doesn't include the last conv for logits
-_C.MODEL.RETINANET.NUM_CONVS = 4
-
-# IoU overlap ratio [bg, fg] for labeling anchors.
-# Anchors with < bg are labeled negative (0)
-# Anchors  with >= bg and < fg are ignored (-1)
-# Anchors with >= fg are labeled positive (1)
-_C.MODEL.RETINANET.IOU_THRESHOLDS = [0.4, 0.5]
-_C.MODEL.RETINANET.IOU_LABELS = [0, -1, 1]
-
-# Prior prob for rare case (i.e. foreground) at the beginning of training.
-# This is used to set the bias for the logits layer of the classifier subnet.
-# This improves training stability in the case of heavy class imbalance.
-_C.MODEL.RETINANET.PRIOR_PROB = 0.01
-
-# Inference cls score threshold, only anchors with score > INFERENCE_TH are
-# considered for inference (to improve speed)
-_C.MODEL.RETINANET.SCORE_THRESH_TEST = 0.05
-# Select topk candidates before NMS
-_C.MODEL.RETINANET.TOPK_CANDIDATES_TEST = 1000
-_C.MODEL.RETINANET.NMS_THRESH_TEST = 0.5
-
-# Weights on (dx, dy, dw, dh) for normalizing Retinanet anchor regression targets
-_C.MODEL.RETINANET.BBOX_REG_WEIGHTS = (1.0, 1.0, 1.0, 1.0)
-
-# Loss parameters
-_C.MODEL.RETINANET.FOCAL_LOSS_GAMMA = 2.0
-_C.MODEL.RETINANET.FOCAL_LOSS_ALPHA = 0.25
-_C.MODEL.RETINANET.SMOOTH_L1_LOSS_BETA = 0.1
-# Options are: "smooth_l1", "giou", "diou", "ciou"
-_C.MODEL.RETINANET.BBOX_REG_LOSS_TYPE = "smooth_l1"
-
-# One of BN, SyncBN, FrozenBN, GN
-# Only supports GN until unshared norm is implemented
-_C.MODEL.RETINANET.NORM = ""
-
-
-# ---------------------------------------------------------------------------- #
-# ResNe[X]t options (ResNets = {ResNet, ResNeXt}
-# Note that parts of a resnet may be used for both the backbone and the head
-# These options apply to both
-# ---------------------------------------------------------------------------- #
-_C.MODEL.RESNETS = CN()
-
-_C.MODEL.RESNETS.DEPTH = 50
-_C.MODEL.RESNETS.OUT_FEATURES = ["res4"]  # res4 for C4 backbone, res2..5 for FPN backbone
-
-# Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
-_C.MODEL.RESNETS.NUM_GROUPS = 1
-
-# Options: FrozenBN, GN, "SyncBN", "BN"
-_C.MODEL.RESNETS.NORM = "FrozenBN"
-
-# Baseline width of each group.
-# Scaling this parameters will scale the width of all bottleneck layers.
-_C.MODEL.RESNETS.WIDTH_PER_GROUP = 64
-
-# Place the stride 2 conv on the 1x1 filter
-# Use True only for the original MSRA ResNet; use False for C2 and Torch models
-_C.MODEL.RESNETS.STRIDE_IN_1X1 = True
-
-# Apply dilation in stage "res5"
-_C.MODEL.RESNETS.RES5_DILATION = 1
-
-# Output width of res2. Scaling this parameters will scale the width of all 1x1 convs in ResNet
-# For R18 and R34, this needs to be set to 64
-_C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
-_C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
-
-# Apply Deformable Convolution in stages
-# Specify if apply deform_conv on Res2, Res3, Res4, Res5
-_C.MODEL.RESNETS.DEFORM_ON_PER_STAGE = [False, False, False, False]
-# Use True to use modulated deform_conv (DeformableV2, https://arxiv.org/abs/1811.11168);
-# Use False for DeformableV1.
-_C.MODEL.RESNETS.DEFORM_MODULATED = False
-# Number of groups in deformable conv.
-_C.MODEL.RESNETS.DEFORM_NUM_GROUPS = 1
-
-
-# ---------------------------------------------------------------------------- #
-# Solver
-# ---------------------------------------------------------------------------- #
-_C.SOLVER = CN()
-
-# Options: WarmupMultiStepLR, WarmupCosineLR.
-# See detectron2/solver/build.py for definition.
-_C.SOLVER.LR_SCHEDULER_NAME = "WarmupMultiStepLR"
-
-_C.SOLVER.MAX_ITER = 40000
-
-_C.SOLVER.BASE_LR = 0.001
-# The end lr, only used by WarmupCosineLR
-_C.SOLVER.BASE_LR_END = 0.0
-
-_C.SOLVER.MOMENTUM = 0.9
-
-_C.SOLVER.NESTEROV = False
-
-_C.SOLVER.WEIGHT_DECAY = 0.0001
-# The weight decay that's applied to parameters of normalization layers
-# (typically the affine transformation)
-_C.SOLVER.WEIGHT_DECAY_NORM = 0.0
-
-_C.SOLVER.GAMMA = 0.1
-# The iteration number to decrease learning rate by GAMMA.
-_C.SOLVER.STEPS = (30000,)
-# Number of decays in WarmupStepWithFixedGammaLR schedule
-_C.SOLVER.NUM_DECAYS = 3
-
-_C.SOLVER.WARMUP_FACTOR = 1.0 / 1000
-_C.SOLVER.WARMUP_ITERS = 1000
-_C.SOLVER.WARMUP_METHOD = "linear"
-# Whether to rescale the interval for the learning schedule after warmup
-_C.SOLVER.RESCALE_INTERVAL = False
-
-# Save a checkpoint after every this number of iterations
-_C.SOLVER.CHECKPOINT_PERIOD = 5000
-
-# Number of images per batch across all machines. This is also the number
-# of training images per step (i.e. per iteration). If we use 16 GPUs
-# and IMS_PER_BATCH = 32, each GPU will see 2 images per batch.
-# May be adjusted automatically if REFERENCE_WORLD_SIZE is set.
-_C.SOLVER.IMS_PER_BATCH = 16
-
-# The reference number of workers (GPUs) this config is meant to train with.
-# It takes no effect when set to 0.
-# With a non-zero value, it will be used by DefaultTrainer to compute a desired
-# per-worker batch size, and then scale the other related configs (total batch size,
-# learning rate, etc) to match the per-worker batch size.
-# See documentation of `DefaultTrainer.auto_scale_workers` for details:
-_C.SOLVER.REFERENCE_WORLD_SIZE = 0
-
-# Detectron v1 (and previous detection code) used a 2x higher LR and 0 WD for
-# biases. This is not useful (at least for recent models). You should avoid
-# changing these and they exist only to reproduce Detectron v1 training if
-# desired.
-_C.SOLVER.BIAS_LR_FACTOR = 1.0
-_C.SOLVER.WEIGHT_DECAY_BIAS = None  # None means following WEIGHT_DECAY
-
-# Gradient clipping
-_C.SOLVER.CLIP_GRADIENTS = CN({"ENABLED": False})
-# Type of gradient clipping, currently 2 values are supported:
-# - "value": the absolute values of elements of each gradients are clipped
-# - "norm": the norm of the gradient for each parameter is clipped thus
-#   affecting all elements in the parameter
-_C.SOLVER.CLIP_GRADIENTS.CLIP_TYPE = "value"
-# Maximum absolute value used for clipping gradients
-_C.SOLVER.CLIP_GRADIENTS.CLIP_VALUE = 1.0
-# Floating point number p for L-p norm to be used with the "norm"
-# gradient clipping type; for L-inf, please specify .inf
-_C.SOLVER.CLIP_GRADIENTS.NORM_TYPE = 2.0
-
-# Enable automatic mixed precision for training
-# Note that this does not change model's inference behavior.
-# To use AMP in inference, run inference under autocast()
-_C.SOLVER.AMP = CN({"ENABLED": False})
-
-# ---------------------------------------------------------------------------- #
-# Specific test options
-# ---------------------------------------------------------------------------- #
-_C.TEST = CN()
-# For end-to-end tests to verify the expected accuracy.
-# Each item is [task, metric, value, tolerance]
-# e.g.: [['bbox', 'AP', 38.5, 0.2]]
-_C.TEST.EXPECTED_RESULTS = []
-# The period (in terms of steps) to evaluate the model during training.
-# Set to 0 to disable.
-_C.TEST.EVAL_PERIOD = 0
-# The sigmas used to calculate keypoint OKS. See http://cocodataset.org/#keypoints-eval
-# When empty, it will use the defaults in COCO.
-# Otherwise it should be a list[float] with the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
-_C.TEST.KEYPOINT_OKS_SIGMAS = []
-# Maximum number of detections to return per image during inference (100 is
-# based on the limit established for the COCO dataset).
-_C.TEST.DETECTIONS_PER_IMAGE = 100
-
-_C.TEST.AUG = CN({"ENABLED": False})
-_C.TEST.AUG.MIN_SIZES = (400, 500, 600, 700, 800, 900, 1000, 1100, 1200)
-_C.TEST.AUG.MAX_SIZE = 4000
-_C.TEST.AUG.FLIP = True
-
-_C.TEST.PRECISE_BN = CN({"ENABLED": False})
-_C.TEST.PRECISE_BN.NUM_ITER = 200
-
-# ---------------------------------------------------------------------------- #
-# Misc options
-# ---------------------------------------------------------------------------- #
-# Directory where output files are written
-_C.OUTPUT_DIR = "./output"
-# Set seed to negative to fully randomize everything.
-# Set seed to positive to use a fixed seed. Note that a fixed seed increases
-# reproducibility but does not guarantee fully deterministic behavior.
-# Disabling all parallelism further increases reproducibility.
-_C.SEED = -1
-# Benchmark different cudnn algorithms.
-# If input images have very different sizes, this option will have large overhead
-# for about 10k iterations. It usually hurts total time, but can benefit for certain models.
-# If input images have the same or similar sizes, benchmark is often helpful.
-_C.CUDNN_BENCHMARK = False
-# The period (in terms of steps) for minibatch visualization at train time.
-# Set to 0 to disable.
-_C.VIS_PERIOD = 0
-
-# global config is for quick hack purposes.
-# You can set them in command line or config files,
-# and access it with:
-#
-# from detectron2.config import global_cfg
-# print(global_cfg.HACK)
-#
-# Do not commit any configs into it.
-_C.GLOBAL = CN()
-_C.GLOBAL.HACK = 1.0
diff --git a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/css__WgYtuddKnvLANWPD8vo73t49SpwJTUwCd8yIhtijuDU__gW8u-y-0IH.css b/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/css__WgYtuddKnvLANWPD8vo73t49SpwJTUwCd8yIhtijuDU__gW8u-y-0IH.css
deleted file mode 100644
index 5da16ffab349521719f37be2f1fbc4848c9c0cdf..0000000000000000000000000000000000000000
--- a/spaces/nmitchko/AI-in-Healthcare/Developer Meetup in Boston Generative AI Use Cases in Healthcare _files/css__WgYtuddKnvLANWPD8vo73t49SpwJTUwCd8yIhtijuDU__gW8u-y-0IH.css	
+++ /dev/null
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stroke-linejoin='round'/%3E%3C/svg%3E%0A");background-repeat:no-repeat;background-position:right 10px center;}#intsys-search-base-form .tt-input{padding:5px 10px 5px 10px!important;font-size:16px;box-shadow:none;}.input-group-btn{right:84px;}.twitter-typeahead{background-color:#fff;border-radius:3px 5px 5px 3px;} 
-}@media (max-width:361px) {
- .navbar-default ul.navbar-nav > .tb-megamenu-item .tb-megamenu-subnav a{text-overflow:ellipsis;white-space:nowrap;overflow:hidden;}.personal-mobile-link{width:200px;} 
-}@media (min-width:768px) {
- .intsys-search-group{margin:24px auto 0 auto;width:674px;}.navbar-default ul.navbar-nav > .tb-megamenu-item > a{padding-left:10px;}.intsys-navbar-top-show{display:block !important;}.intsys-navbar-link-show,.personal-mobile{display:none !important;}#tb-megamenu-user-menu,#tb-megamenu-user-menu-spanish,#tb-megamenu-user-menu-russian,#tb-megamenu-user-menu-japanese,#tb-megamenu-user-menu-english{margin-right:100px;}.navbar-default ul.navbar-nav{background-color:transparent !important;}.language-switcher-locale-url{z-index:50000;position:absolute;display:none;padding:13px 0;}#block-locale-language{display:inline-block;float:right;margin-top:24px;}#block-locale-language h2{display:inline-block;width:40px;font-size:15px;font-weight:400;color:#919191;cursor:pointer;padding:3px;margin:0 16px 0 0;}#block-locale-language h2:hover span{color:#666;}#block-locale-language h2:hover{border-radius:2px;background-color:#F0F2F3;}#block-locale-language 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- #navbar .block-intsys-search-area{width:calc(100% - 470px);}body.navbar-is-fixed-top.admin-menu .intsys-search-group{width:calc(100vw - 470px);} 
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a.active::after,#block-translate-language li a.active::after{content:url("data:image/svg+xml,%3Csvg width='12' height='10' viewBox='0 0 12 10' fill='none' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M10.8536 1.15954C10.6583 0.94682 10.3417 0.94682 10.1465 1.15954L4.15614 7.68516L1.85356 5.17684C1.65831 4.96412 1.34175 4.96414 1.14646 5.17684C0.951182 5.38954 0.951182 5.73439 1.14646 5.94711L3.80259 8.84053C3.99778 9.05323 4.31458 9.05308 4.5097 8.84053L10.8536 1.92983C11.0488 1.71713 11.0488 1.37226 10.8536 1.15954Z' fill='%2343484D' stroke='%2343484D' stroke-width='0.3'/%3E%3C/svg%3E%0A");margin-left:4px;}.intsys-img-user-login{display:inline-block;vertical-align:middle;border-radius:100%;margin-right:5px;background-size:cover;background-position:center;}.intsys-noimg-user-login{margin:7px 5px 0 0;}.btn-login-intsys::before,.btn-create-intsys::before,.btn-search-intsys::before{position:relative;top:1px;display:inline-block;font-family:'Glyphicons 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-}@media (max-width:768px) {
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-.anchor-wrapper{position:relative}code .anchor-wrapper,kbd .anchor-wrapper{padding:2px 4px;margin:-2px -4px}.anchor-link{position:absolute;display:block;min-width:1.25em;font-size:1em}h1 .anchor-link,h2 .anchor-link,h3 .anchor-link,h4 .anchor-link,h5 .anchor-link,h6 .anchor-link{font-size:.75em}.anchor-link.fade a{-webkit-transition:color 300ms ease-in-out,opacity 300ms ease-in-out;-o-transition:color 300ms ease-in-out,opacity 300ms ease-in-out;transition:color 300ms ease-in-out,opacity 300ms ease-in-out}.anchor-link a{padding:.25em;color:#333;text-decoration:none;filter:alpha(opacity=35);opacity:.35}.anchor-link a:hover{color:#337ab7;text-decoration:none;filter:alpha(opacity=100);opacity:1}
-/*})'"*/
-button#ihavecookiesBtn{margin-left:0px !important;}#gdpr-cookie-message{position:fixed;min-width:100%;height:auto;font-size:13px;line-height:20px;left:0;text-align:center;box-sizing:border-box;z-index:5000;bottom:0;min-height:72px;max-width:100%;background-color:rgba(51,51,51,.8) !important;box-shadow:6px -6px 15px rgba(0,0,0,0.05);}#gdpr-cookie-message h4{color:#2A2A2A;font-size:18px;font-weight:bold;margin-bottom:10px;}#gdpr-cookie-message h5{color:red;font-size:15px;font-weight:500;margin-bottom:10px;}#gdpr-cookie-message p,#gdpr-cookie-message ul{color:#fff;font-size:15px;font-weight:400;text-align:center;vertical-align:baseline;line-height:32px;margin-top:18px;}#gdpr-cookie-message p:last-child{}#gdpr-cookie-message li{width:49%;display:inline-block;}#gdpr-cookie-message a{text-decoration:underline;transition:all 0.2s ease-in;color:#fff;}#gdpr-cookie-message a:hover{color:#00b2a9;transition:all 0.3s ease-in;}#gdpr-cookie-message button,button#ihavecookiesBtn{background:#18A99E;margin-top:18px;border-radius:4px;font-size:13px;line-height:20px;border:0;padding:6px 16px;color:#fff;text-align:center;line-height:20px;text-decoration:none;-webkit-transition:all .1s ease-in-out;transition:all .1s ease-in-out;vertical-align:baseline;}#gdpr-cookie-message button:hover{background-color:#139187;}button#gdpr-cookie-advanced{background:white;color:red;display:none;}#gdpr-cookie-message button:disabled{opacity:0.3;}#gdpr-cookie-message input[type="checkbox"]{float:none;margin-top:0;margin-right:5px;}button#ihavecookiesBtn{margin-left:0px !important;}#usl-cookie-message{position:fixed;margin-right:30px;bottom:30px;max-width:100%;background-color:#f7f7f7;border:1px solid #e0e0e0;padding:20px;border-radius:5px;box-shadow:0px 5px 6px rgba(0,0,0,0.25);margin-left:30px;z-index:500;}#usl-cookie-message h4{color:#2A2A2A;font-size:18px;font-weight:bold;margin-bottom:10px;}#usl-cookie-message h5{color:red;font-size:15px;font-weight:500;margin-bottom:10px;}#usl-cookie-message p,#usl-cookie-message ul{color:#000;font-size:15px;line-height:1.5em;}#usl-cookie-message p:last-child{margin-bottom:0;text-align:right;}#usl-cookie-message li{width:49%;display:inline-block;}#usl-cookie-message a{font-weight:bold;color:#fff;transition:all 0.3s ease-in;}#usl-cookie-message a:hover{color:#fff;transition:all 0.3s ease-in;}#usl-cookie-message button,button#ihavecookiesBtn{border:none;background:#27318a;color:white;font-size:15px;padding:7px;border-radius:3px;margin-left:15px;cursor:pointer;transition:all 0.3s ease-in;}#usl-cookie-message button:hover{background:#00b2a9;color:white;transition:all 0.3s ease-in;}button#usl-cookie-advanced{background:white;color:red;display:none;}#usl-cookie-message button:disabled{opacity:0.3;}#usl-cookie-message input[type="checkbox"]{float:none;margin-top:0;margin-right:5px;}@media (max-width:768px) {
- #gdpr-cookie-message button,button#ihavecookiesBtn{margin:14px;}#gdpr-cookie-message p,#gdpr-cookie-message ul{line-height:20px;margin-top:14px;margin-bottom:0;} 
-}
-/*})'"*/
diff --git a/spaces/nomic-ai/wangrui6_Zhihu-KOL/index.html b/spaces/nomic-ai/wangrui6_Zhihu-KOL/index.html
deleted file mode 100644
index 2da248d8f7283f3c0c61c70ffba0a60038b31ec4..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/wangrui6_Zhihu-KOL/index.html
+++ /dev/null
@@ -1,42 +0,0 @@
-<html>
-
-<head>
-  <title>wangrui6/Zhihu-KOL</title>
-  <style>
-    body {
-      font-family: Arial, sans-serif;
-      background-color: #f0f0f0;
-      display: flex;
-      justify-content: center;
-      align-items: center;
-      height: 100vh;
-      margin: 0;
-      padding: 0;
-      color: #333;
-    }
-
-    .iframe-container {
-      border: 1px solid #ccc;
-      border-radius: 10px;
-      overflow: hidden;
-      width: 80%;
-      height: 80%;
-      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
-    }
-
-    iframe {
-      width: 100%;
-      height: 100%;
-      border: none;
-    }
-  </style>
-</head>
-
-<body>
-  <div class="iframe-container">
-    <iframe src="https://atlas.nomic.ai/map/7793e84c-9d48-4a45-9004-cb8a871287fc/a1483509-0f80-48bd-8bc5-7cd5392f24ca" allow="clipboard-read; clipboard-write"
-      title="Nomic Atlas"></iframe>
-  </div>
-</body>
-
-</html>
\ No newline at end of file
diff --git a/spaces/oliver2023/chatgpt-on-wechat/docker/build.alpine.sh b/spaces/oliver2023/chatgpt-on-wechat/docker/build.alpine.sh
deleted file mode 100644
index 8f102ddc899a80b1c6b5d4e75c72b6f686a965d7..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/docker/build.alpine.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-
-# fetch latest release tag
-CHATGPT_ON_WECHAT_TAG=`curl -sL "https://api.github.com/repos/zhayujie/chatgpt-on-wechat/releases/latest" | \
-     grep '"tag_name":' | \
-     sed -E 's/.*"([^"]+)".*/\1/'`
-
-# build image
-docker build -f Dockerfile.alpine \
-             --build-arg CHATGPT_ON_WECHAT_VER=$CHATGPT_ON_WECHAT_TAG \
-             -t zhayujie/chatgpt-on-wechat .
-
-# tag image
-docker tag zhayujie/chatgpt-on-wechat zhayujie/chatgpt-on-wechat:alpine 
-docker tag zhayujie/chatgpt-on-wechat zhayujie/chatgpt-on-wechat:$CHATGPT_ON_WECHAT_TAG-alpine
-           
\ No newline at end of file
diff --git a/spaces/openskyml/README/README.md b/spaces/openskyml/README/README.md
deleted file mode 100644
index a9b4978c75d33b1b95e91bed044aee9b44ef8a1f..0000000000000000000000000000000000000000
--- a/spaces/openskyml/README/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: README
-emoji: 🐢
-colorFrom: red
-colorTo: gray
-sdk: static
-pinned: false
----
-
diff --git a/spaces/optimum/optimum-benchmark-ui/run.py b/spaces/optimum/optimum-benchmark-ui/run.py
deleted file mode 100644
index d8ac452600c1a33b29b3b4248fcf7617b71218e8..0000000000000000000000000000000000000000
--- a/spaces/optimum/optimum-benchmark-ui/run.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import subprocess
-import gradio as gr
-import pandas as pd
-from ansi2html import Ansi2HTMLConverter
-
-ansi2html_converter = Ansi2HTMLConverter(inline=True)
-
-
-def run_benchmark(kwargs):
-    for key, value in kwargs.copy().items():
-        if key.label == "compare_to_baseline":
-            baseline = value
-            kwargs.pop(key)
-        elif key.label == "experiment_name":
-            experiment_name = value
-            kwargs.pop(key)
-        elif key.label == "model":
-            model = value
-            kwargs.pop(key)
-        elif key.label == "task":
-            task = value
-            kwargs.pop(key)
-        elif key.label == "device":
-            device = value
-            kwargs.pop(key)
-        elif key.label == "backend":
-            backend = value
-            kwargs.pop(key)
-        elif key.label == "benchmark":
-            benchmark = value
-            kwargs.pop(key)
-        else:
-            continue
-
-    if baseline:
-        baseline_arguments = [
-            "optimum-benchmark",
-            "--config-dir",
-            "./configs",
-            "--config-name",
-            "base_config",
-            f"backend=pytorch",
-            f"task={task}",
-            f"model={model}",
-            f"device={device}",
-            f"benchmark={benchmark}",
-            f"experiment_name=baseline_{experiment_name}",
-        ]
-        for component, value in kwargs.items():
-            if f"{benchmark}." in component.label:
-                label = component.label.replace(f"{benchmark}.", "benchmark.")
-                if isinstance(component, gr.Dataframe):
-                    for sub_key, sub_value in zip(component.headers, value[0]):
-                        baseline_arguments.append(f"++{label}.{sub_key}={sub_value}")
-                else:
-                    baseline_arguments.append(f"{label}={value}")
-
-        # yield from run_experiment(baseline_arguments) but get the return code
-        baseline_return_code, html_text = yield from run_experiment(baseline_arguments, "")
-        if baseline_return_code is not None and baseline_return_code != 0:
-            yield gr.update(value=html_text), gr.update(interactive=True), gr.update(visible=False)
-            return
-    else:
-        html_text = ""
-
-    arguments = [
-        "optimum-benchmark",
-        "--config-dir",
-        "./configs",
-        "--config-name",
-        "base_config",
-        f"task={task}",
-        f"model={model}",
-        f"device={device}",
-        f"backend={backend}",
-        f"benchmark={benchmark}",
-        f"experiment_name={experiment_name}",
-    ]
-    for component, value in kwargs.items():
-        if f"{backend}." in component.label or f"{benchmark}." in component.label:
-            label = component.label.replace(f"{backend}.", "backend.").replace(f"{benchmark}.", "benchmark.")
-
-            if isinstance(component, gr.Dataframe):
-                for sub_key, sub_value in zip(component.headers, value[0]):
-                    arguments.append(f"++{label}.{sub_key}={sub_value}")
-            else:
-                arguments.append(f"{label}={value}")
-
-    return_code, html_text = yield from run_experiment(arguments, html_text)
-    if return_code is not None and return_code != 0:
-        yield gr.update(value=html_text), gr.update(interactive=True), gr.update(visible=False)
-        return
-
-    if baseline:
-        baseline_table = pd.read_csv(f"runs/baseline_{experiment_name}/{benchmark}_results.csv", index_col=0)
-        table = pd.read_csv(f"runs/{experiment_name}/{benchmark}_results.csv", index_col=0)
-        # concat tables
-        table = pd.concat([baseline_table, table], axis=0)
-        table = postprocess_table(table, experiment_name)
-    else:
-        table = pd.read_csv(f"runs/{experiment_name}/{benchmark}_results.csv", index_col=0)
-
-    table_update = gr.update(visible=True, value={"headers": list(table.columns), "data": table.values.tolist()})
-    yield gr.update(value=html_text), gr.update(interactive=True), table_update
-    return
-
-
-def run_experiment(args, html_text=""):
-    command = "<br>".join(args)
-    html_text += f"<h3>Running command:</h3>{command}"
-    yield gr.update(value=html_text), gr.update(interactive=False), gr.update(visible=False)
-
-    # stream subprocess output
-    process = subprocess.Popen(
-        args,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.STDOUT,
-        universal_newlines=True,
-    )
-
-    curr_ansi_text = ""
-    for ansi_line in iter(process.stdout.readline, ""):
-        if process.returncode is not None and process.returncode != 0:
-            break
-
-        # stream process output to stdout
-        print(ansi_line, end="")
-        # skip torch.distributed.nn.jit.instantiator messages
-        if "torch.distributed.nn.jit.instantiator" in ansi_line:
-            continue
-        # process download messages
-        if "Downloading " in curr_ansi_text and "Downloading " in ansi_line:
-            curr_ansi_text = curr_ansi_text.split("\n")[:-2]
-            print(curr_ansi_text)
-            curr_ansi_text.append(ansi_line)
-            curr_ansi_text = "\n".join(curr_ansi_text)
-        else:
-            # append line to ansi text
-            curr_ansi_text += ansi_line
-        # convert ansi to html
-        curr_html_text = ansi2html_converter.convert(curr_ansi_text)
-        # stream html output to gradio
-        cumul_html_text = html_text + "<br><h3>Streaming logs:</h3>" + curr_html_text
-        yield gr.update(value=cumul_html_text), gr.update(interactive=False), gr.update(visible=False)
-
-    return process.returncode, cumul_html_text
-
-
-def postprocess_table(table, experiment_name):
-    table["experiment_name"] = ["baseline", experiment_name]
-    table = table.set_index("experiment_name")
-    table.reset_index(inplace=True)
-    if "forward.latency(s)" in table.columns:
-        table["forward.latency.reduction(%)"] = (
-            table["forward.latency(s)"] / table["forward.latency(s)"].iloc[0] - 1
-        ) * 100
-        table["forward.latency.reduction(%)"] = table["forward.latency.reduction(%)"].round(2)
-
-    if "forward.throughput(samples/s)" in table.columns:
-        table["forward.throughput.speedup(%)"] = (
-            table["forward.throughput(samples/s)"] / table["forward.throughput(samples/s)"].iloc[0] - 1
-        ) * 100
-        table["forward.throughput.speedup(%)"] = table["forward.throughput.speedup(%)"].round(2)
-
-    if "forward.peak_memory(MB)" in table.columns:
-        table["forward.peak_memory.reduction(%)"] = (
-            table["forward.peak_memory(MB)"] / table["forward.peak_memory(MB)"].iloc[0] - 1
-        ) * 100
-        table["forward.peak_memory.reduction(%)"] = table["forward.peak_memory.reduction(%)"].round(2)
-
-    if "generate.latency(s)" in table.columns:
-        table["generate.latency.reduction(%)"] = (
-            table["generate.latency(s)"] / table["generate.latency(s)"].iloc[0] - 1
-        ) * 100
-        table["generate.latency.reduction(%)"] = table["generate.latency.reduction(%)"].round(2)
-
-    if "generate.throughput(tokens/s)" in table.columns:
-        table["generate.throughput.speedup(%)"] = (
-            table["generate.throughput(tokens/s)"] / table["generate.throughput(tokens/s)"].iloc[0] - 1
-        ) * 100
-        table["generate.throughput.speedup(%)"] = table["generate.throughput.speedup(%)"].round(2)
-
-    if "generate.peak_memory(MB)" in table.columns:
-        table["generate.peak_memory.reduction(%)"] = (
-            table["generate.peak_memory(MB)"] / table["generate.peak_memory(MB)"].iloc[0] - 1
-        ) * 100
-        table["generate.peak_memory.reduction(%)"] = table["generate.peak_memory.reduction(%)"].round(2)
-
-    return table
diff --git a/spaces/osanseviero/SMILES_RDKit_Py3DMOL_FORK/README.md b/spaces/osanseviero/SMILES_RDKit_Py3DMOL_FORK/README.md
deleted file mode 100644
index ed1abb3f213d2fa8352d57996a798fb776c0d766..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/SMILES_RDKit_Py3DMOL_FORK/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: SMILES_RDKit_Py3DMOL_FORK
-emoji: 🐨
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/audio_diffusion.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/audio_diffusion.md
deleted file mode 100644
index cc52c70a8e9ec6814d9d2b928c70d0694a3b9e71..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/pipelines/audio_diffusion.md
+++ /dev/null
@@ -1,37 +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.
--->
-
-# Audio Diffusion
-
-[Audio Diffusion](https://github.com/teticio/audio-diffusion) is by Robert Dargavel Smith, and it leverages the recent advances in image generation from diffusion models by converting audio samples to and from Mel spectrogram images.
-
-The original codebase, training scripts and example notebooks can be found at [teticio/audio-diffusion](https://github.com/teticio/audio-diffusion).
-
-<Tip>
-
-Make sure to check out the Schedulers [guide](/using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](/using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
-
-</Tip>
-
-## AudioDiffusionPipeline
-[[autodoc]] AudioDiffusionPipeline
-	- all
-	- __call__
-
-## AudioPipelineOutput
-[[autodoc]] pipelines.AudioPipelineOutput
-
-## ImagePipelineOutput
-[[autodoc]] pipelines.ImagePipelineOutput
-
-## Mel
-[[autodoc]] Mel
diff --git a/spaces/pallesureshnaidu/MyGenAIChatBot/README.md b/spaces/pallesureshnaidu/MyGenAIChatBot/README.md
deleted file mode 100644
index 8d1f81ac6d796e14ff3093d22a710903adcece8a..0000000000000000000000000000000000000000
--- a/spaces/pallesureshnaidu/MyGenAIChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIChatBot
-emoji: 🌖
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/patgpt4/MusicGen/audiocraft/__init__.py b/spaces/patgpt4/MusicGen/audiocraft/__init__.py
deleted file mode 100644
index 2befac60faf6f406f78ff7b7da05225dbfe7b111..0000000000000000000000000000000000000000
--- a/spaces/patgpt4/MusicGen/audiocraft/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from . import data, modules, models
-
-__version__ = '0.0.2a1'
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_tokenizer.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_tokenizer.py
deleted file mode 100644
index dd0d648d49a7c1a62d25ce5c9107aa448a8a22d1..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pkg_resources/_vendor/packaging/_tokenizer.py
+++ /dev/null
@@ -1,192 +0,0 @@
-import contextlib
-import re
-from dataclasses import dataclass
-from typing import Dict, Iterator, NoReturn, Optional, Tuple, Union
-
-from .specifiers import Specifier
-
-
-@dataclass
-class Token:
-    name: str
-    text: str
-    position: int
-
-
-class ParserSyntaxError(Exception):
-    """The provided source text could not be parsed correctly."""
-
-    def __init__(
-        self,
-        message: str,
-        *,
-        source: str,
-        span: Tuple[int, int],
-    ) -> None:
-        self.span = span
-        self.message = message
-        self.source = source
-
-        super().__init__()
-
-    def __str__(self) -> str:
-        marker = " " * self.span[0] + "~" * (self.span[1] - self.span[0]) + "^"
-        return "\n    ".join([self.message, self.source, marker])
-
-
-DEFAULT_RULES: "Dict[str, Union[str, re.Pattern[str]]]" = {
-    "LEFT_PARENTHESIS": r"\(",
-    "RIGHT_PARENTHESIS": r"\)",
-    "LEFT_BRACKET": r"\[",
-    "RIGHT_BRACKET": r"\]",
-    "SEMICOLON": r";",
-    "COMMA": r",",
-    "QUOTED_STRING": re.compile(
-        r"""
-            (
-                ('[^']*')
-                |
-                ("[^"]*")
-            )
-        """,
-        re.VERBOSE,
-    ),
-    "OP": r"(===|==|~=|!=|<=|>=|<|>)",
-    "BOOLOP": r"\b(or|and)\b",
-    "IN": r"\bin\b",
-    "NOT": r"\bnot\b",
-    "VARIABLE": re.compile(
-        r"""
-            \b(
-                python_version
-                |python_full_version
-                |os[._]name
-                |sys[._]platform
-                |platform_(release|system)
-                |platform[._](version|machine|python_implementation)
-                |python_implementation
-                |implementation_(name|version)
-                |extra
-            )\b
-        """,
-        re.VERBOSE,
-    ),
-    "SPECIFIER": re.compile(
-        Specifier._operator_regex_str + Specifier._version_regex_str,
-        re.VERBOSE | re.IGNORECASE,
-    ),
-    "AT": r"\@",
-    "URL": r"[^ \t]+",
-    "IDENTIFIER": r"\b[a-zA-Z0-9][a-zA-Z0-9._-]*\b",
-    "VERSION_PREFIX_TRAIL": r"\.\*",
-    "VERSION_LOCAL_LABEL_TRAIL": r"\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*",
-    "WS": r"[ \t]+",
-    "END": r"$",
-}
-
-
-class Tokenizer:
-    """Context-sensitive token parsing.
-
-    Provides methods to examine the input stream to check whether the next token
-    matches.
-    """
-
-    def __init__(
-        self,
-        source: str,
-        *,
-        rules: "Dict[str, Union[str, re.Pattern[str]]]",
-    ) -> None:
-        self.source = source
-        self.rules: Dict[str, re.Pattern[str]] = {
-            name: re.compile(pattern) for name, pattern in rules.items()
-        }
-        self.next_token: Optional[Token] = None
-        self.position = 0
-
-    def consume(self, name: str) -> None:
-        """Move beyond provided token name, if at current position."""
-        if self.check(name):
-            self.read()
-
-    def check(self, name: str, *, peek: bool = False) -> bool:
-        """Check whether the next token has the provided name.
-
-        By default, if the check succeeds, the token *must* be read before
-        another check. If `peek` is set to `True`, the token is not loaded and
-        would need to be checked again.
-        """
-        assert (
-            self.next_token is None
-        ), f"Cannot check for {name!r}, already have {self.next_token!r}"
-        assert name in self.rules, f"Unknown token name: {name!r}"
-
-        expression = self.rules[name]
-
-        match = expression.match(self.source, self.position)
-        if match is None:
-            return False
-        if not peek:
-            self.next_token = Token(name, match[0], self.position)
-        return True
-
-    def expect(self, name: str, *, expected: str) -> Token:
-        """Expect a certain token name next, failing with a syntax error otherwise.
-
-        The token is *not* read.
-        """
-        if not self.check(name):
-            raise self.raise_syntax_error(f"Expected {expected}")
-        return self.read()
-
-    def read(self) -> Token:
-        """Consume the next token and return it."""
-        token = self.next_token
-        assert token is not None
-
-        self.position += len(token.text)
-        self.next_token = None
-
-        return token
-
-    def raise_syntax_error(
-        self,
-        message: str,
-        *,
-        span_start: Optional[int] = None,
-        span_end: Optional[int] = None,
-    ) -> NoReturn:
-        """Raise ParserSyntaxError at the given position."""
-        span = (
-            self.position if span_start is None else span_start,
-            self.position if span_end is None else span_end,
-        )
-        raise ParserSyntaxError(
-            message,
-            source=self.source,
-            span=span,
-        )
-
-    @contextlib.contextmanager
-    def enclosing_tokens(
-        self, open_token: str, close_token: str, *, around: str
-    ) -> Iterator[None]:
-        if self.check(open_token):
-            open_position = self.position
-            self.read()
-        else:
-            open_position = None
-
-        yield
-
-        if open_position is None:
-            return
-
-        if not self.check(close_token):
-            self.raise_syntax_error(
-                f"Expected matching {close_token} for {open_token}, after {around}",
-                span_start=open_position,
-            )
-
-        self.read()
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/version.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/version.py
deleted file mode 100644
index 74c40d7bfd5fc63826864345837f1ce66ac613c7..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/version.py
+++ /dev/null
@@ -1,357 +0,0 @@
-#
-# distutils/version.py
-#
-# Implements multiple version numbering conventions for the
-# Python Module Distribution Utilities.
-#
-# $Id$
-#
-
-"""Provides classes to represent module version numbers (one class for
-each style of version numbering).  There are currently two such classes
-implemented: StrictVersion and LooseVersion.
-
-Every version number class implements the following interface:
-  * the 'parse' method takes a string and parses it to some internal
-    representation; if the string is an invalid version number,
-    'parse' raises a ValueError exception
-  * the class constructor takes an optional string argument which,
-    if supplied, is passed to 'parse'
-  * __str__ reconstructs the string that was passed to 'parse' (or
-    an equivalent string -- ie. one that will generate an equivalent
-    version number instance)
-  * __repr__ generates Python code to recreate the version number instance
-  * _cmp compares the current instance with either another instance
-    of the same class or a string (which will be parsed to an instance
-    of the same class, thus must follow the same rules)
-"""
-
-import re
-import warnings
-import contextlib
-
-
-@contextlib.contextmanager
-def suppress_known_deprecation():
-    with warnings.catch_warnings(record=True) as ctx:
-        warnings.filterwarnings(
-            action='default',
-            category=DeprecationWarning,
-            message="distutils Version classes are deprecated.",
-        )
-        yield ctx
-
-
-class Version:
-    """Abstract base class for version numbering classes.  Just provides
-    constructor (__init__) and reproducer (__repr__), because those
-    seem to be the same for all version numbering classes; and route
-    rich comparisons to _cmp.
-    """
-
-    def __init__(self, vstring=None):
-        if vstring:
-            self.parse(vstring)
-        warnings.warn(
-            "distutils Version classes are deprecated. "
-            "Use packaging.version instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-    def __repr__(self):
-        return "{} ('{}')".format(self.__class__.__name__, str(self))
-
-    def __eq__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c == 0
-
-    def __lt__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c < 0
-
-    def __le__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c <= 0
-
-    def __gt__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c > 0
-
-    def __ge__(self, other):
-        c = self._cmp(other)
-        if c is NotImplemented:
-            return c
-        return c >= 0
-
-
-# Interface for version-number classes -- must be implemented
-# by the following classes (the concrete ones -- Version should
-# be treated as an abstract class).
-#    __init__ (string) - create and take same action as 'parse'
-#                        (string parameter is optional)
-#    parse (string)    - convert a string representation to whatever
-#                        internal representation is appropriate for
-#                        this style of version numbering
-#    __str__ (self)    - convert back to a string; should be very similar
-#                        (if not identical to) the string supplied to parse
-#    __repr__ (self)   - generate Python code to recreate
-#                        the instance
-#    _cmp (self, other) - compare two version numbers ('other' may
-#                        be an unparsed version string, or another
-#                        instance of your version class)
-
-
-class StrictVersion(Version):
-
-    """Version numbering for anal retentives and software idealists.
-    Implements the standard interface for version number classes as
-    described above.  A version number consists of two or three
-    dot-separated numeric components, with an optional "pre-release" tag
-    on the end.  The pre-release tag consists of the letter 'a' or 'b'
-    followed by a number.  If the numeric components of two version
-    numbers are equal, then one with a pre-release tag will always
-    be deemed earlier (lesser) than one without.
-
-    The following are valid version numbers (shown in the order that
-    would be obtained by sorting according to the supplied cmp function):
-
-        0.4       0.4.0  (these two are equivalent)
-        0.4.1
-        0.5a1
-        0.5b3
-        0.5
-        0.9.6
-        1.0
-        1.0.4a3
-        1.0.4b1
-        1.0.4
-
-    The following are examples of invalid version numbers:
-
-        1
-        2.7.2.2
-        1.3.a4
-        1.3pl1
-        1.3c4
-
-    The rationale for this version numbering system will be explained
-    in the distutils documentation.
-    """
-
-    version_re = re.compile(
-        r'^(\d+) \. (\d+) (\. (\d+))? ([ab](\d+))?$', re.VERBOSE | re.ASCII
-    )
-
-    def parse(self, vstring):
-        match = self.version_re.match(vstring)
-        if not match:
-            raise ValueError("invalid version number '%s'" % vstring)
-
-        (major, minor, patch, prerelease, prerelease_num) = match.group(1, 2, 4, 5, 6)
-
-        if patch:
-            self.version = tuple(map(int, [major, minor, patch]))
-        else:
-            self.version = tuple(map(int, [major, minor])) + (0,)
-
-        if prerelease:
-            self.prerelease = (prerelease[0], int(prerelease_num))
-        else:
-            self.prerelease = None
-
-    def __str__(self):
-        if self.version[2] == 0:
-            vstring = '.'.join(map(str, self.version[0:2]))
-        else:
-            vstring = '.'.join(map(str, self.version))
-
-        if self.prerelease:
-            vstring = vstring + self.prerelease[0] + str(self.prerelease[1])
-
-        return vstring
-
-    def _cmp(self, other):  # noqa: C901
-        if isinstance(other, str):
-            with suppress_known_deprecation():
-                other = StrictVersion(other)
-        elif not isinstance(other, StrictVersion):
-            return NotImplemented
-
-        if self.version != other.version:
-            # numeric versions don't match
-            # prerelease stuff doesn't matter
-            if self.version < other.version:
-                return -1
-            else:
-                return 1
-
-        # have to compare prerelease
-        # case 1: neither has prerelease; they're equal
-        # case 2: self has prerelease, other doesn't; other is greater
-        # case 3: self doesn't have prerelease, other does: self is greater
-        # case 4: both have prerelease: must compare them!
-
-        if not self.prerelease and not other.prerelease:
-            return 0
-        elif self.prerelease and not other.prerelease:
-            return -1
-        elif not self.prerelease and other.prerelease:
-            return 1
-        elif self.prerelease and other.prerelease:
-            if self.prerelease == other.prerelease:
-                return 0
-            elif self.prerelease < other.prerelease:
-                return -1
-            else:
-                return 1
-        else:
-            assert False, "never get here"
-
-
-# end class StrictVersion
-
-
-# The rules according to Greg Stein:
-# 1) a version number has 1 or more numbers separated by a period or by
-#    sequences of letters. If only periods, then these are compared
-#    left-to-right to determine an ordering.
-# 2) sequences of letters are part of the tuple for comparison and are
-#    compared lexicographically
-# 3) recognize the numeric components may have leading zeroes
-#
-# The LooseVersion class below implements these rules: a version number
-# string is split up into a tuple of integer and string components, and
-# comparison is a simple tuple comparison.  This means that version
-# numbers behave in a predictable and obvious way, but a way that might
-# not necessarily be how people *want* version numbers to behave.  There
-# wouldn't be a problem if people could stick to purely numeric version
-# numbers: just split on period and compare the numbers as tuples.
-# However, people insist on putting letters into their version numbers;
-# the most common purpose seems to be:
-#   - indicating a "pre-release" version
-#     ('alpha', 'beta', 'a', 'b', 'pre', 'p')
-#   - indicating a post-release patch ('p', 'pl', 'patch')
-# but of course this can't cover all version number schemes, and there's
-# no way to know what a programmer means without asking him.
-#
-# The problem is what to do with letters (and other non-numeric
-# characters) in a version number.  The current implementation does the
-# obvious and predictable thing: keep them as strings and compare
-# lexically within a tuple comparison.  This has the desired effect if
-# an appended letter sequence implies something "post-release":
-# eg. "0.99" < "0.99pl14" < "1.0", and "5.001" < "5.001m" < "5.002".
-#
-# However, if letters in a version number imply a pre-release version,
-# the "obvious" thing isn't correct.  Eg. you would expect that
-# "1.5.1" < "1.5.2a2" < "1.5.2", but under the tuple/lexical comparison
-# implemented here, this just isn't so.
-#
-# Two possible solutions come to mind.  The first is to tie the
-# comparison algorithm to a particular set of semantic rules, as has
-# been done in the StrictVersion class above.  This works great as long
-# as everyone can go along with bondage and discipline.  Hopefully a
-# (large) subset of Python module programmers will agree that the
-# particular flavour of bondage and discipline provided by StrictVersion
-# provides enough benefit to be worth using, and will submit their
-# version numbering scheme to its domination.  The free-thinking
-# anarchists in the lot will never give in, though, and something needs
-# to be done to accommodate them.
-#
-# Perhaps a "moderately strict" version class could be implemented that
-# lets almost anything slide (syntactically), and makes some heuristic
-# assumptions about non-digits in version number strings.  This could
-# sink into special-case-hell, though; if I was as talented and
-# idiosyncratic as Larry Wall, I'd go ahead and implement a class that
-# somehow knows that "1.2.1" < "1.2.2a2" < "1.2.2" < "1.2.2pl3", and is
-# just as happy dealing with things like "2g6" and "1.13++".  I don't
-# think I'm smart enough to do it right though.
-#
-# In any case, I've coded the test suite for this module (see
-# ../test/test_version.py) specifically to fail on things like comparing
-# "1.2a2" and "1.2".  That's not because the *code* is doing anything
-# wrong, it's because the simple, obvious design doesn't match my
-# complicated, hairy expectations for real-world version numbers.  It
-# would be a snap to fix the test suite to say, "Yep, LooseVersion does
-# the Right Thing" (ie. the code matches the conception).  But I'd rather
-# have a conception that matches common notions about version numbers.
-
-
-class LooseVersion(Version):
-
-    """Version numbering for anarchists and software realists.
-    Implements the standard interface for version number classes as
-    described above.  A version number consists of a series of numbers,
-    separated by either periods or strings of letters.  When comparing
-    version numbers, the numeric components will be compared
-    numerically, and the alphabetic components lexically.  The following
-    are all valid version numbers, in no particular order:
-
-        1.5.1
-        1.5.2b2
-        161
-        3.10a
-        8.02
-        3.4j
-        1996.07.12
-        3.2.pl0
-        3.1.1.6
-        2g6
-        11g
-        0.960923
-        2.2beta29
-        1.13++
-        5.5.kw
-        2.0b1pl0
-
-    In fact, there is no such thing as an invalid version number under
-    this scheme; the rules for comparison are simple and predictable,
-    but may not always give the results you want (for some definition
-    of "want").
-    """
-
-    component_re = re.compile(r'(\d+ | [a-z]+ | \.)', re.VERBOSE)
-
-    def parse(self, vstring):
-        # I've given up on thinking I can reconstruct the version string
-        # from the parsed tuple -- so I just store the string here for
-        # use by __str__
-        self.vstring = vstring
-        components = [x for x in self.component_re.split(vstring) if x and x != '.']
-        for i, obj in enumerate(components):
-            try:
-                components[i] = int(obj)
-            except ValueError:
-                pass
-
-        self.version = components
-
-    def __str__(self):
-        return self.vstring
-
-    def __repr__(self):
-        return "LooseVersion ('%s')" % str(self)
-
-    def _cmp(self, other):
-        if isinstance(other, str):
-            other = LooseVersion(other)
-        elif not isinstance(other, LooseVersion):
-            return NotImplemented
-
-        if self.version == other.version:
-            return 0
-        if self.version < other.version:
-            return -1
-        if self.version > other.version:
-            return 1
-
-
-# end class LooseVersion
diff --git a/spaces/playgrdstar/compare-llms/app.py b/spaces/playgrdstar/compare-llms/app.py
deleted file mode 100644
index f04e9e30ad9eeb38a59ca4ab0a1462642159cf82..0000000000000000000000000000000000000000
--- a/spaces/playgrdstar/compare-llms/app.py
+++ /dev/null
@@ -1,274 +0,0 @@
-import os, requests
-import gradio as gr
-HF_READ_API_KEY = os.environ["HF_READ_API_KEY"]
-
-### This code loads the models and undertakes inference locally ###
-
-# from transformers import GPTNeoForCausalLM, GPT2Tokenizer
-# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
-# model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
-# tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
-# tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
-# model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
-
-model_list = ['google/flan-t5-small', 'google/flan-t5-base', 'google/flan-t5-large', 'google/flan-t5-xl', 'google/flan-t5-xxl', 
-              'gpt2-medium', 'gpt2-large', 'gpt2-xl',
-              'EleutherAI/gpt-neo-1.3B', 'EleutherAI/gpt-neo-2.7B', 'EleutherAI/gpt-neo-6b', 'EleutherAI/gpt-neox-20b',
-              'bigscience/bloom-1b7', 'bigscience/bloom-3b', 'bigscience/bloom-7b1'
-              ]
-
-def load_model(model_name):
-    if model_name == 'EleutherAI/gpt-neo-2.7B' or model_name == 'gpt2-medium' or model_name == 'gpt2-large':
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-    else:
-        model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    tokenizer.pad_token = tokenizer.eos_token
-    # tokenizer.padding_side = "left"
-    return model, tokenizer
-
-def maybe_is_truncated(s):
-    punct = [".", "!", "?", '"']
-    if s[-1] in punct:
-        return False
-    return True
-
-def load_and_generate(model_name, prompt):
-    model, tokenizer = load_model(model_name)
-
-    temperature=0.25
-    tokens = tokenizer(prompt, return_tensors="pt")
-    max_length = len(tokens.input_ids[0])+5
-    input_ids = tokens.input_ids
-    attention_mask = tokens.attention_mask
-    # see huggingface.co/docs/transformers/main_classes/text_generation
-    gen_tokens = model.generate(
-        input_ids=input_ids,
-        attention_mask=attention_mask,
-        pad_token_id=tokenizer.eos_token_id,
-        do_sample=True,
-        temperature=temperature,
-        # max_length=max_length,
-        max_new_tokens=max_length, 
-        # use_cache=False, 
-        # penalty_alpha=0.1,
-        # top_k=100, 
-        # early_stopping=False
-    )
-    gen_text = tokenizer.batch_decode(gen_tokens)[0]
-
-    max_times = 20
-    while maybe_is_truncated(gen_text) and max_times > 0:
-        tokens = tokenizer(gen_text, return_tensors="pt")
-        max_length = len(tokens.input_ids[0])+5
-        input_ids = tokens.input_ids
-        attention_mask = tokens.attention_mask
-
-        gen_tokens = model.generate(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            pad_token_id=tokenizer.eos_token_id,
-            do_sample=True,
-            temperature=temperature,
-            max_length=max_length,
-            # max_new_tokens=100,
-            # use_cache=True,
-            # penalty_alpha=0.1,
-            # top_k=100, 
-            # early_stopping=False
-        )
-
-        gen_text = tokenizer.batch_decode(gen_tokens)[0]
-
-        max_times -= 1
-
-    return gen_text.replace("<pad>", "").replace("</s>", "")
-
-### This code for the inference api ###
-
-def generate_from_api(query, model_name, temperature, max_tokens):
-    headers = {f"Authorization": f"Bearer {HF_READ_API_KEY}", 
-               "wait_for_model": "true", 
-               "temperature": str(temperature), 
-               "max_tokens": str(max_tokens),
-               "max_time": str(120)}
-    
-    model_api_url = f"https://api-inference.huggingface.co/models/{model_name}"
-
-    payload = {"inputs": query}
-    response = requests.post(model_api_url, headers=headers, json=payload)
-    while response.status_code != 200:
-        response = requests.post(model_api_url, headers=headers, json=payload)
-    return response.json()[0]['generated_text']
-
-def generate_from_api_check(query, model_name, temperature, max_tokens):
-    headers = {f"Authorization": f"Bearer {HF_READ_API_KEY}", 
-               "wait_for_model": "true", 
-               "temperature": str(temperature), 
-               "max_tokens": str(max_tokens),
-               "max_time": str(120)}
-    
-    model_api_url = f"https://api-inference.huggingface.co/models/{model_name}"
-
-    payload = {"inputs": query}
-    response = requests.post(model_api_url, headers=headers, json=payload)
-    while response.status_code != 200:
-        response = requests.post(model_api_url, headers=headers, json=payload)
-
-    max_times = 20
-    gen_text = response.json()[0]['generated_text']
-    while maybe_is_truncated(gen_text) and max_times > 0:
-        headers = {f"Authorization": f"Bearer {HF_READ_API_KEY}", 
-                    "wait_for_model": "true", 
-                    "temperature": str(temperature), 
-                    "max_tokens": str(max_tokens + len(gen_text)),
-                    "max_time": str(120)}
-        payload = {"inputs": query + ' ' + gen_text}
-        response = requests.post(model_api_url, headers=headers, json=payload)
-        while response.status_code != 200:
-            response = requests.post(model_api_url, headers=headers, json=payload)
-        gen_text = response.json()[0]['generated_text']
-        max_times -= 1
-        
-    return gen_text
-
-
-with gr.Blocks(css='style.css') as demo:
-    gr.HTML("""
-    <div style="text-align: center; max-width: 1240px; margin: 0 auto;">
-    <h1 style="font-weight: 200; font-size: 20px; margin-bottom:8px; margin-top:0px;">
-    Different Strokes (Prompts) for Different Folks (LLMs)
-    </h1>
-    <hr style="margin-bottom:5px; margin-top:5px;">
-    <h4 style="font-weight: 50; font-size: 14px; margin-bottom:0px; margin-top:0px;">
-    After reading <a href="https://github.com/dair-ai/Prompt-Engineering-Guide">Prompt Engineering Guide</a>, which is a good guide when starting to learn about prompts for large language models (LLMs), specifically OpenAI's LLMs, I was interested in seeing the results with for other LLMs. Hence, did up a simple demonstration of different prompts for different popular LLMs of different sizes. The prompt examples are taken from the Prompt Engineering Guide, and the LLMs that you can select below are all available on Hugging Face. If you are interested in comparing them with the prompts from OpenAI's model, you can refer to the writeup in the <a href="https://github.com/dair-ai/Prompt-Engineering-Guide">Prompt Engineering Guide</a> itself.
-    </h4>
-    <hr style="margin-bottom:5px; margin-top:5px;">
-    <h5 style="font-weight: 50; font-size: 12px; margin-bottom:0px; margin-top:0px;">
-    Note: Larger models will take a while, especially on the first run.
-    </h5>
-    </div>
-    """)
-
-    with gr.Column(elem_id="col-container"):  
-        with gr.Row(variant="compact"):
-
-            model_name = gr.Dropdown(
-                 model_list, 
-                 label="Select model",
-                 value=model_list[0],
-                 ).style(
-                container=False,
-            )
-
-            temperature = gr.Slider(
-                0.1, 100.0, value=1.0, label="Temperature",
-            ).style(
-                container=False,
-            )
-
-            max_tokens = gr.Slider(
-                10, 250, step=1, value=100, label="Max. tokens (in output)",
-            ).style(
-                container=False,
-            )
-
-            check_truncated = gr.Checkbox(
-                label="Check for truncated output",
-                value=False,
-            ).style(
-                container=False,
-            )
-
-        with gr.Row(variant="compact"):
-            prompt = gr.Textbox(
-                label="Enter your prompt",
-                show_label=False,
-                # max_lines=2,
-                placeholder="Select your prompt from the examples below",
-            ).style(
-                container=False,
-            )
-            process = gr.Button("Generate").style(full_width=False)
-        
-        with gr.Row():
-            output=gr.Textbox(
-                label="LLM output",
-                show_label=True)
-            
-        gr.HTML("""
-        <div>
-        <h4 style="font-weight: 50; font-size: 14px; margin-bottom:0px; margin-top:0px;">
-        Prompt examples. Select the prompt you would like to test, and it will appear (properly formatted) in the input box above.
-        </h4>
-        </div>
-        """)
-        with gr.Tab("Introduction"):
-            example_set_1 = gr.Examples(label = 'Simple Prompt vs. Instruct then Prompt.', 
-                                        examples=["The sky is ", "Complete the following sentence: The sky is ",],
-                                        inputs=[prompt])
-            example_set_2 = gr.Examples(label = 'Few Shot Prompt.', 
-                                        examples=["This is awesome! // Positive\nThis is bad! // Negative\nWow that movie was rad! // Positive\nWhat a horrible show! //",],
-                                        inputs=[prompt])
-            example_set_3 = gr.Examples(label = 'Explicitly Specify the Instruction', 
-                                        examples=["### Instruction ###\nTranslate the text below to Spanish:\nText: 'hello!'",],
-                                        inputs=[prompt])
-            example_set_4 = gr.Examples(label = 'Be Very Specific', 
-                                        examples=["Extract the name of places in the following text.\nDesired format:\nPlace: <comma_separated_list_of_company_names>\nInput: 'Although these developments are encouraging to researchers, much is still a mystery. “We often have a black box between the brain and the effect we see in the periphery,” says Henrique Veiga-Fernandes, a neuroimmunologist at the Champalimaud Centre for the Unknown in Lisbon. “If we want to use it in the therapeutic context, we actually need to understand the mechanism.'",],
-                                        inputs=[prompt])
-            example_set_5 = gr.Examples(label = 'Precision', 
-                                        examples=["Explain the concept of deep learning. Keep the explanation short, only a few sentences, and don't be too descriptive.", "Use 2-3 sentences to explain the concept of deep learning to a high school student."],
-                                        inputs=[prompt])
-            example_set_6 = gr.Examples(label = 'Focus on What LLM Should Do', 
-                                        examples=["The following is an agent that recommends movies to a customer. The agent is responsible to recommend a movie from the top global trending movies. It should refrain from asking users for their preferences and avoid asking for personal information. If the agent doesn't have a movie to recommend, it should respond 'Sorry, couldn't find a movie to recommend today.'.\nCustomer: Please recommend a movie based on my interests.\nAgent:"],
-                                        inputs=[prompt])      
-              
-        with gr.Tab("Basic Tasks"):
-            example_set_7 = gr.Examples(label = 'Explain vs. Summarize', 
-                                        examples=["Explain antibiotics.\nA:", "Antibiotics are a type of medication used to treat bacterial infections. They work by either killing the bacteria or preventing them from reproducing, allowing the body’s immune system to fight off the infection. Antibiotics are usually taken orally in the form of pills, capsules, or liquid solutions, or sometimes administered intravenously. They are not effective against viral infections, and using them inappropriately can lead to antibiotic resistance.\nExplain the above in one sentence:",],
-                                        inputs=[prompt])
-            example_set_8 = gr.Examples(label = 'Information Extraction', 
-                                        examples=["Author-contribution statements and acknowledgements in research papers should state clearly and specifically whether, and to what extent, the authors used AI technologies such as ChatGPT in the preparation of their manuscript and analysis. They should also indicate which LLMs were used. This will alert editors and reviewers to scrutinize manuscripts more carefully for potential biases, inaccuracies and improper source crediting. Likewise, scientific journals should be transparent about their use of LLMs, for example when selecting submitted manuscripts.\nMention the large language model based product mentioned in the paragraph above:",],
-                                        inputs=[prompt])
-            example_set_9 = gr.Examples(label = 'Question and Answer', 
-                                        examples=["Answer the question based on the context below. Keep the answer short and concise. Respond 'Unsure about answer' if not sure about the answer.\nContext: Teplizumab traces its roots to a New Jersey drug company called Ortho Pharmaceutical. There, scientists generated an early version of the antibody, dubbed OKT3. Originally sourced from mice, the molecule was able to bind to the surface of T cells and limit their cell-killing potential. In 1986, it was approved to help prevent organ rejection after kidney transplants, making it the first therapeutic antibody allowed for human use.\nQuestion: What was OKT3 originally sourced from?\nAnswer:",],
-                                        inputs=[prompt])
-            example_set_10 = gr.Examples(label = 'Text Classification', 
-                                        examples=["Classify the text into neutral, negative or positive.\nText: I think the food was okay.\nSentiment:","Classify the text into neutral, negative or positive.\nText: I think the vacation is okay.\nSentiment: neutral\nText: I think the food was okay.\nSentiment:"],
-                                        inputs=[prompt])    
-            example_set_11 = gr.Examples(label = 'Conversation', 
-                                        examples=["The following is a conversation with an AI research assistant. The assistant tone is technical and scientific.\nHuman: Hello, who are you?\nAI: Greeting! I am an AI research assistant. How can I help you today?\nHuman: Can you tell me about the creation of blackholes?\nAI:", "The following is a conversation with an AI research assistant. The assistant answers should be easy to understand even by primary school students.\nHuman: Hello, who are you?\nAI: Greeting! I am an AI research assistant. How can I help you today?\nHuman: Can you tell me about the creation of black holes?\nAI: "],
-                                        inputs=[prompt])
-            example_set_12 = gr.Examples(label = 'Reasoning', 
-                                        examples=["The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.\nA: ", "The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.\nSolve by breaking the problem into steps. First, identify the odd numbers, add them, and indicate whether the result is odd or even."],
-                                        inputs=[prompt])   
-            
-
-        with gr.Tab("Interesting Techniques"):
-            example_set_13 = gr.Examples(label = 'Zero Shot, i.e., no examples at all', 
-                                        examples=["Classify the text into neutral, negative or positive.\nText: I think the vacation is okay.\nSentiment:",],
-                                        inputs=[prompt])
-            example_set_14 = gr.Examples(label = 'Few Shot, i.e., only a few examples', 
-                                        examples=["The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.\nA: The answer is False.\n\nThe odd numbers in this group add up to an even number: 17,  10, 19, 4, 8, 12, 24.\nA: The answer is True.\n\nThe odd numbers in this group add up to an even number: 16,  11, 14, 4, 8, 13, 24.\nA: The answer is True.\n\nThe odd numbers in this group add up to an even number: 17,  9, 10, 12, 13, 4, 2.\nA: The answer is False.\n\nThe odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.\nA: ",],
-                                        inputs=[prompt])
-            example_set_15 = gr.Examples(label = 'Chain of Thought, i.e., go through a series of rational steps', 
-                                        examples=["The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1.\nA: Adding all the odd numbers (9, 15, 1) gives 25. The answer is False.\n\nThe odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.\nA:",],
-                                        inputs=[prompt])
-            example_set_16 = gr.Examples(label = 'Zero Shot Chain of Thought, i.e., think step by step, but no examples provided', 
-                                        examples=["I went to the market and bought 10 apples. I gave 2 apples to the neighbor and 2 to the repairman. I then went and bought 5 more apples and ate 1. How many apples did I remain with?\nLet's think step by step.",],
-                                        inputs=[prompt])            
-            example_set_17 = gr.Examples(label = 'Self Consistency, i.e., give examples to encourage the model to be consistent', 
-                                        examples=["Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done,there will be 21 trees. How many trees did the grove workers plant today?\nA: We start with 15 trees. Later we have 21 trees. The difference must be the number of trees they planted.\nSo, they must have planted 21 - 15 = 6 trees. The answer is 6.\n\nQ: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?\nA: There are 3 cars in the parking lot already. 2 more arrive. Now there are 3 + 2 = 5 cars. The answer is 5.\n\nQ: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?\nA: She bought 5 bagels for $3 each. This means she spent 5\n\nQ: When I was 6 my sister was half my age. Now I’m 70 how old is my sister?\nA:",],
-                                        inputs=[prompt])
-            example_set_18 = gr.Examples(label = 'Generating Knowledge, i.e., use examples to generate knowledge', 
-                                        examples=["Input: Greece is larger than mexico.\nKnowledge: Greece is approximately 131,957 sq km, while Mexico is approximately 1,964,375 sq km, making Mexico 1,389% larger than Greece.\n\nInput: Glasses always fog up.\nKnowledge: Condensation occurs on eyeglass lenses when water vapor from your sweat, breath, and ambient humidity lands on a cold surface, cools, and then changes into tiny drops of liquid, forming a film that you see as fog. Your lenses will be relatively cool compared to your breath, especially when the outside air is cold.\n\nInput: A fish is capable of thinking.\nKnowledge: Fish are more intelligent than they appear. In many areas, such as memory, their cognitive powers match or exceed those of ’higher’ vertebrates including non-human primates. Fish’s long-term memories help them keep track of complex social relationships.\n\nInput: A common effect of smoking lots of cigarettes in one’s lifetime is a higher than normal chance of getting lung cancer.\nKnowledge: Those who consistently averaged less than one cigarette per day over their lifetime had nine times the risk of dying from lung cancer than never smokers. Among people who smoked between one and 10 cigarettes per day, the risk of dying from lung cancer was nearly 12 times higher than that of never smokers.\n\nInput: Part of golf is trying to get a higher point total than others.\nKnowledge:",],
-                                        inputs=[prompt])
-                        
-        # process.click(load_and_generate, inputs=[model_name, prompt], outputs=[output])
-        if check_truncated:
-            process.click(generate_from_api_check, inputs=[prompt, model_name, temperature, max_tokens], outputs=[output])
-        else:   
-            process.click(generate_from_api, inputs=[prompt, model_name, temperature, max_tokens], outputs=[output])
-
-# demo.launch(server_port=8080)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/plzdontcry/dakubettergpt/src/assets/icons/EditIcon.tsx b/spaces/plzdontcry/dakubettergpt/src/assets/icons/EditIcon.tsx
deleted file mode 100644
index 4b96673b0b9d2b831324a5e1069cf6eeb802650e..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/assets/icons/EditIcon.tsx
+++ /dev/null
@@ -1,23 +0,0 @@
-import React from 'react';
-
-const EditIcon = () => {
-  return (
-    <svg
-      stroke='currentColor'
-      fill='none'
-      strokeWidth='2'
-      viewBox='0 0 24 24'
-      strokeLinecap='round'
-      strokeLinejoin='round'
-      className='h-4 w-4'
-      height='1em'
-      width='1em'
-      xmlns='http://www.w3.org/2000/svg'
-    >
-      <path d='M12 20h9'></path>
-      <path d='M16.5 3.5a2.121 2.121 0 0 1 3 3L7 19l-4 1 1-4L16.5 3.5z'></path>
-    </svg>
-  );
-};
-
-export default EditIcon;
diff --git a/spaces/pn23/HackGT2023/app.py b/spaces/pn23/HackGT2023/app.py
deleted file mode 100644
index 7ed857c6d0a0837e3129e577490c0974c72f74fe..0000000000000000000000000000000000000000
--- a/spaces/pn23/HackGT2023/app.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# import streamlit as st
-# from langchain.callbacks import StreamlitCallbackHandler
-# import streamlit as st
-# from langchain.llms import OpenAI
-# from langchain.agents import AgentType, initialize_agent, load_tools
-# from langchain.callbacks import StreamlitCallbackHandler
-# import streamlit as st
-
-# from scraping import prompty
-
-# import os
-
-
-# st.set_page_config(page_title="NutriMentor", page_icon=":robot:")
-# st.header("NutriMentor")
-
-# from langchain.chat_models import ChatOpenAI
-# from langchain.chains.question_answering import load_qa_chain
-
-
-    
-
-# from langchain.chains import RetrievalQA
-# from langchain.llms import OpenAI
-# from langchain.document_loaders import TextLoader
-# from langchain.document_loaders import PyPDFLoader
-# from langchain.indexes import VectorstoreIndexCreator
-# from langchain.text_splitter import CharacterTextSplitter
-# from langchain.embeddings import OpenAIEmbeddings
-# from langchain.vectorstores import Chroma
-# from langchain.chains.question_answering import load_qa_chain
-
-# if "generated" not in st.session_state:
-#   st.session_state["generated"] = []
-
-# if "past" not in st.session_state:
-#   st.session_state["past"] = []
-
-# if "messages" not in st.session_state:
-#   st.session_state["messages"] = []
-
-# init_alr = False
-# def init_model():
-#     os.environ["OPENAI_API_KEY"] = "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"
-#     llm = ChatOpenAI(
-#       openai_api_key=os.environ.get("OPENAI_API_KEY"),
-#       model='gpt-3.5-turbo-16k',
-#       temperature=0,
-#       streaming=True
-#     )
-#     # load document
-#     loader = PyPDFLoader("./Dietary_Guidelines_for_Americans_2020-2025.pdf")
-#     documents = loader.load()
-#     # split the documents into chunks
-#     text_splitter = CharacterTextSplitter(chunk_size=10000, chunk_overlap=0)
-#     texts = text_splitter.split_documents(documents)
-#     # select which embeddings we want to use
-#     embeddings = OpenAIEmbeddings()
-#     # create the vectorestore to use as the index
-#     db = Chroma.from_documents(texts, embeddings)
-#     # expose this index in a retriever interface
-#     retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":2})
-#     # create a chain to answer questions
-#     qa = RetrievalQA.from_chain_type(
-#         llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=False)
-#     return qa
-
-
-
-
-
-# st_callback = StreamlitCallbackHandler(st.container())
-
-
-
-# # date_input = st.text_input(
-# #         "Enter Date (ex. 10-11) 👇",
-# #         label_visibility=st.session_state.visibility,
-# #         disabled=st.session_state.disabled,
-# #         placeholder=st.session_state.placeholder,
-# #     )
-
-# date_input = st.text_input(label = "Enter Date (ex. 10-11) 👇" )
-
-
-# if prompt := st.chat_input():
-#     st.chat_message("user").write(prompt)
-#     with st.chat_message("assistant"):
-#         #st_callback = StreamlitCallbackHandler(st.container())
-
-#         if init_alr == False:
-#             init_alr = True
-            
-#             qa = init_model()
-#             #Call query generator with text_input
-#             query = prompty(date_input, prompt)
-#             result = qa({"query": query})
-    
-#             st.write(result['result'])
-#             st.stop()
diff --git a/spaces/power2/JoJoGan-powerhow2/op/upfirdn2d_cpu.py b/spaces/power2/JoJoGan-powerhow2/op/upfirdn2d_cpu.py
deleted file mode 100644
index a0f820b4c81e03598589b1ea6b95cf9bef9b04f8..0000000000000000000000000000000000000000
--- a/spaces/power2/JoJoGan-powerhow2/op/upfirdn2d_cpu.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import os
-
-import torch
-from torch.autograd import Function
-from torch.nn import functional as F
-
-
-
-module_path = os.path.dirname(__file__)
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    out = upfirdn2d_native(
-        input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
-    )
-
-    return out
-
-
-def upfirdn2d_native(
-    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-        :,
-        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
-        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
-        :,
-    ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-945aa2ea.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-945aa2ea.js
deleted file mode 100644
index b329b2b7424223e870a6a20c1250ca2770ff91e5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-945aa2ea.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{a as k}from"./Button-89057c03.js";import"./Index-37584f50.js";import"./index-0526d562.js";import"./svelte/svelte.js";const{SvelteComponent:w,create_component:z,destroy_component:B,detach:q,init:C,insert:I,mount_component:S,safe_not_equal:j,set_data:A,text:D,transition_in:E,transition_out:F}=window.__gradio__svelte__internal;function G(a){let i=a[11].i18n(a[3])+"",l;return{c(){l=D(i)},m(e,t){I(e,l,t)},p(e,t){t&2056&&i!==(i=e[11].i18n(e[3])+"")&&A(l,i)},d(e){e&&q(l)}}}function H(a){let i,l;return i=new k({props:{value:a[3],variant:a[4],elem_id:a[0],elem_classes:a[1],size:a[6],scale:a[7],link:a[9],icon:a[8],min_width:a[10],visible:a[2],root:a[12],proxy_url:a[13],disabled:!a[5],$$slots:{default:[G]},$$scope:{ctx:a}}}),i.$on("click",a[14]),{c(){z(i.$$.fragment)},m(e,t){S(i,e,t),l=!0},p(e,[t]){const f={};t&8&&(f.value=e[3]),t&16&&(f.variant=e[4]),t&1&&(f.elem_id=e[0]),t&2&&(f.elem_classes=e[1]),t&64&&(f.size=e[6]),t&128&&(f.scale=e[7]),t&512&&(f.link=e[9]),t&256&&(f.icon=e[8]),t&1024&&(f.min_width=e[10]),t&4&&(f.visible=e[2]),t&4096&&(f.root=e[12]),t&8192&&(f.proxy_url=e[13]),t&32&&(f.disabled=!e[5]),t&34824&&(f.$$scope={dirty:t,ctx:e}),i.$set(f)},i(e){l||(E(i.$$.fragment,e),l=!0)},o(e){F(i.$$.fragment,e),l=!1},d(e){B(i,e)}}}function J(a,i,l){let{elem_id:e=""}=i,{elem_classes:t=[]}=i,{visible:f=!0}=i,{value:u}=i,{variant:m="secondary"}=i,{interactive:s}=i,{size:o="lg"}=i,{scale:c=null}=i,{icon:r=null}=i,{link:v=null}=i,{min_width:g=void 0}=i,{gradio:_}=i,{root:h=""}=i,{proxy_url:b=null}=i;const d=()=>_.dispatch("click");return a.$$set=n=>{"elem_id"in n&&l(0,e=n.elem_id),"elem_classes"in n&&l(1,t=n.elem_classes),"visible"in n&&l(2,f=n.visible),"value"in n&&l(3,u=n.value),"variant"in n&&l(4,m=n.variant),"interactive"in n&&l(5,s=n.interactive),"size"in n&&l(6,o=n.size),"scale"in n&&l(7,c=n.scale),"icon"in n&&l(8,r=n.icon),"link"in n&&l(9,v=n.link),"min_width"in n&&l(10,g=n.min_width),"gradio"in n&&l(11,_=n.gradio),"root"in n&&l(12,h=n.root),"proxy_url"in n&&l(13,b=n.proxy_url)},[e,t,f,u,m,s,o,c,r,v,g,_,h,b,d]}class O extends w{constructor(i){super(),C(this,i,J,H,j,{elem_id:0,elem_classes:1,visible:2,value:3,variant:4,interactive:5,size:6,scale:7,icon:8,link:9,min_width:10,gradio:11,root:12,proxy_url:13})}}export{k as BaseButton,O as default};
-//# sourceMappingURL=Index-945aa2ea.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-b32578d7.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-b32578d7.js
deleted file mode 100644
index aff82d0527c0f43dbf208abfa5449d1bb0bb7bc8..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-b32578d7.js
+++ /dev/null
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label(){return this.$$.ctx[6]}set label(e){this.$$set({label:e}),b()}get show_label(){return this.$$.ctx[7]}set show_label(e){this.$$set({show_label:e}),b()}get height(){return this.$$.ctx[8]}set height(e){this.$$set({height:e}),b()}get proxy_url(){return this.$$.ctx[19]}set proxy_url(e){this.$$set({proxy_url:e}),b()}get _selectable(){return this.$$.ctx[9]}set _selectable(e){this.$$set({_selectable:e}),b()}get loading_status(){return this.$$.ctx[10]}set loading_status(e){this.$$set({loading_status:e}),b()}get container(){return this.$$.ctx[11]}set container(e){this.$$set({container:e}),b()}get scale(){return this.$$.ctx[12]}set scale(e){this.$$set({scale:e}),b()}get min_width(){return this.$$.ctx[13]}set min_width(e){this.$$set({min_width:e}),b()}get gradio(){return this.$$.ctx[14]}set gradio(e){this.$$set({gradio:e}),b()}get file_count(){return this.$$.ctx[15]}set file_count(e){this.$$set({file_count:e}),b()}get file_types(){return this.$$.ctx[16]}set file_types(e){this.$$set({file_types:e}),b()}}export{tl as BaseExample,xe as BaseFile,Ft as BaseFileUpload,ue as FilePreview,$t as default};
-//# sourceMappingURL=Index-b32578d7.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-043aba05.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-043aba05.js
deleted file mode 100644
index b2e2bb80822f6e5c40dab44d4b1dec65a7adf1bc..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-043aba05.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{P as j,N as G,a as E,D as U,b as w,T as b,I as H}from"./Index-7b3f6002.js";class P{constructor(t,e,s,i,h,r,n,a,l,f=0,u){this.p=t,this.stack=e,this.state=s,this.reducePos=i,this.pos=h,this.score=r,this.buffer=n,this.bufferBase=a,this.curContext=l,this.lookAhead=f,this.parent=u}toString(){return`[${this.stack.filter((t,e)=>e%3==0).concat(this.state)}]@${this.pos}${this.score?"!"+this.score:""}`}static start(t,e,s=0){let i=t.parser.context;return new P(t,[],e,s,s,0,[],0,i?new y(i,i.start):null,0,null)}get context(){return this.curContext?this.curContext.context:null}pushState(t,e){this.stack.push(this.state,e,this.bufferBase+this.buffer.length),this.state=t}reduce(t){var e;let s=t>>19,i=t&65535,{parser:h}=this.p,r=h.dynamicPrecedence(i);if(r&&(this.score+=r),s==0){this.pushState(h.getGoto(this.state,i,!0),this.reducePos),i<h.minRepeatTerm&&this.storeNode(i,this.reducePos,this.reducePos,4,!0),this.reduceContext(i,this.reducePos);return}let n=this.stack.length-(s-1)*3-(t&262144?6:0),a=n?this.stack[n-2]:this.p.ranges[0].from,l=this.reducePos-a;l>=2e3&&!(!((e=this.p.parser.nodeSet.types[i])===null||e===void 0)&&e.isAnonymous)&&(a==this.p.lastBigReductionStart?(this.p.bigReductionCount++,this.p.lastBigReductionSize=l):this.p.lastBigReductionSize<l&&(this.p.bigReductionCount=1,this.p.lastBigReductionStart=a,this.p.lastBigReductionSize=l));let f=n?this.stack[n-1]:0,u=this.bufferBase+this.buffer.length-f;if(i<h.minRepeatTerm||t&131072){let c=h.stateFlag(this.state,1)?this.pos:this.reducePos;this.storeNode(i,a,c,u+4,!0)}if(t&262144)this.state=this.stack[n];else{let c=this.stack[n-3];this.state=h.getGoto(c,i,!0)}for(;this.stack.length>n;)this.stack.pop();this.reduceContext(i,a)}storeNode(t,e,s,i=4,h=!1){if(t==0&&(!this.stack.length||this.stack[this.stack.length-1]<this.buffer.length+this.bufferBase)){let r=this,n=this.buffer.length;if(n==0&&r.parent&&(n=r.bufferBase-r.parent.bufferBase,r=r.parent),n>0&&r.buffer[n-4]==0&&r.buffer[n-1]>-1){if(e==s)return;if(r.buffer[n-2]>=e){r.buffer[n-2]=s;return}}}if(!h||this.pos==s)this.buffer.push(t,e,s,i);else{let r=this.buffer.length;if(r>0&&this.buffer[r-4]!=0)for(;r>0&&this.buffer[r-2]>s;)this.buffer[r]=this.buffer[r-4],this.buffer[r+1]=this.buffer[r-3],this.buffer[r+2]=this.buffer[r-2],this.buffer[r+3]=this.buffer[r-1],r-=4,i>4&&(i-=4);this.buffer[r]=t,this.buffer[r+1]=e,this.buffer[r+2]=s,this.buffer[r+3]=i}}shift(t,e,s){let i=this.pos;if(t&131072)this.pushState(t&65535,this.pos);else if(t&262144)this.pos=s,this.shiftContext(e,i),e<=this.p.parser.maxNode&&this.buffer.push(e,i,s,4);else{let h=t,{parser:r}=this.p;(s>this.pos||e<=r.maxNode)&&(this.pos=s,r.stateFlag(h,1)||(this.reducePos=s)),this.pushState(h,i),this.shiftContext(e,i),e<=r.maxNode&&this.buffer.push(e,i,s,4)}}apply(t,e,s){t&65536?this.reduce(t):this.shift(t,e,s)}useNode(t,e){let s=this.p.reused.length-1;(s<0||this.p.reused[s]!=t)&&(this.p.reused.push(t),s++);let i=this.pos;this.reducePos=this.pos=i+t.length,this.pushState(e,i),this.buffer.push(s,i,this.reducePos,-1),this.curContext&&this.updateContext(this.curContext.tracker.reuse(this.curContext.context,t,this,this.p.stream.reset(this.pos-t.length)))}split(){let t=this,e=t.buffer.length;for(;e>0&&t.buffer[e-2]>t.reducePos;)e-=4;let s=t.buffer.slice(e),i=t.bufferBase+e;for(;t&&i==t.bufferBase;)t=t.parent;return new P(this.p,this.stack.slice(),this.state,this.reducePos,this.pos,this.score,s,i,this.curContext,this.lookAhead,t)}recoverByDelete(t,e){let s=t<=this.p.parser.maxNode;s&&this.storeNode(t,this.pos,e,4),this.storeNode(0,this.pos,e,s?8:4),this.pos=this.reducePos=e,this.score-=190}canShift(t){for(let e=new W(this);;){let s=this.p.parser.stateSlot(e.state,4)||this.p.parser.hasAction(e.state,t);if(s==0)return!1;if(!(s&65536))return!0;e.reduce(s)}}recoverByInsert(t){if(this.stack.length>=300)return[];let e=this.p.parser.nextStates(this.state);if(e.length>8||this.stack.length>=120){let i=[];for(let h=0,r;h<e.length;h+=2)(r=e[h+1])!=this.state&&this.p.parser.hasAction(r,t)&&i.push(e[h],r);if(this.stack.length<120)for(let h=0;i.length<8&&h<e.length;h+=2){let r=e[h+1];i.some((n,a)=>a&1&&n==r)||i.push(e[h],r)}e=i}let s=[];for(let i=0;i<e.length&&s.length<4;i+=2){let h=e[i+1];if(h==this.state)continue;let r=this.split();r.pushState(h,this.pos),r.storeNode(0,r.pos,r.pos,4,!0),r.shiftContext(e[i],this.pos),r.score-=200,s.push(r)}return s}forceReduce(){let t=this.p.parser.stateSlot(this.state,5);if(!(t&65536))return!1;let{parser:e}=this.p;if(!e.validAction(this.state,t)){let s=t>>19,i=t&65535,h=this.stack.length-s*3;if(h<0||e.getGoto(this.stack[h],i,!1)<0)return!1;this.storeNode(0,this.reducePos,this.reducePos,4,!0),this.score-=100}return this.reducePos=this.pos,this.reduce(t),!0}forceAll(){for(;!this.p.parser.stateFlag(this.state,2);)if(!this.forceReduce()){this.storeNode(0,this.pos,this.pos,4,!0);break}return this}get deadEnd(){if(this.stack.length!=3)return!1;let{parser:t}=this.p;return t.data[t.stateSlot(this.state,1)]==65535&&!t.stateSlot(this.state,4)}restart(){this.state=this.stack[0],this.stack.length=0}sameState(t){if(this.state!=t.state||this.stack.length!=t.stack.length)return!1;for(let e=0;e<this.stack.length;e+=3)if(this.stack[e]!=t.stack[e])return!1;return!0}get parser(){return this.p.parser}dialectEnabled(t){return this.p.parser.dialect.flags[t]}shiftContext(t,e){this.curContext&&this.updateContext(this.curContext.tracker.shift(this.curContext.context,t,this,this.p.stream.reset(e)))}reduceContext(t,e){this.curContext&&this.updateContext(this.curContext.tracker.reduce(this.curContext.context,t,this,this.p.stream.reset(e)))}emitContext(){let t=this.buffer.length-1;(t<0||this.buffer[t]!=-3)&&this.buffer.push(this.curContext.hash,this.reducePos,this.reducePos,-3)}emitLookAhead(){let t=this.buffer.length-1;(t<0||this.buffer[t]!=-4)&&this.buffer.push(this.lookAhead,this.reducePos,this.reducePos,-4)}updateContext(t){if(t!=this.curContext.context){let e=new y(this.curContext.tracker,t);e.hash!=this.curContext.hash&&this.emitContext(),this.curContext=e}}setLookAhead(t){t>this.lookAhead&&(this.emitLookAhead(),this.lookAhead=t)}close(){this.curContext&&this.curContext.tracker.strict&&this.emitContext(),this.lookAhead>0&&this.emitLookAhead()}}class y{constructor(t,e){this.tracker=t,this.context=e,this.hash=t.strict?t.hash(e):0}}var N;(function(o){o[o.Insert=200]="Insert",o[o.Delete=190]="Delete",o[o.Reduce=100]="Reduce",o[o.MaxNext=4]="MaxNext",o[o.MaxInsertStackDepth=300]="MaxInsertStackDepth",o[o.DampenInsertStackDepth=120]="DampenInsertStackDepth",o[o.MinBigReduction=2e3]="MinBigReduction"})(N||(N={}));class W{constructor(t){this.start=t,this.state=t.state,this.stack=t.stack,this.base=this.stack.length}reduce(t){let e=t&65535,s=t>>19;s==0?(this.stack==this.start.stack&&(this.stack=this.stack.slice()),this.stack.push(this.state,0,0),this.base+=3):this.base-=(s-1)*3;let i=this.start.p.parser.getGoto(this.stack[this.base-3],e,!0);this.state=i}}class C{constructor(t,e,s){this.stack=t,this.pos=e,this.index=s,this.buffer=t.buffer,this.index==0&&this.maybeNext()}static create(t,e=t.bufferBase+t.buffer.length){return new C(t,e,e-t.bufferBase)}maybeNext(){let t=this.stack.parent;t!=null&&(this.index=this.stack.bufferBase-t.bufferBase,this.stack=t,this.buffer=t.buffer)}get id(){return this.buffer[this.index-4]}get start(){return this.buffer[this.index-3]}get end(){return this.buffer[this.index-2]}get size(){return this.buffer[this.index-1]}next(){this.index-=4,this.pos-=4,this.index==0&&this.maybeNext()}fork(){return new C(this.stack,this.pos,this.index)}}function x(o,t=Uint16Array){if(typeof o!="string")return o;let e=null;for(let s=0,i=0;s<o.length;){let h=0;for(;;){let r=o.charCodeAt(s++),n=!1;if(r==126){h=65535;break}r>=92&&r--,r>=34&&r--;let a=r-32;if(a>=46&&(a-=46,n=!0),h+=a,n)break;h*=46}e?e[i++]=h:e=new t(h)}return e}class S{constructor(){this.start=-1,this.value=-1,this.end=-1,this.extended=-1,this.lookAhead=0,this.mask=0,this.context=0}}const D=new S;class q{constructor(t,e){this.input=t,this.ranges=e,this.chunk="",this.chunkOff=0,this.chunk2="",this.chunk2Pos=0,this.next=-1,this.token=D,this.rangeIndex=0,this.pos=this.chunkPos=e[0].from,this.range=e[0],this.end=e[e.length-1].to,this.readNext()}resolveOffset(t,e){let s=this.range,i=this.rangeIndex,h=this.pos+t;for(;h<s.from;){if(!i)return null;let r=this.ranges[--i];h-=s.from-r.to,s=r}for(;e<0?h>s.to:h>=s.to;){if(i==this.ranges.length-1)return null;let r=this.ranges[++i];h+=r.from-s.to,s=r}return h}clipPos(t){if(t>=this.range.from&&t<this.range.to)return t;for(let e of this.ranges)if(e.to>t)return Math.max(t,e.from);return this.end}peek(t){let e=this.chunkOff+t,s,i;if(e>=0&&e<this.chunk.length)s=this.pos+t,i=this.chunk.charCodeAt(e);else{let h=this.resolveOffset(t,1);if(h==null)return-1;if(s=h,s>=this.chunk2Pos&&s<this.chunk2Pos+this.chunk2.length)i=this.chunk2.charCodeAt(s-this.chunk2Pos);else{let r=this.rangeIndex,n=this.range;for(;n.to<=s;)n=this.ranges[++r];this.chunk2=this.input.chunk(this.chunk2Pos=s),s+this.chunk2.length>n.to&&(this.chunk2=this.chunk2.slice(0,n.to-s)),i=this.chunk2.charCodeAt(0)}}return s>=this.token.lookAhead&&(this.token.lookAhead=s+1),i}acceptToken(t,e=0){let s=e?this.resolveOffset(e,-1):this.pos;if(s==null||s<this.token.start)throw new RangeError("Token end out of bounds");this.token.value=t,this.token.end=s}getChunk(){if(this.pos>=this.chunk2Pos&&this.pos<this.chunk2Pos+this.chunk2.length){let{chunk:t,chunkPos:e}=this;this.chunk=this.chunk2,this.chunkPos=this.chunk2Pos,this.chunk2=t,this.chunk2Pos=e,this.chunkOff=this.pos-this.chunkPos}else{this.chunk2=this.chunk,this.chunk2Pos=this.chunkPos;let t=this.input.chunk(this.pos),e=this.pos+t.length;this.chunk=e>this.range.to?t.slice(0,this.range.to-this.pos):t,this.chunkPos=this.pos,this.chunkOff=0}}readNext(){return this.chunkOff>=this.chunk.length&&(this.getChunk(),this.chunkOff==this.chunk.length)?this.next=-1:this.next=this.chunk.charCodeAt(this.chunkOff)}advance(t=1){for(this.chunkOff+=t;this.pos+t>=this.range.to;){if(this.rangeIndex==this.ranges.length-1)return this.setDone();t-=this.range.to-this.pos,this.range=this.ranges[++this.rangeIndex],this.pos=this.range.from}return this.pos+=t,this.pos>=this.token.lookAhead&&(this.token.lookAhead=this.pos+1),this.readNext()}setDone(){return this.pos=this.chunkPos=this.end,this.range=this.ranges[this.rangeIndex=this.ranges.length-1],this.chunk="",this.next=-1}reset(t,e){if(e?(this.token=e,e.start=t,e.lookAhead=t+1,e.value=e.extended=-1):this.token=D,this.pos!=t){if(this.pos=t,t==this.end)return this.setDone(),this;for(;t<this.range.from;)this.range=this.ranges[--this.rangeIndex];for(;t>=this.range.to;)this.range=this.ranges[++this.rangeIndex];t>=this.chunkPos&&t<this.chunkPos+this.chunk.length?this.chunkOff=t-this.chunkPos:(this.chunk="",this.chunkOff=0),this.readNext()}return this}read(t,e){if(t>=this.chunkPos&&e<=this.chunkPos+this.chunk.length)return this.chunk.slice(t-this.chunkPos,e-this.chunkPos);if(t>=this.chunk2Pos&&e<=this.chunk2Pos+this.chunk2.length)return this.chunk2.slice(t-this.chunk2Pos,e-this.chunk2Pos);if(t>=this.range.from&&e<=this.range.to)return this.input.read(t,e);let s="";for(let i of this.ranges){if(i.from>=e)break;i.to>t&&(s+=this.input.read(Math.max(i.from,t),Math.min(i.to,e)))}return s}}class m{constructor(t,e){this.data=t,this.id=e}token(t,e){let{parser:s}=e.p;F(this.data,t,e,this.id,s.data,s.tokenPrecTable)}}m.prototype.contextual=m.prototype.fallback=m.prototype.extend=!1;class J{constructor(t,e,s){this.precTable=e,this.elseToken=s,this.data=typeof t=="string"?x(t):t}token(t,e){let s=t.pos,i;for(;i=t.pos,F(this.data,t,e,0,this.data,this.precTable),!(t.token.value>-1);){if(this.elseToken==null)return;if(t.next<0)break;t.advance(),t.reset(i+1,t.token)}i>s&&(t.reset(s,t.token),t.acceptToken(this.elseToken,i-s))}}J.prototype.contextual=m.prototype.fallback=m.prototype.extend=!1;class tt{constructor(t,e={}){this.token=t,this.contextual=!!e.contextual,this.fallback=!!e.fallback,this.extend=!!e.extend}}function F(o,t,e,s,i,h){let r=0,n=1<<s,{dialect:a}=e.p.parser;t:for(;n&o[r];){let l=o[r+1];for(let p=r+3;p<l;p+=2)if((o[p+1]&n)>0){let d=o[p];if(a.allows(d)&&(t.token.value==-1||t.token.value==d||K(d,t.token.value,i,h))){t.acceptToken(d);break}}let f=t.next,u=0,c=o[r+2];if(t.next<0&&c>u&&o[l+c*3-3]==65535&&o[l+c*3-3]==65535){r=o[l+c*3-1];continue t}for(;u<c;){let p=u+c>>1,d=l+p+(p<<1),L=o[d],$=o[d+1]||65536;if(f<L)c=p;else if(f>=$)u=p+1;else{r=o[d+2],t.advance();continue t}}break}}function I(o,t,e){for(let s=t,i;(i=o[s])!=65535;s++)if(i==e)return s-t;return-1}function K(o,t,e,s){let i=I(e,s,t);return i<0||I(e,s,o)<i}const g=typeof process<"u"&&process.env&&/\bparse\b/.test({}.LOG);let A=null;var z;(function(o){o[o.Margin=25]="Margin"})(z||(z={}));function B(o,t,e){let s=o.cursor(H.IncludeAnonymous);for(s.moveTo(t);;)if(!(e<0?s.childBefore(t):s.childAfter(t)))for(;;){if((e<0?s.to<t:s.from>t)&&!s.type.isError)return e<0?Math.max(0,Math.min(s.to-1,t-25)):Math.min(o.length,Math.max(s.from+1,t+25));if(e<0?s.prevSibling():s.nextSibling())break;if(!s.parent())return e<0?0:o.length}}class Q{constructor(t,e){this.fragments=t,this.nodeSet=e,this.i=0,this.fragment=null,this.safeFrom=-1,this.safeTo=-1,this.trees=[],this.start=[],this.index=[],this.nextFragment()}nextFragment(){let t=this.fragment=this.i==this.fragments.length?null:this.fragments[this.i++];if(t){for(this.safeFrom=t.openStart?B(t.tree,t.from+t.offset,1)-t.offset:t.from,this.safeTo=t.openEnd?B(t.tree,t.to+t.offset,-1)-t.offset:t.to;this.trees.length;)this.trees.pop(),this.start.pop(),this.index.pop();this.trees.push(t.tree),this.start.push(-t.offset),this.index.push(0),this.nextStart=this.safeFrom}else this.nextStart=1e9}nodeAt(t){if(t<this.nextStart)return null;for(;this.fragment&&this.safeTo<=t;)this.nextFragment();if(!this.fragment)return null;for(;;){let e=this.trees.length-1;if(e<0)return this.nextFragment(),null;let s=this.trees[e],i=this.index[e];if(i==s.children.length){this.trees.pop(),this.start.pop(),this.index.pop();continue}let h=s.children[i],r=this.start[e]+s.positions[i];if(r>t)return this.nextStart=r,null;if(h instanceof b){if(r==t){if(r<this.safeFrom)return null;let n=r+h.length;if(n<=this.safeTo){let a=h.prop(w.lookAhead);if(!a||n+a<this.fragment.to)return h}}this.index[e]++,r+h.length>=Math.max(this.safeFrom,t)&&(this.trees.push(h),this.start.push(r),this.index.push(0))}else this.index[e]++,this.nextStart=r+h.length}}}class V{constructor(t,e){this.stream=e,this.tokens=[],this.mainToken=null,this.actions=[],this.tokens=t.tokenizers.map(s=>new S)}getActions(t){let e=0,s=null,{parser:i}=t.p,{tokenizers:h}=i,r=i.stateSlot(t.state,3),n=t.curContext?t.curContext.hash:0,a=0;for(let l=0;l<h.length;l++){if(!(1<<l&r))continue;let f=h[l],u=this.tokens[l];if(!(s&&!f.fallback)&&((f.contextual||u.start!=t.pos||u.mask!=r||u.context!=n)&&(this.updateCachedToken(u,f,t),u.mask=r,u.context=n),u.lookAhead>u.end+25&&(a=Math.max(u.lookAhead,a)),u.value!=0)){let c=e;if(u.extended>-1&&(e=this.addActions(t,u.extended,u.end,e)),e=this.addActions(t,u.value,u.end,e),!f.extend&&(s=u,e>c))break}}for(;this.actions.length>e;)this.actions.pop();return a&&t.setLookAhead(a),!s&&t.pos==this.stream.end&&(s=new S,s.value=t.p.parser.eofTerm,s.start=s.end=t.pos,e=this.addActions(t,s.value,s.end,e)),this.mainToken=s,this.actions}getMainToken(t){if(this.mainToken)return this.mainToken;let e=new S,{pos:s,p:i}=t;return e.start=s,e.end=Math.min(s+1,i.stream.end),e.value=s==i.stream.end?i.parser.eofTerm:0,e}updateCachedToken(t,e,s){let i=this.stream.clipPos(s.pos);if(e.token(this.stream.reset(i,t),s),t.value>-1){let{parser:h}=s.p;for(let r=0;r<h.specialized.length;r++)if(h.specialized[r]==t.value){let n=h.specializers[r](this.stream.read(t.start,t.end),s);if(n>=0&&s.p.parser.dialect.allows(n>>1)){n&1?t.extended=n>>1:t.value=n>>1;break}}}else t.value=0,t.end=this.stream.clipPos(i+1)}putAction(t,e,s,i){for(let h=0;h<i;h+=3)if(this.actions[h]==t)return i;return this.actions[i++]=t,this.actions[i++]=e,this.actions[i++]=s,i}addActions(t,e,s,i){let{state:h}=t,{parser:r}=t.p,{data:n}=r;for(let a=0;a<2;a++)for(let l=r.stateSlot(h,a?2:1);;l+=3){if(n[l]==65535)if(n[l+1]==1)l=k(n,l+2);else{i==0&&n[l+1]==2&&(i=this.putAction(k(n,l+2),e,s,i));break}n[l]==e&&(i=this.putAction(k(n,l+1),e,s,i))}return i}}var M;(function(o){o[o.Distance=5]="Distance",o[o.MaxRemainingPerStep=3]="MaxRemainingPerStep",o[o.MinBufferLengthPrune=500]="MinBufferLengthPrune",o[o.ForceReduceLimit=10]="ForceReduceLimit",o[o.CutDepth=15e3]="CutDepth",o[o.CutTo=9e3]="CutTo",o[o.MaxLeftAssociativeReductionCount=300]="MaxLeftAssociativeReductionCount",o[o.MaxStackCount=12]="MaxStackCount"})(M||(M={}));class X{constructor(t,e,s,i){this.parser=t,this.input=e,this.ranges=i,this.recovering=0,this.nextStackID=9812,this.minStackPos=0,this.reused=[],this.stoppedAt=null,this.lastBigReductionStart=-1,this.lastBigReductionSize=0,this.bigReductionCount=0,this.stream=new q(e,i),this.tokens=new V(t,this.stream),this.topTerm=t.top[1];let{from:h}=i[0];this.stacks=[P.start(this,t.top[0],h)],this.fragments=s.length&&this.stream.end-h>t.bufferLength*4?new Q(s,t.nodeSet):null}get parsedPos(){return this.minStackPos}advance(){let t=this.stacks,e=this.minStackPos,s=this.stacks=[],i,h;if(this.bigReductionCount>300&&t.length==1){let[r]=t;for(;r.forceReduce()&&r.stack.length&&r.stack[r.stack.length-2]>=this.lastBigReductionStart;);this.bigReductionCount=this.lastBigReductionSize=0}for(let r=0;r<t.length;r++){let n=t[r];for(;;){if(this.tokens.mainToken=null,n.pos>e)s.push(n);else{if(this.advanceStack(n,s,t))continue;{i||(i=[],h=[]),i.push(n);let a=this.tokens.getMainToken(n);h.push(a.value,a.end)}}break}}if(!s.length){let r=i&&Z(i);if(r)return this.stackToTree(r);if(this.parser.strict)throw g&&i&&console.log("Stuck with token "+(this.tokens.mainToken?this.parser.getName(this.tokens.mainToken.value):"none")),new SyntaxError("No parse at "+e);this.recovering||(this.recovering=5)}if(this.recovering&&i){let r=this.stoppedAt!=null&&i[0].pos>this.stoppedAt?i[0]:this.runRecovery(i,h,s);if(r)return this.stackToTree(r.forceAll())}if(this.recovering){let r=this.recovering==1?1:this.recovering*3;if(s.length>r)for(s.sort((n,a)=>a.score-n.score);s.length>r;)s.pop();s.some(n=>n.reducePos>e)&&this.recovering--}else if(s.length>1){t:for(let r=0;r<s.length-1;r++){let n=s[r];for(let a=r+1;a<s.length;a++){let l=s[a];if(n.sameState(l)||n.buffer.length>500&&l.buffer.length>500)if((n.score-l.score||n.buffer.length-l.buffer.length)>0)s.splice(a--,1);else{s.splice(r--,1);continue t}}}s.length>12&&s.splice(12,s.length-12)}this.minStackPos=s[0].pos;for(let r=1;r<s.length;r++)s[r].pos<this.minStackPos&&(this.minStackPos=s[r].pos);return null}stopAt(t){if(this.stoppedAt!=null&&this.stoppedAt<t)throw new RangeError("Can't move stoppedAt forward");this.stoppedAt=t}advanceStack(t,e,s){let i=t.pos,{parser:h}=this,r=g?this.stackID(t)+" -> ":"";if(this.stoppedAt!=null&&i>this.stoppedAt)return t.forceReduce()?t:null;if(this.fragments){let l=t.curContext&&t.curContext.tracker.strict,f=l?t.curContext.hash:0;for(let u=this.fragments.nodeAt(i);u;){let c=this.parser.nodeSet.types[u.type.id]==u.type?h.getGoto(t.state,u.type.id):-1;if(c>-1&&u.length&&(!l||(u.prop(w.contextHash)||0)==f))return t.useNode(u,c),g&&console.log(r+this.stackID(t)+` (via reuse of ${h.getName(u.type.id)})`),!0;if(!(u instanceof b)||u.children.length==0||u.positions[0]>0)break;let p=u.children[0];if(p instanceof b&&u.positions[0]==0)u=p;else break}}let n=h.stateSlot(t.state,4);if(n>0)return t.reduce(n),g&&console.log(r+this.stackID(t)+` (via always-reduce ${h.getName(n&65535)})`),!0;if(t.stack.length>=15e3)for(;t.stack.length>9e3&&t.forceReduce(););let a=this.tokens.getActions(t);for(let l=0;l<a.length;){let f=a[l++],u=a[l++],c=a[l++],p=l==a.length||!s,d=p?t:t.split();if(d.apply(f,u,c),g&&console.log(r+this.stackID(d)+` (via ${f&65536?`reduce of ${h.getName(f&65535)}`:"shift"} for ${h.getName(u)} @ ${i}${d==t?"":", split"})`),p)return!0;d.pos>i?e.push(d):s.push(d)}return!1}advanceFully(t,e){let s=t.pos;for(;;){if(!this.advanceStack(t,null,null))return!1;if(t.pos>s)return R(t,e),!0}}runRecovery(t,e,s){let i=null,h=!1;for(let r=0;r<t.length;r++){let n=t[r],a=e[r<<1],l=e[(r<<1)+1],f=g?this.stackID(n)+" -> ":"";if(n.deadEnd&&(h||(h=!0,n.restart(),g&&console.log(f+this.stackID(n)+" (restarted)"),this.advanceFully(n,s))))continue;let u=n.split(),c=f;for(let p=0;u.forceReduce()&&p<10&&(g&&console.log(c+this.stackID(u)+" (via force-reduce)"),!this.advanceFully(u,s));p++)g&&(c=this.stackID(u)+" -> ");for(let p of n.recoverByInsert(a))g&&console.log(f+this.stackID(p)+" (via recover-insert)"),this.advanceFully(p,s);this.stream.end>n.pos?(l==n.pos&&(l++,a=0),n.recoverByDelete(a,l),g&&console.log(f+this.stackID(n)+` (via recover-delete ${this.parser.getName(a)})`),R(n,s)):(!i||i.score<n.score)&&(i=n)}return i}stackToTree(t){return t.close(),b.build({buffer:C.create(t),nodeSet:this.parser.nodeSet,topID:this.topTerm,maxBufferLength:this.parser.bufferLength,reused:this.reused,start:this.ranges[0].from,length:t.pos-this.ranges[0].from,minRepeatType:this.parser.minRepeatTerm})}stackID(t){let e=(A||(A=new WeakMap)).get(t);return e||A.set(t,e=String.fromCodePoint(this.nextStackID++)),e+t}}function R(o,t){for(let e=0;e<t.length;e++){let s=t[e];if(s.pos==o.pos&&s.sameState(o)){t[e].score<o.score&&(t[e]=o);return}}t.push(o)}class Y{constructor(t,e,s){this.source=t,this.flags=e,this.disabled=s}allows(t){return!this.disabled||this.disabled[t]==0}}const T=o=>o;class et{constructor(t){this.start=t.start,this.shift=t.shift||T,this.reduce=t.reduce||T,this.reuse=t.reuse||T,this.hash=t.hash||(()=>0),this.strict=t.strict!==!1}}class v extends j{constructor(t){if(super(),this.wrappers=[],t.version!=14)throw new RangeError(`Parser version (${t.version}) doesn't match runtime version (14)`);let e=t.nodeNames.split(" ");this.minRepeatTerm=e.length;for(let n=0;n<t.repeatNodeCount;n++)e.push("");let s=Object.keys(t.topRules).map(n=>t.topRules[n][1]),i=[];for(let n=0;n<e.length;n++)i.push([]);function h(n,a,l){i[n].push([a,a.deserialize(String(l))])}if(t.nodeProps)for(let n of t.nodeProps){let a=n[0];typeof a=="string"&&(a=w[a]);for(let l=1;l<n.length;){let f=n[l++];if(f>=0)h(f,a,n[l++]);else{let u=n[l+-f];for(let c=-f;c>0;c--)h(n[l++],a,u);l++}}}this.nodeSet=new G(e.map((n,a)=>E.define({name:a>=this.minRepeatTerm?void 0:n,id:a,props:i[a],top:s.indexOf(a)>-1,error:a==0,skipped:t.skippedNodes&&t.skippedNodes.indexOf(a)>-1}))),t.propSources&&(this.nodeSet=this.nodeSet.extend(...t.propSources)),this.strict=!1,this.bufferLength=U;let r=x(t.tokenData);this.context=t.context,this.specializerSpecs=t.specialized||[],this.specialized=new Uint16Array(this.specializerSpecs.length);for(let n=0;n<this.specializerSpecs.length;n++)this.specialized[n]=this.specializerSpecs[n].term;this.specializers=this.specializerSpecs.map(O),this.states=x(t.states,Uint32Array),this.data=x(t.stateData),this.goto=x(t.goto),this.maxTerm=t.maxTerm,this.tokenizers=t.tokenizers.map(n=>typeof n=="number"?new m(r,n):n),this.topRules=t.topRules,this.dialects=t.dialects||{},this.dynamicPrecedences=t.dynamicPrecedences||null,this.tokenPrecTable=t.tokenPrec,this.termNames=t.termNames||null,this.maxNode=this.nodeSet.types.length-1,this.dialect=this.parseDialect(),this.top=this.topRules[Object.keys(this.topRules)[0]]}createParse(t,e,s){let i=new X(this,t,e,s);for(let h of this.wrappers)i=h(i,t,e,s);return i}getGoto(t,e,s=!1){let i=this.goto;if(e>=i[0])return-1;for(let h=i[e+1];;){let r=i[h++],n=r&1,a=i[h++];if(n&&s)return a;for(let l=h+(r>>1);h<l;h++)if(i[h]==t)return a;if(n)return-1}}hasAction(t,e){let s=this.data;for(let i=0;i<2;i++)for(let h=this.stateSlot(t,i?2:1),r;;h+=3){if((r=s[h])==65535)if(s[h+1]==1)r=s[h=k(s,h+2)];else{if(s[h+1]==2)return k(s,h+2);break}if(r==e||r==0)return k(s,h+1)}return 0}stateSlot(t,e){return this.states[t*6+e]}stateFlag(t,e){return(this.stateSlot(t,0)&e)>0}validAction(t,e){if(e==this.stateSlot(t,4))return!0;for(let s=this.stateSlot(t,1);;s+=3){if(this.data[s]==65535)if(this.data[s+1]==1)s=k(this.data,s+2);else return!1;if(e==k(this.data,s+1))return!0}}nextStates(t){let e=[];for(let s=this.stateSlot(t,1);;s+=3){if(this.data[s]==65535)if(this.data[s+1]==1)s=k(this.data,s+2);else break;if(!(this.data[s+2]&1)){let i=this.data[s+1];e.some((h,r)=>r&1&&h==i)||e.push(this.data[s],i)}}return e}configure(t){let e=Object.assign(Object.create(v.prototype),this);if(t.props&&(e.nodeSet=this.nodeSet.extend(...t.props)),t.top){let s=this.topRules[t.top];if(!s)throw new RangeError(`Invalid top rule name ${t.top}`);e.top=s}return t.tokenizers&&(e.tokenizers=this.tokenizers.map(s=>{let i=t.tokenizers.find(h=>h.from==s);return i?i.to:s})),t.specializers&&(e.specializers=this.specializers.slice(),e.specializerSpecs=this.specializerSpecs.map((s,i)=>{let h=t.specializers.find(n=>n.from==s.external);if(!h)return s;let r=Object.assign(Object.assign({},s),{external:h.to});return e.specializers[i]=O(r),r})),t.contextTracker&&(e.context=t.contextTracker),t.dialect&&(e.dialect=this.parseDialect(t.dialect)),t.strict!=null&&(e.strict=t.strict),t.wrap&&(e.wrappers=e.wrappers.concat(t.wrap)),t.bufferLength!=null&&(e.bufferLength=t.bufferLength),e}hasWrappers(){return this.wrappers.length>0}getName(t){return this.termNames?this.termNames[t]:String(t<=this.maxNode&&this.nodeSet.types[t].name||t)}get eofTerm(){return this.maxNode+1}get topNode(){return this.nodeSet.types[this.top[1]]}dynamicPrecedence(t){let e=this.dynamicPrecedences;return e==null?0:e[t]||0}parseDialect(t){let e=Object.keys(this.dialects),s=e.map(()=>!1);if(t)for(let h of t.split(" ")){let r=e.indexOf(h);r>=0&&(s[r]=!0)}let i=null;for(let h=0;h<e.length;h++)if(!s[h])for(let r=this.dialects[e[h]],n;(n=this.data[r++])!=65535;)(i||(i=new Uint8Array(this.maxTerm+1)))[n]=1;return new Y(t,s,i)}static deserialize(t){return new v(t)}}function k(o,t){return o[t]|o[t+1]<<16}function Z(o){let t=null;for(let e of o){let s=e.p.stoppedAt;(e.pos==e.p.stream.end||s!=null&&e.pos>s)&&e.p.parser.stateFlag(e.state,2)&&(!t||t.score<e.score)&&(t=e)}return t}function O(o){if(o.external){let t=o.extend?1:0;return(e,s)=>o.external(e,s)<<1|t}return o.get}export{et as C,tt as E,v as L,J as a};
-//# sourceMappingURL=index-043aba05.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-eaba773f.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-eaba773f.js
deleted file mode 100644
index 56e14bdf13613b356f96608e038cb96d8b0741d1..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-eaba773f.js
+++ /dev/null
@@ -1,2 +0,0 @@
-const{SvelteComponent:p,append:s,attr:e,detach:d,init:g,insert:w,noop:c,safe_not_equal:_,svg_element:o}=window.__gradio__svelte__internal;function u(a){let t,n,r,i;return{c(){t=o("svg"),n=o("rect"),r=o("circle"),i=o("polyline"),e(n,"x","3"),e(n,"y","3"),e(n,"width","18"),e(n,"height","18"),e(n,"rx","2"),e(n,"ry","2"),e(r,"cx","8.5"),e(r,"cy","8.5"),e(r,"r","1.5"),e(i,"points","21 15 16 10 5 21"),e(t,"xmlns","http://www.w3.org/2000/svg"),e(t,"width","100%"),e(t,"height","100%"),e(t,"viewBox","0 0 24 24"),e(t,"fill","none"),e(t,"stroke","currentColor"),e(t,"stroke-width","1.5"),e(t,"stroke-linecap","round"),e(t,"stroke-linejoin","round"),e(t,"class","feather feather-image")},m(l,h){w(l,t,h),s(t,n),s(t,r),s(t,i)},p:c,i:c,o:c,d(l){l&&d(t)}}}class x extends p{constructor(t){super(),g(this,t,null,u,_,{})}}export{x as I};
-//# sourceMappingURL=Image-eaba773f.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/style/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/style/__init__.py
deleted file mode 100644
index 488c6d6ae1ec310cdd84bb209b4103da26fed284..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/style/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from .core import available, context, library, reload_library, use
-
-
-__all__ = ["available", "context", "library", "reload_library", "use"]
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mpl_toolkits/mplot3d/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mpl_toolkits/mplot3d/__init__.py
deleted file mode 100644
index a089fbd6b70ebdafb19f6c902d49e5e3bdc05f43..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/mpl_toolkits/mplot3d/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .axes3d import Axes3D
-
-__all__ = ['Axes3D']
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_value_attrspec.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_value_attrspec.py
deleted file mode 100644
index 83aaf6c9161e0ccc0a388c6b671c63fdbeb3f81d..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/f2py/tests/test_value_attrspec.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import os
-import pytest
-
-from . import util
-
-class TestValueAttr(util.F2PyTest):
-    sources = [util.getpath("tests", "src", "value_attrspec", "gh21665.f90")]
-
-    # gh-21665
-    def test_long_long_map(self):
-        inp = 2
-        out = self.module.fortfuncs.square(inp)
-        exp_out = 4
-        assert out == exp_out
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/dtypes/test_dtypes.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/dtypes/test_dtypes.py
deleted file mode 100644
index f57093c29b733810a8404f216f2aa64738ec4e83..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/dtypes/test_dtypes.py
+++ /dev/null
@@ -1,1214 +0,0 @@
-import re
-import weakref
-
-import numpy as np
-import pytest
-import pytz
-
-from pandas._libs.tslibs.dtypes import NpyDatetimeUnit
-
-from pandas.core.dtypes.base import _registry as registry
-from pandas.core.dtypes.common import (
-    is_bool_dtype,
-    is_categorical_dtype,
-    is_datetime64_any_dtype,
-    is_datetime64_dtype,
-    is_datetime64_ns_dtype,
-    is_datetime64tz_dtype,
-    is_dtype_equal,
-    is_interval_dtype,
-    is_period_dtype,
-    is_string_dtype,
-)
-from pandas.core.dtypes.dtypes import (
-    CategoricalDtype,
-    DatetimeTZDtype,
-    IntervalDtype,
-    PeriodDtype,
-)
-
-import pandas as pd
-from pandas import (
-    Categorical,
-    CategoricalIndex,
-    DatetimeIndex,
-    IntervalIndex,
-    Series,
-    SparseDtype,
-    date_range,
-)
-import pandas._testing as tm
-from pandas.core.arrays.sparse import SparseArray
-
-
-class Base:
-    def test_hash(self, dtype):
-        hash(dtype)
-
-    def test_equality_invalid(self, dtype):
-        assert not dtype == "foo"
-        assert not is_dtype_equal(dtype, np.int64)
-
-    def test_numpy_informed(self, dtype):
-        # npdev 2020-02-02 changed from "data type not understood" to
-        #  "Cannot interpret 'foo' as a data type"
-        msg = "|".join(
-            ["data type not understood", "Cannot interpret '.*' as a data type"]
-        )
-        with pytest.raises(TypeError, match=msg):
-            np.dtype(dtype)
-
-        assert not dtype == np.str_
-        assert not np.str_ == dtype
-
-    def test_pickle(self, dtype):
-        # make sure our cache is NOT pickled
-
-        # clear the cache
-        type(dtype).reset_cache()
-        assert not len(dtype._cache_dtypes)
-
-        # force back to the cache
-        result = tm.round_trip_pickle(dtype)
-        if not isinstance(dtype, PeriodDtype):
-            # Because PeriodDtype has a cython class as a base class,
-            #  it has different pickle semantics, and its cache is re-populated
-            #  on un-pickling.
-            assert not len(dtype._cache_dtypes)
-        assert result == dtype
-
-
-class TestCategoricalDtype(Base):
-    @pytest.fixture
-    def dtype(self):
-        """
-        Class level fixture of dtype for TestCategoricalDtype
-        """
-        return CategoricalDtype()
-
-    def test_hash_vs_equality(self, dtype):
-        dtype2 = CategoricalDtype()
-        assert dtype == dtype2
-        assert dtype2 == dtype
-        assert hash(dtype) == hash(dtype2)
-
-    def test_equality(self, dtype):
-        assert dtype == "category"
-        assert is_dtype_equal(dtype, "category")
-        assert "category" == dtype
-        assert is_dtype_equal("category", dtype)
-
-        assert dtype == CategoricalDtype()
-        assert is_dtype_equal(dtype, CategoricalDtype())
-        assert CategoricalDtype() == dtype
-        assert is_dtype_equal(CategoricalDtype(), dtype)
-
-        assert dtype != "foo"
-        assert not is_dtype_equal(dtype, "foo")
-        assert "foo" != dtype
-        assert not is_dtype_equal("foo", dtype)
-
-    def test_construction_from_string(self, dtype):
-        result = CategoricalDtype.construct_from_string("category")
-        assert is_dtype_equal(dtype, result)
-        msg = "Cannot construct a 'CategoricalDtype' from 'foo'"
-        with pytest.raises(TypeError, match=msg):
-            CategoricalDtype.construct_from_string("foo")
-
-    def test_constructor_invalid(self):
-        msg = "Parameter 'categories' must be list-like"
-        with pytest.raises(TypeError, match=msg):
-            CategoricalDtype("category")
-
-    dtype1 = CategoricalDtype(["a", "b"], ordered=True)
-    dtype2 = CategoricalDtype(["x", "y"], ordered=False)
-    c = Categorical([0, 1], dtype=dtype1)
-
-    @pytest.mark.parametrize(
-        "values, categories, ordered, dtype, expected",
-        [
-            [None, None, None, None, CategoricalDtype()],
-            [None, ["a", "b"], True, None, dtype1],
-            [c, None, None, dtype2, dtype2],
-            [c, ["x", "y"], False, None, dtype2],
-        ],
-    )
-    def test_from_values_or_dtype(self, values, categories, ordered, dtype, expected):
-        result = CategoricalDtype._from_values_or_dtype(
-            values, categories, ordered, dtype
-        )
-        assert result == expected
-
-    @pytest.mark.parametrize(
-        "values, categories, ordered, dtype",
-        [
-            [None, ["a", "b"], True, dtype2],
-            [None, ["a", "b"], None, dtype2],
-            [None, None, True, dtype2],
-        ],
-    )
-    def test_from_values_or_dtype_raises(self, values, categories, ordered, dtype):
-        msg = "Cannot specify `categories` or `ordered` together with `dtype`."
-        with pytest.raises(ValueError, match=msg):
-            CategoricalDtype._from_values_or_dtype(values, categories, ordered, dtype)
-
-    def test_from_values_or_dtype_invalid_dtype(self):
-        msg = "Cannot not construct CategoricalDtype from <class 'object'>"
-        with pytest.raises(ValueError, match=msg):
-            CategoricalDtype._from_values_or_dtype(None, None, None, object)
-
-    def test_is_dtype(self, dtype):
-        assert CategoricalDtype.is_dtype(dtype)
-        assert CategoricalDtype.is_dtype("category")
-        assert CategoricalDtype.is_dtype(CategoricalDtype())
-        assert not CategoricalDtype.is_dtype("foo")
-        assert not CategoricalDtype.is_dtype(np.float64)
-
-    def test_basic(self, dtype):
-        msg = "is_categorical_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_categorical_dtype(dtype)
-
-            factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])
-
-            s = Series(factor, name="A")
-
-            # dtypes
-            assert is_categorical_dtype(s.dtype)
-            assert is_categorical_dtype(s)
-            assert not is_categorical_dtype(np.dtype("float64"))
-
-    def test_tuple_categories(self):
-        categories = [(1, "a"), (2, "b"), (3, "c")]
-        result = CategoricalDtype(categories)
-        assert all(result.categories == categories)
-
-    @pytest.mark.parametrize(
-        "categories, expected",
-        [
-            ([True, False], True),
-            ([True, False, None], True),
-            ([True, False, "a", "b'"], False),
-            ([0, 1], False),
-        ],
-    )
-    def test_is_boolean(self, categories, expected):
-        cat = Categorical(categories)
-        assert cat.dtype._is_boolean is expected
-        assert is_bool_dtype(cat) is expected
-        assert is_bool_dtype(cat.dtype) is expected
-
-    def test_dtype_specific_categorical_dtype(self):
-        expected = "datetime64[ns]"
-        result = str(Categorical(DatetimeIndex([])).categories.dtype)
-        assert result == expected
-
-    def test_not_string(self):
-        # though CategoricalDtype has object kind, it cannot be string
-        assert not is_string_dtype(CategoricalDtype())
-
-    def test_repr_range_categories(self):
-        rng = pd.Index(range(3))
-        dtype = CategoricalDtype(categories=rng, ordered=False)
-        result = repr(dtype)
-
-        expected = (
-            "CategoricalDtype(categories=range(0, 3), ordered=False, "
-            "categories_dtype=int64)"
-        )
-        assert result == expected
-
-    def test_update_dtype(self):
-        # GH 27338
-        result = CategoricalDtype(["a"]).update_dtype(Categorical(["b"], ordered=True))
-        expected = CategoricalDtype(["b"], ordered=True)
-        assert result == expected
-
-    def test_repr(self):
-        cat = Categorical(pd.Index([1, 2, 3], dtype="int32"))
-        result = cat.dtype.__repr__()
-        expected = (
-            "CategoricalDtype(categories=[1, 2, 3], ordered=False, "
-            "categories_dtype=int32)"
-        )
-        assert result == expected
-
-
-class TestDatetimeTZDtype(Base):
-    @pytest.fixture
-    def dtype(self):
-        """
-        Class level fixture of dtype for TestDatetimeTZDtype
-        """
-        return DatetimeTZDtype("ns", "US/Eastern")
-
-    def test_alias_to_unit_raises(self):
-        # 23990
-        with pytest.raises(ValueError, match="Passing a dtype alias"):
-            DatetimeTZDtype("datetime64[ns, US/Central]")
-
-    def test_alias_to_unit_bad_alias_raises(self):
-        # 23990
-        with pytest.raises(TypeError, match=""):
-            DatetimeTZDtype("this is a bad string")
-
-        with pytest.raises(TypeError, match=""):
-            DatetimeTZDtype("datetime64[ns, US/NotATZ]")
-
-    def test_hash_vs_equality(self, dtype):
-        # make sure that we satisfy is semantics
-        dtype2 = DatetimeTZDtype("ns", "US/Eastern")
-        dtype3 = DatetimeTZDtype(dtype2)
-        assert dtype == dtype2
-        assert dtype2 == dtype
-        assert dtype3 == dtype
-        assert hash(dtype) == hash(dtype2)
-        assert hash(dtype) == hash(dtype3)
-
-        dtype4 = DatetimeTZDtype("ns", "US/Central")
-        assert dtype2 != dtype4
-        assert hash(dtype2) != hash(dtype4)
-
-    def test_construction_non_nanosecond(self):
-        res = DatetimeTZDtype("ms", "US/Eastern")
-        assert res.unit == "ms"
-        assert res._creso == NpyDatetimeUnit.NPY_FR_ms.value
-        assert res.str == "|M8[ms]"
-        assert str(res) == "datetime64[ms, US/Eastern]"
-        assert res.base == np.dtype("M8[ms]")
-
-    def test_day_not_supported(self):
-        msg = "DatetimeTZDtype only supports s, ms, us, ns units"
-        with pytest.raises(ValueError, match=msg):
-            DatetimeTZDtype("D", "US/Eastern")
-
-    def test_subclass(self):
-        a = DatetimeTZDtype.construct_from_string("datetime64[ns, US/Eastern]")
-        b = DatetimeTZDtype.construct_from_string("datetime64[ns, CET]")
-
-        assert issubclass(type(a), type(a))
-        assert issubclass(type(a), type(b))
-
-    def test_compat(self, dtype):
-        msg = "is_datetime64tz_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_datetime64tz_dtype(dtype)
-            assert is_datetime64tz_dtype("datetime64[ns, US/Eastern]")
-        assert is_datetime64_any_dtype(dtype)
-        assert is_datetime64_any_dtype("datetime64[ns, US/Eastern]")
-        assert is_datetime64_ns_dtype(dtype)
-        assert is_datetime64_ns_dtype("datetime64[ns, US/Eastern]")
-        assert not is_datetime64_dtype(dtype)
-        assert not is_datetime64_dtype("datetime64[ns, US/Eastern]")
-
-    def test_construction_from_string(self, dtype):
-        result = DatetimeTZDtype.construct_from_string("datetime64[ns, US/Eastern]")
-        assert is_dtype_equal(dtype, result)
-
-    @pytest.mark.parametrize(
-        "string",
-        [
-            "foo",
-            "datetime64[ns, notatz]",
-            # non-nano unit
-            "datetime64[ps, UTC]",
-            # dateutil str that returns None from gettz
-            "datetime64[ns, dateutil/invalid]",
-        ],
-    )
-    def test_construct_from_string_invalid_raises(self, string):
-        msg = f"Cannot construct a 'DatetimeTZDtype' from '{string}'"
-        with pytest.raises(TypeError, match=re.escape(msg)):
-            DatetimeTZDtype.construct_from_string(string)
-
-    def test_construct_from_string_wrong_type_raises(self):
-        msg = "'construct_from_string' expects a string, got <class 'list'>"
-        with pytest.raises(TypeError, match=msg):
-            DatetimeTZDtype.construct_from_string(["datetime64[ns, notatz]"])
-
-    def test_is_dtype(self, dtype):
-        assert not DatetimeTZDtype.is_dtype(None)
-        assert DatetimeTZDtype.is_dtype(dtype)
-        assert DatetimeTZDtype.is_dtype("datetime64[ns, US/Eastern]")
-        assert DatetimeTZDtype.is_dtype("M8[ns, US/Eastern]")
-        assert not DatetimeTZDtype.is_dtype("foo")
-        assert DatetimeTZDtype.is_dtype(DatetimeTZDtype("ns", "US/Pacific"))
-        assert not DatetimeTZDtype.is_dtype(np.float64)
-
-    def test_equality(self, dtype):
-        assert is_dtype_equal(dtype, "datetime64[ns, US/Eastern]")
-        assert is_dtype_equal(dtype, "M8[ns, US/Eastern]")
-        assert is_dtype_equal(dtype, DatetimeTZDtype("ns", "US/Eastern"))
-        assert not is_dtype_equal(dtype, "foo")
-        assert not is_dtype_equal(dtype, DatetimeTZDtype("ns", "CET"))
-        assert not is_dtype_equal(
-            DatetimeTZDtype("ns", "US/Eastern"), DatetimeTZDtype("ns", "US/Pacific")
-        )
-
-        # numpy compat
-        assert is_dtype_equal(np.dtype("M8[ns]"), "datetime64[ns]")
-
-        assert dtype == "M8[ns, US/Eastern]"
-
-    def test_basic(self, dtype):
-        msg = "is_datetime64tz_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_datetime64tz_dtype(dtype)
-
-        dr = date_range("20130101", periods=3, tz="US/Eastern")
-        s = Series(dr, name="A")
-
-        # dtypes
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_datetime64tz_dtype(s.dtype)
-            assert is_datetime64tz_dtype(s)
-            assert not is_datetime64tz_dtype(np.dtype("float64"))
-            assert not is_datetime64tz_dtype(1.0)
-
-    def test_dst(self):
-        dr1 = date_range("2013-01-01", periods=3, tz="US/Eastern")
-        s1 = Series(dr1, name="A")
-        assert isinstance(s1.dtype, DatetimeTZDtype)
-
-        dr2 = date_range("2013-08-01", periods=3, tz="US/Eastern")
-        s2 = Series(dr2, name="A")
-        assert isinstance(s2.dtype, DatetimeTZDtype)
-        assert s1.dtype == s2.dtype
-
-    @pytest.mark.parametrize("tz", ["UTC", "US/Eastern"])
-    @pytest.mark.parametrize("constructor", ["M8", "datetime64"])
-    def test_parser(self, tz, constructor):
-        # pr #11245
-        dtz_str = f"{constructor}[ns, {tz}]"
-        result = DatetimeTZDtype.construct_from_string(dtz_str)
-        expected = DatetimeTZDtype("ns", tz)
-        assert result == expected
-
-    def test_empty(self):
-        with pytest.raises(TypeError, match="A 'tz' is required."):
-            DatetimeTZDtype()
-
-    def test_tz_standardize(self):
-        # GH 24713
-        tz = pytz.timezone("US/Eastern")
-        dr = date_range("2013-01-01", periods=3, tz="US/Eastern")
-        dtype = DatetimeTZDtype("ns", dr.tz)
-        assert dtype.tz == tz
-        dtype = DatetimeTZDtype("ns", dr[0].tz)
-        assert dtype.tz == tz
-
-
-class TestPeriodDtype(Base):
-    @pytest.fixture
-    def dtype(self):
-        """
-        Class level fixture of dtype for TestPeriodDtype
-        """
-        return PeriodDtype("D")
-
-    def test_hash_vs_equality(self, dtype):
-        # make sure that we satisfy is semantics
-        dtype2 = PeriodDtype("D")
-        dtype3 = PeriodDtype(dtype2)
-        assert dtype == dtype2
-        assert dtype2 == dtype
-        assert dtype3 == dtype
-        assert dtype is not dtype2
-        assert dtype2 is not dtype
-        assert dtype3 is not dtype
-        assert hash(dtype) == hash(dtype2)
-        assert hash(dtype) == hash(dtype3)
-
-    def test_construction(self):
-        with pytest.raises(ValueError, match="Invalid frequency: xx"):
-            PeriodDtype("xx")
-
-        for s in ["period[D]", "Period[D]", "D"]:
-            dt = PeriodDtype(s)
-            assert dt.freq == pd.tseries.offsets.Day()
-
-        for s in ["period[3D]", "Period[3D]", "3D"]:
-            dt = PeriodDtype(s)
-            assert dt.freq == pd.tseries.offsets.Day(3)
-
-        for s in [
-            "period[26H]",
-            "Period[26H]",
-            "26H",
-            "period[1D2H]",
-            "Period[1D2H]",
-            "1D2H",
-        ]:
-            dt = PeriodDtype(s)
-            assert dt.freq == pd.tseries.offsets.Hour(26)
-
-    def test_cannot_use_custom_businessday(self):
-        # GH#52534
-        msg = "CustomBusinessDay cannot be used with Period or PeriodDtype"
-        msg2 = r"PeriodDtype\[B\] is deprecated"
-        with pytest.raises(TypeError, match=msg):
-            with tm.assert_produces_warning(FutureWarning, match=msg2):
-                PeriodDtype("C")
-        with pytest.raises(TypeError, match=msg):
-            with tm.assert_produces_warning(FutureWarning, match=msg2):
-                PeriodDtype(pd.offsets.CustomBusinessDay())
-
-    def test_subclass(self):
-        a = PeriodDtype("period[D]")
-        b = PeriodDtype("period[3D]")
-
-        assert issubclass(type(a), type(a))
-        assert issubclass(type(a), type(b))
-
-    def test_identity(self):
-        assert PeriodDtype("period[D]") == PeriodDtype("period[D]")
-        assert PeriodDtype("period[D]") is not PeriodDtype("period[D]")
-
-        assert PeriodDtype("period[3D]") == PeriodDtype("period[3D]")
-        assert PeriodDtype("period[3D]") is not PeriodDtype("period[3D]")
-
-        assert PeriodDtype("period[1S1U]") == PeriodDtype("period[1000001U]")
-        assert PeriodDtype("period[1S1U]") is not PeriodDtype("period[1000001U]")
-
-    def test_compat(self, dtype):
-        assert not is_datetime64_ns_dtype(dtype)
-        assert not is_datetime64_ns_dtype("period[D]")
-        assert not is_datetime64_dtype(dtype)
-        assert not is_datetime64_dtype("period[D]")
-
-    def test_construction_from_string(self, dtype):
-        result = PeriodDtype("period[D]")
-        assert is_dtype_equal(dtype, result)
-        result = PeriodDtype.construct_from_string("period[D]")
-        assert is_dtype_equal(dtype, result)
-
-        with pytest.raises(TypeError, match="list"):
-            PeriodDtype.construct_from_string([1, 2, 3])
-
-    @pytest.mark.parametrize(
-        "string",
-        [
-            "foo",
-            "period[foo]",
-            "foo[D]",
-            "datetime64[ns]",
-            "datetime64[ns, US/Eastern]",
-        ],
-    )
-    def test_construct_dtype_from_string_invalid_raises(self, string):
-        msg = f"Cannot construct a 'PeriodDtype' from '{string}'"
-        with pytest.raises(TypeError, match=re.escape(msg)):
-            PeriodDtype.construct_from_string(string)
-
-    def test_is_dtype(self, dtype):
-        assert PeriodDtype.is_dtype(dtype)
-        assert PeriodDtype.is_dtype("period[D]")
-        assert PeriodDtype.is_dtype("period[3D]")
-        assert PeriodDtype.is_dtype(PeriodDtype("3D"))
-        assert PeriodDtype.is_dtype("period[U]")
-        assert PeriodDtype.is_dtype("period[S]")
-        assert PeriodDtype.is_dtype(PeriodDtype("U"))
-        assert PeriodDtype.is_dtype(PeriodDtype("S"))
-
-        assert not PeriodDtype.is_dtype("D")
-        assert not PeriodDtype.is_dtype("3D")
-        assert not PeriodDtype.is_dtype("U")
-        assert not PeriodDtype.is_dtype("S")
-        assert not PeriodDtype.is_dtype("foo")
-        assert not PeriodDtype.is_dtype(np.object_)
-        assert not PeriodDtype.is_dtype(np.int64)
-        assert not PeriodDtype.is_dtype(np.float64)
-
-    def test_equality(self, dtype):
-        assert is_dtype_equal(dtype, "period[D]")
-        assert is_dtype_equal(dtype, PeriodDtype("D"))
-        assert is_dtype_equal(dtype, PeriodDtype("D"))
-        assert is_dtype_equal(PeriodDtype("D"), PeriodDtype("D"))
-
-        assert not is_dtype_equal(dtype, "D")
-        assert not is_dtype_equal(PeriodDtype("D"), PeriodDtype("2D"))
-
-    def test_basic(self, dtype):
-        msg = "is_period_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_period_dtype(dtype)
-
-            pidx = pd.period_range("2013-01-01 09:00", periods=5, freq="H")
-
-            assert is_period_dtype(pidx.dtype)
-            assert is_period_dtype(pidx)
-
-            s = Series(pidx, name="A")
-
-            assert is_period_dtype(s.dtype)
-            assert is_period_dtype(s)
-
-            assert not is_period_dtype(np.dtype("float64"))
-            assert not is_period_dtype(1.0)
-
-    def test_freq_argument_required(self):
-        # GH#27388
-        msg = "missing 1 required positional argument: 'freq'"
-        with pytest.raises(TypeError, match=msg):
-            PeriodDtype()
-
-        msg = "PeriodDtype argument should be string or BaseOffset, got NoneType"
-        with pytest.raises(TypeError, match=msg):
-            # GH#51790
-            PeriodDtype(None)
-
-    def test_not_string(self):
-        # though PeriodDtype has object kind, it cannot be string
-        assert not is_string_dtype(PeriodDtype("D"))
-
-    def test_perioddtype_caching_dateoffset_normalize(self):
-        # GH 24121
-        per_d = PeriodDtype(pd.offsets.YearEnd(normalize=True))
-        assert per_d.freq.normalize
-
-        per_d2 = PeriodDtype(pd.offsets.YearEnd(normalize=False))
-        assert not per_d2.freq.normalize
-
-    def test_dont_keep_ref_after_del(self):
-        # GH 54184
-        dtype = PeriodDtype("D")
-        ref = weakref.ref(dtype)
-        del dtype
-        assert ref() is None
-
-
-class TestIntervalDtype(Base):
-    @pytest.fixture
-    def dtype(self):
-        """
-        Class level fixture of dtype for TestIntervalDtype
-        """
-        return IntervalDtype("int64", "right")
-
-    def test_hash_vs_equality(self, dtype):
-        # make sure that we satisfy is semantics
-        dtype2 = IntervalDtype("int64", "right")
-        dtype3 = IntervalDtype(dtype2)
-        assert dtype == dtype2
-        assert dtype2 == dtype
-        assert dtype3 == dtype
-        assert dtype is not dtype2
-        assert dtype2 is not dtype3
-        assert dtype3 is not dtype
-        assert hash(dtype) == hash(dtype2)
-        assert hash(dtype) == hash(dtype3)
-
-        dtype1 = IntervalDtype("interval")
-        dtype2 = IntervalDtype(dtype1)
-        dtype3 = IntervalDtype("interval")
-        assert dtype2 == dtype1
-        assert dtype2 == dtype2
-        assert dtype2 == dtype3
-        assert dtype2 is not dtype1
-        assert dtype2 is dtype2
-        assert dtype2 is not dtype3
-        assert hash(dtype2) == hash(dtype1)
-        assert hash(dtype2) == hash(dtype2)
-        assert hash(dtype2) == hash(dtype3)
-
-    @pytest.mark.parametrize(
-        "subtype", ["interval[int64]", "Interval[int64]", "int64", np.dtype("int64")]
-    )
-    def test_construction(self, subtype):
-        i = IntervalDtype(subtype, closed="right")
-        assert i.subtype == np.dtype("int64")
-        msg = "is_interval_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_interval_dtype(i)
-
-    @pytest.mark.parametrize(
-        "subtype", ["interval[int64]", "Interval[int64]", "int64", np.dtype("int64")]
-    )
-    def test_construction_allows_closed_none(self, subtype):
-        # GH#38394
-        dtype = IntervalDtype(subtype)
-
-        assert dtype.closed is None
-
-    def test_closed_mismatch(self):
-        msg = "'closed' keyword does not match value specified in dtype string"
-        with pytest.raises(ValueError, match=msg):
-            IntervalDtype("interval[int64, left]", "right")
-
-    @pytest.mark.parametrize("subtype", [None, "interval", "Interval"])
-    def test_construction_generic(self, subtype):
-        # generic
-        i = IntervalDtype(subtype)
-        assert i.subtype is None
-        msg = "is_interval_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_interval_dtype(i)
-
-    @pytest.mark.parametrize(
-        "subtype",
-        [
-            CategoricalDtype(list("abc"), False),
-            CategoricalDtype(list("wxyz"), True),
-            object,
-            str,
-            "<U10",
-            "interval[category]",
-            "interval[object]",
-        ],
-    )
-    def test_construction_not_supported(self, subtype):
-        # GH 19016
-        msg = (
-            "category, object, and string subtypes are not supported "
-            "for IntervalDtype"
-        )
-        with pytest.raises(TypeError, match=msg):
-            IntervalDtype(subtype)
-
-    @pytest.mark.parametrize("subtype", ["xx", "IntervalA", "Interval[foo]"])
-    def test_construction_errors(self, subtype):
-        msg = "could not construct IntervalDtype"
-        with pytest.raises(TypeError, match=msg):
-            IntervalDtype(subtype)
-
-    def test_closed_must_match(self):
-        # GH#37933
-        dtype = IntervalDtype(np.float64, "left")
-
-        msg = "dtype.closed and 'closed' do not match"
-        with pytest.raises(ValueError, match=msg):
-            IntervalDtype(dtype, closed="both")
-
-    def test_closed_invalid(self):
-        with pytest.raises(ValueError, match="closed must be one of"):
-            IntervalDtype(np.float64, "foo")
-
-    def test_construction_from_string(self, dtype):
-        result = IntervalDtype("interval[int64, right]")
-        assert is_dtype_equal(dtype, result)
-        result = IntervalDtype.construct_from_string("interval[int64, right]")
-        assert is_dtype_equal(dtype, result)
-
-    @pytest.mark.parametrize("string", [0, 3.14, ("a", "b"), None])
-    def test_construction_from_string_errors(self, string):
-        # these are invalid entirely
-        msg = f"'construct_from_string' expects a string, got {type(string)}"
-
-        with pytest.raises(TypeError, match=re.escape(msg)):
-            IntervalDtype.construct_from_string(string)
-
-    @pytest.mark.parametrize("string", ["foo", "foo[int64]", "IntervalA"])
-    def test_construction_from_string_error_subtype(self, string):
-        # this is an invalid subtype
-        msg = (
-            "Incorrectly formatted string passed to constructor. "
-            r"Valid formats include Interval or Interval\[dtype\] "
-            "where dtype is numeric, datetime, or timedelta"
-        )
-
-        with pytest.raises(TypeError, match=msg):
-            IntervalDtype.construct_from_string(string)
-
-    def test_subclass(self):
-        a = IntervalDtype("interval[int64, right]")
-        b = IntervalDtype("interval[int64, right]")
-
-        assert issubclass(type(a), type(a))
-        assert issubclass(type(a), type(b))
-
-    def test_is_dtype(self, dtype):
-        assert IntervalDtype.is_dtype(dtype)
-        assert IntervalDtype.is_dtype("interval")
-        assert IntervalDtype.is_dtype(IntervalDtype("float64"))
-        assert IntervalDtype.is_dtype(IntervalDtype("int64"))
-        assert IntervalDtype.is_dtype(IntervalDtype(np.int64))
-        assert IntervalDtype.is_dtype(IntervalDtype("float64", "left"))
-        assert IntervalDtype.is_dtype(IntervalDtype("int64", "right"))
-        assert IntervalDtype.is_dtype(IntervalDtype(np.int64, "both"))
-
-        assert not IntervalDtype.is_dtype("D")
-        assert not IntervalDtype.is_dtype("3D")
-        assert not IntervalDtype.is_dtype("U")
-        assert not IntervalDtype.is_dtype("S")
-        assert not IntervalDtype.is_dtype("foo")
-        assert not IntervalDtype.is_dtype("IntervalA")
-        assert not IntervalDtype.is_dtype(np.object_)
-        assert not IntervalDtype.is_dtype(np.int64)
-        assert not IntervalDtype.is_dtype(np.float64)
-
-    def test_equality(self, dtype):
-        assert is_dtype_equal(dtype, "interval[int64, right]")
-        assert is_dtype_equal(dtype, IntervalDtype("int64", "right"))
-        assert is_dtype_equal(
-            IntervalDtype("int64", "right"), IntervalDtype("int64", "right")
-        )
-
-        assert not is_dtype_equal(dtype, "interval[int64]")
-        assert not is_dtype_equal(dtype, IntervalDtype("int64"))
-        assert not is_dtype_equal(
-            IntervalDtype("int64", "right"), IntervalDtype("int64")
-        )
-
-        assert not is_dtype_equal(dtype, "int64")
-        assert not is_dtype_equal(
-            IntervalDtype("int64", "neither"), IntervalDtype("float64", "right")
-        )
-        assert not is_dtype_equal(
-            IntervalDtype("int64", "both"), IntervalDtype("int64", "left")
-        )
-
-        # invalid subtype comparisons do not raise when directly compared
-        dtype1 = IntervalDtype("float64", "left")
-        dtype2 = IntervalDtype("datetime64[ns, US/Eastern]", "left")
-        assert dtype1 != dtype2
-        assert dtype2 != dtype1
-
-    @pytest.mark.parametrize(
-        "subtype",
-        [
-            None,
-            "interval",
-            "Interval",
-            "int64",
-            "uint64",
-            "float64",
-            "complex128",
-            "datetime64",
-            "timedelta64",
-            PeriodDtype("Q"),
-        ],
-    )
-    def test_equality_generic(self, subtype):
-        # GH 18980
-        closed = "right" if subtype is not None else None
-        dtype = IntervalDtype(subtype, closed=closed)
-        assert is_dtype_equal(dtype, "interval")
-        assert is_dtype_equal(dtype, IntervalDtype())
-
-    @pytest.mark.parametrize(
-        "subtype",
-        [
-            "int64",
-            "uint64",
-            "float64",
-            "complex128",
-            "datetime64",
-            "timedelta64",
-            PeriodDtype("Q"),
-        ],
-    )
-    def test_name_repr(self, subtype):
-        # GH 18980
-        closed = "right" if subtype is not None else None
-        dtype = IntervalDtype(subtype, closed=closed)
-        expected = f"interval[{subtype}, {closed}]"
-        assert str(dtype) == expected
-        assert dtype.name == "interval"
-
-    @pytest.mark.parametrize("subtype", [None, "interval", "Interval"])
-    def test_name_repr_generic(self, subtype):
-        # GH 18980
-        dtype = IntervalDtype(subtype)
-        assert str(dtype) == "interval"
-        assert dtype.name == "interval"
-
-    def test_basic(self, dtype):
-        msg = "is_interval_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_interval_dtype(dtype)
-
-            ii = IntervalIndex.from_breaks(range(3))
-
-            assert is_interval_dtype(ii.dtype)
-            assert is_interval_dtype(ii)
-
-            s = Series(ii, name="A")
-
-            assert is_interval_dtype(s.dtype)
-            assert is_interval_dtype(s)
-
-    def test_basic_dtype(self):
-        msg = "is_interval_dtype is deprecated"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            assert is_interval_dtype("interval[int64, both]")
-            assert is_interval_dtype(IntervalIndex.from_tuples([(0, 1)]))
-            assert is_interval_dtype(IntervalIndex.from_breaks(np.arange(4)))
-            assert is_interval_dtype(
-                IntervalIndex.from_breaks(date_range("20130101", periods=3))
-            )
-            assert not is_interval_dtype("U")
-            assert not is_interval_dtype("S")
-            assert not is_interval_dtype("foo")
-            assert not is_interval_dtype(np.object_)
-            assert not is_interval_dtype(np.int64)
-            assert not is_interval_dtype(np.float64)
-
-    def test_caching(self):
-        # GH 54184: Caching not shown to improve performance
-        IntervalDtype.reset_cache()
-        dtype = IntervalDtype("int64", "right")
-        assert len(IntervalDtype._cache_dtypes) == 0
-
-        IntervalDtype("interval")
-        assert len(IntervalDtype._cache_dtypes) == 0
-
-        IntervalDtype.reset_cache()
-        tm.round_trip_pickle(dtype)
-        assert len(IntervalDtype._cache_dtypes) == 0
-
-    def test_not_string(self):
-        # GH30568: though IntervalDtype has object kind, it cannot be string
-        assert not is_string_dtype(IntervalDtype())
-
-    def test_unpickling_without_closed(self):
-        # GH#38394
-        dtype = IntervalDtype("interval")
-
-        assert dtype._closed is None
-
-        tm.round_trip_pickle(dtype)
-
-    def test_dont_keep_ref_after_del(self):
-        # GH 54184
-        dtype = IntervalDtype("int64", "right")
-        ref = weakref.ref(dtype)
-        del dtype
-        assert ref() is None
-
-
-class TestCategoricalDtypeParametrized:
-    @pytest.mark.parametrize(
-        "categories",
-        [
-            list("abcd"),
-            np.arange(1000),
-            ["a", "b", 10, 2, 1.3, True],
-            [True, False],
-            date_range("2017", periods=4),
-        ],
-    )
-    def test_basic(self, categories, ordered):
-        c1 = CategoricalDtype(categories, ordered=ordered)
-        tm.assert_index_equal(c1.categories, pd.Index(categories))
-        assert c1.ordered is ordered
-
-    def test_order_matters(self):
-        categories = ["a", "b"]
-        c1 = CategoricalDtype(categories, ordered=True)
-        c2 = CategoricalDtype(categories, ordered=False)
-        c3 = CategoricalDtype(categories, ordered=None)
-        assert c1 is not c2
-        assert c1 is not c3
-
-    @pytest.mark.parametrize("ordered", [False, None])
-    def test_unordered_same(self, ordered):
-        c1 = CategoricalDtype(["a", "b"], ordered=ordered)
-        c2 = CategoricalDtype(["b", "a"], ordered=ordered)
-        assert hash(c1) == hash(c2)
-
-    def test_categories(self):
-        result = CategoricalDtype(["a", "b", "c"])
-        tm.assert_index_equal(result.categories, pd.Index(["a", "b", "c"]))
-        assert result.ordered is False
-
-    def test_equal_but_different(self):
-        c1 = CategoricalDtype([1, 2, 3])
-        c2 = CategoricalDtype([1.0, 2.0, 3.0])
-        assert c1 is not c2
-        assert c1 != c2
-
-    @pytest.mark.parametrize("v1, v2", [([1, 2, 3], [1, 2, 3]), ([1, 2, 3], [3, 2, 1])])
-    def test_order_hashes_different(self, v1, v2):
-        c1 = CategoricalDtype(v1, ordered=False)
-        c2 = CategoricalDtype(v2, ordered=True)
-        c3 = CategoricalDtype(v1, ordered=None)
-        assert c1 is not c2
-        assert c1 is not c3
-
-    def test_nan_invalid(self):
-        msg = "Categorical categories cannot be null"
-        with pytest.raises(ValueError, match=msg):
-            CategoricalDtype([1, 2, np.nan])
-
-    def test_non_unique_invalid(self):
-        msg = "Categorical categories must be unique"
-        with pytest.raises(ValueError, match=msg):
-            CategoricalDtype([1, 2, 1])
-
-    def test_same_categories_different_order(self):
-        c1 = CategoricalDtype(["a", "b"], ordered=True)
-        c2 = CategoricalDtype(["b", "a"], ordered=True)
-        assert c1 is not c2
-
-    @pytest.mark.parametrize("ordered1", [True, False, None])
-    @pytest.mark.parametrize("ordered2", [True, False, None])
-    def test_categorical_equality(self, ordered1, ordered2):
-        # same categories, same order
-        # any combination of None/False are equal
-        # True/True is the only combination with True that are equal
-        c1 = CategoricalDtype(list("abc"), ordered1)
-        c2 = CategoricalDtype(list("abc"), ordered2)
-        result = c1 == c2
-        expected = bool(ordered1) is bool(ordered2)
-        assert result is expected
-
-        # same categories, different order
-        # any combination of None/False are equal (order doesn't matter)
-        # any combination with True are not equal (different order of cats)
-        c1 = CategoricalDtype(list("abc"), ordered1)
-        c2 = CategoricalDtype(list("cab"), ordered2)
-        result = c1 == c2
-        expected = (bool(ordered1) is False) and (bool(ordered2) is False)
-        assert result is expected
-
-        # different categories
-        c2 = CategoricalDtype([1, 2, 3], ordered2)
-        assert c1 != c2
-
-        # none categories
-        c1 = CategoricalDtype(list("abc"), ordered1)
-        c2 = CategoricalDtype(None, ordered2)
-        c3 = CategoricalDtype(None, ordered1)
-        assert c1 != c2
-        assert c2 != c1
-        assert c2 == c3
-
-    def test_categorical_dtype_equality_requires_categories(self):
-        # CategoricalDtype with categories=None is *not* equal to
-        #  any fully-initialized CategoricalDtype
-        first = CategoricalDtype(["a", "b"])
-        second = CategoricalDtype()
-        third = CategoricalDtype(ordered=True)
-
-        assert second == second
-        assert third == third
-
-        assert first != second
-        assert second != first
-        assert first != third
-        assert third != first
-        assert second == third
-        assert third == second
-
-    @pytest.mark.parametrize("categories", [list("abc"), None])
-    @pytest.mark.parametrize("other", ["category", "not a category"])
-    def test_categorical_equality_strings(self, categories, ordered, other):
-        c1 = CategoricalDtype(categories, ordered)
-        result = c1 == other
-        expected = other == "category"
-        assert result is expected
-
-    def test_invalid_raises(self):
-        with pytest.raises(TypeError, match="ordered"):
-            CategoricalDtype(["a", "b"], ordered="foo")
-
-        with pytest.raises(TypeError, match="'categories' must be list-like"):
-            CategoricalDtype("category")
-
-    def test_mixed(self):
-        a = CategoricalDtype(["a", "b", 1, 2])
-        b = CategoricalDtype(["a", "b", "1", "2"])
-        assert hash(a) != hash(b)
-
-    def test_from_categorical_dtype_identity(self):
-        c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
-        # Identity test for no changes
-        c2 = CategoricalDtype._from_categorical_dtype(c1)
-        assert c2 is c1
-
-    def test_from_categorical_dtype_categories(self):
-        c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
-        # override categories
-        result = CategoricalDtype._from_categorical_dtype(c1, categories=[2, 3])
-        assert result == CategoricalDtype([2, 3], ordered=True)
-
-    def test_from_categorical_dtype_ordered(self):
-        c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
-        # override ordered
-        result = CategoricalDtype._from_categorical_dtype(c1, ordered=False)
-        assert result == CategoricalDtype([1, 2, 3], ordered=False)
-
-    def test_from_categorical_dtype_both(self):
-        c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
-        # override ordered
-        result = CategoricalDtype._from_categorical_dtype(
-            c1, categories=[1, 2], ordered=False
-        )
-        assert result == CategoricalDtype([1, 2], ordered=False)
-
-    def test_str_vs_repr(self, ordered):
-        c1 = CategoricalDtype(["a", "b"], ordered=ordered)
-        assert str(c1) == "category"
-        # Py2 will have unicode prefixes
-        pat = (
-            r"CategoricalDtype\(categories=\[.*\], ordered={ordered}, "
-            r"categories_dtype=object\)"
-        )
-        assert re.match(pat.format(ordered=ordered), repr(c1))
-
-    def test_categorical_categories(self):
-        # GH17884
-        c1 = CategoricalDtype(Categorical(["a", "b"]))
-        tm.assert_index_equal(c1.categories, pd.Index(["a", "b"]))
-        c1 = CategoricalDtype(CategoricalIndex(["a", "b"]))
-        tm.assert_index_equal(c1.categories, pd.Index(["a", "b"]))
-
-    @pytest.mark.parametrize(
-        "new_categories", [list("abc"), list("cba"), list("wxyz"), None]
-    )
-    @pytest.mark.parametrize("new_ordered", [True, False, None])
-    def test_update_dtype(self, ordered, new_categories, new_ordered):
-        original_categories = list("abc")
-        dtype = CategoricalDtype(original_categories, ordered)
-        new_dtype = CategoricalDtype(new_categories, new_ordered)
-
-        result = dtype.update_dtype(new_dtype)
-        expected_categories = pd.Index(new_categories or original_categories)
-        expected_ordered = new_ordered if new_ordered is not None else dtype.ordered
-
-        tm.assert_index_equal(result.categories, expected_categories)
-        assert result.ordered is expected_ordered
-
-    def test_update_dtype_string(self, ordered):
-        dtype = CategoricalDtype(list("abc"), ordered)
-        expected_categories = dtype.categories
-        expected_ordered = dtype.ordered
-        result = dtype.update_dtype("category")
-        tm.assert_index_equal(result.categories, expected_categories)
-        assert result.ordered is expected_ordered
-
-    @pytest.mark.parametrize("bad_dtype", ["foo", object, np.int64, PeriodDtype("Q")])
-    def test_update_dtype_errors(self, bad_dtype):
-        dtype = CategoricalDtype(list("abc"), False)
-        msg = "a CategoricalDtype must be passed to perform an update, "
-        with pytest.raises(ValueError, match=msg):
-            dtype.update_dtype(bad_dtype)
-
-
-@pytest.mark.parametrize(
-    "dtype", [CategoricalDtype, IntervalDtype, DatetimeTZDtype, PeriodDtype]
-)
-def test_registry(dtype):
-    assert dtype in registry.dtypes
-
-
-@pytest.mark.parametrize(
-    "dtype, expected",
-    [
-        ("int64", None),
-        ("interval", IntervalDtype()),
-        ("interval[int64, neither]", IntervalDtype()),
-        ("interval[datetime64[ns], left]", IntervalDtype("datetime64[ns]", "left")),
-        ("period[D]", PeriodDtype("D")),
-        ("category", CategoricalDtype()),
-        ("datetime64[ns, US/Eastern]", DatetimeTZDtype("ns", "US/Eastern")),
-    ],
-)
-def test_registry_find(dtype, expected):
-    assert registry.find(dtype) == expected
-
-
-@pytest.mark.parametrize(
-    "dtype, expected",
-    [
-        (str, False),
-        (int, False),
-        (bool, True),
-        (np.bool_, True),
-        (np.array(["a", "b"]), False),
-        (Series([1, 2]), False),
-        (np.array([True, False]), True),
-        (Series([True, False]), True),
-        (SparseArray([True, False]), True),
-        (SparseDtype(bool), True),
-    ],
-)
-def test_is_bool_dtype(dtype, expected):
-    result = is_bool_dtype(dtype)
-    assert result is expected
-
-
-def test_is_bool_dtype_sparse():
-    result = is_bool_dtype(Series(SparseArray([True, False])))
-    assert result is True
-
-
-@pytest.mark.parametrize(
-    "check",
-    [
-        is_categorical_dtype,
-        is_datetime64tz_dtype,
-        is_period_dtype,
-        is_datetime64_ns_dtype,
-        is_datetime64_dtype,
-        is_interval_dtype,
-        is_datetime64_any_dtype,
-        is_string_dtype,
-        is_bool_dtype,
-    ],
-)
-def test_is_dtype_no_warning(check):
-    data = pd.DataFrame({"A": [1, 2]})
-
-    warn = None
-    msg = f"{check.__name__} is deprecated"
-    if (
-        check is is_categorical_dtype
-        or check is is_interval_dtype
-        or check is is_datetime64tz_dtype
-        or check is is_period_dtype
-    ):
-        warn = FutureWarning
-
-    with tm.assert_produces_warning(warn, match=msg):
-        check(data)
-
-    with tm.assert_produces_warning(warn, match=msg):
-        check(data["A"])
-
-
-def test_period_dtype_compare_to_string():
-    # https://github.com/pandas-dev/pandas/issues/37265
-    dtype = PeriodDtype(freq="M")
-    assert (dtype == "period[M]") is True
-    assert (dtype != "period[M]") is False
-
-
-def test_compare_complex_dtypes():
-    # GH 28050
-    df = pd.DataFrame(np.arange(5).astype(np.complex128))
-    msg = "'<' not supported between instances of 'complex' and 'complex'"
-
-    with pytest.raises(TypeError, match=msg):
-        df < df.astype(object)
-
-    with pytest.raises(TypeError, match=msg):
-        df.lt(df.astype(object))
-
-
-def test_cast_string_to_complex():
-    # GH 4895
-    expected = pd.DataFrame(["1.0+5j", "1.5-3j"], dtype=complex)
-    result = pd.DataFrame(["1.0+5j", "1.5-3j"]).astype(complex)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_categorical_complex():
-    result = Categorical([1, 2 + 2j])
-    expected = Categorical([1.0 + 0.0j, 2.0 + 2.0j])
-    tm.assert_categorical_equal(result, expected)
-    result = Categorical([1, 2, 2 + 2j])
-    expected = Categorical([1.0 + 0.0j, 2.0 + 0.0j, 2.0 + 2.0j])
-    tm.assert_categorical_equal(result, expected)
-
-
-def test_multi_column_dtype_assignment():
-    # GH #27583
-    df = pd.DataFrame({"a": [0.0], "b": 0.0})
-    expected = pd.DataFrame({"a": [0], "b": 0})
-
-    df[["a", "b"]] = 0
-    tm.assert_frame_equal(df, expected)
-
-    df["b"] = 0
-    tm.assert_frame_equal(df, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/logging.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/logging.py
deleted file mode 100644
index 002f1f7bf1c6857bdcce388f78ac1e415ef8d3f5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/logging.py
+++ /dev/null
@@ -1,268 +0,0 @@
-import logging
-from datetime import datetime
-from logging import Handler, LogRecord
-from pathlib import Path
-from typing import ClassVar, List, Optional, Type, Union
-
-from . import get_console
-from ._log_render import LogRender, FormatTimeCallable
-from .console import Console, ConsoleRenderable
-from .highlighter import Highlighter, ReprHighlighter
-from .text import Text
-from .traceback import Traceback
-
-
-class RichHandler(Handler):
-    """A logging handler that renders output with Rich. The time / level / message and file are displayed in columns.
-    The level is color coded, and the message is syntax highlighted.
-
-    Note:
-        Be careful when enabling console markup in log messages if you have configured logging for libraries not
-        under your control. If a dependency writes messages containing square brackets, it may not produce the intended output.
-
-    Args:
-        level (Union[int, str], optional): Log level. Defaults to logging.NOTSET.
-        console (:class:`~rich.console.Console`, optional): Optional console instance to write logs.
-            Default will use a global console instance writing to stdout.
-        show_time (bool, optional): Show a column for the time. Defaults to True.
-        omit_repeated_times (bool, optional): Omit repetition of the same time. Defaults to True.
-        show_level (bool, optional): Show a column for the level. Defaults to True.
-        show_path (bool, optional): Show the path to the original log call. Defaults to True.
-        enable_link_path (bool, optional): Enable terminal link of path column to file. Defaults to True.
-        highlighter (Highlighter, optional): Highlighter to style log messages, or None to use ReprHighlighter. Defaults to None.
-        markup (bool, optional): Enable console markup in log messages. Defaults to False.
-        rich_tracebacks (bool, optional): Enable rich tracebacks with syntax highlighting and formatting. Defaults to False.
-        tracebacks_width (Optional[int], optional): Number of characters used to render tracebacks, or None for full width. Defaults to None.
-        tracebacks_extra_lines (int, optional): Additional lines of code to render tracebacks, or None for full width. Defaults to None.
-        tracebacks_theme (str, optional): Override pygments theme used in traceback.
-        tracebacks_word_wrap (bool, optional): Enable word wrapping of long tracebacks lines. Defaults to True.
-        tracebacks_show_locals (bool, optional): Enable display of locals in tracebacks. Defaults to False.
-        locals_max_length (int, optional): Maximum length of containers before abbreviating, or None for no abbreviation.
-            Defaults to 10.
-        locals_max_string (int, optional): Maximum length of string before truncating, or None to disable. Defaults to 80.
-        log_time_format (Union[str, TimeFormatterCallable], optional): If ``log_time`` is enabled, either string for strftime or callable that formats the time. Defaults to "[%x %X] ".
-    """
-
-    KEYWORDS: ClassVar[Optional[List[str]]] = [
-        "GET",
-        "POST",
-        "HEAD",
-        "PUT",
-        "DELETE",
-        "OPTIONS",
-        "TRACE",
-        "PATCH",
-    ]
-    HIGHLIGHTER_CLASS: ClassVar[Type[Highlighter]] = ReprHighlighter
-
-    def __init__(
-        self,
-        level: Union[int, str] = logging.NOTSET,
-        console: Optional[Console] = None,
-        *,
-        show_time: bool = True,
-        omit_repeated_times: bool = True,
-        show_level: bool = True,
-        show_path: bool = True,
-        enable_link_path: bool = True,
-        highlighter: Optional[Highlighter] = None,
-        markup: bool = False,
-        rich_tracebacks: bool = False,
-        tracebacks_width: Optional[int] = None,
-        tracebacks_extra_lines: int = 3,
-        tracebacks_theme: Optional[str] = None,
-        tracebacks_word_wrap: bool = True,
-        tracebacks_show_locals: bool = False,
-        locals_max_length: int = 10,
-        locals_max_string: int = 80,
-        log_time_format: Union[str, FormatTimeCallable] = "[%x %X]",
-    ) -> None:
-        super().__init__(level=level)
-        self.console = console or get_console()
-        self.highlighter = highlighter or self.HIGHLIGHTER_CLASS()
-        self._log_render = LogRender(
-            show_time=show_time,
-            show_level=show_level,
-            show_path=show_path,
-            time_format=log_time_format,
-            omit_repeated_times=omit_repeated_times,
-            level_width=None,
-        )
-        self.enable_link_path = enable_link_path
-        self.markup = markup
-        self.rich_tracebacks = rich_tracebacks
-        self.tracebacks_width = tracebacks_width
-        self.tracebacks_extra_lines = tracebacks_extra_lines
-        self.tracebacks_theme = tracebacks_theme
-        self.tracebacks_word_wrap = tracebacks_word_wrap
-        self.tracebacks_show_locals = tracebacks_show_locals
-        self.locals_max_length = locals_max_length
-        self.locals_max_string = locals_max_string
-
-    def get_level_text(self, record: LogRecord) -> Text:
-        """Get the level name from the record.
-
-        Args:
-            record (LogRecord): LogRecord instance.
-
-        Returns:
-            Text: A tuple of the style and level name.
-        """
-        level_name = record.levelname
-        level_text = Text.styled(
-            level_name.ljust(8), f"logging.level.{level_name.lower()}"
-        )
-        return level_text
-
-    def emit(self, record: LogRecord) -> None:
-        """Invoked by logging."""
-        message = self.format(record)
-        traceback = None
-        if (
-            self.rich_tracebacks
-            and record.exc_info
-            and record.exc_info != (None, None, None)
-        ):
-            exc_type, exc_value, exc_traceback = record.exc_info
-            assert exc_type is not None
-            assert exc_value is not None
-            traceback = Traceback.from_exception(
-                exc_type,
-                exc_value,
-                exc_traceback,
-                width=self.tracebacks_width,
-                extra_lines=self.tracebacks_extra_lines,
-                theme=self.tracebacks_theme,
-                word_wrap=self.tracebacks_word_wrap,
-                show_locals=self.tracebacks_show_locals,
-                locals_max_length=self.locals_max_length,
-                locals_max_string=self.locals_max_string,
-            )
-            message = record.getMessage()
-            if self.formatter:
-                record.message = record.getMessage()
-                formatter = self.formatter
-                if hasattr(formatter, "usesTime") and formatter.usesTime():
-                    record.asctime = formatter.formatTime(record, formatter.datefmt)
-                message = formatter.formatMessage(record)
-
-        message_renderable = self.render_message(record, message)
-        log_renderable = self.render(
-            record=record, traceback=traceback, message_renderable=message_renderable
-        )
-        try:
-            self.console.print(log_renderable)
-        except Exception:
-            self.handleError(record)
-
-    def render_message(self, record: LogRecord, message: str) -> "ConsoleRenderable":
-        """Render message text in to Text.
-
-        record (LogRecord): logging Record.
-        message (str): String containing log message.
-
-        Returns:
-            ConsoleRenderable: Renderable to display log message.
-        """
-        use_markup = getattr(record, "markup", self.markup)
-        message_text = Text.from_markup(message) if use_markup else Text(message)
-
-        highlighter = getattr(record, "highlighter", self.highlighter)
-        if highlighter:
-            message_text = highlighter(message_text)
-
-        if self.KEYWORDS:
-            message_text.highlight_words(self.KEYWORDS, "logging.keyword")
-        return message_text
-
-    def render(
-        self,
-        *,
-        record: LogRecord,
-        traceback: Optional[Traceback],
-        message_renderable: "ConsoleRenderable",
-    ) -> "ConsoleRenderable":
-        """Render log for display.
-
-        Args:
-            record (LogRecord): logging Record.
-            traceback (Optional[Traceback]): Traceback instance or None for no Traceback.
-            message_renderable (ConsoleRenderable): Renderable (typically Text) containing log message contents.
-
-        Returns:
-            ConsoleRenderable: Renderable to display log.
-        """
-        path = Path(record.pathname).name
-        level = self.get_level_text(record)
-        time_format = None if self.formatter is None else self.formatter.datefmt
-        log_time = datetime.fromtimestamp(record.created)
-
-        log_renderable = self._log_render(
-            self.console,
-            [message_renderable] if not traceback else [message_renderable, traceback],
-            log_time=log_time,
-            time_format=time_format,
-            level=level,
-            path=path,
-            line_no=record.lineno,
-            link_path=record.pathname if self.enable_link_path else None,
-        )
-        return log_renderable
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from time import sleep
-
-    FORMAT = "%(message)s"
-    # FORMAT = "%(asctime)-15s - %(levelname)s - %(message)s"
-    logging.basicConfig(
-        level="NOTSET",
-        format=FORMAT,
-        datefmt="[%X]",
-        handlers=[RichHandler(rich_tracebacks=True, tracebacks_show_locals=True)],
-    )
-    log = logging.getLogger("rich")
-
-    log.info("Server starting...")
-    log.info("Listening on http://127.0.0.1:8080")
-    sleep(1)
-
-    log.info("GET /index.html 200 1298")
-    log.info("GET /imgs/backgrounds/back1.jpg 200 54386")
-    log.info("GET /css/styles.css 200 54386")
-    log.warning("GET /favicon.ico 404 242")
-    sleep(1)
-
-    log.debug(
-        "JSONRPC request\n--> %r\n<-- %r",
-        {
-            "version": "1.1",
-            "method": "confirmFruitPurchase",
-            "params": [["apple", "orange", "mangoes", "pomelo"], 1.123],
-            "id": "194521489",
-        },
-        {"version": "1.1", "result": True, "error": None, "id": "194521489"},
-    )
-    log.debug(
-        "Loading configuration file /adasd/asdasd/qeqwe/qwrqwrqwr/sdgsdgsdg/werwerwer/dfgerert/ertertert/ertetert/werwerwer"
-    )
-    log.error("Unable to find 'pomelo' in database!")
-    log.info("POST /jsonrpc/ 200 65532")
-    log.info("POST /admin/ 401 42234")
-    log.warning("password was rejected for admin site.")
-
-    def divide() -> None:
-        number = 1
-        divisor = 0
-        foos = ["foo"] * 100
-        log.debug("in divide")
-        try:
-            number / divisor
-        except:
-            log.exception("An error of some kind occurred!")
-
-    divide()
-    sleep(1)
-    log.critical("Out of memory!")
-    log.info("Server exited with code=-1")
-    log.info("[bold]EXITING...[/bold]", extra=dict(markup=True))
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/openscad.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/openscad.py
deleted file mode 100644
index de8fdaf61bb2d77a04442e62db46afd3ae6aacd9..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/openscad.py
+++ /dev/null
@@ -1,97 +0,0 @@
-"""
-    pygments.lexers.openscad
-    ~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Lexers for the OpenSCAD languages.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from pygments.lexer import RegexLexer, bygroups, words, include
-from pygments.token import Text, Comment, Punctuation, Operator, Keyword, Name, Number, Whitespace, Literal, String
-
-__all__ = ['OpenScadLexer']
-
-
-class OpenScadLexer(RegexLexer):
-    """For openSCAD code.
-
-    .. versionadded:: 2.16
-    """
-    name = "OpenSCAD"
-    url = "https://openscad.org/"
-    aliases = ["openscad"]
-    filenames = ["*.scad"]
-    mimetypes = ["application/x-openscad"]
-
-    tokens = {
-        "root": [
-            (r"[^\S\n]+", Whitespace),
-            (r'//', Comment.Single, 'comment-single'),
-            (r'/\*', Comment.Multiline, 'comment-multi'),
-            (r"[{}\[\]\(\),;:]", Punctuation),
-            (r"[*!#%\-+=?/]", Operator),
-            (r"<=|<|==|!=|>=|>|&&|\|\|", Operator),
-            (r"\$(f[asn]|t|vp[rtd]|children)", Operator),
-            (r"(undef|PI)\b", Keyword.Constant),
-            (
-                r"(use|include)((?:\s|\\\\s)+)",
-                bygroups(Keyword.Namespace, Text),
-                "includes",
-            ),
-            (r"(module)(\s*)([^\s\(]+)",
-             bygroups(Keyword.Namespace, Whitespace, Name.Namespace)),
-            (r"(function)(\s*)([^\s\(]+)",
-             bygroups(Keyword.Declaration, Whitespace, Name.Function)),
-            (words(("true", "false"), prefix=r"\b", suffix=r"\b"), Literal),
-            (words((
-                "function", "module", "include", "use", "for",
-                "intersection_for", "if", "else", "return"
-                ), prefix=r"\b", suffix=r"\b"), Keyword
-            ),
-            (words((
-                "circle", "square", "polygon", "text", "sphere", "cube",
-                "cylinder", "polyhedron", "translate", "rotate", "scale",
-                "resize", "mirror", "multmatrix", "color", "offset", "hull",
-                "minkowski", "union", "difference", "intersection", "abs",
-                "sign", "sin", "cos", "tan", "acos", "asin", "atan", "atan2",
-                "floor", "round", "ceil", "ln", "log", "pow", "sqrt", "exp",
-                "rands", "min", "max", "concat", "lookup", "str", "chr",
-                "search", "version", "version_num", "norm", "cross",
-                "parent_module", "echo", "import", "import_dxf",
-                "dxf_linear_extrude", "linear_extrude", "rotate_extrude",
-                "surface", "projection", "render", "dxf_cross",
-                "dxf_dim", "let", "assign", "len"
-                ), prefix=r"\b", suffix=r"\b"),
-                Name.Builtin
-            ),
-            (r"\bchildren\b", Name.Builtin.Pseudo),
-            (r'""".*?"""', String.Double),
-            (r'"(\\\\|\\[^\\]|[^"\\])*"', String.Double),
-            (r"-?\d+(\.\d+)?(e[+-]?\d+)?", Number),
-            (r"\w+", Name),
-        ],
-        "includes": [
-            (
-                r"(<)([^>]*)(>)",
-                bygroups(Punctuation, Comment.PreprocFile, Punctuation),
-            ),
-        ],
-        'comment': [
-            (r':param: [a-zA-Z_]\w*|:returns?:|(FIXME|MARK|TODO):',
-             Comment.Special)
-        ],
-        'comment-single': [
-            (r'\n', Text, '#pop'),
-            include('comment'),
-            (r'[^\n]+', Comment.Single)
-        ],
-        'comment-multi': [
-            include('comment'),
-            (r'[^*/]+', Comment.Multiline),
-            (r'/\*', Comment.Multiline, '#push'),
-            (r'\*/', Comment.Multiline, '#pop'),
-            (r'[*/]', Comment.Multiline)
-        ],
-    }
diff --git a/spaces/pyodide-demo/self-hosted/docutils.js b/spaces/pyodide-demo/self-hosted/docutils.js
deleted file mode 100644
index 9c3911b42e914004a3d91d39d1efe5941b3004a2..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/docutils.js
+++ /dev/null
@@ -1 +0,0 @@
-var Module=typeof globalThis.__pyodide_module!=="undefined"?globalThis.__pyodide_module:{};if(!Module.expectedDataFileDownloads){Module.expectedDataFileDownloads=0}Module.expectedDataFileDownloads++;(function(){var loadPackage=function(metadata){var PACKAGE_PATH="";if(typeof window==="object"){PACKAGE_PATH=window["encodeURIComponent"](window.location.pathname.toString().substring(0,window.location.pathname.toString().lastIndexOf("/"))+"/")}else if(typeof process==="undefined"&&typeof location!=="undefined"){PACKAGE_PATH=encodeURIComponent(location.pathname.toString().substring(0,location.pathname.toString().lastIndexOf("/"))+"/")}var PACKAGE_NAME="docutils.data";var REMOTE_PACKAGE_BASE="docutils.data";if(typeof Module["locateFilePackage"]==="function"&&!Module["locateFile"]){Module["locateFile"]=Module["locateFilePackage"];err("warning: you defined Module.locateFilePackage, that has been renamed to Module.locateFile (using your locateFilePackage for now)")}var REMOTE_PACKAGE_NAME=Module["locateFile"]?Module["locateFile"](REMOTE_PACKAGE_BASE,""):REMOTE_PACKAGE_BASE;var REMOTE_PACKAGE_SIZE=metadata["remote_package_size"];var PACKAGE_UUID=metadata["package_uuid"];function fetchRemotePackage(packageName,packageSize,callback,errback){if(typeof process==="object"){require("fs").readFile(packageName,(function(err,contents){if(err){errback(err)}else{callback(contents.buffer)}}));return}var xhr=new XMLHttpRequest;xhr.open("GET",packageName,true);xhr.responseType="arraybuffer";xhr.onprogress=function(event){var url=packageName;var size=packageSize;if(event.total)size=event.total;if(event.loaded){if(!xhr.addedTotal){xhr.addedTotal=true;if(!Module.dataFileDownloads)Module.dataFileDownloads={};Module.dataFileDownloads[url]={loaded:event.loaded,total:size}}else{Module.dataFileDownloads[url].loaded=event.loaded}var total=0;var loaded=0;var num=0;for(var download in Module.dataFileDownloads){var data=Module.dataFileDownloads[download];total+=data.total;loaded+=data.loaded;num++}total=Math.ceil(total*Module.expectedDataFileDownloads/num);if(Module["setStatus"])Module["setStatus"]("Downloading data... 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\ No newline at end of file
diff --git a/spaces/qdd319/ChuanhuChatGPT/modules/llama_func.py b/spaces/qdd319/ChuanhuChatGPT/modules/llama_func.py
deleted file mode 100644
index 9f4f799882b4e7c34aa8df815ebeb90ed822ba46..0000000000000000000000000000000000000000
--- a/spaces/qdd319/ChuanhuChatGPT/modules/llama_func.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import os
-import logging
-
-from llama_index import download_loader
-from llama_index import (
-    Document,
-    LLMPredictor,
-    PromptHelper,
-    QuestionAnswerPrompt,
-    RefinePrompt,
-)
-import colorama
-import PyPDF2
-from tqdm import tqdm
-
-from modules.presets import *
-from modules.utils import *
-
-def get_index_name(file_src):
-    file_paths = [x.name for x in file_src]
-    file_paths.sort(key=lambda x: os.path.basename(x))
-
-    md5_hash = hashlib.md5()
-    for file_path in file_paths:
-        with open(file_path, "rb") as f:
-            while chunk := f.read(8192):
-                md5_hash.update(chunk)
-
-    return md5_hash.hexdigest()
-
-def block_split(text):
-    blocks = []
-    while len(text) > 0:
-        blocks.append(Document(text[:1000]))
-        text = text[1000:]
-    return blocks
-
-def get_documents(file_src):
-    documents = []
-    logging.debug("Loading documents...")
-    logging.debug(f"file_src: {file_src}")
-    for file in file_src:
-        filepath = file.name
-        filename = os.path.basename(filepath)
-        file_type = os.path.splitext(filepath)[1]
-        logging.info(f"loading file: {filename}")
-        if file_type == ".pdf":
-            logging.debug("Loading PDF...")
-            try:
-                from modules.pdf_func import parse_pdf
-                from modules.config import advance_docs
-                two_column = advance_docs["pdf"].get("two_column", False)
-                pdftext = parse_pdf(filepath, two_column).text
-            except:
-                pdftext = ""
-                with open(filepath, 'rb') as pdfFileObj:
-                    pdfReader = PyPDF2.PdfReader(pdfFileObj)
-                    for page in tqdm(pdfReader.pages):
-                        pdftext += page.extract_text()
-            text_raw = pdftext
-        elif file_type == ".docx":
-            logging.debug("Loading Word...")
-            DocxReader = download_loader("DocxReader")
-            loader = DocxReader()
-            text_raw = loader.load_data(file=filepath)[0].text
-        elif file_type == ".epub":
-            logging.debug("Loading EPUB...")
-            EpubReader = download_loader("EpubReader")
-            loader = EpubReader()
-            text_raw = loader.load_data(file=filepath)[0].text
-        elif file_type == ".xlsx":
-            logging.debug("Loading Excel...")
-            text_raw = excel_to_string(filepath)
-        else:
-            logging.debug("Loading text file...")
-            with open(filepath, "r", encoding="utf-8") as f:
-                text_raw = f.read()
-        text = add_space(text_raw)
-        # text = block_split(text)
-        # documents += text
-        documents += [Document(text)]
-    logging.debug("Documents loaded.")
-    return documents
-
-
-def construct_index(
-        api_key,
-        file_src,
-        max_input_size=4096,
-        num_outputs=5,
-        max_chunk_overlap=20,
-        chunk_size_limit=600,
-        embedding_limit=None,
-        separator=" "
-):
-    from langchain.chat_models import ChatOpenAI
-    from llama_index import GPTSimpleVectorIndex, ServiceContext
-
-    os.environ["OPENAI_API_KEY"] = api_key
-    chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
-    embedding_limit = None if embedding_limit == 0 else embedding_limit
-    separator = " " if separator == "" else separator
-
-    llm_predictor = LLMPredictor(
-        llm=ChatOpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key)
-    )
-    prompt_helper = PromptHelper(max_input_size = max_input_size, num_output = num_outputs, max_chunk_overlap = max_chunk_overlap, embedding_limit=embedding_limit, chunk_size_limit=600, separator=separator)
-    index_name = get_index_name(file_src)
-    if os.path.exists(f"./index/{index_name}.json"):
-        logging.info("找到了缓存的索引文件，加载中……")
-        return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
-    else:
-        try:
-            documents = get_documents(file_src)
-            logging.info("构建索引中……")
-            with retrieve_proxy():
-                service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper, chunk_size_limit=chunk_size_limit)
-                index = GPTSimpleVectorIndex.from_documents(
-                    documents,  service_context=service_context
-                )
-            logging.debug("索引构建完成！")
-            os.makedirs("./index", exist_ok=True)
-            index.save_to_disk(f"./index/{index_name}.json")
-            logging.debug("索引已保存至本地!")
-            return index
-
-        except Exception as e:
-            logging.error("索引构建失败！", e)
-            print(e)
-            return None
-
-
-def add_space(text):
-    punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
-    for cn_punc, en_punc in punctuations.items():
-        text = text.replace(cn_punc, en_punc)
-    return text
diff --git "a/spaces/qingxu98/gpt-academic/crazy_functions/\350\276\205\345\212\251\345\212\237\350\203\275.py" "b/spaces/qingxu98/gpt-academic/crazy_functions/\350\276\205\345\212\251\345\212\237\350\203\275.py"
deleted file mode 100644
index 16854e087eec27f4990aaafcf1b2e9f8ee9e1e30..0000000000000000000000000000000000000000
--- "a/spaces/qingxu98/gpt-academic/crazy_functions/\350\276\205\345\212\251\345\212\237\350\203\275.py"
+++ /dev/null
@@ -1,42 +0,0 @@
-# encoding: utf-8
-# @Time   : 2023/4/19
-# @Author : Spike
-# @Descr   :
-from toolbox import update_ui, get_conf
-from toolbox import CatchException
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-
-
-@CatchException
-def 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    if txt:
-        show_say = txt
-        prompt = txt+'\n回答完问题后，再列出用户可能提出的三个问题。'
-    else:
-        prompt = history[-1]+"\n分析上述回答，再列出用户可能提出的三个问题。"
-        show_say = '分析上述回答，再列出用户可能提出的三个问题。'
-    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-        inputs=prompt,
-        inputs_show_user=show_say,
-        llm_kwargs=llm_kwargs,
-        chatbot=chatbot,
-        history=history,
-        sys_prompt=system_prompt
-    )
-    chatbot[-1] = (show_say, gpt_say)
-    history.extend([show_say, gpt_say])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-
-@CatchException
-def 清除缓存(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    chatbot.append(['清除本地缓存数据', '执行中. 删除数据'])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
-
-    import shutil, os
-    PATH_PRIVATE_UPLOAD, PATH_LOGGING = get_conf('PATH_PRIVATE_UPLOAD', 'PATH_LOGGING')
-    shutil.rmtree(PATH_LOGGING, ignore_errors=True)
-    shutil.rmtree(PATH_PRIVATE_UPLOAD, ignore_errors=True)
-
-    chatbot.append(['清除本地缓存数据', '执行完成'])
-    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
\ No newline at end of file
diff --git a/spaces/qinzhu/diy-girlfriend-online/transforms.py b/spaces/qinzhu/diy-girlfriend-online/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/qinzhu/diy-girlfriend-online/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Descargar Dkz Studio 092b Para Pes 6 18.md b/spaces/quidiaMuxgu/Expedit-SAM/Descargar Dkz Studio 092b Para Pes 6 18.md
deleted file mode 100644
index 8bcda266adbc0b2b1e250682fa31d82cdb07fbb2..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Descargar Dkz Studio 092b Para Pes 6 18.md	
+++ /dev/null
@@ -1,12 +0,0 @@
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Download [New Release] Crack 38 Dictionnaires Et Recueils De Correspondance 8.md b/spaces/quidiaMuxgu/Expedit-SAM/Download [New Release] Crack 38 Dictionnaires Et Recueils De Correspondance 8.md
deleted file mode 100644
index bcdc63cc40cb7a7cbe449a9e0304bd1f13e1e8dd..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Download [New Release] Crack 38 Dictionnaires Et Recueils De Correspondance 8.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Maxi Biewer Strapse 57.md b/spaces/quidiaMuxgu/Expedit-SAM/Maxi Biewer Strapse 57.md
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diff --git a/spaces/r3gm/Aesthetic_RVC_Inference_HF/go-applio.sh b/spaces/r3gm/Aesthetic_RVC_Inference_HF/go-applio.sh
deleted file mode 100644
index 46c9018ad21b5dd8d689ed8ca75d53c318f6ad98..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Aesthetic_RVC_Inference_HF/go-applio.sh
+++ /dev/null
@@ -1,56 +0,0 @@
-#!/bin/bash
-echo -e "\033]0;Applio - Start\007"
-source .venv/bin/activate
-menu() {
-  while true; do
-    clear
-echo " :::"
-echo " :::                       _ _ "
-echo " :::     /\               | (_) "
-echo " :::    /  \   _ __  _ __ | |_  ___ "
-echo " :::   / /\ \ | '_ \| '_ \| | |/ _ \ "
-echo " :::  / ____ \| |_) | |_) | | | (_) | "
-echo " ::: /_/    \_\ .__/| .__/|_|_|\___/ "
-echo " :::          | |   | | "
-echo " :::          |_|   |_| "
-echo " ::: "
-echo " ::: "
-    echo ""
-    echo "[1] Start Applio (Nvidia/AMD Support)"
-    echo "[2] Start Applio (Intel GPU/CPU)"
-    echo ""
-    echo "[3] Exit"
-    echo ""
-
-    read -p "Select an option: " choice
-    case $choice in
-    1)
-      clear
-      python infer-web.py --pycmd python --port 7897 --theme dark
-      read -p "Press Enter to continue..."
-      ;;
-   2)
-      clear
-      python -m sklearnex infer-web.py --pycmd python --port 7897 --theme dark
-      read -p "Press Enter to continue..."
-      ;;
-    3)
-      finish
-      ;;
-    *)
-      clear
-      echo "Invalid option. Please enter a number from 1 to 3."
-      echo ""
-      read -n 1 -s -r -p "Press 'Enter' to access the main menu..."
-      ;;
-    esac
-  done
-}
-
-finish() {
-  clear
-  echo "Exiting Applio..."
-  exit 0
-}
-
-menu
diff --git a/spaces/r3gm/Fast_Stable_diffusion_CPU/lcm_pipeline.py b/spaces/r3gm/Fast_Stable_diffusion_CPU/lcm_pipeline.py
deleted file mode 100644
index 4cf74d2e772108efebfb3db00130ed5781d8373c..0000000000000000000000000000000000000000
--- a/spaces/r3gm/Fast_Stable_diffusion_CPU/lcm_pipeline.py
+++ /dev/null
@@ -1,274 +0,0 @@
-import torch
-from diffusers import DiffusionPipeline, AutoencoderKL, UNet2DConditionModel
-from transformers import CLIPTokenizer, CLIPTextModel, CLIPImageProcessor
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.image_processor import VaeImageProcessor
-
-from typing import List, Optional, Tuple, Union, Dict, Any
-
-from diffusers import logging
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-class LatentConsistencyModelPipeline(DiffusionPipeline):
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: None,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True
-    ):
-        super().__init__()
-        
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        
-    
-    def _encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        prompt_embeds: None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per prompt
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-        """
-
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        if prompt_embeds is None:
-
-            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
-            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}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            prompt_embeds = self.text_encoder(
-                text_input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            prompt_embeds = prompt_embeds[0]
-
-        if self.text_encoder is not None:
-            prompt_embeds_dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            prompt_embeds_dtype = self.unet.dtype
-        else:
-            prompt_embeds_dtype = prompt_embeds.dtype
-
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # Don't need to get uncond prompt embedding because of LCM Guided Distillation
-        return prompt_embeds
-
-
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if latents is None:
-            latents = torch.randn(shape, dtype=dtype).to(device)
-        else:
-            latents = latents.to(device)
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-    
-    def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
-        """
-        see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
-        Args:
-        timesteps: torch.Tensor: generate embedding vectors at these timesteps
-        embedding_dim: int: dimension of the embeddings to generate
-        dtype: data type of the generated embeddings
-
-        Returns:
-        embedding vectors with shape `(len(timesteps), embedding_dim)`
-        """
-        assert len(w.shape) == 1
-        w = w * 1000.
-
-        half_dim = embedding_dim // 2
-        emb = torch.log(torch.tensor(10000.)) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
-        emb = w.to(dtype)[:, None] * emb[None, :]
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
-        if embedding_dim % 2 == 1:  # zero pad
-            emb = torch.nn.functional.pad(emb, (0, 1))
-        assert emb.shape == (w.shape[0], embedding_dim)
-        return emb
-    
-    
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]] = None,
-        height: Optional[int] = 768,
-        width: Optional[int] = 768,
-        guidance_scale: float = 7.5,
-        num_images_per_prompt: Optional[int] = 1,
-        latents: Optional[torch.FloatTensor] = None,
-        num_inference_steps: int = 4, 
-        lcm_origin_steps: int = 50,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
-        
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-        
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-        
-        device = self._execution_device
-        # do_classifier_free_guidance = guidance_scale > 0.0  # In LCM Implementation:  cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
-        
-        # 3. Encode input prompt
-        prompt_embeds = self._encode_prompt(
-            prompt,
-            device,
-            num_images_per_prompt,
-            prompt_embeds=prompt_embeds,
-        )
-        
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps)
-        timesteps = self.scheduler.timesteps
-        
-        # 5. Prepare latent variable
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            latents,
-        )
-        bs = batch_size * num_images_per_prompt
-        
-        # 6. Get Guidance Scale Embedding
-        w = torch.tensor(guidance_scale).repeat(bs)
-        w_embedding = self.get_w_embedding(w, embedding_dim=256).to(device=device, dtype=latents.dtype)
-        
-        # 7. LCM MultiStep Sampling Loop:
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                
-                ts = torch.full((bs,), t, device=device, dtype=torch.long)
-                latents = latents.to(prompt_embeds.dtype)
-                
-                # model prediction (v-prediction, eps, x)
-                model_pred = self.unet(
-                    latents,
-                    ts,
-                    timestep_cond=w_embedding,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs, 
-                    return_dict=False)[0]
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents, denoised = self.scheduler.step(model_pred, i, t, latents, return_dict=False)
-                
-                # # call the callback, if provided
-                # if i == len(timesteps) - 1:
-                progress_bar.update()
-            
-        denoised = denoised.to(prompt_embeds.dtype)
-        if not output_type == "latent":
-            image = self.vae.decode(denoised / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
-        else:
-            image = denoised
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-        
-        
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Ahmet Maranki Kitab PDF Bitkilerle Tedavi Edilen Hastalklar.md b/spaces/raedeXanto/academic-chatgpt-beta/Ahmet Maranki Kitab PDF Bitkilerle Tedavi Edilen Hastalklar.md
deleted file mode 100644
index 379226b87e54f342f5573aa3d0f8be26fc813a43..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Ahmet Maranki Kitab PDF Bitkilerle Tedavi Edilen Hastalklar.md	
+++ /dev/null
@@ -1,136 +0,0 @@
-<br />
-<h1>Ahmet Maranki Kitab PDF: A Guide to Healing Plants by Ahmet Maranki</h1>
- <p>If you are interested in learning about natural remedies and healing plants, you may want to check out <strong>ahmet maranki kitab pdf</strong>, a book by Ahmet Maranki, a Turkish herbalist and naturopath. In this book, you will find a wealth of information on how to use various plants for treating different health problems, from common colds and allergies to chronic diseases and infections. You will also learn about the history, culture, and science behind healing plants, as well as how to prepare and use them safely and effectively.</p>
- <p>In this article, we will give you an overview of ahmet maranki kitab pdf, its benefits, contents, and how you can download or read it online. We hope that this article will inspire you to explore the wonderful world of healing plants and discover their amazing potential for your health and wellness.</p>
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- <h2>The Benefits of Healing Plants</h2>
- <p>Healing plants are plants that have medicinal properties and can be used to prevent, treat, or cure various ailments. They have been used by humans for thousands of years, across different cultures and regions, as a natural and holistic way of healing. Some of the benefits of healing plants are:</p>
- <ul>
-<li>They are natural and organic, meaning they do not contain synthetic chemicals or additives that may harm your body or the environment.</li>
-<li>They are affordable and accessible, meaning you can easily find them in your local markets, gardens, or forests, or grow them yourself at home.</li>
-<li>They are versatile and adaptable, meaning you can use them in different forms and ways, such as teas, oils, creams, capsules, tinctures, syrups, poultices, compresses, inhalations, baths, etc.</li>
-<li>They are effective and safe, meaning they can address the root cause of your health problem, not just the symptoms, and they have fewer or milder side effects than conventional drugs.</li>
-</ul>
- <p>Of course, healing plants are not a magic bullet or a substitute for professional medical advice. You should always consult your doctor before using any healing plant, especially if you have a pre-existing condition, are pregnant or breastfeeding, or are taking any medication. You should also follow the dosage and instructions carefully, and be aware of any possible interactions or allergies.</p>
- <h2>The Contents of Ahmet Maranki Kitab PDF</h2>
- <p>Ahmet Maranki Kitab PDF is a comprehensive guide to healing plants by Ahmet Maranki, who is a renowned expert in herbal medicine and natural therapies. He has written several books and articles on this topic, as well as hosted TV shows and seminars. He has also founded a center for natural health education and research in Turkey.</p>
- <p>The book is divided into four parts:</p>
- <ol>
-<li>The first part introduces the concept and history of healing plants, as well as their classification, cultivation, harvesting, preservation, preparation, and administration.</li>
-<li>The second part provides detailed information on more than 300 healing plants, including their botanical names, common names, descriptions, habitats, properties, uses, dosages, contraindications, side effects, and recipes.</li>
-<li>The third part covers more than 100 common diseases and disorders that can be treated with healing plants,</li>
-<li>The fourth part offers some tips and advice on how to improve your overall health and wellness with healing plants.</li>
-</ol>
- <p>Here are some examples of healing plants and their uses that you can find in ahmet maranki kitab pdf:</p>
- <table>
-<tr><th>Plant</th><th>Use</th></tr>
-<tr><td>Lavender</td><td>Relieves stress, anxiety, insomnia, headaches,</td></tr>
-<tr><td>Mint</td><td>Eases digestion,</td></tr>
-<tr><td>Ginger</td><td>Fights nausea,</td></tr>
-<tr><td>Turmeric</td><td>Reduces inflammation,</td></tr>
-<tr><td>Garlic</td><td>Boosts immunity,</td></tr>
-<tr><td>Aloe vera</td><td>Soothes burns,</td></tr>
-<tr><td>Chamomile</td><td>Calm nerves,</td></tr>
-<tr><td>Echinacea</td><td>Prevents colds,</td></tr>
-<tr><td>Rosemary</td><td>Improves memory,</td></tr>
-<tr><td>Lemon balm</td><td>Lifts mood,</td></tr>
-</table>
- <p>You can apply the information in ahmet maranki kitab pdf to your own health and wellness by following these steps:</p>
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- <ul>
-<li>Identify your health problem or goal.</li>
-<li>Find the appropriate healing plant or combination of plants that can help you.</li>
-<li>Choose the best form and method of using the plant (e.g., tea,</li>
-<li>oil,</li>
-<li>cream,</li>
-<li>etc.).</li>
-<li>Follow the dosage and instructions carefully.</li>
-<li>Monitor your results and adjust accordingly.</li>
-<li>Enjoy the benefits of healing plants!</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Ahmet Maranki Kitab PDF is a valuable resource for anyone who wants to learn about natural remedies and healing plants. It offers a comprehensive overview of more than 300 healing plants,<strong></strong> their properties,<strong></strong> uses,<strong></strong> dosages,<strong></strong> contraindications,<strong></strong> side effects,<strong></strong> recipes,<strong></strong> etc.<strong></strong>, as well as more than 100 common diseases<em></em> that can be treated with them.<em></em>. It also provides some tips<em></em> on how to improve your overall health<em></em> with healing plants.<em></em>.</p>
- <p>If you are interested in downloading or reading ahmet maranki kitab pdf online,<strong></strong> you can visit <a href="https://www.scribd.com/document/27681735/Ahmet-Maranki-%C5%9Eifal%C4%B1-Bitkiler-Kitab%C4%B1">this link</a>, <a href="https://idoc.pub/documents/ahmet-maranki-ifal-bitkiler-kitab-k546x9ook748">this link</a>, or <a href="https://idoc.pub/download/ahmet-maranki-ifal-bitkiler-kitab-k546x9ook748">this link</a>. You may need to sign up for a free account or pay a small fee to access the full content.</p>
- <p>We hope that this article has given you some useful information on ahmet maranki kitab pdf<em>.</em>. We thank you for your attention<em>,</em>. Please feel free to share your feedback<em>,</em>. questions<em>,</em>. comments<em>,</em>. or suggestions<em>.</em>. with us<em>.</em>.</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <p>Here are some frequently asked questions (FAQs) related to ahmet maranki kitab pdf:</p>
- <h3>Q: Who is Ahmet Maranki?</h3>
- <h3>Q: What are some of the healing plants that are featured in ahmet maranki kitab pdf?</h3>
- <p>A: Some of the healing plants that are featured in ahmet maranki kitab pdf are lavender, mint, ginger, turmeric, garlic, aloe vera, chamomile, echinacea, rosemary, and lemon balm. These are just some examples of the more than 300 healing plants that are discussed in the book.</p>
- <h3>Q: How can I download or read ahmet maranki kitab pdf online?</h3>
- <p>A: You can download or read ahmet maranki kitab pdf online by visiting one of these links: <a href="https://www.scribd.com/document/27681735/Ahmet-Maranki-%C5%9Eifal%C4%B1-Bitkiler-Kitab%C4%B1">this link</a>, <a href="https://idoc.pub/documents/ahmet-maranki-ifal-bitkiler-kitab-k546x9ook748">this link</a>, or <a href="https://idoc.pub/download/ahmet-maranki-ifal-bitkiler-kitab-k546x9ook748">this link</a>. You may need to sign up for a free account or pay a small fee to access the full content.</p>
- <h3>Q: How can I use the information in ahmet maranki kitab pdf to improve my health and wellness?</h3>
- <p>A: You can use the information in ahmet maranki kitab pdf to improve your health and wellness by following these steps:</p>
- <ul>
-<li>Identify your health problem or goal.</li>
-<li>Find the appropriate healing plant or combination of plants that can help you.</li>
-<li>Choose the best form and method of using the plant (e.g., tea, oil, cream, etc.).</li>
-<li>Follow the dosage and instructions carefully.</li>
-<li>Monitor your results and adjust accordingly.</li>
-<li>Enjoy the benefits of healing plants!</li>
-</ul>
- <h3>Q: What are some of the precautions and side effects of using healing plants?</h3>
- <p>A: Some of the precautions and side effects of using healing plants are:</p>
- <ul>
-<li>You should always consult your doctor before using any healing plant, especially if you have a pre-existing condition, are pregnant or breastfeeding, or are taking any medication.</li>
-<li>You should also follow the dosage and instructions carefully, and be aware of any possible interactions or allergies.</li>
-<li>You should not use healing plants as a substitute for professional medical advice or treatment.</li>
-<li>You should stop using healing plants if you experience any adverse reactions or symptoms.</li>
-</ul>
- <h3>Q: Where can I find more information on healing plants and natural therapies?</h3>
- <p>A: You can find more information on healing plants and natural therapies by:</p>
- <ul>
-<li>Reading other books and articles by Ahmet Maranki or other experts in this field.</li>
-<li>Watching TV shows and videos on this topic.</li>
-<li>Attending seminars and workshops on this topic.</li>
-<li>Visiting websites and blogs on this topic.</li>
-<li>Joining online forums and communities on this topic.</li>
-</ul>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Alexander 2004 In Hindi Free NEW! Download.md b/spaces/raedeXanto/academic-chatgpt-beta/Alexander 2004 In Hindi Free NEW! Download.md
deleted file mode 100644
index ccf54400713fa9351ff944a15082f852dedfb15c..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Alexander 2004 In Hindi Free NEW! Download.md	
+++ /dev/null
@@ -1,19 +0,0 @@
-<br />
-<h1>How to Watch Alexander (2004) in Hindi for Free Online</h1>
-<p>Alexander (2004) is an epic historical drama film directed by Oliver Stone and starring Colin Farrell as Alexander the Great, the legendary king of Macedonia who conquered most of the known world in the 4th century BC. The film also features Angelina Jolie, Val Kilmer, Jared Leto, Anthony Hopkins, and other famous actors in supporting roles.</p>
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-<p>However, not everyone is impressed by Alexander (2004) as a movie. The film received mostly negative reviews from critics and audiences, who criticized its length, script, performances, historical accuracy, and portrayal of Alexander's sexuality. The film has a 16% rating on Rotten Tomatoes, based on 206 reviews, with an average score of 4.2/10. The website's critics consensus reads: "Even at nearly three hours long, this ponderous, talky, and emotionally distant biopic fails to illuminate Alexander's life."[^2^]</p>
-<p></p>
-<p>Some of the harsher comments include: "Alexander the person was great. This movie isn't."[^3^] "Farrell, a remarkable actor on most occasions, is fatally miscast. His Alexander is a petulant crybaby, and it is hard to imagine anyone - let alone armies and nations galore - bending themselves to his will."[^2^] "Stone has made a film that is not only dull but also historically dubious."[^2^] "Alexander is not a success, but it is ambitious and risky, and incapable of the inanities of Troy. Fascinated by his subject, he has things he urgently wants to say about Alexander, but his urgency outraces his narrative; he gives us provocative notes and sketches but not a final draft."[^1^]</p>
-<p>The film also performed poorly at the box office, grossing only $167 million worldwide against a budget of $155 million. It was nominated for six Golden Raspberry Awards, including Worst Picture, Worst Actor (Farrell), Worst Director (Stone), Worst Screenplay (Stone, Christopher Kyle, and Laeta Kalogridis), Worst Supporting Actress (Jolie), and Worst Screen Couple (Farrell and either Jolie or Leto). It did not win any of them.[^4^]</p> 81aa517590<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Callofdutyblackops2v1001trainer The Ultimate Cheat Tool for COD BO2 Fans.md b/spaces/raedeXanto/academic-chatgpt-beta/Callofdutyblackops2v1001trainer The Ultimate Cheat Tool for COD BO2 Fans.md
deleted file mode 100644
index 0e811ed5047636bbfa8454797ff65f7e5e5a0578..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Callofdutyblackops2v1001trainer The Ultimate Cheat Tool for COD BO2 Fans.md	
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-callofdutyblackops2v1001trainer questions and answers<br />
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-callofdutyblackops2v1001trainer latest news and updates</p>
-<ol>
-<li>Download callofdutyblackops2v1001trainer from a trusted source such as GameCopyWorld</li>
-<li>Extract the trainer file from the archive using a software such as 7-Zip</li>
-<li>Run the trainer file as an administrator</li>
-<li>Start Call of Duty: Black Ops 2 v1.0.0.1</li>
-<li>Press F1 at the main menu to activate the trainer</li>
-<li>Hear a voice saying "Trainer activated"</li>
-<li>Enjoy the trainer features by pressing the corresponding keys</li>
-</ol>
-<h2>How to activate and deactivate the trainer features</h2>
-<p>To activate and deactivate the trainer features, you need to press the following keys on your keyboard:</p>
- <table style="border-collapse: collapse; width: 50%;">
-<tbody>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;"><strong>Key</strong></td>
-<td style="width: 50%; height: 21px;"><strong>Feature</strong></td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;">Numpad 1</td>
-<td style="width: 50%; height: 21px;">Infinite health</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;">Numpad 2</td>
-<td style="width: 50%; height: 21px;">Infinite ammo</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;">Numpad 3</td>
-<td style="width: 50%; height: 21px;">Infinite grenades</td>
-</tr>
-<tr style="height: 21px;">
-<td style="width: 50%; height: 21px;">Numpad 4</td>
-<td style="width: 50%; height: 21px;">Infinite money</td>
-</tr>
- <tr style="height: 21px;">
-<td style="width: 50%; height: 21px;">Numpad 5</td>
-<td style="width: 50%; height: 21px;">Infinite sprint</td>
- <tr style="height:  21px;">
-<td style="width: 50%; height: 21px;">Numpad  6  </td><td  style=" width: 50%; height: 21px; ">Infinite  experience  </td></tr><tr  style=" height: 21px; "><td  style=" width: 50%; height: 21px; ">Numpad  7  </td><td  style=" width: 50%; height: 21px; ">Unlimited  jump  </td></tr><tr  style=" height: 21px; "><td  style=" width: 50%; height: 21px; ">Numpad  8  </td><td  style=" width: 50%; height: 21px; ">Long  jump  </td></tr><tr  style=" height: 21px; "><td  style=" width: 50%; height: 21px; ">Numpad  9  </td><td  style=" width: 50%; height: 21px; ">Long  dash  </td></tr><tr  style=" height: 21px; "><td  style=" width: 50%; height: 21px; ">F1  </td><td  style=" width: 50%; height: 21px; ">Speed  dash  </td></tr><tr  style=" height: 21px; "><td  style=" width: 50%; height: 21px; ">F2  </td><td  style=" width: 50%; height: 21px; ">One hit kills  </td></tr><tr  style=" height: 21px; "><td  style=" width: 50%; height: 21px; ">F3  </td><td  style=" width: 50%; height: 21px; ">Disable collisions  </td></tr></tbody></table>
- <p>You can also press F4 at any time to deactivate all features at once.</p>
- <p>Callofdutyblackops2v1001trainer does not work with multiplayer mode or online servers. If you try to use it in multiplayer mode or online servers, you may get banned or penalized by the game's anti-cheat system or online moderators. Therefore, you should only use the trainer in single-player mode or offline mode.</p>
-<h3>How to update callofdutyblackops2v1001trainer if the game patches?</h3>
-<p>Callofdutyblackops2v1001trainer is compatible with the v1.0.0.1 version of Call of Duty: Black Ops 2, which was released on November 13, 2012. If the game patches or updates to a newer version, the trainer may not work properly or cause crashes or errors. Therefore, you should check the trainer's website or source for any updates or new versions of the trainer that are compatible with the latest version of the game.</p>
-<h3>Where can I find more trainers for Call of Duty: Black Ops 2?</h3>
-<p>Callofdutyblackops2v1001trainer is one of the many trainers that are available for Call of Duty: Black Ops 2. You can find more trainers for Call of Duty: Black Ops 2 on websites such as GameCopyWorld, Gamepressure, or other sources that provide game patches, fixes, trainers, cheats, and more. However, be careful when downloading files from unknown sources as they may contain viruses or malware.</p>
-<h3>How can I contact the trainer developer or report a bug?</h3>
-<p>Callofdutyblackops2v1001trainer was created by GameCopyWorld, a website that provides game patches, fixes, trainers, cheats, and more. You can contact the trainer developer or report a bug by visiting GameCopyWorld's website and following their instructions or guidelines. You can also leave a comment or feedback on their website or forum.</p>
- </p> 0a6ba089eb<br />
-<br />
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\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Download the Official 2001 Hyundai Elantra Service Manual in PDF Format.md b/spaces/raedeXanto/academic-chatgpt-beta/Download the Official 2001 Hyundai Elantra Service Manual in PDF Format.md
deleted file mode 100644
index 7890468c3913b61d4f2c6dcfe573ae58a81d6000..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Download the Official 2001 Hyundai Elantra Service Manual in PDF Format.md	
+++ /dev/null
@@ -1,168 +0,0 @@
-<br />
-<br> H4: Motor Era <br> H4: All Car Manuals | | H3: Offline Sources | H4: Dealerships <br> H4: Libraries <br> H4: Bookstores | | H2: How to Download a 2001 Hyundai Elantra Service Manual? | | | H3: Choose a Reliable Source | | | H3: Select the Right Format | | | H3: Follow the Instructions | | | H2: How to Use a 2001 Hyundai Elantra Service Manual? | | | H3: Read the Manual Carefully | | | H3: Follow the Maintenance Schedule | | | H3: Perform the Repairs and Adjustments | | | H2: Conclusion | | **Table 2: Article with HTML formatting** <h1>How to Download a 2001 Hyundai Elantra Service Manual</h1>
- <p>If you own a 2001 Hyundai Elantra, you might be wondering how to download a service manual for your car. A service manual is a valuable resource that can help you maintain, repair, and troubleshoot your vehicle. In this article, we will explain what a service manual is, why you need one, what it contains, where to find one, how to download one, and how to use one. By the end of this article, you will be able to download a 2001 Hyundai Elantra service manual and keep your car in good shape.</p>
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- <h2>What is a Service Manual?</h2>
- <p>A service manual is a document that provides detailed information about a specific vehicle model. It covers all aspects of the vehicle, such as engine, transmission, suspension, brakes, steering, electrical systems, body, interior, and exterior. A service manual also includes diagrams, illustrations, photos, specifications, and instructions on how to perform various tasks on the vehicle.</p>
- <h3>Why Do You Need a Service Manual?</h3>
- <p>A service manual is essential for anyone who owns or drives a vehicle. It can help you:</p>
- <ul>
-<li>Learn more about your vehicle and its features</li>
-<li>Understand how your vehicle works and what components it has</li>
-<li>Prevent potential problems and malfunctions by following the recommended maintenance schedule</li>
-<li>Diagnose and fix common issues by using the troubleshooting guide</li>
-<li>Save money and time by doing some repairs and adjustments yourself</li>
-<li>Enhance your vehicle's performance and safety by following the best practices</li>
-<li>Prolong your vehicle's lifespan and resale value by keeping it in good condition</li>
-</ul>
- <h3>What Does a Service Manual Contain?</h3>
- <p>A service manual contains various sections that cover different topics related to your vehicle. Some of the common sections are:</p>
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- <ul>
-<li>General information: This section provides an overview of your vehicle and its history, as well as some general precautions and tips.</li>
-<li>Engine and transmission system: This section covers the operation, maintenance, repair, and replacement of the engine and transmission components.</li>
-<li>Electrical system: This section covers the wiring, fuses, relays, switches, sensors, lights, and other electrical components.</li>
-<li>Fuel system: This section covers the fuel pump, filter, injectors, lines, tank, and other fuel-related components.</li>
-<li>Cooling system: This section covers the radiator, hoses, thermostat, water pump, fan, and other cooling-related components.</li>
-<li>Exhaust system: This section covers the muffler, catalytic converter, pipes, and other exhaust-related components.</li>
-<li>Suspension system: This section covers the springs, shocks, struts, arms, bushings, bearings, and other suspension-related components.</li>
-<li>Brake system: This section covers the brake pads, rotors, calipers, lines, master cylinder, anti-lock brake system (ABS), and other brake-related components.</li>
-<li>Steering system: This section covers the steering wheel, column, rack, pinion, pump, fluid, and other steering-related components.</li>
-<li>Body system: This section covers the doors, hood, trunk, fenders, bumpers, grilles, mirrors, and other body-related components.</li>
-<li>Interior system: This section covers the seats, seat belts, airbags, dashboard, console, gauges, controls, and other interior-related components.</li>
-<li>Exterior system: This section covers the windshield, windows, wipers, washers, sunroof, antenna, and other exterior-related components.</li>
-<li>Air conditioning system: This section covers the compressor, condenser, evaporator, expansion valve, dryer, blower, heater core, and other air conditioning-related components.</li>
-</ul>
- <p>In addition to these sections, a service manual may also include: </p>
- <ul>
-<li>A table of contents and an index for easy navigation</li>
-<li>A glossary of terms and abbreviations for easy reference</li>
-<li>A list of tools and equipment needed for servicing your vehicle</li>
-<li>A list of specifications and data for your vehicle</li>
-<li>A list of warning signs and symbols for your safety</li>
-<li>A list of service intervals and maintenance schedules for your convenience</li>
-</ul>
- <h2>Where to Find a 2001 Hyundai Elantra Service Manual?</h2>
- <p>If you are looking for a 2001 Hyundai Elantra service manual, you have two options: online sources or offline sources. Let's take a look at each option in detail. </p>
- <h3>Online Sources</h3>
- <p>The internet is a great place to find a 2001 Hyundai Elantra service manual. There are many websites that offer service manuals for various vehicles, including Hyundai Elantra. Some of these websites are: </p>
- <h4>Factory Manuals</h4>
- <p>This website provides OEM (original equipment manufacturer) factory service manuals for Hyundai Elantra models from 1996 to 2016. These manuals are in PDF format and can be downloaded instantly after payment. The manuals are comprehensive, accurate, and easy to follow. They also include wiring diagrams, illustrations, and photos. You can visit this website at <a href="https://www.factory-manuals.com/repairmanual/hyundai-elantra-2000-2001-2002-2003-2004-2005-2006-repair-manual/">https://www.factory-manuals.com/repairmanual/hyundai-elantra-2000-2001-2002-2003-2004-2005-2006-repair-manual/</a>.</p>
- <h4>Motor Era</h4>
- <p>This website provides service repair manuals for various Hyundai models, including Elantra. These manuals are in PDF format and can be downloaded instantly after payment. The manuals are detailed, informative, and user-friendly. They also include diagrams, specifications, and instructions. You can visit this website at <a href="https://www.motorera.com/auto-service-repair-manual/Hyundai/Elantra/2001">https://www.motorera.com/auto-service-repair-manual/Hyundai/Elantra/2001</a>.</p>
- <h4>All Car Manuals</h4>
- <p>This website provides factory workshop manuals for various Hyundai models, including Elantra. These manuals are in ZIP format containing PDF files. They can be downloaded for free after registration. The manuals are complete, reliable, and clear. They also include diagrams, photos, and data. You can visit this website at <a href="https://www.allcarmanuals.com/factory-service-manual-259-Hyundai-Elantra-XD.html">https://www.allcarmanuals.com/factory-service-manual-259-Hyundai-Elantra-XD.html</a>.</p>
- <h3>Offline Sources</h3>
- <p>If you prefer to have a physical copy of a 2001 Hyundai Elantra service manual, you can also look for offline sources. Some of these sources are: </p>
- <h4>Dealerships</h4>
- <p>You can contact your local Hyundai dealership and ask if they have a service manual for your car model. They may have one in stock or they may be able to order one for you. However, this option may be expensive or unavailable depending on your location and availability.</p>
- <h4>Libraries</h4>
- such as year, model, and engine</li>
-<li>Selecting the service manual you want to download</li>
-<li>Adding the service manual to your cart and proceeding to checkout</li>
-<li>Paying for the service manual using your preferred method</li>
-<li>Receiving a confirmation email with a download link or a download code</li>
-<li>Clicking on the download link or entering the download code to start the download</li>
-<li>Waiting for the download to finish and checking the file for errors</li>
-</ul>
- <p>Once you have downloaded your service manual, you can save it on your device or print it out for future use.</p>
- <h2>How to Use a 2001 Hyundai Elantra Service Manual?</h2>
- <p>After you have downloaded your service manual, you can use it to service your vehicle. Here are some tips on how to use your service manual effectively: </p>
- <h3>Read the Manual Carefully</h3>
- <p>The first tip is to read the manual carefully and familiarize yourself with its contents. You should pay attention to: </p>
- <ul>
-<li>The table of contents and the index to find the sections you need</li>
-<li>The glossary of terms and abbreviations to understand the language used</li>
-<li>The list of tools and equipment to prepare the necessary items</li>
-<li>The list of specifications and data to know the correct values and measurements</li>
-<li>The list of warning signs and symbols to follow the safety precautions</li>
-<li>The list of service intervals and maintenance schedules to plan your service routine</li>
-</ul>
- <p>You should also read the general information section to learn more about your vehicle and its history.</p>
- <h3>Follow the Maintenance Schedule</h3>
- <p>The second tip is to follow the maintenance schedule provided by the manual. The maintenance schedule tells you when and how often you need to perform certain tasks on your vehicle, such as: </p>
- <ul>
-<li>Changing the oil and filter</li>
-<li>Checking and adjusting the tire pressure</li>
-<li>Replacing the spark plugs and wires</li>
-<li>Inspecting and cleaning the air filter</li>
-<li>Flushing and refilling the coolant</li>
-<li>Bleeding and replacing the brake fluid</li>
-<li>Lubricating and tightening the belts and hoses</li>
-<li>Testing and replacing the battery and alternator</li>
-<li>Cleaning and polishing the exterior and interior</li>
-</ul>
- <p>Following the maintenance schedule can help you prevent potential problems and malfunctions, as well as enhance your vehicle's performance and safety.</p>
- <h3>Perform the Repairs and Adjustments</h3>
- <p>The third tip is to perform the repairs and adjustments that are needed on your vehicle. The manual provides detailed instructions on how to perform various tasks on your vehicle, such as: </p>
- <ul>
-<li>Removing and installing components</li>
-<li>Disassembling and assembling parts</li>
-<li>Cleaning and inspecting parts</li>
-<li>Measuring and testing parts</li>
-<li>Adjusting and aligning parts</li>
-<li>Troubleshooting and diagnosing problems</li>
-<li>Solving and fixing problems</li>
-</ul>
- <p>The manual also provides diagrams, illustrations, photos, specifications, and instructions that can help you perform these tasks correctly and efficiently.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, a 2001 Hyundai Elantra service manual is a valuable resource that can help you maintain, repair, and troubleshoot your vehicle. You can download a service manual from various online or offline sources, but you need to choose a reliable source, select the right format, and follow the instructions. You can also use a service manual effectively by reading it carefully, following the maintenance schedule, and performing the repairs and adjustments. By doing so, you can keep your car in good shape and enjoy driving it for a long time.</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
- <p>Here are some frequently asked questions (FAQs) about 2001 Hyundai Elantra service manuals:</p>
- <ol>
-<li><b>How much does a 2001 Hyundai Elantra service manual cost?</b><br>The cost of a 2001 Hyundai Elantra service manual depends on the source you choose. Online sources usually charge between $10 to $30 for a downloadable PDF or ZIP file. Offline sources usually charge between $20 to $50 for a printed book or CD-ROM.</li>
-<li><b>How long does it take to download a 2001 Hyundai Elantra service manual?</b><br>The time it takes to download a 2001 Hyundai Elantra service manual depends on your internet speed and file size. Online sources usually offer instant downloads after payment. However, it may take a few minutes to an hour for larger files or slower connections.</li>
-<li><b>How can I edit a 2001 Hyundai Elantra service manual?</b><br>You can edit a 2001 Hyundai Elantra service manual by opening it on your device or software and selecting the edit option. You may need to have a compatible software that can open and modify PDF or ZIP files. You may also need to have some basic editing skills and knowledge.</li>
-<li><b>How can I share a 2001 Hyundai Elantra service manual?</b><br>You can share a 2001 Hyundai Elantra service manual by sending it as an attachment or a link to your email, social media, cloud storage, or other platforms. You may need to have permission from the source or the owner of the service manual before sharing it. You may also need to respect the copyright and privacy laws and policies of the service manual.</li>
-</ol>
- </p> 0a6ba089eb<br />
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\ No newline at end of file
diff --git a/spaces/rainbowemoji/etf-assistant/app.py b/spaces/rainbowemoji/etf-assistant/app.py
deleted file mode 100644
index c83f4fb3af8c78ce7d0fa0d940aa2ff7d5b4a7b0..0000000000000000000000000000000000000000
--- a/spaces/rainbowemoji/etf-assistant/app.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os
-import pinecone
-import openai
-
-from langchain.embeddings.openai import OpenAIEmbeddings
-# from langchain.text_splitter import CharacterTextSplitter
-# from langchain.document_loaders import PyPDFLoader
-from langchain.vectorstores import Pinecone
-from langchain.chains import ConversationalRetrievalChain
-from langchain.chat_models import ChatOpenAI
-import streamlit as st
-
-
-def _initialize_env():
-    pinecone.init(
-        api_key=st.secrets["pinecone_api_key"],
-        environment=st.secrets["pinecone_env"]
-    )
-    openai.api_key = st.secrets["openai_api_key"]
-    os.environ['OPENAI_API_KEY'] = st.secrets["openai_api_key"]
-
-
-def _initialize_indexes():
-    embeddings = OpenAIEmbeddings()
-
-    # loader = PyPDFLoader("./etf-book.pdf")
-    # documents = loader.load()
-    # text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    # docs = text_splitter.split_documents(documents)
-    # db = Pinecone.from_documents(
-    #     docs,
-    #     embeddings,
-    #     index_name=st.secrets["pinecone_table_name"]
-    # )
-
-    db_object = Pinecone.from_existing_index(
-        st.secrets["pinecone_table_name"],
-        embeddings
-    )
-    return db_object
-
-
-def _initialize_retriever(db_object):
-    retriever = db_object.as_retriever(search_type="similarity", search_kwargs={"k": 2})
-    qa = ConversationalRetrievalChain.from_llm(
-        ChatOpenAI(temperature=0),
-        retriever
-    )
-    return qa
-
-
-chat_history = []
-
-
-def answer(user_input):
-    if user_input == '':
-        return
-    global chat_history
-    result = qa({
-        "question": user_input,
-        "chat_history": chat_history
-    })
-    chat_history.append((user_input, result["answer"]))
-    chat_history = chat_history[-10:]
-    st.write(
-        "Bot: ",
-        result["answer"]
-    )
-
-
-_initialize_env()
-db = _initialize_indexes()
-qa = _initialize_retriever(db)
-
-question = st.text_input('Question')
-answer(question)
diff --git a/spaces/ramiin2/AutoGPT/tests/context.py b/spaces/ramiin2/AutoGPT/tests/context.py
deleted file mode 100644
index cef969db69ab189109b935bba9ed06696cf5337a..0000000000000000000000000000000000000000
--- a/spaces/ramiin2/AutoGPT/tests/context.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import os
-import sys
-
-sys.path.insert(
-    0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../scripts"))
-)
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Avenement Du Roi Sorcier Crack 2.01.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Avenement Du Roi Sorcier Crack 2.01.md
deleted file mode 100644
index df6d4104924f9a1dff780578168b0bc05455d154..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Avenement Du Roi Sorcier Crack 2.01.md	
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Contra O Tempo 1080p Download.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Contra O Tempo 1080p Download.md
deleted file mode 100644
index 8125b8ec178206e55da073d8782c64ea9ae655c4..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Contra O Tempo 1080p Download.md	
+++ /dev/null
@@ -1,7 +0,0 @@
-
-<p><strong>ficha tcnica</strong><br /> <strong>ttulo original</strong>: the audition<br /> <strong>temporada:</strong> 1<br /> <strong>episdios: 03 dub</strong><br /> <strong>ano de lanamento: </strong>2020<br /> <strong>idiomas:</strong> portugus, ingls<br /> <strong>legendas:</strong> portugus externa<br /> <strong>gnero:</strong> ao drama<br /> <b>qualidade: </b>web-dl<br /> <b>tamanho:</b> 3.08 gb, 9.15 gb<b><br /> </b><b>formato: </b>mkv<br /> <b>durao: </b>cerca de 50 min.<br /> <strong>imdb:</strong> 7,5/10<strong><br /> </strong><br /> <strong>sinopse:<br /> </strong>encontramos aos platinas a insisteo dl o carnaval do amor, que sera emprestado ao biblioteca do prapo u (munho) e que eventualmente sera incluido na guia (digs). a danha, entretanto, sabe que, alto lhe a temco, o conto devera ficar para um segundo disco de catro minutos. a determinao do presidente (lld.), a danha carrego uma canelada de plata como indulto aos persolllldos da guia (lld.). por creste, toda a obreta</p>
-<p>a formao dos estados nacionais dividiu em dois o curso do tempo na amrica latina. a repblica moderna procurou assentar seu programa de governo entre o desejo da ordem e a aspirao do progresso. todas as vezes que foi invocado, o progresso desatou foras poderosas. em torno dele se forjou um horizonte de expectativas em que o futuro era concebido como um tempo de redeno. a tal ponto, que o progresso diria bolvar echeverra chegou a se confundir com o tempo, se identificou com ele, o determinando e o redefinindo sua medida. simultaneamente, o passado foi associado..</p>
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diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Ford Travelpilot Nx Dvd Navigation Europe 2010.iso.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Ford Travelpilot Nx Dvd Navigation Europe 2010.iso.md
deleted file mode 100644
index 8bca3d35af286917628298db8b0baa0a5e2cd2cb..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/Ford Travelpilot Nx Dvd Navigation Europe 2010.iso.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-
-<p>the main advantage of sd media is that the software is automatically updated in the system without requiring manual intervention. storing the updates on a sd card, however, requires that it is plugged into the system all the time. if the sd card is removed during the update process, the updates cannot be installed. if this happens, you must download the updates again and install them. this also applies to updating the maps by using the navigation application. therefore, you should always carry the sd card with you when driving, so that you will not have to download the updates again when the unit is out of the vehicle. in other words, you should always prepare the sd card before you start driving because it is not the driver’s responsibility to keep the whole system updated. the sd card is not removable and the content of the sd card is not copied to a local hard disk. if you need more than 1gb on the sd card, you can mount additional sd cards. the sd card can be removed completely from the system after the update is finished, and is then not in the system anymore. thus, the system does not need to be shut down when the sd card is removed, and it can be used as is. this makes in-vehicle navigation systems particularly suitable for users who do not have an easy access to a network connection at the road.</p>
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diff --git a/spaces/renatotn7/teste2/app.py b/spaces/renatotn7/teste2/app.py
deleted file mode 100644
index 40d3c822f6dc9a557e124ddb9344018720c62cc2..0000000000000000000000000000000000000000
--- a/spaces/renatotn7/teste2/app.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import streamlit as st  
-import base64
-import os.path  
-import os
-import cv2  
-import random
-   
-os.system("ls")
-col1, col2 = st.columns(2)
-
-title = "Melhoria de imagens"
-st.title(title)
-os.system("ls")
-description = "Sistema para automação。"
-st.header(description)
-article = "<p style='text-align: center'><a href='https://huggingface.co/spaces/akhaliq/GFPGAN/' target='_blank'>clone from akhaliq@huggingface with little change</a> | <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>GFPGAN Github Repo</a></p><center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>"
-
-
-uploaded_file = st.file_uploader("Choose a file")
-if uploaded_file is not None:
-    # To read file as bytes:
-    bytes_data = uploaded_file.getvalue()
-    st.video(bytes_data)
-    os.system("rm -R /tmp/*")
-    os.system("mkdir /tmp/_input")
-    os.system("mkdir /tmp/_output")
-    os.system("mkdir /tmp/_outputf")
-    os.system("mkdir /tmp/videoSaida")
-    with open("/tmp/inputvideo", "wb") as binary_file:
-       binary_file.write(bytes_data)
-    vcap = cv2.VideoCapture('/tmp/inputvideo') # 0=camera
-    width=0
-    height=0
-    if vcap.isOpened(): 
-              # get vcap property 
-              width  = vcap.get(3)   # float `width`
-              height = vcap.get(4)  # float `height`
-              
-              st.write(str(width)+"x"+str(height))
-              fps = vcap.get(5)
-              st.write(str(fps)+" fps")
-@st.experimental_memo(suppress_st_warning=True)
-def chamada():
-          
-
-          percent_complete=0
-          my_bar = st.progress(0)   
-          percent_complete= percent_complete+ 10
-          with col1:
-           my_bar.progress(percent_complete )    
-       #exec=True 
-#           st.write("ffmpeg separando imagens")
-       #if not os.path.isfile("./_input/imagem-0001.png"):
-          vcap = cv2.VideoCapture('/tmp/inputvideo') # 0=camera
-          width=0
-          height=0
-          if vcap.isOpened(): 
-              # get vcap property 
-              width  = vcap.get(3)   # float `width`
-              height = vcap.get(4)  # float `height`
-              fps = vcap.get(5)
-
-        #  os.system("ffmpeg -i /tmp/inputvideo -vn -acodec copy /tmp/inputvideo.acc")
-          os.system("ffmpeg -i /tmp/inputvideo -compression_level 10 -pred mixed -pix_fmt rgb24 -sws_flags +accurate_rnd+full_chroma_int -s "+str(int(width/2))+"x"+str(int(height/2))+" -r   29.97 /tmp/_input/imagem-%4d.png")
-          percent_complete= percent_complete+ 30
-          with col1:
-           my_bar.progress(percent_complete )   
- #          st.write("testando imagem")
-          input_img = cv2.imread("/tmp/_input/imagem-0002.png" , cv2.IMREAD_COLOR)
-          input_img= cv2.cvtColor(input_img,cv2.COLOR_BGR2RGB) 
-          with col1:
-           st.image(input_img)
-          
-          os.system("ls /tmp/_input") 
-          if 'myVar' not in globals():
-              myVar=""
-  #        st.write("melhorando faces")
-          with col2:
-           with st.spinner('Wait for it...'):
-             # os.system("pip install git+https://github.com/TencentARC/GFPGAN.git")
-                    os.system("python3 inference_gfpgan.py -i /tmp/_input -o /tmp/_output -v 1.4 -s 2")
-          with col1:
-           percent_complete= percent_complete+ 30
-           my_bar.progress(percent_complete )   
-         
-          os.system("ls /tmp/_output")
-          os.system("echo ----")
-          os.system("ls /tmp/_output/cmp")
-          os.system("echo ----")
-          os.system("ls /tmp/_output/restored_imgs")
-          os.system("echo ----")
-#          s 1480x2560
-       #   with col1:
-   #        st.write("recompilando video")
-          #ffmpeg -r 60 -f image2 -s 1920x1080 -i _output/restored_imgs/imagem-%4d.png   -pix_fmt yuv420p ./videoSaida/output.mp4
-          os.system("ffmpeg -y -r 29.97 -f image2  -i /tmp/_output/restored_imgs/imagem-%4d.png   -pix_fmt yuv420p /tmp/videoSaida/output.mp4")
-         # os.system("ffmpeg -i /tmp/videoSaida/outputp1.mp4 -i /tmp/inputvideo.acc -c:v copy -c:a aac /tmp/output.mp4")
-          os.system("ls /tmp/videoSaida")
-          #st.video("./videoSaida/output.mp4" )
-          with col1:
-        #   st.write("preparando para download do video")
-           percent_complete= percent_complete+ 30
-           my_bar.progress(percent_complete ) 
-          with col2:
-           with open("/tmp/videoSaida/output.mp4", "rb") as file:
-              st.video(file)
-
-              btn = st.download_button(
-          
-                      label="Download video",
-          
-                      data=file,
-          
-                      file_name="output.mp4",
-          
-                      mime="video/mp4"
-          
-                    )
-          #st.download_button("download video", data, file_name='output.mp4', mime='video/mp4',)
-          #stremio
-          #input_img = cv2.imread("./_output/cmp/imagem-0001_0000.png" , cv2.IMREAD_COLOR)
-          #input_img = cv2.imread("./_output/cmp/imagem-0001_0000.png" , cv2.IMREAD_COLOR)
-          with col2:
-         #  st.write("demonstrando imagem restaurada")
-           input_img = cv2.imread("/tmp/_output/restored_imgs/imagem-0002.png" , cv2.IMREAD_COLOR)
-           input_img= cv2.cvtColor(input_img,cv2.COLOR_BGR2RGB) 
-          
-           st.image(input_img)
-          exec=False
-       #return Image.fromarray(restored_faces[0][:,:,::-1])
-load = st.checkbox("load")
-
-if load:
-       chamada()
-   
-#st.button('Imagem',on_click=inference)
-exec=True
-     
\ No newline at end of file
diff --git a/spaces/riccorl/relik-entity-linking/relik/retriever/data/utils.py b/spaces/riccorl/relik-entity-linking/relik/retriever/data/utils.py
deleted file mode 100644
index 928dfb833919ce2e30c9e90fed539c46f4bd3dec..0000000000000000000000000000000000000000
--- a/spaces/riccorl/relik-entity-linking/relik/retriever/data/utils.py
+++ /dev/null
@@ -1,176 +0,0 @@
-import json
-import os
-from collections import defaultdict
-from typing import Any, Dict, Iterable, List, Optional, Union
-
-import numpy as np
-import transformers as tr
-from tqdm import tqdm
-
-
-class HardNegativesManager:
-    def __init__(
-        self,
-        tokenizer: tr.PreTrainedTokenizer,
-        data: Union[List[Dict], os.PathLike, Dict[int, List]] = None,
-        max_length: int = 64,
-        batch_size: int = 1000,
-        lazy: bool = False,
-    ) -> None:
-        self._db: dict = None
-        self.tokenizer = tokenizer
-
-        if data is None:
-            self._db = {}
-        else:
-            if isinstance(data, Dict):
-                self._db = data
-            elif isinstance(data, os.PathLike):
-                with open(data) as f:
-                    self._db = json.load(f)
-            else:
-                raise ValueError(
-                    f"Data type {type(data)} not supported, only Dict and os.PathLike are supported."
-                )
-        # add the tokenizer to the class for future use
-        self.tokenizer = tokenizer
-
-        # invert the db to have a passage -> sample_idx mapping
-        self._passage_db = defaultdict(set)
-        for sample_idx, passages in self._db.items():
-            for passage in passages:
-                self._passage_db[passage].add(sample_idx)
-
-        self._passage_hard_negatives = {}
-        if not lazy:
-            # create a dictionary of passage -> hard_negative mapping
-            batch_size = min(batch_size, len(self._passage_db))
-            unique_passages = list(self._passage_db.keys())
-            for i in tqdm(
-                range(0, len(unique_passages), batch_size),
-                desc="Tokenizing Hard Negatives",
-            ):
-                batch = unique_passages[i : i + batch_size]
-                tokenized_passages = self.tokenizer(
-                    batch,
-                    max_length=max_length,
-                    truncation=True,
-                )
-                for i, passage in enumerate(batch):
-                    self._passage_hard_negatives[passage] = {
-                        k: tokenized_passages[k][i] for k in tokenized_passages.keys()
-                    }
-
-    def __len__(self) -> int:
-        return len(self._db)
-
-    def __getitem__(self, idx: int) -> Dict:
-        return self._db[idx]
-
-    def __iter__(self):
-        for sample in self._db:
-            yield sample
-
-    def __contains__(self, idx: int) -> bool:
-        return idx in self._db
-
-    def get(self, idx: int) -> List[str]:
-        """Get the hard negatives for a given sample index."""
-        if idx not in self._db:
-            raise ValueError(f"Sample index {idx} not in the database.")
-
-        passages = self._db[idx]
-
-        output = []
-        for passage in passages:
-            if passage not in self._passage_hard_negatives:
-                self._passage_hard_negatives[passage] = self._tokenize(passage)
-            output.append(self._passage_hard_negatives[passage])
-
-        return output
-
-    def _tokenize(self, passage: str) -> Dict:
-        return self.tokenizer(passage, max_length=self.max_length, truncation=True)
-
-
-class NegativeSampler:
-    def __init__(
-        self, num_elements: int, probabilities: Optional[Union[List, np.ndarray]] = None
-    ):
-        if not isinstance(probabilities, np.ndarray):
-            probabilities = np.array(probabilities)
-
-        if probabilities is None:
-            # probabilities should sum to 1
-            probabilities = np.random.random(num_elements)
-            probabilities /= np.sum(probabilities)
-        self.probabilities = probabilities
-
-    def __call__(
-        self,
-        sample_size: int,
-        num_samples: int = 1,
-        probabilities: np.array = None,
-        exclude: List[int] = None,
-    ) -> np.array:
-        """
-        Fast sampling of `sample_size` elements from `num_elements` elements.
-        The sampling is done by randomly shifting the probabilities and then
-        finding the smallest of the negative numbers. This is much faster than
-        sampling from a multinomial distribution.
-
-        Args:
-            sample_size (`int`):
-                number of elements to sample
-            num_samples (`int`, optional):
-                number of samples to draw. Defaults to 1.
-            probabilities (`np.array`, optional):
-                probabilities of each element. Defaults to None.
-            exclude (`List[int]`, optional):
-                indices of elements to exclude. Defaults to None.
-
-        Returns:
-            `np.array`: array of sampled indices
-        """
-        if probabilities is None:
-            probabilities = self.probabilities
-
-        if exclude is not None:
-            probabilities[exclude] = 0
-            # re-normalize?
-            # probabilities /= np.sum(probabilities)
-
-        # replicate probabilities as many times as `num_samples`
-        replicated_probabilities = np.tile(probabilities, (num_samples, 1))
-        # get random shifting numbers & scale them correctly
-        random_shifts = np.random.random(replicated_probabilities.shape)
-        random_shifts /= random_shifts.sum(axis=1)[:, np.newaxis]
-        # shift by numbers & find largest (by finding the smallest of the negative)
-        shifted_probabilities = random_shifts - replicated_probabilities
-        sampled_indices = np.argpartition(shifted_probabilities, sample_size, axis=1)[
-            :, :sample_size
-        ]
-        return sampled_indices
-
-
-def batch_generator(samples: Iterable[Any], batch_size: int) -> Iterable[Any]:
-    """
-    Generate batches from samples.
-
-    Args:
-        samples (`Iterable[Any]`): Iterable of samples.
-        batch_size (`int`): Batch size.
-
-    Returns:
-        `Iterable[Any]`: Iterable of batches.
-    """
-    batch = []
-    for sample in samples:
-        batch.append(sample)
-        if len(batch) == batch_size:
-            yield batch
-            batch = []
-
-    # leftover batch
-    if len(batch) > 0:
-        yield batch
diff --git a/spaces/rishi9440/remove-photo-background/src/utils.py b/spaces/rishi9440/remove-photo-background/src/utils.py
deleted file mode 100644
index 6fb2e1b6b9d6109cf578c7183a5770f33aae4663..0000000000000000000000000000000000000000
--- a/spaces/rishi9440/remove-photo-background/src/utils.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Credits to https://github.com/ZHKKKe/MODNet for the model.
-import streamlit as st
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-import os
-from PIL import Image, ImageColor
-from copy import deepcopy
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision.transforms as transforms
-
-from src.models.modnet import MODNet
-from src.st_style import apply_prod_style
-
-# apply(st)
-
-MODEL = "./assets/modnet_photographic_portrait_matting.ckpt"
-
-
-def change_background(image, matte, background_alpha: float=1.0, background_hex: str="#000000"):
-    """ 
-    image: PIL Image (RGBA)
-    matte: PIL Image (grayscale, if 255 it is foreground)
-    background_alpha: float
-    background_hex: string
-    """
-    img = deepcopy(image)
-    if image.mode != "RGBA":
-        img = img.convert("RGBA")
-        
-    background_color = ImageColor.getrgb(background_hex)
-    background_alpha = int(255 * background_alpha)
-    background = Image.new("RGBA", img.size, color=background_color + (background_alpha,))
-    background.paste(img, mask=matte)
-    return background
-
-
-def matte(image):
-    # define hyper-parameters
-    ref_size = 512
-
-    # define image to tensor transform
-    im_transform = transforms.Compose(
-        [
-            transforms.ToTensor(),
-            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-        ]
-    )
-
-    # create MODNet and load the pre-trained ckpt
-    modnet = MODNet(backbone_pretrained=False)
-    modnet = nn.DataParallel(modnet)
-
-    if torch.cuda.is_available():
-        modnet = modnet.cuda()
-        weights = torch.load(MODEL)
-    else:
-        weights = torch.load(MODEL, map_location=torch.device('cpu'))
-    modnet.load_state_dict(weights)
-    modnet.eval()
-
-    # read image
-    im = deepcopy(image)
-
-    # unify image channels to 3
-    im = np.asarray(im)
-    if len(im.shape) == 2:
-        im = im[:, :, None]
-    if im.shape[2] == 1:
-        im = np.repeat(im, 3, axis=2)
-    elif im.shape[2] == 4:
-        im = im[:, :, 0:3]
-
-    # convert image to PyTorch tensor
-    im = Image.fromarray(im)
-    im = im_transform(im)
-
-    # add mini-batch dim
-    im = im[None, :, :, :]
-
-    # resize image for input
-    im_b, im_c, im_h, im_w = im.shape
-    if max(im_h, im_w) < ref_size or min(im_h, im_w) > ref_size:
-        if im_w >= im_h:
-            im_rh = ref_size
-            im_rw = int(im_w / im_h * ref_size)
-        elif im_w < im_h:
-            im_rw = ref_size
-            im_rh = int(im_h / im_w * ref_size)
-    else:
-        im_rh = im_h
-        im_rw = im_w
-    
-    im_rw = im_rw - im_rw % 32
-    im_rh = im_rh - im_rh % 32
-    im = F.interpolate(im, size=(im_rh, im_rw), mode='area')
-
-    # inference
-    _, _, matte = modnet(im.cuda() if torch.cuda.is_available() else im, True)
-
-    # resize and save matte
-    matte = F.interpolate(matte, size=(im_h, im_w), mode='area')
-    matte = matte[0][0].data.cpu().numpy()
-    return Image.fromarray(((matte * 255).astype('uint8')), mode='L')
\ No newline at end of file
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/datasets/samplers/infinite_sampler.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/datasets/samplers/infinite_sampler.py
deleted file mode 100644
index d42487e6ac0c3e63cd8c4a0bb5ead9644b09a0ea..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/datasets/samplers/infinite_sampler.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import itertools
-
-import numpy as np
-import torch
-from mmcv.runner import get_dist_info
-from torch.utils.data.sampler import Sampler
-
-from mmdet.core.utils import sync_random_seed
-
-
-class InfiniteGroupBatchSampler(Sampler):
-    """Similar to `BatchSampler` warping a `GroupSampler. It is designed for
-    iteration-based runners like `IterBasedRunner` and yields a mini-batch
-    indices each time, all indices in a batch should be in the same group.
-
-    The implementation logic is referred to
-    https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/samplers/grouped_batch_sampler.py
-
-    Args:
-        dataset (object): The dataset.
-        batch_size (int): When model is :obj:`DistributedDataParallel`,
-            it is the number of training samples on each GPU.
-            When model is :obj:`DataParallel`, it is
-            `num_gpus * samples_per_gpu`.
-            Default : 1.
-        world_size (int, optional): Number of processes participating in
-            distributed training. Default: None.
-        rank (int, optional): Rank of current process. Default: None.
-        seed (int): Random seed. Default: 0.
-        shuffle (bool): Whether shuffle the indices of a dummy `epoch`, it
-            should be noted that `shuffle` can not guarantee that you can
-            generate sequential indices because it need to ensure
-            that all indices in a batch is in a group. Default: True.
-    """  # noqa: W605
-
-    def __init__(self,
-                 dataset,
-                 batch_size=1,
-                 world_size=None,
-                 rank=None,
-                 seed=0,
-                 shuffle=True):
-        _rank, _world_size = get_dist_info()
-        if world_size is None:
-            world_size = _world_size
-        if rank is None:
-            rank = _rank
-        self.rank = rank
-        self.world_size = world_size
-        self.dataset = dataset
-        self.batch_size = batch_size
-        # In distributed sampling, different ranks should sample
-        # non-overlapped data in the dataset. Therefore, this function
-        # is used to make sure that each rank shuffles the data indices
-        # in the same order based on the same seed. Then different ranks
-        # could use different indices to select non-overlapped data from the
-        # same data list.
-        self.seed = sync_random_seed(seed)
-        self.shuffle = shuffle
-
-        assert hasattr(self.dataset, 'flag')
-        self.flag = self.dataset.flag
-        self.group_sizes = np.bincount(self.flag)
-        # buffer used to save indices of each group
-        self.buffer_per_group = {k: [] for k in range(len(self.group_sizes))}
-
-        self.size = len(dataset)
-        self.indices = self._indices_of_rank()
-
-    def _infinite_indices(self):
-        """Infinitely yield a sequence of indices."""
-        g = torch.Generator()
-        g.manual_seed(self.seed)
-        while True:
-            if self.shuffle:
-                yield from torch.randperm(self.size, generator=g).tolist()
-
-            else:
-                yield from torch.arange(self.size).tolist()
-
-    def _indices_of_rank(self):
-        """Slice the infinite indices by rank."""
-        yield from itertools.islice(self._infinite_indices(), self.rank, None,
-                                    self.world_size)
-
-    def __iter__(self):
-        # once batch size is reached, yield the indices
-        for idx in self.indices:
-            flag = self.flag[idx]
-            group_buffer = self.buffer_per_group[flag]
-            group_buffer.append(idx)
-            if len(group_buffer) == self.batch_size:
-                yield group_buffer[:]
-                del group_buffer[:]
-
-    def __len__(self):
-        """Length of base dataset."""
-        return self.size
-
-    def set_epoch(self, epoch):
-        """Not supported in `IterationBased` runner."""
-        raise NotImplementedError
-
-
-class InfiniteBatchSampler(Sampler):
-    """Similar to `BatchSampler` warping a `DistributedSampler. It is designed
-    iteration-based runners like `IterBasedRunner` and yields a mini-batch
-    indices each time.
-
-    The implementation logic is referred to
-    https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/samplers/grouped_batch_sampler.py
-
-    Args:
-        dataset (object): The dataset.
-        batch_size (int): When model is :obj:`DistributedDataParallel`,
-            it is the number of training samples on each GPU,
-            When model is :obj:`DataParallel`, it is
-            `num_gpus * samples_per_gpu`.
-            Default : 1.
-        world_size (int, optional): Number of processes participating in
-            distributed training. Default: None.
-        rank (int, optional): Rank of current process. Default: None.
-        seed (int): Random seed. Default: 0.
-        shuffle (bool): Whether shuffle the dataset or not. Default: True.
-    """  # noqa: W605
-
-    def __init__(self,
-                 dataset,
-                 batch_size=1,
-                 world_size=None,
-                 rank=None,
-                 seed=0,
-                 shuffle=True):
-        _rank, _world_size = get_dist_info()
-        if world_size is None:
-            world_size = _world_size
-        if rank is None:
-            rank = _rank
-        self.rank = rank
-        self.world_size = world_size
-        self.dataset = dataset
-        self.batch_size = batch_size
-        # In distributed sampling, different ranks should sample
-        # non-overlapped data in the dataset. Therefore, this function
-        # is used to make sure that each rank shuffles the data indices
-        # in the same order based on the same seed. Then different ranks
-        # could use different indices to select non-overlapped data from the
-        # same data list.
-        self.seed = sync_random_seed(seed)
-        self.shuffle = shuffle
-        self.size = len(dataset)
-        self.indices = self._indices_of_rank()
-
-    def _infinite_indices(self):
-        """Infinitely yield a sequence of indices."""
-        g = torch.Generator()
-        g.manual_seed(self.seed)
-        while True:
-            if self.shuffle:
-                yield from torch.randperm(self.size, generator=g).tolist()
-
-            else:
-                yield from torch.arange(self.size).tolist()
-
-    def _indices_of_rank(self):
-        """Slice the infinite indices by rank."""
-        yield from itertools.islice(self._infinite_indices(), self.rank, None,
-                                    self.world_size)
-
-    def __iter__(self):
-        # once batch size is reached, yield the indices
-        batch_buffer = []
-        for idx in self.indices:
-            batch_buffer.append(idx)
-            if len(batch_buffer) == self.batch_size:
-                yield batch_buffer
-                batch_buffer = []
-
-    def __len__(self):
-        """Length of base dataset."""
-        return self.size
-
-    def set_epoch(self, epoch):
-        """Not supported in `IterationBased` runner."""
-        raise NotImplementedError
diff --git a/spaces/rorallitri/biomedical-language-models/logs/ABCD Any Body Can Dance Movie 720p Download Kickass.md b/spaces/rorallitri/biomedical-language-models/logs/ABCD Any Body Can Dance Movie 720p Download Kickass.md
deleted file mode 100644
index 1f7d24627fb7cc278f5f1c1c2ee1661f6fc7326f..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/ABCD Any Body Can Dance Movie 720p Download Kickass.md	
+++ /dev/null
@@ -1,38 +0,0 @@
-<h2>ABCD Any Body Can Dance Movie 720p Download Kickass</h2><br /><p><b><b>DOWNLOAD</b> &#9989; <a href="https://tinurll.com/2uzoAl">https://tinurll.com/2uzoAl</a></b></p><br /><br />
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-
-Not Found
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-
-2].mkv was not found on this server.
-
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-A:
-
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-
-See: 
-
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-
-Josip Bozanić (2 November 1886 – 1 June 1968) was a Croatian Orthodox priest, composer and linguist, born in Slunj, Kingdom of Croatia-Slavonia. He is regarded as one of the foremost Croatian composers, and as an early example of Croatian polyphonic music.
-
-Bozanić is known for his Christian hymns, and is also credited with the introduction of several musical techniques that later came to the forefront in Croatia, such as the introduction of the melodic cycle (16th century) and polyphonic composition (early 19th century). He also wrote music, which combined ethnic elements of the surrounding Slavic cultures (Kajkavian dialects, Serbian folk music, etc.). Bozanić was interested in the history of his native region and the popular literature of Croatia and Dalmatia, and he worked extensively as a translator from Italian, German and Slovene.
-
-Josip Bozanić died in Zagreb in 1968. His compositions include ten collections of works for the liturgy, choral music, and sacred songs, as well as many other works that he published in other genres, such as love music, classical and modern.
-
-Compositions 
-
-The following is a complete list of Bozanić's compositions, arranged chronologically by genre:
-
-Liturgical
-
-Psalms and Hymns for all the feasts of the year (published by the publishing house Đura Daničić)
-
-Psalms and Hymns (solo 4fefd39f24<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download HOT! Mocap Device Plug-in Crack.md b/spaces/rorallitri/biomedical-language-models/logs/Download HOT! Mocap Device Plug-in Crack.md
deleted file mode 100644
index 6a47a9dd831254fbdba1234b9d0cea2cb38c23e8..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download HOT! Mocap Device Plug-in Crack.md	
+++ /dev/null
@@ -1,50 +0,0 @@
-<br />
-<h1>Download Mocap Device Plug-in Crack: A Guide for Animation Lovers</h1>
-
-<p>If you are looking for a way to create realistic animations with your own body movements, you might want to try the Mocap Device Plug-in. This is a software that allows you to use the Kinect motion sensor to control virtual actors in real-time. You can use this plug-in with iClone, a 3D animation software that lets you create and edit animations with ease.</p>
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-
-<h2>What is a Mocap Device Plug-in Crack?</h2>
-
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-
-<p>However, downloading a mocap device plug-in crack is not as simple as it sounds. You need to follow some steps to install and activate the crack, such as:</p>
-
-<ul>
-<li>Download the crack file from the website.</li>
-<li>Extract the file using a password provided by the website.</li>
-<li>Copy and paste the file into the iClone installation folder.</li>
-<li>Run the file as administrator and follow the instructions.</li>
-<li>Restart your computer and launch iClone.</li>
-</ul>
-
-<p>If everything goes well, you should be able to use the Mocap Device Plug-in without any problems. However, there is no guarantee that the crack will work or that it will not cause any harm to your computer or your data.</p>
-
-<h2>What are the Risks of Downloading a Mocap Device Plug-in Crack?</h2>
-
-<p>Downloading a mocap device plug-in crack may seem like an easy and cheap way to enjoy the benefits of motion capture animation, but it comes with many risks and disadvantages. Some of them are:</p>
-<p></p>
-
-<ul>
-<li><strong>Legal issues:</strong> Downloading a mocap device plug-in crack is illegal and violates the intellectual property rights of Reallusion. You may face legal consequences if you are caught using or distributing a cracked version of the plug-in.</li>
-<li><strong>Virus infection:</strong> Downloading a mocap device plug-in crack from an unknown source may expose your computer to viruses, malware, or spyware. These malicious programs can damage your system, steal your personal information, or compromise your online security.</li>
-<li><strong>Poor performance:</strong> Downloading a mocap device plug-in crack may affect the performance and quality of your animations. The crack may not be compatible with your iClone version or your Kinect sensor. It may also cause errors, crashes, or glitches during your animation process.</li>
-<li><strong>No updates or support:</strong> Downloading a mocap device plug-in crack means that you will not receive any updates or support from Reallusion. You will miss out on new features, bug fixes, or improvements that Reallusion may release for the plug-in. You will also not be able to contact Reallusion for any technical issues or questions regarding the plug-in.</li>
-</ul>
-
-<h2>Conclusion: Download Mocap Device Plug-in Crack</h2>
-
-<p>Download mocap device plug-in crack is a risky and illegal way to use the Mocap Device Plug-in for iClone. It may seem like a good idea at first, but it can cause more harm than good in the long run. You may face legal troubles, virus infections, poor performance, or no updates or support from Reallusion.</p>
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-<p>If you want to enjoy the benefits of motion capture animation with your own body movements, you should consider purchasing the Mocap Device Plug-in from Reallusion's official website. This way, you will get a safe and reliable software that works well with iClone and Kinect. You will also get access to updates and support from Reallusion.</p>
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Fix Gen V1 1 Samsung Rapidshare.md b/spaces/rorallitri/biomedical-language-models/logs/Fix Gen V1 1 Samsung Rapidshare.md
deleted file mode 100644
index 8cafa7364e4a23b2c4027b6c4ee2be39069e2010..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Fix Gen V1 1 Samsung Rapidshare.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
-<p>6.1 samsung, in its sole discretion, reserves the right to modify the samsung tv app sdk at any time without any notice to you. such changes, including but not limited to the addition of new features, functionality, and capabilities to the samsung tv app sdk may be made in the future and are subject to change by samsung at any time without notice to you. by using the samsung tv app sdk, you agree to be bound by any changes to the samsung tv app sdk. samsung is not responsible for any damage or loss that you may incur, directly or indirectly, as a result of any such change or of your use of the samsung tv app sdk after such change has been made.<br><br> <br><br> 7.1 samsung does not provide warranties or representation, express or implied, as to the operation or performance of the samsung tv app sdk. samsung disclaims all implied warranties of merchantability, fitness for a particular purpose, accuracy of data, title, quiet enjoyment, and non-infringement. samsung does not warrant that the samsung tv app sdk will be compatible with all equipment or software, nor that the samsung tv app sdk will meet all of your requirements. samsung does not guarantee uninterrupted or error-free operation, and assumes no liability for any damages of any kind, whether direct, incidental, indirect, consequential or otherwise, arising out of your use of the samsung tv app sdk.<br><br> <br><br> 8.</p>
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diff --git a/spaces/ruboin/faster-whisper-webui/app.py b/spaces/ruboin/faster-whisper-webui/app.py
deleted file mode 100644
index b87cf044822410d851f0567497ca4adfbd324144..0000000000000000000000000000000000000000
--- a/spaces/ruboin/faster-whisper-webui/app.py
+++ /dev/null
@@ -1,544 +0,0 @@
-from datetime import datetime
-import math
-from typing import Iterator, Union
-import argparse
-
-from io import StringIO
-import os
-import pathlib
-import tempfile
-import zipfile
-import numpy as np
-
-import torch
-
-from src.config import ApplicationConfig, VadInitialPromptMode
-from src.hooks.progressListener import ProgressListener
-from src.hooks.subTaskProgressListener import SubTaskProgressListener
-from src.hooks.whisperProgressHook import create_progress_listener_handle
-from src.languages import get_language_names
-from src.modelCache import ModelCache
-from src.source import get_audio_source_collection
-from src.vadParallel import ParallelContext, ParallelTranscription
-
-# External programs
-import ffmpeg
-
-# UI
-import gradio as gr
-
-from src.download import ExceededMaximumDuration, download_url
-from src.utils import slugify, write_srt, write_vtt
-from src.vad import AbstractTranscription, NonSpeechStrategy, PeriodicTranscriptionConfig, TranscriptionConfig, VadPeriodicTranscription, VadSileroTranscription
-from src.whisper.abstractWhisperContainer import AbstractWhisperContainer
-from src.whisper.whisperFactory import create_whisper_container
-
-# Configure more application defaults in config.json5
-
-# Gradio seems to truncate files without keeping the extension, so we need to truncate the file prefix ourself 
-MAX_FILE_PREFIX_LENGTH = 17
-
-# Limit auto_parallel to a certain number of CPUs (specify vad_cpu_cores to get a higher number)
-MAX_AUTO_CPU_CORES = 8
-
-WHISPER_MODELS = ["tiny", "base", "small", "medium", "large", "large-v1", "large-v2"]
-
-class VadOptions:
-    def __init__(self, vad: str = None, vadMergeWindow: float = 5, vadMaxMergeSize: float = 150, vadPadding: float = 1, vadPromptWindow: float = 1, 
-                                        vadInitialPromptMode: Union[VadInitialPromptMode, str] = VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
-        self.vad = vad
-        self.vadMergeWindow = vadMergeWindow
-        self.vadMaxMergeSize = vadMaxMergeSize
-        self.vadPadding = vadPadding
-        self.vadPromptWindow = vadPromptWindow
-        self.vadInitialPromptMode = vadInitialPromptMode if isinstance(vadInitialPromptMode, VadInitialPromptMode) \
-                                        else VadInitialPromptMode.from_string(vadInitialPromptMode)
-
-class WhisperTranscriber:
-    def __init__(self, input_audio_max_duration: float = None, vad_process_timeout: float = None, 
-                 vad_cpu_cores: int = 1, delete_uploaded_files: bool = False, output_dir: str = None, 
-                 app_config: ApplicationConfig = None):
-        self.model_cache = ModelCache()
-        self.parallel_device_list = None
-        self.gpu_parallel_context = None
-        self.cpu_parallel_context = None
-        self.vad_process_timeout = vad_process_timeout
-        self.vad_cpu_cores = vad_cpu_cores
-
-        self.vad_model = None
-        self.inputAudioMaxDuration = input_audio_max_duration
-        self.deleteUploadedFiles = delete_uploaded_files
-        self.output_dir = output_dir
-
-        self.app_config = app_config
-
-    def set_parallel_devices(self, vad_parallel_devices: str):
-        self.parallel_device_list = [ device.strip() for device in vad_parallel_devices.split(",") ] if vad_parallel_devices else None
-
-    def set_auto_parallel(self, auto_parallel: bool):
-        if auto_parallel:
-            if torch.cuda.is_available():
-                self.parallel_device_list = [ str(gpu_id) for gpu_id in range(torch.cuda.device_count())]
-
-            self.vad_cpu_cores = min(os.cpu_count(), MAX_AUTO_CPU_CORES)
-            print("[Auto parallel] Using GPU devices " + str(self.parallel_device_list) + " and " + str(self.vad_cpu_cores) + " CPU cores for VAD/transcription.")
-
-    # Entry function for the simple tab
-    def transcribe_webui_simple(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, 
-                                progress=gr.Progress()):
-        
-        vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, self.app_config.vad_initial_prompt_mode)
-
-        return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vadOptions, progress=progress)
-
-    # Entry function for the full tab
-    def transcribe_webui_full(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                                    vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode, 
-                                    initial_prompt: str, temperature: float, best_of: int, beam_size: int, patience: float, length_penalty: float, suppress_tokens: str, 
-                                    condition_on_previous_text: bool, fp16: bool, temperature_increment_on_fallback: float, 
-                                    compression_ratio_threshold: float, logprob_threshold: float, no_speech_threshold: float, 
-                                    progress=gr.Progress()):
-
-        # Handle temperature_increment_on_fallback
-        if temperature_increment_on_fallback is not None:
-            temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback))
-        else:
-            temperature = [temperature]
-
-        vadOptions = VadOptions(vad, vadMergeWindow, vadMaxMergeSize, vadPadding, vadPromptWindow, vadInitialPromptMode)
-
-        return self.transcribe_webui(modelName, languageName, urlData, multipleFiles, microphoneData, task, vadOptions,
-                                     initial_prompt=initial_prompt, temperature=temperature, best_of=best_of, beam_size=beam_size, patience=patience, length_penalty=length_penalty, suppress_tokens=suppress_tokens,
-                                     condition_on_previous_text=condition_on_previous_text, fp16=fp16,
-                                     compression_ratio_threshold=compression_ratio_threshold, logprob_threshold=logprob_threshold, no_speech_threshold=no_speech_threshold, 
-                                     progress=progress)
-
-    def transcribe_webui(self, modelName, languageName, urlData, multipleFiles, microphoneData, task, 
-                         vadOptions: VadOptions, progress: gr.Progress = None, **decodeOptions: dict):
-        try:
-            sources = self.__get_source(urlData, multipleFiles, microphoneData)
-            
-            try:
-                selectedLanguage = languageName.lower() if len(languageName) > 0 else None
-                selectedModel = modelName if modelName is not None else "base"
-
-                model = create_whisper_container(whisper_implementation=self.app_config.whisper_implementation, 
-                                                 model_name=selectedModel, compute_type=self.app_config.compute_type, 
-                                                 cache=self.model_cache, models=self.app_config.models)
-
-                # Result
-                download = []
-                zip_file_lookup = {}
-                text = ""
-                vtt = ""
-
-                # Write result
-                downloadDirectory = tempfile.mkdtemp()
-                source_index = 0
-
-                outputDirectory = self.output_dir if self.output_dir is not None else downloadDirectory
-
-                # Progress
-                total_duration = sum([source.get_audio_duration() for source in sources])
-                current_progress = 0
-
-                # A listener that will report progress to Gradio
-                root_progress_listener = self._create_progress_listener(progress)
-
-                # Execute whisper
-                for source in sources:
-                    source_prefix = ""
-                    source_audio_duration = source.get_audio_duration()
-
-                    if (len(sources) > 1):
-                        # Prefix (minimum 2 digits)
-                        source_index += 1
-                        source_prefix = str(source_index).zfill(2) + "_"
-                        print("Transcribing ", source.source_path)
-
-                    scaled_progress_listener = SubTaskProgressListener(root_progress_listener, 
-                                                   base_task_total=total_duration,
-                                                   sub_task_start=current_progress,
-                                                   sub_task_total=source_audio_duration)
-
-                    # Transcribe
-                    result = self.transcribe_file(model, source.source_path, selectedLanguage, task, vadOptions, scaled_progress_listener, **decodeOptions)
-                    filePrefix = slugify(source_prefix + source.get_short_name(), allow_unicode=True)
-
-                    # Update progress
-                    current_progress += source_audio_duration
-
-                    source_download, source_text, source_vtt = self.write_result(result, filePrefix, outputDirectory)
-
-                    if len(sources) > 1:
-                        # Add new line separators
-                        if (len(source_text) > 0):
-                            source_text += os.linesep + os.linesep
-                        if (len(source_vtt) > 0):
-                            source_vtt += os.linesep + os.linesep
-
-                        # Append file name to source text too
-                        source_text = source.get_full_name() + ":" + os.linesep + source_text
-                        source_vtt = source.get_full_name() + ":" + os.linesep + source_vtt
-
-                    # Add to result
-                    download.extend(source_download)
-                    text += source_text
-                    vtt += source_vtt
-
-                    if (len(sources) > 1):
-                        # Zip files support at least 260 characters, but we'll play it safe and use 200
-                        zipFilePrefix = slugify(source_prefix + source.get_short_name(max_length=200), allow_unicode=True)
-
-                        # File names in ZIP file can be longer
-                        for source_download_file in source_download:
-                            # Get file postfix (after last -)
-                            filePostfix = os.path.basename(source_download_file).split("-")[-1]
-                            zip_file_name = zipFilePrefix + "-" + filePostfix
-                            zip_file_lookup[source_download_file] = zip_file_name
-
-                # Create zip file from all sources
-                if len(sources) > 1:
-                    downloadAllPath = os.path.join(downloadDirectory, "All_Output-" + datetime.now().strftime("%Y%m%d-%H%M%S") + ".zip")
-
-                    with zipfile.ZipFile(downloadAllPath, 'w', zipfile.ZIP_DEFLATED) as zip:
-                        for download_file in download:
-                            # Get file name from lookup
-                            zip_file_name = zip_file_lookup.get(download_file, os.path.basename(download_file))
-                            zip.write(download_file, arcname=zip_file_name)
-
-                    download.insert(0, downloadAllPath)
-
-                return download, text, vtt
-
-            finally:
-                # Cleanup source
-                if self.deleteUploadedFiles:
-                    for source in sources:
-                        print("Deleting source file " + source.source_path)
-
-                        try:
-                            os.remove(source.source_path)
-                        except Exception as e:
-                            # Ignore error - it's just a cleanup
-                            print("Error deleting source file " + source.source_path + ": " + str(e))
-        
-        except ExceededMaximumDuration as e:
-            return [], ("[ERROR]: Maximum remote video length is " + str(e.maxDuration) + "s, file was " + str(e.videoDuration) + "s"), "[ERROR]"
-
-    def transcribe_file(self, model: AbstractWhisperContainer, audio_path: str, language: str, task: str = None, 
-                        vadOptions: VadOptions = VadOptions(), 
-                        progressListener: ProgressListener = None, **decodeOptions: dict):
-        
-        initial_prompt = decodeOptions.pop('initial_prompt', None)
-
-        if progressListener is None:
-            # Default progress listener
-            progressListener = ProgressListener()
-
-        if ('task' in decodeOptions):
-            task = decodeOptions.pop('task')
-
-        # Callable for processing an audio file
-        whisperCallable = model.create_callback(language, task, initial_prompt, initial_prompt_mode=vadOptions.vadInitialPromptMode, **decodeOptions)
-
-        # The results
-        if (vadOptions.vad == 'silero-vad'):
-            # Silero VAD where non-speech gaps are transcribed
-            process_gaps = self._create_silero_config(NonSpeechStrategy.CREATE_SEGMENT, vadOptions)
-            result = self.process_vad(audio_path, whisperCallable, self.vad_model, process_gaps, progressListener=progressListener)
-        elif (vadOptions.vad == 'silero-vad-skip-gaps'):
-            # Silero VAD where non-speech gaps are simply ignored
-            skip_gaps = self._create_silero_config(NonSpeechStrategy.SKIP, vadOptions)
-            result = self.process_vad(audio_path, whisperCallable, self.vad_model, skip_gaps, progressListener=progressListener)
-        elif (vadOptions.vad == 'silero-vad-expand-into-gaps'):
-            # Use Silero VAD where speech-segments are expanded into non-speech gaps
-            expand_gaps = self._create_silero_config(NonSpeechStrategy.EXPAND_SEGMENT, vadOptions)
-            result = self.process_vad(audio_path, whisperCallable, self.vad_model, expand_gaps, progressListener=progressListener)
-        elif (vadOptions.vad == 'periodic-vad'):
-            # Very simple VAD - mark every 5 minutes as speech. This makes it less likely that Whisper enters an infinite loop, but
-            # it may create a break in the middle of a sentence, causing some artifacts.
-            periodic_vad = VadPeriodicTranscription()
-            period_config = PeriodicTranscriptionConfig(periodic_duration=vadOptions.vadMaxMergeSize, max_prompt_window=vadOptions.vadPromptWindow)
-            result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
-
-        else:
-            if (self._has_parallel_devices()):
-                # Use a simple period transcription instead, as we need to use the parallel context
-                periodic_vad = VadPeriodicTranscription()
-                period_config = PeriodicTranscriptionConfig(periodic_duration=math.inf, max_prompt_window=1)
-
-                result = self.process_vad(audio_path, whisperCallable, periodic_vad, period_config, progressListener=progressListener)
-            else:
-                # Default VAD
-                result = whisperCallable.invoke(audio_path, 0, None, None, progress_listener=progressListener)
-
-        return result
-
-    def _create_progress_listener(self, progress: gr.Progress):
-        if (progress is None):
-            # Dummy progress listener
-            return ProgressListener()
-        
-        class ForwardingProgressListener(ProgressListener):
-            def __init__(self, progress: gr.Progress):
-                self.progress = progress
-
-            def on_progress(self, current: Union[int, float], total: Union[int, float]):
-                # From 0 to 1
-                self.progress(current / total)
-
-            def on_finished(self):
-                self.progress(1)
-
-        return ForwardingProgressListener(progress)
-
-    def process_vad(self, audio_path, whisperCallable, vadModel: AbstractTranscription, vadConfig: TranscriptionConfig, 
-                    progressListener: ProgressListener = None):
-        if (not self._has_parallel_devices()):
-            # No parallel devices, so just run the VAD and Whisper in sequence
-            return vadModel.transcribe(audio_path, whisperCallable, vadConfig, progressListener=progressListener)
-
-        gpu_devices = self.parallel_device_list
-
-        if (gpu_devices is None or len(gpu_devices) == 0):
-            # No GPU devices specified, pass the current environment variable to the first GPU process. This may be NULL.
-            gpu_devices = [os.environ.get("CUDA_VISIBLE_DEVICES", None)]
-
-        # Create parallel context if needed
-        if (self.gpu_parallel_context is None):
-            # Create a context wih processes and automatically clear the pool after 1 hour of inactivity
-            self.gpu_parallel_context = ParallelContext(num_processes=len(gpu_devices), auto_cleanup_timeout_seconds=self.vad_process_timeout)
-        # We also need a CPU context for the VAD
-        if (self.cpu_parallel_context is None):
-            self.cpu_parallel_context = ParallelContext(num_processes=self.vad_cpu_cores, auto_cleanup_timeout_seconds=self.vad_process_timeout)
-
-        parallel_vad = ParallelTranscription()
-        return parallel_vad.transcribe_parallel(transcription=vadModel, audio=audio_path, whisperCallable=whisperCallable,  
-                                                config=vadConfig, cpu_device_count=self.vad_cpu_cores, gpu_devices=gpu_devices, 
-                                                cpu_parallel_context=self.cpu_parallel_context, gpu_parallel_context=self.gpu_parallel_context, 
-                                                progress_listener=progressListener) 
-
-    def _has_parallel_devices(self):
-        return (self.parallel_device_list is not None and len(self.parallel_device_list) > 0) or self.vad_cpu_cores > 1
-
-    def _concat_prompt(self, prompt1, prompt2):
-        if (prompt1 is None):
-            return prompt2
-        elif (prompt2 is None):
-            return prompt1
-        else:
-            return prompt1 + " " + prompt2
-
-    def _create_silero_config(self, non_speech_strategy: NonSpeechStrategy, vadOptions: VadOptions):
-        # Use Silero VAD 
-        if (self.vad_model is None):
-            self.vad_model = VadSileroTranscription()
-
-        config = TranscriptionConfig(non_speech_strategy = non_speech_strategy, 
-                max_silent_period=vadOptions.vadMergeWindow, max_merge_size=vadOptions.vadMaxMergeSize, 
-                segment_padding_left=vadOptions.vadPadding, segment_padding_right=vadOptions.vadPadding, 
-                max_prompt_window=vadOptions.vadPromptWindow)
-
-        return config
-
-    def write_result(self, result: dict, source_name: str, output_dir: str):
-        if not os.path.exists(output_dir):
-            os.makedirs(output_dir)
-
-        text = result["text"]
-        language = result["language"]
-        languageMaxLineWidth = self.__get_max_line_width(language)
-
-        print("Max line width " + str(languageMaxLineWidth))
-        vtt = self.__get_subs(result["segments"], "vtt", languageMaxLineWidth)
-        srt = self.__get_subs(result["segments"], "srt", languageMaxLineWidth)
-
-        output_files = []
-        output_files.append(self.__create_file(srt, output_dir, source_name + "-subs.srt"));
-        output_files.append(self.__create_file(vtt, output_dir, source_name + "-subs.vtt"));
-        output_files.append(self.__create_file(text, output_dir, source_name + "-transcript.txt"));
-
-        return output_files, text, vtt
-
-    def clear_cache(self):
-        self.model_cache.clear()
-        self.vad_model = None
-
-    def __get_source(self, urlData, multipleFiles, microphoneData):
-        return get_audio_source_collection(urlData, multipleFiles, microphoneData, self.inputAudioMaxDuration)
-
-    def __get_max_line_width(self, language: str) -> int:
-        if (language and language.lower() in ["japanese", "ja", "chinese", "zh"]):
-            # Chinese characters and kana are wider, so limit line length to 40 characters
-            return 40
-        else:
-            # TODO: Add more languages
-            # 80 latin characters should fit on a 1080p/720p screen
-            return 80
-
-    def __get_subs(self, segments: Iterator[dict], format: str, maxLineWidth: int) -> str:
-        segmentStream = StringIO()
-
-        if format == 'vtt':
-            write_vtt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-        elif format == 'srt':
-            write_srt(segments, file=segmentStream, maxLineWidth=maxLineWidth)
-        else:
-            raise Exception("Unknown format " + format)
-
-        segmentStream.seek(0)
-        return segmentStream.read()
-
-    def __create_file(self, text: str, directory: str, fileName: str) -> str:
-        # Write the text to a file
-        with open(os.path.join(directory, fileName), 'w+', encoding="utf-8") as file:
-            file.write(text)
-
-        return file.name
-
-    def close(self):
-        print("Closing parallel contexts")
-        self.clear_cache()
-
-        if (self.gpu_parallel_context is not None):
-            self.gpu_parallel_context.close()
-        if (self.cpu_parallel_context is not None):
-            self.cpu_parallel_context.close()
-
-
-def create_ui(app_config: ApplicationConfig):
-    ui = WhisperTranscriber(app_config.input_audio_max_duration, app_config.vad_process_timeout, app_config.vad_cpu_cores, 
-                            app_config.delete_uploaded_files, app_config.output_dir, app_config)
-
-    # Specify a list of devices to use for parallel processing
-    ui.set_parallel_devices(app_config.vad_parallel_devices)
-    ui.set_auto_parallel(app_config.auto_parallel)
-
-    is_whisper = False
-
-    if app_config.whisper_implementation == "whisper":
-        implementation_name = "Whisper"
-        is_whisper = True
-    elif app_config.whisper_implementation in ["faster-whisper", "faster_whisper"]:
-        implementation_name = "Faster Whisper"
-    else:
-        # Try to convert from camel-case to title-case
-        implementation_name = app_config.whisper_implementation.title().replace("_", " ").replace("-", " ")
-
-    ui_description = implementation_name + " is a general-purpose speech recognition model. It is trained on a large dataset of diverse " 
-    ui_description += " audio and is also a multi-task model that can perform multilingual speech recognition "
-    ui_description += " as well as speech translation and language identification. "
-
-    ui_description += "\n\n\n\nFor longer audio files (>10 minutes) not in English, it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option."
-
-    # Recommend faster-whisper
-    if is_whisper:
-        ui_description += "\n\n\n\nFor faster inference on GPU, try [faster-whisper](https://huggingface.co/spaces/aadnk/faster-whisper-webui)."
-
-    if app_config.input_audio_max_duration > 0:
-        ui_description += "\n\n" + "Max audio file length: " + str(app_config.input_audio_max_duration) + " s"
-
-    ui_article = "Read the [documentation here](https://gitlab.com/aadnk/whisper-webui/-/blob/main/docs/options.md)."
-
-    whisper_models = app_config.get_model_names()
-
-    simple_inputs = lambda : [
-        gr.Dropdown(choices=whisper_models, value=app_config.default_model_name, label="Model"),
-        gr.Dropdown(choices=sorted(get_language_names()), label="Language", value=app_config.language),
-        gr.Text(label="URL (YouTube, etc.)"),
-        gr.File(label="Upload Files", file_count="multiple"),
-        gr.Audio(source="microphone", type="filepath", label="Microphone Input"),
-        gr.Dropdown(choices=["transcribe", "translate"], label="Task", value=app_config.task),
-        gr.Dropdown(choices=["none", "silero-vad", "silero-vad-skip-gaps", "silero-vad-expand-into-gaps", "periodic-vad"], value=app_config.default_vad, label="VAD"),
-        gr.Number(label="VAD - Merge Window (s)", precision=0, value=app_config.vad_merge_window),
-        gr.Number(label="VAD - Max Merge Size (s)", precision=0, value=app_config.vad_max_merge_size),
-        gr.Number(label="VAD - Padding (s)", precision=None, value=app_config.vad_padding),
-        gr.Number(label="VAD - Prompt Window (s)", precision=None, value=app_config.vad_prompt_window),
-    ]
-
-    simple_transcribe = gr.Interface(fn=ui.transcribe_webui_simple, description=ui_description, article=ui_article, inputs=simple_inputs(), outputs=[
-        gr.File(label="Download"),
-        gr.Text(label="Transcription"), 
-        gr.Text(label="Segments")
-    ])
-
-    full_description = ui_description + "\n\n\n\n" + "Be careful when changing some of the options in the full interface - this can cause the model to crash."
-
-    full_transcribe = gr.Interface(fn=ui.transcribe_webui_full, description=full_description, article=ui_article, inputs=[
-        *simple_inputs(),
-        gr.Dropdown(choices=["prepend_first_segment", "prepend_all_segments"], value=app_config.vad_initial_prompt_mode, label="VAD - Initial Prompt Mode"),
-        gr.TextArea(label="Initial Prompt"),
-        gr.Number(label="Temperature", value=app_config.temperature),
-        gr.Number(label="Best Of - Non-zero temperature", value=app_config.best_of, precision=0),
-        gr.Number(label="Beam Size - Zero temperature", value=app_config.beam_size, precision=0),
-        gr.Number(label="Patience - Zero temperature", value=app_config.patience),
-        gr.Number(label="Length Penalty - Any temperature", value=app_config.length_penalty), 
-        gr.Text(label="Suppress Tokens - Comma-separated list of token IDs", value=app_config.suppress_tokens),
-        gr.Checkbox(label="Condition on previous text", value=app_config.condition_on_previous_text),
-        gr.Checkbox(label="FP16", value=app_config.fp16),
-        gr.Number(label="Temperature increment on fallback", value=app_config.temperature_increment_on_fallback),
-        gr.Number(label="Compression ratio threshold", value=app_config.compression_ratio_threshold),
-        gr.Number(label="Logprob threshold", value=app_config.logprob_threshold),
-        gr.Number(label="No speech threshold", value=app_config.no_speech_threshold)
-    ], outputs=[
-        gr.File(label="Download"),
-        gr.Text(label="Transcription"), 
-        gr.Text(label="Segments")
-    ])
-
-    demo = gr.TabbedInterface([simple_transcribe, full_transcribe], tab_names=["Simple", "Full"])
-
-    # Queue up the demo
-    if app_config.queue_concurrency_count is not None and app_config.queue_concurrency_count > 0:
-        demo.queue(concurrency_count=app_config.queue_concurrency_count)
-   
-    demo.launch(share=app_config.share, server_name=app_config.server_name, server_port=app_config.server_port)
-    
-    # Clean up
-    ui.close()
-
-if __name__ == '__main__':
-    default_app_config = ApplicationConfig.create_default()
-    whisper_models = default_app_config.get_model_names()
-
-    # Environment variable overrides
-    default_whisper_implementation = os.environ.get("WHISPER_IMPLEMENTATION", default_app_config.whisper_implementation)
-
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("--input_audio_max_duration", type=int, default=default_app_config.input_audio_max_duration, \
-                        help="Maximum audio file length in seconds, or -1 for no limit.") # 600
-    parser.add_argument("--share", type=bool, default=default_app_config.share, \
-                        help="True to share the app on HuggingFace.") # False
-    parser.add_argument("--server_name", type=str, default=default_app_config.server_name, \
-                        help="The host or IP to bind to. If None, bind to localhost.") # None
-    parser.add_argument("--server_port", type=int, default=default_app_config.server_port, \
-                        help="The port to bind to.") # 7860
-    parser.add_argument("--queue_concurrency_count", type=int, default=default_app_config.queue_concurrency_count, \
-                        help="The number of concurrent requests to process.") # 1
-    parser.add_argument("--default_model_name", type=str, choices=whisper_models, default=default_app_config.default_model_name, \
-                        help="The default model name.") # medium
-    parser.add_argument("--default_vad", type=str, default=default_app_config.default_vad, \
-                        help="The default VAD.") # silero-vad
-    parser.add_argument("--vad_initial_prompt_mode", type=str, default=default_app_config.vad_initial_prompt_mode, choices=["prepend_all_segments", "prepend_first_segment"], \
-                        help="Whether or not to prepend the initial prompt to each VAD segment (prepend_all_segments), or just the first segment (prepend_first_segment)") # prepend_first_segment
-    parser.add_argument("--vad_parallel_devices", type=str, default=default_app_config.vad_parallel_devices, \
-                        help="A commma delimited list of CUDA devices to use for parallel processing. If None, disable parallel processing.") # ""
-    parser.add_argument("--vad_cpu_cores", type=int, default=default_app_config.vad_cpu_cores, \
-                        help="The number of CPU cores to use for VAD pre-processing.") # 1
-    parser.add_argument("--vad_process_timeout", type=float, default=default_app_config.vad_process_timeout, \
-                        help="The number of seconds before inactivate processes are terminated. Use 0 to close processes immediately, or None for no timeout.") # 1800
-    parser.add_argument("--auto_parallel", type=bool, default=default_app_config.auto_parallel, \
-                        help="True to use all available GPUs and CPU cores for processing. Use vad_cpu_cores/vad_parallel_devices to specify the number of CPU cores/GPUs to use.") # False
-    parser.add_argument("--output_dir", "-o", type=str, default=default_app_config.output_dir, \
-                        help="directory to save the outputs")
-    parser.add_argument("--whisper_implementation", type=str, default=default_whisper_implementation, choices=["whisper", "faster-whisper"],\
-                        help="the Whisper implementation to use")
-    parser.add_argument("--compute_type", type=str, default=default_app_config.compute_type, choices=["default", "auto", "int8", "int8_float16", "int16", "float16", "float32"], \
-                        help="the compute type to use for inference")
-
-    args = parser.parse_args().__dict__
-
-    updated_config = default_app_config.update(**args)
-
-    create_ui(app_config=updated_config)
\ No newline at end of file
diff --git a/spaces/ruboin/faster-whisper-webui/src/whisper/abstractWhisperContainer.py b/spaces/ruboin/faster-whisper-webui/src/whisper/abstractWhisperContainer.py
deleted file mode 100644
index d14fb23d24256e3f1c12d8ae1db6ece891d49ec8..0000000000000000000000000000000000000000
--- a/spaces/ruboin/faster-whisper-webui/src/whisper/abstractWhisperContainer.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import abc
-from typing import List
-from src.config import ModelConfig, VadInitialPromptMode
-
-from src.hooks.progressListener import ProgressListener
-from src.modelCache import GLOBAL_MODEL_CACHE, ModelCache
-
-class AbstractWhisperCallback:
-    @abc.abstractmethod
-    def invoke(self, audio, segment_index: int, prompt: str, detected_language: str, progress_listener: ProgressListener = None):
-        """
-        Peform the transcription of the given audio file or data.
-
-        Parameters
-        ----------
-        audio: Union[str, np.ndarray, torch.Tensor]
-            The audio file to transcribe, or the audio data as a numpy array or torch tensor.
-        segment_index: int
-            The target language of the transcription. If not specified, the language will be inferred from the audio content.
-        task: str
-            The task - either translate or transcribe.
-        progress_listener: ProgressListener
-            A callback to receive progress updates.
-        """
-        raise NotImplementedError()
-
-    def _get_initial_prompt(self, initial_prompt: str, initial_prompt_mode: VadInitialPromptMode, 
-                               prompt: str, segment_index: int):
-        if (initial_prompt_mode == VadInitialPromptMode.PREPEND_ALL_SEGMENTS):
-            return self._concat_prompt(initial_prompt, prompt)
-        elif (initial_prompt_mode == VadInitialPromptMode.PREPREND_FIRST_SEGMENT):
-            return self._concat_prompt(initial_prompt, prompt) if segment_index == 0 else prompt
-        else:
-            raise ValueError(f"Unknown initial prompt mode {initial_prompt_mode}")
-
-    def _concat_prompt(self, prompt1, prompt2):
-        if (prompt1 is None):
-            return prompt2
-        elif (prompt2 is None):
-            return prompt1
-        else:
-            return prompt1 + " " + prompt2
-
-class AbstractWhisperContainer:
-    def __init__(self, model_name: str, device: str = None, compute_type: str = "float16",
-                 download_root: str = None,
-                 cache: ModelCache = None, models: List[ModelConfig] = []):
-        self.model_name = model_name
-        self.device = device
-        self.compute_type = compute_type
-        self.download_root = download_root
-        self.cache = cache
-
-        # Will be created on demand
-        self.model = None
-
-        # List of known models
-        self.models = models
-    
-    def get_model(self):
-        if self.model is None:
-
-            if (self.cache is None):
-                self.model = self._create_model()
-            else:
-                model_key = "WhisperContainer." + self.model_name + ":" + (self.device if self.device else '')
-                self.model = self.cache.get(model_key, self._create_model)
-        return self.model
-    
-    @abc.abstractmethod
-    def _create_model(self):
-        raise NotImplementedError()
-
-    def ensure_downloaded(self):
-        pass
-
-    @abc.abstractmethod
-    def create_callback(self, language: str = None, task: str = None, initial_prompt: str = None, 
-                        initial_prompt_mode: VadInitialPromptMode = VadInitialPromptMode.PREPREND_FIRST_SEGMENT, 
-                        **decodeOptions: dict) -> AbstractWhisperCallback:
-        """
-        Create a WhisperCallback object that can be used to transcript audio files.
-
-        Parameters
-        ----------
-        language: str
-            The target language of the transcription. If not specified, the language will be inferred from the audio content.
-        task: str
-            The task - either translate or transcribe.
-        initial_prompt: str
-            The initial prompt to use for the transcription.
-        initial_prompt_mode: VadInitialPromptMode
-            The mode to use for the initial prompt. If set to PREPEND_FIRST_SEGMENT, the initial prompt will be prepended to the first segment of audio.
-            If set to PREPEND_ALL_SEGMENTS, the initial prompt will be prepended to all segments of audio.
-        decodeOptions: dict
-            Additional options to pass to the decoder. Must be pickleable.
-
-        Returns
-        -------
-        A WhisperCallback object.
-        """
-        raise NotImplementedError()
-
-    # This is required for multiprocessing
-    def __getstate__(self):
-        return { 
-            "model_name": self.model_name, 
-            "device": self.device, 
-            "download_root": self.download_root, 
-            "models": self.models, 
-            "compute_type": self.compute_type 
-        }
-
-    def __setstate__(self, state):
-        self.model_name = state["model_name"]
-        self.device = state["device"]
-        self.download_root = state["download_root"]
-        self.models = state["models"]
-        self.compute_type = state["compute_type"]
-        self.model = None
-        # Depickled objects must use the global cache
-        self.cache = GLOBAL_MODEL_CACHE
\ No newline at end of file
diff --git a/spaces/safi842/FashionGen/models/stylegan/model.py b/spaces/safi842/FashionGen/models/stylegan/model.py
deleted file mode 100644
index a230961c4d1bf0bd2d1efe7972b4baa33c5d7013..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/models/stylegan/model.py
+++ /dev/null
@@ -1,456 +0,0 @@
-# Copyright 2020 Erik Härkönen. All rights reserved.
-# This file is licensed to you 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 REPRESENTATIONS
-# OF ANY KIND, either express or implied. See the License for the specific language
-# governing permissions and limitations under the License.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from collections import OrderedDict
-from pathlib import Path
-import requests
-import pickle
-import sys
-
-import numpy as np
-
-# Reimplementation of StyleGAN in PyTorch
-# Source: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
-
-class MyLinear(nn.Module):
-    """Linear layer with equalized learning rate and custom learning rate multiplier."""
-    def __init__(self, input_size, output_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True):
-        super().__init__()
-        he_std = gain * input_size**(-0.5) # He init
-        # Equalized learning rate and custom learning rate multiplier.
-        if use_wscale:
-            init_std = 1.0 / lrmul
-            self.w_mul = he_std * lrmul
-        else:
-            init_std = he_std / lrmul
-            self.w_mul = lrmul
-        self.weight = torch.nn.Parameter(torch.randn(output_size, input_size) * init_std)
-        if bias:
-            self.bias = torch.nn.Parameter(torch.zeros(output_size))
-            self.b_mul = lrmul
-        else:
-            self.bias = None
-
-    def forward(self, x):
-        bias = self.bias
-        if bias is not None:
-            bias = bias * self.b_mul
-        return F.linear(x, self.weight * self.w_mul, bias)
-
-class MyConv2d(nn.Module):
-    """Conv layer with equalized learning rate and custom learning rate multiplier."""
-    def __init__(self, input_channels, output_channels, kernel_size, gain=2**(0.5), use_wscale=False, lrmul=1, bias=True,
-                intermediate=None, upscale=False):
-        super().__init__()
-        if upscale:
-            self.upscale = Upscale2d()
-        else:
-            self.upscale = None
-        he_std = gain * (input_channels * kernel_size ** 2) ** (-0.5) # He init
-        self.kernel_size = kernel_size
-        if use_wscale:
-            init_std = 1.0 / lrmul
-            self.w_mul = he_std * lrmul
-        else:
-            init_std = he_std / lrmul
-            self.w_mul = lrmul
-        self.weight = torch.nn.Parameter(torch.randn(output_channels, input_channels, kernel_size, kernel_size) * init_std)
-        if bias:
-            self.bias = torch.nn.Parameter(torch.zeros(output_channels))
-            self.b_mul = lrmul
-        else:
-            self.bias = None
-        self.intermediate = intermediate
-
-    def forward(self, x):
-        bias = self.bias
-        if bias is not None:
-            bias = bias * self.b_mul
-        
-        have_convolution = False
-        if self.upscale is not None and min(x.shape[2:]) * 2 >= 128:
-            # this is the fused upscale + conv from StyleGAN, sadly this seems incompatible with the non-fused way
-            # this really needs to be cleaned up and go into the conv...
-            w = self.weight * self.w_mul
-            w = w.permute(1, 0, 2, 3)
-            # probably applying a conv on w would be more efficient. also this quadruples the weight (average)?!
-            w = F.pad(w, (1,1,1,1))
-            w = w[:, :, 1:, 1:]+ w[:, :, :-1, 1:] + w[:, :, 1:, :-1] + w[:, :, :-1, :-1]
-            x = F.conv_transpose2d(x, w, stride=2, padding=(w.size(-1)-1)//2)
-            have_convolution = True
-        elif self.upscale is not None:
-            x = self.upscale(x)
-    
-        if not have_convolution and self.intermediate is None:
-            return F.conv2d(x, self.weight * self.w_mul, bias, padding=self.kernel_size//2)
-        elif not have_convolution:
-            x = F.conv2d(x, self.weight * self.w_mul, None, padding=self.kernel_size//2)
-        
-        if self.intermediate is not None:
-            x = self.intermediate(x)
-        if bias is not None:
-            x = x + bias.view(1, -1, 1, 1)
-        return x
-
-class NoiseLayer(nn.Module):
-    """adds noise. noise is per pixel (constant over channels) with per-channel weight"""
-    def __init__(self, channels):
-        super().__init__()
-        self.weight = nn.Parameter(torch.zeros(channels))
-        self.noise = None
-    
-    def forward(self, x, noise=None):
-        if noise is None and self.noise is None:
-            noise = torch.randn(x.size(0), 1, x.size(2), x.size(3), device=x.device, dtype=x.dtype)
-        elif noise is None:
-            # here is a little trick: if you get all the noiselayers and set each
-            # modules .noise attribute, you can have pre-defined noise.
-            # Very useful for analysis
-            noise = self.noise
-        x = x + self.weight.view(1, -1, 1, 1) * noise
-        return x
-
-class StyleMod(nn.Module):
-    def __init__(self, latent_size, channels, use_wscale):
-        super(StyleMod, self).__init__()
-        self.lin = MyLinear(latent_size,
-                            channels * 2,
-                            gain=1.0, use_wscale=use_wscale)
-        
-    def forward(self, x, latent):
-        style = self.lin(latent) # style => [batch_size, n_channels*2]
-        shape = [-1, 2, x.size(1)] + (x.dim() - 2) * [1]
-        style = style.view(shape)  # [batch_size, 2, n_channels, ...]
-        x = x * (style[:, 0] + 1.) + style[:, 1]
-        return x
-
-class PixelNormLayer(nn.Module):
-    def __init__(self, epsilon=1e-8):
-        super().__init__()
-        self.epsilon = epsilon
-    def forward(self, x):
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + self.epsilon)
-
-class BlurLayer(nn.Module):
-    def __init__(self, kernel=[1, 2, 1], normalize=True, flip=False, stride=1):
-        super(BlurLayer, self).__init__()
-        kernel=[1, 2, 1]
-        kernel = torch.tensor(kernel, dtype=torch.float32)
-        kernel = kernel[:, None] * kernel[None, :]
-        kernel = kernel[None, None]
-        if normalize:
-            kernel = kernel / kernel.sum()
-        if flip:
-            kernel = kernel[:, :, ::-1, ::-1]
-        self.register_buffer('kernel', kernel)
-        self.stride = stride
-    
-    def forward(self, x):
-        # expand kernel channels
-        kernel = self.kernel.expand(x.size(1), -1, -1, -1)
-        x = F.conv2d(
-            x,
-            kernel,
-            stride=self.stride,
-            padding=int((self.kernel.size(2)-1)/2),
-            groups=x.size(1)
-        )
-        return x
-
-def upscale2d(x, factor=2, gain=1):
-    assert x.dim() == 4
-    if gain != 1:
-        x = x * gain
-    if factor != 1:
-        shape = x.shape
-        x = x.view(shape[0], shape[1], shape[2], 1, shape[3], 1).expand(-1, -1, -1, factor, -1, factor)
-        x = x.contiguous().view(shape[0], shape[1], factor * shape[2], factor * shape[3])
-    return x
-
-class Upscale2d(nn.Module):
-    def __init__(self, factor=2, gain=1):
-        super().__init__()
-        assert isinstance(factor, int) and factor >= 1
-        self.gain = gain
-        self.factor = factor
-    def forward(self, x):
-        return upscale2d(x, factor=self.factor, gain=self.gain)
-
-class G_mapping(nn.Sequential):
-    def __init__(self, nonlinearity='lrelu', use_wscale=True):
-        act, gain = {'relu': (torch.relu, np.sqrt(2)),
-                     'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
-        layers = [
-            ('pixel_norm', PixelNormLayer()),
-            ('dense0', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense0_act', act),
-            ('dense1', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense1_act', act),
-            ('dense2', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense2_act', act),
-            ('dense3', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense3_act', act),
-            ('dense4', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense4_act', act),
-            ('dense5', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense5_act', act),
-            ('dense6', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense6_act', act),
-            ('dense7', MyLinear(512, 512, gain=gain, lrmul=0.01, use_wscale=use_wscale)),
-            ('dense7_act', act)
-        ]
-        super().__init__(OrderedDict(layers))
-        
-    def forward(self, x):
-        return super().forward(x)
-
-class Truncation(nn.Module):
-    def __init__(self, avg_latent, max_layer=8, threshold=0.7):
-        super().__init__()
-        self.max_layer = max_layer
-        self.threshold = threshold
-        self.register_buffer('avg_latent', avg_latent)
-    def forward(self, x):
-        assert x.dim() == 3
-        interp = torch.lerp(self.avg_latent, x, self.threshold)
-        do_trunc = (torch.arange(x.size(1)) < self.max_layer).view(1, -1, 1)
-        return torch.where(do_trunc, interp, x)
-
-class LayerEpilogue(nn.Module):
-    """Things to do at the end of each layer."""
-    def __init__(self, channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
-        super().__init__()
-        layers = []
-        if use_noise:
-            layers.append(('noise', NoiseLayer(channels)))
-        layers.append(('activation', activation_layer))
-        if use_pixel_norm:
-            layers.append(('pixel_norm', PixelNorm()))
-        if use_instance_norm:
-            layers.append(('instance_norm', nn.InstanceNorm2d(channels)))
-        self.top_epi = nn.Sequential(OrderedDict(layers))
-        if use_styles:
-            self.style_mod = StyleMod(dlatent_size, channels, use_wscale=use_wscale)
-        else:
-            self.style_mod = None
-    def forward(self, x, dlatents_in_slice=None):
-        x = self.top_epi(x)
-        if self.style_mod is not None:
-            x = self.style_mod(x, dlatents_in_slice)
-        else:
-            assert dlatents_in_slice is None
-        return x
-
-
-class InputBlock(nn.Module):
-    def __init__(self, nf, dlatent_size, const_input_layer, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
-        super().__init__()
-        self.const_input_layer = const_input_layer
-        self.nf = nf
-        if self.const_input_layer:
-            # called 'const' in tf
-            self.const = nn.Parameter(torch.ones(1, nf, 4, 4))
-            self.bias = nn.Parameter(torch.ones(nf))
-        else:
-            self.dense = MyLinear(dlatent_size, nf*16, gain=gain/4, use_wscale=use_wscale) # tweak gain to match the official implementation of Progressing GAN
-        self.epi1 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-        self.conv = MyConv2d(nf, nf, 3, gain=gain, use_wscale=use_wscale)
-        self.epi2 = LayerEpilogue(nf, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-        
-    def forward(self, dlatents_in_range):
-        batch_size = dlatents_in_range.size(0)
-        if self.const_input_layer:
-            x = self.const.expand(batch_size, -1, -1, -1)
-            x = x + self.bias.view(1, -1, 1, 1)
-        else:
-            x = self.dense(dlatents_in_range[:, 0]).view(batch_size, self.nf, 4, 4)
-        x = self.epi1(x, dlatents_in_range[:, 0])
-        x = self.conv(x)
-        x = self.epi2(x, dlatents_in_range[:, 1])
-        return x
-
-
-class GSynthesisBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer):
-        # 2**res x 2**res # res = 3..resolution_log2
-        super().__init__()
-        if blur_filter:
-            blur = BlurLayer(blur_filter)
-        else:
-            blur = None
-        self.conv0_up = MyConv2d(in_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale,
-                                 intermediate=blur, upscale=True)
-        self.epi1 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-        self.conv1 = MyConv2d(out_channels, out_channels, kernel_size=3, gain=gain, use_wscale=use_wscale)
-        self.epi2 = LayerEpilogue(out_channels, dlatent_size, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, activation_layer)
-            
-    def forward(self, x, dlatents_in_range):
-        x = self.conv0_up(x)
-        x = self.epi1(x, dlatents_in_range[:, 0])
-        x = self.conv1(x)
-        x = self.epi2(x, dlatents_in_range[:, 1])
-        return x
-
-class G_synthesis(nn.Module):
-    def __init__(self,
-            dlatent_size        = 512,          # Disentangled latent (W) dimensionality.
-            num_channels        = 3,            # Number of output color channels.
-            resolution          = 1024,         # Output resolution.
-            fmap_base           = 8192,         # Overall multiplier for the number of feature maps.
-            fmap_decay          = 1.0,          # log2 feature map reduction when doubling the resolution.
-            fmap_max            = 512,          # Maximum number of feature maps in any layer.
-            use_styles          = True,         # Enable style inputs?
-            const_input_layer   = True,         # First layer is a learned constant?
-            use_noise           = True,         # Enable noise inputs?
-            randomize_noise     = True,         # True = randomize noise inputs every time (non-deterministic), False = read noise inputs from variables.
-            nonlinearity        = 'lrelu',      # Activation function: 'relu', 'lrelu'
-            use_wscale          = True,         # Enable equalized learning rate?
-            use_pixel_norm      = False,        # Enable pixelwise feature vector normalization?
-            use_instance_norm   = True,         # Enable instance normalization?
-            dtype               = torch.float32,  # Data type to use for activations and outputs.
-            blur_filter         = [1,2,1],      # Low-pass filter to apply when resampling activations. None = no filtering.
-        ):
-        
-        super().__init__()
-        def nf(stage):
-            return min(int(fmap_base / (2.0 ** (stage * fmap_decay))), fmap_max)
-        self.dlatent_size = dlatent_size
-        resolution_log2 = int(np.log2(resolution))
-        assert resolution == 2**resolution_log2 and resolution >= 4
-
-        act, gain = {'relu': (torch.relu, np.sqrt(2)),
-                     'lrelu': (nn.LeakyReLU(negative_slope=0.2), np.sqrt(2))}[nonlinearity]
-        num_layers = resolution_log2 * 2 - 2
-        num_styles = num_layers if use_styles else 1
-        torgbs = []
-        blocks = []
-        for res in range(2, resolution_log2 + 1):
-            channels = nf(res-1)
-            name = '{s}x{s}'.format(s=2**res)
-            if res == 2:
-                blocks.append((name,
-                               InputBlock(channels, dlatent_size, const_input_layer, gain, use_wscale,
-                                      use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
-                
-            else:
-                blocks.append((name,
-                               GSynthesisBlock(last_channels, channels, blur_filter, dlatent_size, gain, use_wscale, use_noise, use_pixel_norm, use_instance_norm, use_styles, act)))
-            last_channels = channels
-        self.torgb = MyConv2d(channels, num_channels, 1, gain=1, use_wscale=use_wscale)
-        self.blocks = nn.ModuleDict(OrderedDict(blocks))
-        
-    def forward(self, dlatents_in):
-        # Input: Disentangled latents (W) [minibatch, num_layers, dlatent_size].
-        # lod_in = tf.cast(tf.get_variable('lod', initializer=np.float32(0), trainable=False), dtype)
-        batch_size = dlatents_in.size(0)       
-        for i, m in enumerate(self.blocks.values()):
-            if i == 0:
-                x = m(dlatents_in[:, 2*i:2*i+2])
-            else:
-                x = m(x, dlatents_in[:, 2*i:2*i+2])
-        rgb = self.torgb(x)
-        return rgb
-
-
-class StyleGAN_G(nn.Sequential):
-    def __init__(self, resolution, truncation=1.0):
-        self.resolution = resolution
-        self.layers = OrderedDict([
-            ('g_mapping', G_mapping()),
-            #('truncation', Truncation(avg_latent)),
-            ('g_synthesis', G_synthesis(resolution=resolution)),
-        ])
-        super().__init__(self.layers)
-
-    def forward(self, x, latent_is_w=False):
-        if isinstance(x, list):
-            assert len(x) == 18, 'Must provide 1 or 18 latents'
-            if not latent_is_w:
-                x = [self.layers['g_mapping'].forward(l) for l in x]
-            x = torch.stack(x, dim=1)
-        else:
-            if not latent_is_w:
-                x = self.layers['g_mapping'].forward(x)
-            x = x.unsqueeze(1).expand(-1, 18, -1)
-
-        x = self.layers['g_synthesis'].forward(x)
-
-        return x
-
-    # From: https://github.com/lernapparat/lernapparat/releases/download/v2019-02-01/
-    def load_weights(self, checkpoint):    
-        self.load_state_dict(torch.load(checkpoint))
-
-    def export_from_tf(self, pickle_path):
-        module_path = Path(__file__).parent / 'stylegan_tf'
-        sys.path.append(str(module_path.resolve()))
-
-        import dnnlib, dnnlib.tflib, pickle, torch, collections
-        dnnlib.tflib.init_tf()
-
-        weights = pickle.load(open(pickle_path,'rb'))
-        weights_pt = [collections.OrderedDict([(k, torch.from_numpy(v.value().eval())) for k,v in w.trainables.items()]) for w in weights]
-        #torch.save(weights_pt, pytorch_name)
-
-        # then on the PyTorch side run
-        state_G, state_D, state_Gs = weights_pt #torch.load('./karras2019stylegan-ffhq-1024x1024.pt')
-        def key_translate(k):
-            k = k.lower().split('/')
-            if k[0] == 'g_synthesis':
-                if not k[1].startswith('torgb'):
-                    k.insert(1, 'blocks')
-                k = '.'.join(k)
-                k = (k.replace('const.const','const').replace('const.bias','bias').replace('const.stylemod','epi1.style_mod.lin')
-                    .replace('const.noise.weight','epi1.top_epi.noise.weight')
-                    .replace('conv.noise.weight','epi2.top_epi.noise.weight')
-                    .replace('conv.stylemod','epi2.style_mod.lin')
-                    .replace('conv0_up.noise.weight', 'epi1.top_epi.noise.weight')
-                    .replace('conv0_up.stylemod','epi1.style_mod.lin')
-                    .replace('conv1.noise.weight', 'epi2.top_epi.noise.weight')
-                    .replace('conv1.stylemod','epi2.style_mod.lin')
-                    .replace('torgb_lod0','torgb'))
-            else:
-                k = '.'.join(k)
-            return k
-
-        def weight_translate(k, w):
-            k = key_translate(k)
-            if k.endswith('.weight'):
-                if w.dim() == 2:
-                    w = w.t()
-                elif w.dim() == 1:
-                    pass
-                else:
-                    assert w.dim() == 4
-                    w = w.permute(3, 2, 0, 1)
-            return w
-
-        # we delete the useless torgb filters
-        param_dict = {key_translate(k) : weight_translate(k, v) for k,v in state_Gs.items() if 'torgb_lod' not in key_translate(k)}
-        if 1:
-            sd_shapes = {k : v.shape for k,v in self.state_dict().items()}
-            param_shapes = {k : v.shape for k,v in param_dict.items() }
-
-            for k in list(sd_shapes)+list(param_shapes):
-                pds = param_shapes.get(k)
-                sds = sd_shapes.get(k)
-                if pds is None:
-                    print ("sd only", k, sds)
-                elif sds is None:
-                    print ("pd only", k, pds)
-                elif sds != pds:
-                    print ("mismatch!", k, pds, sds)
-
-        self.load_state_dict(param_dict, strict=False) # needed for the blur kernels
-        torch.save(self.state_dict(), Path(pickle_path).with_suffix('.pt'))
\ No newline at end of file
diff --git a/spaces/sarinam/speaker-anonymization-gan/IMSToucan/Layers/VariancePredictor.py b/spaces/sarinam/speaker-anonymization-gan/IMSToucan/Layers/VariancePredictor.py
deleted file mode 100644
index cb4ccf522acb3e7cf36b1af59fe87d4fc50725a5..0000000000000000000000000000000000000000
--- a/spaces/sarinam/speaker-anonymization-gan/IMSToucan/Layers/VariancePredictor.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright 2019 Tomoki Hayashi
-# MIT License (https://opensource.org/licenses/MIT)
-# Adapted by Florian Lux 2021
-
-from abc import ABC
-
-import torch
-
-from .LayerNorm import LayerNorm
-
-
-class VariancePredictor(torch.nn.Module, ABC):
-    """
-    Variance predictor module.
-
-    This is a module of variance predictor described in `FastSpeech 2:
-    Fast and High-Quality End-to-End Text to Speech`_.
-
-    .. _`FastSpeech 2: Fast and High-Quality End-to-End Text to Speech`:
-        https://arxiv.org/abs/2006.04558
-
-    """
-
-    def __init__(self, idim, n_layers=2, n_chans=384, kernel_size=3, bias=True, dropout_rate=0.5, ):
-        """
-        Initilize duration predictor module.
-
-        Args:
-            idim (int): Input dimension.
-            n_layers (int, optional): Number of convolutional layers.
-            n_chans (int, optional): Number of channels of convolutional layers.
-            kernel_size (int, optional): Kernel size of convolutional layers.
-            dropout_rate (float, optional): Dropout rate.
-        """
-        super().__init__()
-        self.conv = torch.nn.ModuleList()
-        for idx in range(n_layers):
-            in_chans = idim if idx == 0 else n_chans
-            self.conv += [
-                torch.nn.Sequential(torch.nn.Conv1d(in_chans, n_chans, kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias, ), torch.nn.ReLU(),
-                                    LayerNorm(n_chans, dim=1), torch.nn.Dropout(dropout_rate), )]
-        self.linear = torch.nn.Linear(n_chans, 1)
-
-    def forward(self, xs, x_masks=None):
-        """
-        Calculate forward propagation.
-
-        Args:
-            xs (Tensor): Batch of input sequences (B, Tmax, idim).
-            x_masks (ByteTensor, optional):
-                Batch of masks indicating padded part (B, Tmax).
-
-        Returns:
-            Tensor: Batch of predicted sequences (B, Tmax, 1).
-        """
-        xs = xs.transpose(1, -1)  # (B, idim, Tmax)
-        for f in self.conv:
-            xs = f(xs)  # (B, C, Tmax)
-
-        xs = self.linear(xs.transpose(1, 2))  # (B, Tmax, 1)
-
-        if x_masks is not None:
-            xs = xs.masked_fill(x_masks, 0.0)
-
-        return xs
diff --git a/spaces/sayakpaul/sots-indoor-dehazing-maxim/maxim/__init__.py b/spaces/sayakpaul/sots-indoor-dehazing-maxim/maxim/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/scedlatioru/img-to-music/example/Buku Siapa Menabur Angin Akan Menuai Badai Pdf 26.md b/spaces/scedlatioru/img-to-music/example/Buku Siapa Menabur Angin Akan Menuai Badai Pdf 26.md
deleted file mode 100644
index e9a0f8af1503ad95de66c524a4e02cd84372e8ba..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Buku Siapa Menabur Angin Akan Menuai Badai Pdf 26.md	
+++ /dev/null
@@ -1,8 +0,0 @@
-
-<p>However, several of these are regularly cited in the. Download the FOXIT PDF Editor for... Siapa Anwar Sadat menabur angin dan ilusi rupawan rinci seharusnya menggunakan buku Siapa Menabur Angin Akan Menuai Badai (Wer Wind st wird einen Sturm ernten). Kami akan memberitahumu tahu apa yang Saya. </p>
-<h2>buku siapa menabur angin akan menuai badai pdf 26</h2><br /><p><b><b>Download File</b> &#11088; <a href="https://gohhs.com/2uEA1p">https://gohhs.com/2uEA1p</a></b></p><br /><br />
-<p> PDF-Editor Version 1. . Siapa yang menabur angin, akan menuai puting beliung (Hos. 8: 7). Perasaan Anti Fundamentalisme Yang Semakin Meningkat Pada pertengahan tujuhpuluhan Richard. TexEditor Version 1. Described in this book are the three aspects. " This Third Edition is revised and rewritten. SOPPANYU, too, had his own version, the Siapa Menabur Angin Akan Menuai Badai, a book. Buku Siapa Menabur Angin Akan Menuai Badai Kuta Tingkat. If you buy the online version of Siapa Menabur Angin Akan Menuai Badai Kuta Tingkat. 9/24 9: 08. Ruang-Ruang Kerja Mendiskriminasi (LPDKI) Pada 1996, oleh TPSP / lh/ anonim(?/prajuritmilitan/). Daftar kustannus/keuntungan tingkat2. Ruang-Ruang Kerja Pendidikan Indonesia. Download Buku Siapa Menabur Angin Akan Menuai Badai Kuta Tingkat. This is a one-stop shop for collecting an assortment of Dr. PADI Pro Open Water (PADIPOD. </p>
-<p>if you. download bkubuku-siapa-menabur-angin-akan-menuai-badi... PADI Pro Advanced Open Water (PADIPOD) for FREE. PDFEditor Version 1. Siapa Menabur Angin Akan Menuai Badai (Wer Wind st wird einen Sturm ernten). PDF Editor is a complete PDF editing solution with support for. </p>
-<p>One of the great advantages of modern psychology is that it can be very effective in the treatment of depression. Daenen's The Gardener and the Psychiatrist How. https://coub.com/stories/2313499-buku-siapa-menabur-angin-akan-menuai-badai-26-pdf-hot </p> 899543212b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/sdeeas/ChuanhuChatGPT/ChuanhuChatbot.py b/spaces/sdeeas/ChuanhuChatGPT/ChuanhuChatbot.py
deleted file mode 100644
index ef01092a9ed4253955d5ce053105837a494ac3ed..0000000000000000000000000000000000000000
--- a/spaces/sdeeas/ChuanhuChatGPT/ChuanhuChatbot.py
+++ /dev/null
@@ -1,474 +0,0 @@
-# -*- coding:utf-8 -*-
-import os
-import logging
-import sys
-
-import gradio as gr
-
-from modules import config
-from modules.config import *
-from modules.utils import *
-from modules.presets import *
-from modules.overwrites import *
-from modules.models.models import get_model
-
-
-gr.Chatbot._postprocess_chat_messages = postprocess_chat_messages
-gr.Chatbot.postprocess = postprocess
-PromptHelper.compact_text_chunks = compact_text_chunks
-
-with open("assets/custom.css", "r", encoding="utf-8") as f:
-    customCSS = f.read()
-
-def create_new_model():
-    return get_model(model_name = MODELS[DEFAULT_MODEL], access_key = my_api_key)[0]
-
-with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo:
-    user_name = gr.State("")
-    promptTemplates = gr.State(load_template(get_template_names(plain=True)[0], mode=2))
-    user_question = gr.State("")
-    assert type(my_api_key)==str
-    user_api_key = gr.State(my_api_key)
-    current_model = gr.State(create_new_model)
-
-    topic = gr.State(i18n("未命名对话历史记录"))
-
-    with gr.Row():
-        gr.HTML(CHUANHU_TITLE, elem_id="app_title")
-        status_display = gr.Markdown(get_geoip(), elem_id="status_display")
-    with gr.Row(elem_id="float_display"):
-        user_info = gr.Markdown(value="getting user info...", elem_id="user_info")
-
-    with gr.Row().style(equal_height=True):
-        with gr.Column(scale=5):
-            with gr.Row():
-                chatbot = gr.Chatbot(label="Chuanhu Chat", elem_id="chuanhu_chatbot").style(height="100%")
-            with gr.Row():
-                with gr.Column(min_width=225, scale=12):
-                    user_input = gr.Textbox(
-                        elem_id="user_input_tb",
-                        show_label=False, placeholder=i18n("在这里输入")
-                    ).style(container=False)
-                with gr.Column(min_width=42, scale=1):
-                    submitBtn = gr.Button(value="", variant="primary", elem_id="submit_btn")
-                    cancelBtn = gr.Button(value="", variant="secondary", visible=False, elem_id="cancel_btn")
-            with gr.Row():
-                emptyBtn = gr.Button(
-                    i18n("🧹 新的对话"), elem_id="empty_btn"
-                )
-                retryBtn = gr.Button(i18n("🔄 重新生成"))
-                delFirstBtn = gr.Button(i18n("🗑️ 删除最旧对话"))
-                delLastBtn = gr.Button(i18n("🗑️ 删除最新对话"))
-                with gr.Row(visible=False) as like_dislike_area:
-                    with gr.Column(min_width=20, scale=1):
-                        likeBtn = gr.Button(i18n("👍"))
-                    with gr.Column(min_width=20, scale=1):
-                        dislikeBtn = gr.Button(i18n("👎"))
-
-        with gr.Column():
-            with gr.Column(min_width=50, scale=1):
-                with gr.Tab(label=i18n("模型")):
-                    keyTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=f"Your API-key...",
-                        value=hide_middle_chars(user_api_key.value),
-                        type="password",
-                        visible=not HIDE_MY_KEY,
-                        label="API-Key",
-                    )
-                    if multi_api_key:
-                        usageTxt = gr.Markdown(i18n("多账号模式已开启，无需输入key，可直接开始对话"), elem_id="usage_display", elem_classes="insert_block")
-                    else:
-                        usageTxt = gr.Markdown(i18n("**发送消息** 或 **提交key** 以显示额度"), elem_id="usage_display", elem_classes="insert_block")
-                    model_select_dropdown = gr.Dropdown(
-                        label=i18n("选择模型"), choices=MODELS, multiselect=False, value=MODELS[DEFAULT_MODEL], interactive=True
-                    )
-                    lora_select_dropdown = gr.Dropdown(
-                        label=i18n("选择LoRA模型"), choices=[], multiselect=False, interactive=True, visible=False
-                    )
-                    with gr.Row():
-                        single_turn_checkbox = gr.Checkbox(label=i18n("单轮对话"), value=False)
-                        use_websearch_checkbox = gr.Checkbox(label=i18n("使用在线搜索"), value=False)
-                        # render_latex_checkbox = gr.Checkbox(label=i18n("渲染LaTeX公式"), value=render_latex, interactive=True, elem_id="render_latex_checkbox")
-                    language_select_dropdown = gr.Dropdown(
-                        label=i18n("选择回复语言（针对搜索&索引功能）"),
-                        choices=REPLY_LANGUAGES,
-                        multiselect=False,
-                        value=REPLY_LANGUAGES[0],
-                    )
-                    index_files = gr.Files(label=i18n("上传"), type="file")
-                    two_column = gr.Checkbox(label=i18n("双栏pdf"), value=advance_docs["pdf"].get("two_column", False))
-                    # TODO: 公式ocr
-                    # formula_ocr = gr.Checkbox(label=i18n("识别公式"), value=advance_docs["pdf"].get("formula_ocr", False))
-
-                with gr.Tab(label="Prompt"):
-                    systemPromptTxt = gr.Textbox(
-                        show_label=True,
-                        placeholder=i18n("在这里输入System Prompt..."),
-                        label="System prompt",
-                        value=INITIAL_SYSTEM_PROMPT,
-                        lines=10,
-                    ).style(container=False)
-                    with gr.Accordion(label=i18n("加载Prompt模板"), open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    templateFileSelectDropdown = gr.Dropdown(
-                                        label=i18n("选择Prompt模板集合文件"),
-                                        choices=get_template_names(plain=True),
-                                        multiselect=False,
-                                        value=get_template_names(plain=True)[0],
-                                    ).style(container=False)
-                                with gr.Column(scale=1):
-                                    templateRefreshBtn = gr.Button(i18n("🔄 刷新"))
-                            with gr.Row():
-                                with gr.Column():
-                                    templateSelectDropdown = gr.Dropdown(
-                                        label=i18n("从Prompt模板中加载"),
-                                        choices=load_template(
-                                            get_template_names(plain=True)[0], mode=1
-                                        ),
-                                        multiselect=False,
-                                    ).style(container=False)
-
-                with gr.Tab(label=i18n("保存/加载")):
-                    with gr.Accordion(label=i18n("保存/加载对话历史记录"), open=True):
-                        with gr.Column():
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    historyFileSelectDropdown = gr.Dropdown(
-                                        label=i18n("从列表中加载对话"),
-                                        choices=get_history_names(plain=True),
-                                        multiselect=False
-                                    )
-                                with gr.Column(scale=1):
-                                    historyRefreshBtn = gr.Button(i18n("🔄 刷新"))
-                            with gr.Row():
-                                with gr.Column(scale=6):
-                                    saveFileName = gr.Textbox(
-                                        show_label=True,
-                                        placeholder=i18n("设置文件名: 默认为.json，可选为.md"),
-                                        label=i18n("设置保存文件名"),
-                                        value=i18n("对话历史记录"),
-                                    ).style(container=True)
-                                with gr.Column(scale=1):
-                                    saveHistoryBtn = gr.Button(i18n("💾 保存对话"))
-                                    exportMarkdownBtn = gr.Button(i18n("📝 导出为Markdown"))
-                                    gr.Markdown(i18n("默认保存于history文件夹"))
-                            with gr.Row():
-                                with gr.Column():
-                                    downloadFile = gr.File(interactive=True)
-
-                with gr.Tab(label=i18n("高级")):
-                    gr.Markdown(i18n("# ⚠️ 务必谨慎更改 ⚠️\n\n如果无法使用请恢复默认设置"))
-                    gr.HTML(APPEARANCE_SWITCHER, elem_classes="insert_block")
-                    use_streaming_checkbox = gr.Checkbox(
-                            label=i18n("实时传输回答"), value=True, visible=ENABLE_STREAMING_OPTION
-                        )
-                    with gr.Accordion(i18n("参数"), open=False):
-                        temperature_slider = gr.Slider(
-                            minimum=-0,
-                            maximum=2.0,
-                            value=1.0,
-                            step=0.1,
-                            interactive=True,
-                            label="temperature",
-                        )
-                        top_p_slider = gr.Slider(
-                            minimum=-0,
-                            maximum=1.0,
-                            value=1.0,
-                            step=0.05,
-                            interactive=True,
-                            label="top-p",
-                        )
-                        n_choices_slider = gr.Slider(
-                            minimum=1,
-                            maximum=10,
-                            value=1,
-                            step=1,
-                            interactive=True,
-                            label="n choices",
-                        )
-                        stop_sequence_txt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("在这里输入停止符，用英文逗号隔开..."),
-                            label="stop",
-                            value="",
-                            lines=1,
-                        )
-                        max_context_length_slider = gr.Slider(
-                            minimum=1,
-                            maximum=32768,
-                            value=2000,
-                            step=1,
-                            interactive=True,
-                            label="max context",
-                        )
-                        max_generation_slider = gr.Slider(
-                            minimum=1,
-                            maximum=32768,
-                            value=1000,
-                            step=1,
-                            interactive=True,
-                            label="max generations",
-                        )
-                        presence_penalty_slider = gr.Slider(
-                            minimum=-2.0,
-                            maximum=2.0,
-                            value=0.0,
-                            step=0.01,
-                            interactive=True,
-                            label="presence penalty",
-                        )
-                        frequency_penalty_slider = gr.Slider(
-                            minimum=-2.0,
-                            maximum=2.0,
-                            value=0.0,
-                            step=0.01,
-                            interactive=True,
-                            label="frequency penalty",
-                        )
-                        logit_bias_txt = gr.Textbox(
-                            show_label=True,
-                            placeholder=f"word:likelihood",
-                            label="logit bias",
-                            value="",
-                            lines=1,
-                        )
-                        user_identifier_txt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("用于定位滥用行为"),
-                            label=i18n("用户名"),
-                            value=user_name.value,
-                            lines=1,
-                        )
-
-                    with gr.Accordion(i18n("网络设置"), open=False, visible=False):
-                        # 优先展示自定义的api_host
-                        apihostTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("在这里输入API-Host..."),
-                            label="API-Host",
-                            value=config.api_host or shared.API_HOST,
-                            lines=1,
-                        )
-                        changeAPIURLBtn = gr.Button(i18n("🔄 切换API地址"))
-                        proxyTxt = gr.Textbox(
-                            show_label=True,
-                            placeholder=i18n("在这里输入代理地址..."),
-                            label=i18n("代理地址（示例：http://127.0.0.1:10809）"),
-                            value="",
-                            lines=2,
-                        )
-                        changeProxyBtn = gr.Button(i18n("🔄 设置代理地址"))
-                        default_btn = gr.Button(i18n("🔙 恢复默认设置"))
-
-    gr.Markdown(CHUANHU_DESCRIPTION, elem_id="description")
-    gr.HTML(FOOTER.format(versions=versions_html()), elem_id="footer")
-
-    # https://github.com/gradio-app/gradio/pull/3296
-    def create_greeting(request: gr.Request):
-        if hasattr(request, "username") and request.username: # is not None or is not ""
-            logging.info(f"Get User Name: {request.username}")
-            user_info, user_name = gr.Markdown.update(value=f"User: {request.username}"), request.username
-        else:
-            user_info, user_name = gr.Markdown.update(value=f"", visible=False), ""
-        current_model = get_model(model_name = MODELS[DEFAULT_MODEL], access_key = my_api_key)[0]
-        current_model.set_user_identifier(user_name)
-        chatbot = gr.Chatbot.update(label=MODELS[DEFAULT_MODEL])
-        return user_info, user_name, current_model, toggle_like_btn_visibility(DEFAULT_MODEL), *current_model.auto_load(), get_history_names(False, user_name), chatbot
-    demo.load(create_greeting, inputs=None, outputs=[user_info, user_name, current_model, like_dislike_area, systemPromptTxt, chatbot, historyFileSelectDropdown, chatbot], api_name="load")
-    chatgpt_predict_args = dict(
-        fn=predict,
-        inputs=[
-            current_model,
-            user_question,
-            chatbot,
-            use_streaming_checkbox,
-            use_websearch_checkbox,
-            index_files,
-            language_select_dropdown,
-        ],
-        outputs=[chatbot, status_display],
-        show_progress=True,
-    )
-
-    start_outputing_args = dict(
-        fn=start_outputing,
-        inputs=[],
-        outputs=[submitBtn, cancelBtn],
-        show_progress=True,
-    )
-
-    end_outputing_args = dict(
-        fn=end_outputing, inputs=[], outputs=[submitBtn, cancelBtn]
-    )
-
-    reset_textbox_args = dict(
-        fn=reset_textbox, inputs=[], outputs=[user_input]
-    )
-
-    transfer_input_args = dict(
-        fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn, cancelBtn], show_progress=True
-    )
-
-    get_usage_args = dict(
-        fn=billing_info, inputs=[current_model], outputs=[usageTxt], show_progress=False
-    )
-
-    load_history_from_file_args = dict(
-        fn=load_chat_history,
-        inputs=[current_model, historyFileSelectDropdown, user_name],
-        outputs=[saveFileName, systemPromptTxt, chatbot]
-    )
-
-
-    # Chatbot
-    cancelBtn.click(interrupt, [current_model], [])
-
-    user_input.submit(**transfer_input_args).then(**chatgpt_predict_args).then(**end_outputing_args)
-    user_input.submit(**get_usage_args)
-
-    submitBtn.click(**transfer_input_args).then(**chatgpt_predict_args, api_name="predict").then(**end_outputing_args)
-    submitBtn.click(**get_usage_args)
-
-    index_files.change(handle_file_upload, [current_model, index_files, chatbot], [index_files, chatbot, status_display])
-
-    emptyBtn.click(
-        reset,
-        inputs=[current_model],
-        outputs=[chatbot, status_display],
-        show_progress=True,
-    )
-
-    retryBtn.click(**start_outputing_args).then(
-        retry,
-        [
-            current_model,
-            chatbot,
-            use_streaming_checkbox,
-            use_websearch_checkbox,
-            index_files,
-            language_select_dropdown,
-        ],
-        [chatbot, status_display],
-        show_progress=True,
-    ).then(**end_outputing_args)
-    retryBtn.click(**get_usage_args)
-
-    delFirstBtn.click(
-        delete_first_conversation,
-        [current_model],
-        [status_display],
-    )
-
-    delLastBtn.click(
-        delete_last_conversation,
-        [current_model, chatbot],
-        [chatbot, status_display],
-        show_progress=False
-    )
-
-    likeBtn.click(
-        like,
-        [current_model],
-        [status_display],
-        show_progress=False
-    )
-
-    dislikeBtn.click(
-        dislike,
-        [current_model],
-        [status_display],
-        show_progress=False
-    )
-
-    two_column.change(update_doc_config, [two_column], None)
-
-    # LLM Models
-    keyTxt.change(set_key, [current_model, keyTxt], [user_api_key, status_display], api_name="set_key").then(**get_usage_args)
-    keyTxt.submit(**get_usage_args)
-    single_turn_checkbox.change(set_single_turn, [current_model, single_turn_checkbox], None)
-    model_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt, user_name], [current_model, status_display, chatbot, lora_select_dropdown], show_progress=True, api_name="get_model")
-    model_select_dropdown.change(toggle_like_btn_visibility, [model_select_dropdown], [like_dislike_area], show_progress=False)
-    lora_select_dropdown.change(get_model, [model_select_dropdown, lora_select_dropdown, user_api_key, temperature_slider, top_p_slider, systemPromptTxt, user_name], [current_model, status_display, chatbot], show_progress=True)
-
-    # Template
-    systemPromptTxt.change(set_system_prompt, [current_model, systemPromptTxt], None)
-    templateRefreshBtn.click(get_template_names, None, [templateFileSelectDropdown])
-    templateFileSelectDropdown.change(
-        load_template,
-        [templateFileSelectDropdown],
-        [promptTemplates, templateSelectDropdown],
-        show_progress=True,
-    )
-    templateSelectDropdown.change(
-        get_template_content,
-        [promptTemplates, templateSelectDropdown, systemPromptTxt],
-        [systemPromptTxt],
-        show_progress=True,
-    )
-
-    # S&L
-    saveHistoryBtn.click(
-        save_chat_history,
-        [current_model, saveFileName, chatbot, user_name],
-        downloadFile,
-        show_progress=True,
-    )
-    saveHistoryBtn.click(get_history_names, [gr.State(False), user_name], [historyFileSelectDropdown])
-    exportMarkdownBtn.click(
-        export_markdown,
-        [current_model, saveFileName, chatbot, user_name],
-        downloadFile,
-        show_progress=True,
-    )
-    historyRefreshBtn.click(get_history_names, [gr.State(False), user_name], [historyFileSelectDropdown])
-    historyFileSelectDropdown.change(**load_history_from_file_args)
-    downloadFile.change(upload_chat_history, [current_model, downloadFile, user_name], [saveFileName, systemPromptTxt, chatbot])
-
-    # Advanced
-    max_context_length_slider.change(set_token_upper_limit, [current_model, max_context_length_slider], None)
-    temperature_slider.change(set_temperature, [current_model, temperature_slider], None)
-    top_p_slider.change(set_top_p, [current_model, top_p_slider], None)
-    n_choices_slider.change(set_n_choices, [current_model, n_choices_slider], None)
-    stop_sequence_txt.change(set_stop_sequence, [current_model, stop_sequence_txt], None)
-    max_generation_slider.change(set_max_tokens, [current_model, max_generation_slider], None)
-    presence_penalty_slider.change(set_presence_penalty, [current_model, presence_penalty_slider], None)
-    frequency_penalty_slider.change(set_frequency_penalty, [current_model, frequency_penalty_slider], None)
-    logit_bias_txt.change(set_logit_bias, [current_model, logit_bias_txt], None)
-    user_identifier_txt.change(set_user_identifier, [current_model, user_identifier_txt], None)
-
-    default_btn.click(
-        reset_default, [], [apihostTxt, proxyTxt, status_display], show_progress=True
-    )
-    changeAPIURLBtn.click(
-        change_api_host,
-        [apihostTxt],
-        [status_display],
-        show_progress=True,
-    )
-    changeProxyBtn.click(
-        change_proxy,
-        [proxyTxt],
-        [status_display],
-        show_progress=True,
-    )
-
-logging.info(
-    colorama.Back.GREEN
-    + "\n川虎的温馨提示：访问 http://localhost:7860 查看界面"
-    + colorama.Style.RESET_ALL
-)
-# 默认开启本地服务器，默认可以直接从IP访问，默认不创建公开分享链接
-demo.title = i18n("川虎Chat 🚀")
-
-if __name__ == "__main__":
-    reload_javascript()
-    demo.queue(concurrency_count=CONCURRENT_COUNT).launch(
-        favicon_path="./assets/favicon.ico",
-    )
-    # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860, share=False) # 可自定义端口
-    # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(server_name="0.0.0.0", server_port=7860,auth=("在这里填写用户名", "在这里填写密码")) # 可设置用户名与密码
-    # demo.queue(concurrency_count=CONCURRENT_COUNT).launch(auth=("在这里填写用户名", "在这里填写密码")) # 适合Nginx反向代理
diff --git a/spaces/shibing624/ChatPDF/modules/models/__init__.py b/spaces/shibing624/ChatPDF/modules/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/silencewing/server/youyou/.history/math_20230613231204.html b/spaces/silencewing/server/youyou/.history/math_20230613231204.html
deleted file mode 100644
index c656b559e31b4f6e9c10e705dbd4bde61a474593..0000000000000000000000000000000000000000
--- a/spaces/silencewing/server/youyou/.history/math_20230613231204.html
+++ /dev/null
@@ -1,229 +0,0 @@
-<!-- /*
- * @Author: Chauncey Yuan 
- * @Date: 2019-08-01 18:25:30 
- * @Last Modified by:   Chauncey Yuan 
- * @Last Modified time: 2019-08-03 08:24:27 
- */ -->
-
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <meta http-equiv="X-UA-Compatible" content="ie=edge">
-    <title>Document</title>
-    <style>
-        html {
-            height: 100%;
-            background: #b3b4a6;
-        }
-        .cal_game {
-            width: 100vw;
-            margin: 0 auto;
-        }
-        
-        table {
-            margin: 0 auto;
-        }
-        
-        td {
-            width: 150px;
-            height: 50px;
-            text-align: center;
-            line-height: 40px;
-            border: 1px solid #2b7e71;
-        }
-        
-        /* .btn {
-            width: 150px;
-            height: 50px;
-            color: #2b7e71;;
-            border: 1px solid #2b7e71;
-            background-color: transparent;
-        }
-        
-        .btn:hover {
-            background-color: #2b7e71;
-            color: #fff;
-            cursor: pointer;
-        } */
-        
-        input {
-            height: 30px;
-            /* border: 1px solid #999999; */
-            background-color: transparent;
-
-        }
-    </style>
-</head>
-
-<body>
-    <div class="cal_game">
-        <table>
-            <tr>
-                <td id="output" colspan="3"></td>
-            </tr>
-            <tr>
-                <td>题目</td>
-                <td id="eq" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>答案</td>
-                <td colspan="2"><input type="text" name="" id="input" placeholder="请输入计算结果："></td>
-                <!-- <td><button class="btn" onclick="xun()">计算</button></td> -->
-            </tr>
-            <tr>
-                <td>正误</td>
-                <td id="result" colspan="2"></td>
-            </tr>
-            <tr>
-                <td>得分</td>
-                <td id="score" colspan="2"></td>
-            </tr>
-            <tr>
-                <td id="accuracy" colspan="3"></td>
-            </tr>
-        </table>
-    </div>
-    <script>
-        // 定义题号，使用时+1
-        var i = 0;
-        // 定义计算正确的次数
-        var right_times = 0;
-        // 定义分数
-        var score = 0;
-        // 定义正确率
-        var accuracy = 0;
-        // 定义加减法符号数组，用于后边产生0或1的随机数，来确定加减法
-        var sign_operation_list = ["+", "-"];
-        // 生成0或者1的随机数，确定加减法
-        var sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-
-        var max_num = 20;
-        // 显示题号
-        document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-        document.onkeydown = function (e) {
-            if(e.which == "13" && document.getElementById("input").value){
-                xun()
-            }
-            else{
-                document.getElementById("input").focus()
-            }
-        }
-
-        // 如果是减法
-        if (sign_operation == 1) {
-            // 则被减数应该小于减数，结果才不会为负数
-            // 减数范围为0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 被减数范围为0-减数
-            var num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-        }
-        // 如果是加法
-        if (sign_operation == 0) {
-            // 两数和应小于max_num
-            // 第一个数的范围是0-max_num
-            var num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-            // 第二个数是0-（max_num-第一个数）
-            var num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-        }
-        // 组成算式，显示给用户看
-        var eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-        // 页面中显示算式
-        document.getElementById("eq").innerHTML = eq;
-        // console.log(eq);
-
-        // 定义函数，当按钮按下是执行一次
-        function xun() {
-            // console.log(num1, num2);
-            // 题号加1
-            i++;
-            // 获取用户输入的结果
-            var input = Number(document.getElementById("input").value);
-            // console.log(input, num1, num2);
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 定义真实结果
-                var calResult = num1 + num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            } // 如果是减法
-            if (sign_operation == 1) {
-                // 定义真实结果
-                var calResult = num1 - num2;
-                // 如果用户输入的结果和真实结果相同
-                if (input == calResult) {
-                    // 分数加10分
-                    score += 10;
-                    // console.log("正确！");
-                    // 显示正确信息
-                    document.getElementById("result").innerHTML = "正确";
-                    // 正确的次数加1
-                    right_times++;
-                }
-                // 如果用户输入的结果和真实结果不同
-                if (input != calResult) {
-                    // 分数减10分
-                    score -= 10;
-                    // console.log("错误！");
-                    // 显示错误信息
-                    document.getElementById("result").innerHTML = "错误";
-                }
-            }
-            // 判断结果后，用户输入框清空
-            document.getElementById("input").value = "";
-            // 显示分数
-            document.getElementById("score").innerHTML = score;
-            // 显示正确率
-            document.getElementById("accuracy").innerHTML = (((right_times / i) * 100).toFixed(2)) + "%";
-
-            if (score >= 100) {
-                window.location.href = "game.html";
-            }
-            // 生成0或者1的随机数，确定加减法
-            sign_operation = Math.floor((Math.random() * (1 - 0 + 1)) + 0);
-            // 显示题号
-            document.getElementById("output").innerHTML = "第" + (i + 1) + "题";
-            // 如果是减法
-            if (sign_operation == 1) {
-                // 则被减数应该小于减数，结果才不会为负数
-                // 减数范围为0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 被减数范围为0-减数
-                num2 = Math.floor(Math.random() * (num1 - 0 + 1) + 0);
-            }
-            // 如果是加法
-            if (sign_operation == 0) {
-                // 两数和应小于max_num
-                // 第一个数的范围是0-max_num
-                num1 = Math.floor(Math.random() * (max_num - 0 + 1) + 0);
-                // 第二个数是0-（max_num-第一个数）
-                num2 = Math.floor(Math.random() * (max_num - num1 - 0 + 1) + 0);
-            }
-            // 组成算式，显示给用户看
-            eq = String(num1) + sign_operation_list[sign_operation] + String(num2) + "=";
-            // 页面中显示算式
-            document.getElementById("eq").innerHTML = eq;
-            // console.log(eq);
-        }
-    </script>
-</body>
-
-</html>
diff --git a/spaces/simonduerr/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn/protein_mpnn_utils.py b/spaces/simonduerr/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn/protein_mpnn_utils.py
deleted file mode 100644
index 9c2a6b393eaae5cc3ed8f564e59a2d4eab863758..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/ProteinMPNN/vanilla_proteinmpnn/protein_mpnn_utils.py
+++ /dev/null
@@ -1,1119 +0,0 @@
-from __future__ import print_function
-import json, time, os, sys, glob
-import shutil
-import numpy as np
-import torch
-from torch import optim
-from torch.utils.data import DataLoader
-from torch.utils.data.dataset import random_split, Subset
-
-import copy
-import torch.nn as nn
-import torch.nn.functional as F
-import random
-import itertools
-
-#A number of functions/classes are adopted from: https://github.com/jingraham/neurips19-graph-protein-design
-
-def _scores(S, log_probs, mask):
-    """ Negative log probabilities """
-    criterion = torch.nn.NLLLoss(reduction='none')
-    loss = criterion(
-        log_probs.contiguous().view(-1,log_probs.size(-1)),
-        S.contiguous().view(-1)
-    ).view(S.size())
-    scores = torch.sum(loss * mask, dim=-1) / torch.sum(mask, dim=-1)
-    return scores
-
-def _S_to_seq(S, mask):
-    alphabet = 'ACDEFGHIKLMNPQRSTVWYX'
-    seq = ''.join([alphabet[c] for c, m in zip(S.tolist(), mask.tolist()) if m > 0])
-    return seq
-
-def parse_PDB_biounits(x, atoms=['N','CA','C'], chain=None):
-  '''
-  input:  x = PDB filename
-          atoms = atoms to extract (optional)
-  output: (length, atoms, coords=(x,y,z)), sequence
-  '''
-
-  alpha_1 = list("ARNDCQEGHILKMFPSTWYV-")
-  states = len(alpha_1)
-  alpha_3 = ['ALA','ARG','ASN','ASP','CYS','GLN','GLU','GLY','HIS','ILE',
-             'LEU','LYS','MET','PHE','PRO','SER','THR','TRP','TYR','VAL','GAP']
-  
-  aa_1_N = {a:n for n,a in enumerate(alpha_1)}
-  aa_3_N = {a:n for n,a in enumerate(alpha_3)}
-  aa_N_1 = {n:a for n,a in enumerate(alpha_1)}
-  aa_1_3 = {a:b for a,b in zip(alpha_1,alpha_3)}
-  aa_3_1 = {b:a for a,b in zip(alpha_1,alpha_3)}
-  
-  def AA_to_N(x):
-    # ["ARND"] -> [[0,1,2,3]]
-    x = np.array(x);
-    if x.ndim == 0: x = x[None]
-    return [[aa_1_N.get(a, states-1) for a in y] for y in x]
-  
-  def N_to_AA(x):
-    # [[0,1,2,3]] -> ["ARND"]
-    x = np.array(x);
-    if x.ndim == 1: x = x[None]
-    return ["".join([aa_N_1.get(a,"-") for a in y]) for y in x]
-
-  xyz,seq,min_resn,max_resn = {},{},1e6,-1e6
-  for line in open(x,"rb"):
-    line = line.decode("utf-8","ignore").rstrip()
-
-    if line[:6] == "HETATM" and line[17:17+3] == "MSE":
-      line = line.replace("HETATM","ATOM  ")
-      line = line.replace("MSE","MET")
-
-    if line[:4] == "ATOM":
-      ch = line[21:22]
-      if ch == chain or chain is None:
-        atom = line[12:12+4].strip()
-        resi = line[17:17+3]
-        resn = line[22:22+5].strip()
-        x,y,z = [float(line[i:(i+8)]) for i in [30,38,46]]
-
-        if resn[-1].isalpha(): 
-            resa,resn = resn[-1],int(resn[:-1])-1
-        else: 
-            resa,resn = "",int(resn)-1
-#         resn = int(resn)
-        if resn < min_resn: 
-            min_resn = resn
-        if resn > max_resn: 
-            max_resn = resn
-        if resn not in xyz: 
-            xyz[resn] = {}
-        if resa not in xyz[resn]: 
-            xyz[resn][resa] = {}
-        if resn not in seq: 
-            seq[resn] = {}
-        if resa not in seq[resn]: 
-            seq[resn][resa] = resi
-
-        if atom not in xyz[resn][resa]:
-          xyz[resn][resa][atom] = np.array([x,y,z])
-
-  # convert to numpy arrays, fill in missing values
-  seq_,xyz_ = [],[]
-  try:
-      for resn in range(min_resn,max_resn+1):
-        if resn in seq:
-          for k in sorted(seq[resn]): seq_.append(aa_3_N.get(seq[resn][k],20))
-        else: seq_.append(20)
-        if resn in xyz:
-          for k in sorted(xyz[resn]):
-            for atom in atoms:
-              if atom in xyz[resn][k]: xyz_.append(xyz[resn][k][atom])
-              else: xyz_.append(np.full(3,np.nan))
-        else:
-          for atom in atoms: xyz_.append(np.full(3,np.nan))
-      return np.array(xyz_).reshape(-1,len(atoms),3), N_to_AA(np.array(seq_))
-  except TypeError:
-      return 'no_chain', 'no_chain'
-
-def parse_PDB(path_to_pdb, input_chain_list=None):
-    c=0
-    pdb_dict_list = []
-    init_alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G','H', 'I', 'J','K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T','U', 'V','W','X', 'Y', 'Z', 'a', 'b', 'c', 'd', 'e', 'f', 'g','h', 'i', 'j','k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't','u', 'v','w','x', 'y', 'z']
-    extra_alphabet = [str(item) for item in list(np.arange(300))]
-    chain_alphabet = init_alphabet + extra_alphabet
-     
-    if input_chain_list:
-        chain_alphabet = input_chain_list  
- 
-
-    biounit_names = [path_to_pdb]
-    for biounit in biounit_names:
-        my_dict = {}
-        s = 0
-        concat_seq = ''
-        concat_N = []
-        concat_CA = []
-        concat_C = []
-        concat_O = []
-        concat_mask = []
-        coords_dict = {}
-        for letter in chain_alphabet:
-            xyz, seq = parse_PDB_biounits(biounit, atoms=['N','CA','C','O'], chain=letter)
-            if type(xyz) != str:
-                concat_seq += seq[0]
-                my_dict['seq_chain_'+letter]=seq[0]
-                coords_dict_chain = {}
-                coords_dict_chain['N_chain_'+letter]=xyz[:,0,:].tolist()
-                coords_dict_chain['CA_chain_'+letter]=xyz[:,1,:].tolist()
-                coords_dict_chain['C_chain_'+letter]=xyz[:,2,:].tolist()
-                coords_dict_chain['O_chain_'+letter]=xyz[:,3,:].tolist()
-                my_dict['coords_chain_'+letter]=coords_dict_chain
-                s += 1
-        fi = biounit.rfind("/")
-        my_dict['name']=biounit[(fi+1):-4]
-        my_dict['num_of_chains'] = s
-        my_dict['seq'] = concat_seq
-        if s <= len(chain_alphabet):
-            pdb_dict_list.append(my_dict)
-            c+=1
-    return pdb_dict_list
-
-
-
-def tied_featurize(batch, device, chain_dict, fixed_position_dict=None, omit_AA_dict=None, tied_positions_dict=None, pssm_dict=None, bias_by_res_dict=None):
-    """ Pack and pad batch into torch tensors """
-    alphabet = 'ACDEFGHIKLMNPQRSTVWYX'
-    B = len(batch)
-    lengths = np.array([len(b['seq']) for b in batch], dtype=np.int32) #sum of chain seq lengths
-    L_max = max([len(b['seq']) for b in batch])
-    X = np.zeros([B, L_max, 4, 3])
-    residue_idx = -100*np.ones([B, L_max], dtype=np.int32)
-    chain_M = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted
-    pssm_coef_all = np.zeros([B, L_max], dtype=np.float32) #1.0 for the bits that need to be predicted
-    pssm_bias_all = np.zeros([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted
-    pssm_log_odds_all = 10000.0*np.ones([B, L_max, 21], dtype=np.float32) #1.0 for the bits that need to be predicted
-    chain_M_pos = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted
-    bias_by_res_all = np.zeros([B, L_max, 21], dtype=np.float32)
-    chain_encoding_all = np.zeros([B, L_max], dtype=np.int32) #1.0 for the bits that need to be predicted
-    S = np.zeros([B, L_max], dtype=np.int32)
-    omit_AA_mask = np.zeros([B, L_max, len(alphabet)], dtype=np.int32)
-    # Build the batch
-    letter_list_list = []
-    visible_list_list = []
-    masked_list_list = []
-    masked_chain_length_list_list = []
-    tied_pos_list_of_lists_list = []
-    #shuffle all chains before the main loop
-    for i, b in enumerate(batch):
-        if chain_dict != None:
-            masked_chains, visible_chains = chain_dict[b['name']] #masked_chains a list of chain letters to predict [A, D, F]
-        else:
-            masked_chains = [item[-1:] for item in list(b) if item[:10]=='seq_chain_']
-            visible_chains = []
-        num_chains = b['num_of_chains']
-        all_chains = masked_chains + visible_chains
-        #random.shuffle(all_chains)
-    for i, b in enumerate(batch):
-        mask_dict = {}
-        a = 0
-        x_chain_list = []
-        chain_mask_list = []
-        chain_seq_list = []
-        chain_encoding_list = []
-        c = 1
-        letter_list = []
-        global_idx_start_list = [0]
-        visible_list = []
-        masked_list = []
-        masked_chain_length_list = []
-        fixed_position_mask_list = []
-        omit_AA_mask_list = []
-        pssm_coef_list = []
-        pssm_bias_list = []
-        pssm_log_odds_list = []
-        bias_by_res_list = []
-        l0 = 0
-        l1 = 0
-        for step, letter in enumerate(all_chains):
-            if letter in visible_chains:
-                letter_list.append(letter)
-                visible_list.append(letter)
-                chain_seq = b[f'seq_chain_{letter}']
-                chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])
-                chain_length = len(chain_seq)
-                global_idx_start_list.append(global_idx_start_list[-1]+chain_length)
-                chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary
-                chain_mask = np.zeros(chain_length) #0.0 for visible chains
-                x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3]
-                x_chain_list.append(x_chain)
-                chain_mask_list.append(chain_mask)
-                chain_seq_list.append(chain_seq)
-                chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))
-                l1 += chain_length
-                residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)
-                l0 += chain_length
-                c+=1
-                fixed_position_mask = np.ones(chain_length)
-                fixed_position_mask_list.append(fixed_position_mask)
-                omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)
-                omit_AA_mask_list.append(omit_AA_mask_temp)
-                pssm_coef = np.zeros(chain_length)
-                pssm_bias = np.zeros([chain_length, 21])
-                pssm_log_odds = 10000.0*np.ones([chain_length, 21])
-                pssm_coef_list.append(pssm_coef)
-                pssm_bias_list.append(pssm_bias)
-                pssm_log_odds_list.append(pssm_log_odds)
-                bias_by_res_list.append(np.zeros([chain_length, 21]))
-            if letter in masked_chains:
-                masked_list.append(letter)
-                letter_list.append(letter)
-                chain_seq = b[f'seq_chain_{letter}']
-                chain_seq = ''.join([a if a!='-' else 'X' for a in chain_seq])
-                chain_length = len(chain_seq)
-                global_idx_start_list.append(global_idx_start_list[-1]+chain_length)
-                masked_chain_length_list.append(chain_length)
-                chain_coords = b[f'coords_chain_{letter}'] #this is a dictionary
-                chain_mask = np.ones(chain_length) #1.0 for masked
-                x_chain = np.stack([chain_coords[c] for c in [f'N_chain_{letter}', f'CA_chain_{letter}', f'C_chain_{letter}', f'O_chain_{letter}']], 1) #[chain_lenght,4,3]
-                x_chain_list.append(x_chain)
-                chain_mask_list.append(chain_mask)
-                chain_seq_list.append(chain_seq)
-                chain_encoding_list.append(c*np.ones(np.array(chain_mask).shape[0]))
-                l1 += chain_length
-                residue_idx[i, l0:l1] = 100*(c-1)+np.arange(l0, l1)
-                l0 += chain_length
-                c+=1
-                fixed_position_mask = np.ones(chain_length)
-                if fixed_position_dict!=None:
-                    fixed_pos_list = fixed_position_dict[b['name']][letter]
-                    if fixed_pos_list:
-                        fixed_position_mask[np.array(fixed_pos_list)-1] = 0.0
-                fixed_position_mask_list.append(fixed_position_mask)
-                omit_AA_mask_temp = np.zeros([chain_length, len(alphabet)], np.int32)
-                if omit_AA_dict!=None:
-                    for item in omit_AA_dict[b['name']][letter]:
-                        idx_AA = np.array(item[0])-1
-                        AA_idx = np.array([np.argwhere(np.array(list(alphabet))== AA)[0][0] for AA in item[1]]).repeat(idx_AA.shape[0])
-                        idx_ = np.array([[a, b] for a in idx_AA for b in AA_idx])
-                        omit_AA_mask_temp[idx_[:,0], idx_[:,1]] = 1
-                omit_AA_mask_list.append(omit_AA_mask_temp)
-                pssm_coef = np.zeros(chain_length)
-                pssm_bias = np.zeros([chain_length, 21])
-                pssm_log_odds = 10000.0*np.ones([chain_length, 21])
-                if pssm_dict:
-                    if pssm_dict[b['name']][letter]:
-                        pssm_coef = pssm_dict[b['name']][letter]['pssm_coef']
-                        pssm_bias = pssm_dict[b['name']][letter]['pssm_bias']
-                        pssm_log_odds = pssm_dict[b['name']][letter]['pssm_log_odds']
-                pssm_coef_list.append(pssm_coef)
-                pssm_bias_list.append(pssm_bias)
-                pssm_log_odds_list.append(pssm_log_odds)
-                if bias_by_res_dict:
-                    bias_by_res_list.append(bias_by_res_dict[b['name']][letter])
-                else:
-                    bias_by_res_list.append(np.zeros([chain_length, 21]))
-
-       
-        letter_list_np = np.array(letter_list)
-        tied_pos_list_of_lists = []
-        tied_beta = np.ones(L_max)
-        if tied_positions_dict!=None:
-            tied_pos_list = tied_positions_dict[b['name']]
-            if tied_pos_list:
-                set_chains_tied = set(list(itertools.chain(*[list(item) for item in tied_pos_list])))
-                for tied_item in tied_pos_list:
-                    one_list = []
-                    for k, v in tied_item.items():
-                        start_idx = global_idx_start_list[np.argwhere(letter_list_np == k)[0][0]]
-                        if isinstance(v[0], list):
-                            for v_count in range(len(v[0])):
-                                one_list.append(start_idx+v[0][v_count]-1)#make 0 to be the first
-                                tied_beta[start_idx+v[0][v_count]-1] = v[1][v_count]
-                        else:
-                            for v_ in v:
-                                one_list.append(start_idx+v_-1)#make 0 to be the first
-                    tied_pos_list_of_lists.append(one_list)
-        tied_pos_list_of_lists_list.append(tied_pos_list_of_lists)
-
-
- 
-        x = np.concatenate(x_chain_list,0) #[L, 4, 3]
-        all_sequence = "".join(chain_seq_list)
-        m = np.concatenate(chain_mask_list,0) #[L,], 1.0 for places that need to be predicted
-        chain_encoding = np.concatenate(chain_encoding_list,0)
-        m_pos = np.concatenate(fixed_position_mask_list,0) #[L,], 1.0 for places that need to be predicted
-
-        pssm_coef_ = np.concatenate(pssm_coef_list,0) #[L,], 1.0 for places that need to be predicted
-        pssm_bias_ = np.concatenate(pssm_bias_list,0) #[L,], 1.0 for places that need to be predicted
-        pssm_log_odds_ = np.concatenate(pssm_log_odds_list,0) #[L,], 1.0 for places that need to be predicted
-
-        bias_by_res_ = np.concatenate(bias_by_res_list, 0)  #[L,21], 0.0 for places where AA frequencies don't need to be tweaked
-
-        l = len(all_sequence)
-        x_pad = np.pad(x, [[0,L_max-l], [0,0], [0,0]], 'constant', constant_values=(np.nan, ))
-        X[i,:,:,:] = x_pad
-
-        m_pad = np.pad(m, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
-        m_pos_pad = np.pad(m_pos, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
-        omit_AA_mask_pad = np.pad(np.concatenate(omit_AA_mask_list,0), [[0,L_max-l]], 'constant', constant_values=(0.0, ))
-        chain_M[i,:] = m_pad
-        chain_M_pos[i,:] = m_pos_pad
-        omit_AA_mask[i,] = omit_AA_mask_pad
-
-        chain_encoding_pad = np.pad(chain_encoding, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
-        chain_encoding_all[i,:] = chain_encoding_pad
-
-        pssm_coef_pad = np.pad(pssm_coef_, [[0,L_max-l]], 'constant', constant_values=(0.0, ))
-        pssm_bias_pad = np.pad(pssm_bias_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))
-        pssm_log_odds_pad = np.pad(pssm_log_odds_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))
-
-        pssm_coef_all[i,:] = pssm_coef_pad
-        pssm_bias_all[i,:] = pssm_bias_pad
-        pssm_log_odds_all[i,:] = pssm_log_odds_pad
-
-        bias_by_res_pad = np.pad(bias_by_res_, [[0,L_max-l], [0,0]], 'constant', constant_values=(0.0, ))
-        bias_by_res_all[i,:] = bias_by_res_pad
-
-        # Convert to labels
-        indices = np.asarray([alphabet.index(a) for a in all_sequence], dtype=np.int32)
-        S[i, :l] = indices
-        letter_list_list.append(letter_list)
-        visible_list_list.append(visible_list)
-        masked_list_list.append(masked_list)
-        masked_chain_length_list_list.append(masked_chain_length_list)
-
-
-    isnan = np.isnan(X)
-    mask = np.isfinite(np.sum(X,(2,3))).astype(np.float32)
-    X[isnan] = 0.
-
-    # Conversion
-    pssm_coef_all = torch.from_numpy(pssm_coef_all).to(dtype=torch.float32, device=device)
-    pssm_bias_all = torch.from_numpy(pssm_bias_all).to(dtype=torch.float32, device=device)
-    pssm_log_odds_all = torch.from_numpy(pssm_log_odds_all).to(dtype=torch.float32, device=device)
-
-    tied_beta = torch.from_numpy(tied_beta).to(dtype=torch.float32, device=device)
-
-    jumps = ((residue_idx[:,1:]-residue_idx[:,:-1])==1).astype(np.float32)
-    bias_by_res_all = torch.from_numpy(bias_by_res_all).to(dtype=torch.float32, device=device)
-    phi_mask = np.pad(jumps, [[0,0],[1,0]])
-    psi_mask = np.pad(jumps, [[0,0],[0,1]])
-    omega_mask = np.pad(jumps, [[0,0],[0,1]])
-    dihedral_mask = np.concatenate([phi_mask[:,:,None], psi_mask[:,:,None], omega_mask[:,:,None]], -1) #[B,L,3]
-    dihedral_mask = torch.from_numpy(dihedral_mask).to(dtype=torch.float32, device=device)
-    residue_idx = torch.from_numpy(residue_idx).to(dtype=torch.long,device=device)
-    S = torch.from_numpy(S).to(dtype=torch.long,device=device)
-    X = torch.from_numpy(X).to(dtype=torch.float32, device=device)
-    mask = torch.from_numpy(mask).to(dtype=torch.float32, device=device)
-    chain_M = torch.from_numpy(chain_M).to(dtype=torch.float32, device=device)
-    chain_M_pos = torch.from_numpy(chain_M_pos).to(dtype=torch.float32, device=device)
-    omit_AA_mask = torch.from_numpy(omit_AA_mask).to(dtype=torch.float32, device=device)
-    chain_encoding_all = torch.from_numpy(chain_encoding_all).to(dtype=torch.long, device=device)
-    return X, S, mask, lengths, chain_M, chain_encoding_all, letter_list_list, visible_list_list, masked_list_list, masked_chain_length_list_list, chain_M_pos, omit_AA_mask, residue_idx, dihedral_mask, tied_pos_list_of_lists_list, pssm_coef_all, pssm_bias_all, pssm_log_odds_all, bias_by_res_all, tied_beta
-
-
-
-def loss_nll(S, log_probs, mask):
-    """ Negative log probabilities """
-    criterion = torch.nn.NLLLoss(reduction='none')
-    loss = criterion(
-        log_probs.contiguous().view(-1, log_probs.size(-1)), S.contiguous().view(-1)
-    ).view(S.size())
-    loss_av = torch.sum(loss * mask) / torch.sum(mask)
-    return loss, loss_av
-
-
-def loss_smoothed(S, log_probs, mask, weight=0.1):
-    """ Negative log probabilities """
-    S_onehot = torch.nn.functional.one_hot(S, 21).float()
-
-    # Label smoothing
-    S_onehot = S_onehot + weight / float(S_onehot.size(-1))
-    S_onehot = S_onehot / S_onehot.sum(-1, keepdim=True)
-
-    loss = -(S_onehot * log_probs).sum(-1)
-    loss_av = torch.sum(loss * mask) / torch.sum(mask)
-    return loss, loss_av
-
-class StructureDataset():
-    def __init__(self, jsonl_file, verbose=True, truncate=None, max_length=100,
-        alphabet='ACDEFGHIKLMNPQRSTVWYX-'):
-        alphabet_set = set([a for a in alphabet])
-        discard_count = {
-            'bad_chars': 0,
-            'too_long': 0,
-            'bad_seq_length': 0
-        }
-
-        with open(jsonl_file) as f:
-            self.data = []
-
-            lines = f.readlines()
-            start = time.time()
-            for i, line in enumerate(lines):
-                entry = json.loads(line)
-                seq = entry['seq'] 
-                name = entry['name']
-
-                # Convert raw coords to np arrays
-                #for key, val in entry['coords'].items():
-                #    entry['coords'][key] = np.asarray(val)
-
-                # Check if in alphabet
-                bad_chars = set([s for s in seq]).difference(alphabet_set)
-                if len(bad_chars) == 0:
-                    if len(entry['seq']) <= max_length:
-                        if True:
-                            self.data.append(entry)
-                        else:
-                            discard_count['bad_seq_length'] += 1
-                    else:
-                        discard_count['too_long'] += 1
-                else:
-                    print(name, bad_chars, entry['seq'])
-                    discard_count['bad_chars'] += 1
-
-                # Truncate early
-                if truncate is not None and len(self.data) == truncate:
-                    return
-
-                if verbose and (i + 1) % 1000 == 0:
-                    elapsed = time.time() - start
-                    print('{} entries ({} loaded) in {:.1f} s'.format(len(self.data), i+1, elapsed))
-
-            print('discarded', discard_count)
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        return self.data[idx]
-    
-
-class StructureDatasetPDB():
-    def __init__(self, pdb_dict_list, verbose=True, truncate=None, max_length=100,
-        alphabet='ACDEFGHIKLMNPQRSTVWYX-'):
-        alphabet_set = set([a for a in alphabet])
-        discard_count = {
-            'bad_chars': 0,
-            'too_long': 0,
-            'bad_seq_length': 0
-        }
-
-        self.data = []
-
-        start = time.time()
-        for i, entry in enumerate(pdb_dict_list):
-            seq = entry['seq']
-            name = entry['name']
-
-            bad_chars = set([s for s in seq]).difference(alphabet_set)
-            if len(bad_chars) == 0:
-                if len(entry['seq']) <= max_length:
-                    self.data.append(entry)
-                else:
-                    discard_count['too_long'] += 1
-            else:
-                discard_count['bad_chars'] += 1
-
-            # Truncate early
-            if truncate is not None and len(self.data) == truncate:
-                return
-
-            if verbose and (i + 1) % 1000 == 0:
-                elapsed = time.time() - start
-
-            #print('Discarded', discard_count)
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, idx):
-        return self.data[idx]
-
-
-    
-class StructureLoader():
-    def __init__(self, dataset, batch_size=100, shuffle=True,
-        collate_fn=lambda x:x, drop_last=False):
-        self.dataset = dataset
-        self.size = len(dataset)
-        self.lengths = [len(dataset[i]['seq']) for i in range(self.size)]
-        self.batch_size = batch_size
-        sorted_ix = np.argsort(self.lengths)
-
-        # Cluster into batches of similar sizes
-        clusters, batch = [], []
-        batch_max = 0
-        for ix in sorted_ix:
-            size = self.lengths[ix]
-            if size * (len(batch) + 1) <= self.batch_size:
-                batch.append(ix)
-                batch_max = size
-            else:
-                clusters.append(batch)
-                batch, batch_max = [], 0
-        if len(batch) > 0:
-            clusters.append(batch)
-        self.clusters = clusters
-
-    def __len__(self):
-        return len(self.clusters)
-
-    def __iter__(self):
-        np.random.shuffle(self.clusters)
-        for b_idx in self.clusters:
-            batch = [self.dataset[i] for i in b_idx]
-            yield batch
-            
-            
-            
-# The following gather functions
-def gather_edges(edges, neighbor_idx):
-    # Features [B,N,N,C] at Neighbor indices [B,N,K] => Neighbor features [B,N,K,C]
-    neighbors = neighbor_idx.unsqueeze(-1).expand(-1, -1, -1, edges.size(-1))
-    edge_features = torch.gather(edges, 2, neighbors)
-    return edge_features
-
-def gather_nodes(nodes, neighbor_idx):
-    # Features [B,N,C] at Neighbor indices [B,N,K] => [B,N,K,C]
-    # Flatten and expand indices per batch [B,N,K] => [B,NK] => [B,NK,C]
-    neighbors_flat = neighbor_idx.view((neighbor_idx.shape[0], -1))
-    neighbors_flat = neighbors_flat.unsqueeze(-1).expand(-1, -1, nodes.size(2))
-    # Gather and re-pack
-    neighbor_features = torch.gather(nodes, 1, neighbors_flat)
-    neighbor_features = neighbor_features.view(list(neighbor_idx.shape)[:3] + [-1])
-    return neighbor_features
-
-def gather_nodes_t(nodes, neighbor_idx):
-    # Features [B,N,C] at Neighbor index [B,K] => Neighbor features[B,K,C]
-    idx_flat = neighbor_idx.unsqueeze(-1).expand(-1, -1, nodes.size(2))
-    neighbor_features = torch.gather(nodes, 1, idx_flat)
-    return neighbor_features
-
-def cat_neighbors_nodes(h_nodes, h_neighbors, E_idx):
-    h_nodes = gather_nodes(h_nodes, E_idx)
-    h_nn = torch.cat([h_neighbors, h_nodes], -1)
-    return h_nn
-
-
-class EncLayer(nn.Module):
-    def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30):
-        super(EncLayer, self).__init__()
-        self.num_hidden = num_hidden
-        self.num_in = num_in
-        self.scale = scale
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-        self.dropout3 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(num_hidden)
-        self.norm2 = nn.LayerNorm(num_hidden)
-        self.norm3 = nn.LayerNorm(num_hidden)
-
-        self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
-        self.W2 = nn.Linear(num_hidden, num_hidden, bias=True)
-        self.W3 = nn.Linear(num_hidden, num_hidden, bias=True)
-        self.W11 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
-        self.W12 = nn.Linear(num_hidden, num_hidden, bias=True)
-        self.W13 = nn.Linear(num_hidden, num_hidden, bias=True)
-        self.act = torch.nn.GELU()
-        self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4)
-
-    def forward(self, h_V, h_E, E_idx, mask_V=None, mask_attend=None):
-        """ Parallel computation of full transformer layer """
-
-        h_EV = cat_neighbors_nodes(h_V, h_E, E_idx)
-        h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1)
-        h_EV = torch.cat([h_V_expand, h_EV], -1)
-        h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV)))))
-        if mask_attend is not None:
-            h_message = mask_attend.unsqueeze(-1) * h_message
-        dh = torch.sum(h_message, -2) / self.scale
-        h_V = self.norm1(h_V + self.dropout1(dh))
-
-        dh = self.dense(h_V)
-        h_V = self.norm2(h_V + self.dropout2(dh))
-        if mask_V is not None:
-            mask_V = mask_V.unsqueeze(-1)
-            h_V = mask_V * h_V
-
-        h_EV = cat_neighbors_nodes(h_V, h_E, E_idx)
-        h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_EV.size(-2),-1)
-        h_EV = torch.cat([h_V_expand, h_EV], -1)
-        h_message = self.W13(self.act(self.W12(self.act(self.W11(h_EV)))))
-        h_E = self.norm3(h_E + self.dropout3(h_message))
-        return h_V, h_E
-
-
-class DecLayer(nn.Module):
-    def __init__(self, num_hidden, num_in, dropout=0.1, num_heads=None, scale=30):
-        super(DecLayer, self).__init__()
-        self.num_hidden = num_hidden
-        self.num_in = num_in
-        self.scale = scale
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(num_hidden)
-        self.norm2 = nn.LayerNorm(num_hidden)
-
-        self.W1 = nn.Linear(num_hidden + num_in, num_hidden, bias=True)
-        self.W2 = nn.Linear(num_hidden, num_hidden, bias=True)
-        self.W3 = nn.Linear(num_hidden, num_hidden, bias=True)
-        self.act = torch.nn.GELU()
-        self.dense = PositionWiseFeedForward(num_hidden, num_hidden * 4)
-
-    def forward(self, h_V, h_E, mask_V=None, mask_attend=None):
-        """ Parallel computation of full transformer layer """
-
-        # Concatenate h_V_i to h_E_ij
-        h_V_expand = h_V.unsqueeze(-2).expand(-1,-1,h_E.size(-2),-1)
-        h_EV = torch.cat([h_V_expand, h_E], -1)
-
-        h_message = self.W3(self.act(self.W2(self.act(self.W1(h_EV)))))
-        if mask_attend is not None:
-            h_message = mask_attend.unsqueeze(-1) * h_message
-        dh = torch.sum(h_message, -2) / self.scale
-
-        h_V = self.norm1(h_V + self.dropout1(dh))
-
-        # Position-wise feedforward
-        dh = self.dense(h_V)
-        h_V = self.norm2(h_V + self.dropout2(dh))
-
-        if mask_V is not None:
-            mask_V = mask_V.unsqueeze(-1)
-            h_V = mask_V * h_V
-        return h_V 
-
-
-
-class PositionWiseFeedForward(nn.Module):
-    def __init__(self, num_hidden, num_ff):
-        super(PositionWiseFeedForward, self).__init__()
-        self.W_in = nn.Linear(num_hidden, num_ff, bias=True)
-        self.W_out = nn.Linear(num_ff, num_hidden, bias=True)
-        self.act = torch.nn.GELU()
-    def forward(self, h_V):
-        h = self.act(self.W_in(h_V))
-        h = self.W_out(h)
-        return h
-
-class PositionalEncodings(nn.Module):
-    def __init__(self, num_embeddings, max_relative_feature=32):
-        super(PositionalEncodings, self).__init__()
-        self.num_embeddings = num_embeddings
-        self.max_relative_feature = max_relative_feature
-        self.linear = nn.Linear(2*max_relative_feature+1+1, num_embeddings)
-
-    def forward(self, offset, mask):
-        d = torch.clip(offset + self.max_relative_feature, 0, 2*self.max_relative_feature)*mask + (1-mask)*(2*self.max_relative_feature+1)
-        d_onehot = torch.nn.functional.one_hot(d, 2*self.max_relative_feature+1+1)
-        E = self.linear(d_onehot.float())
-        return E
-
-class ProteinFeatures(nn.Module):
-    def __init__(self, edge_features, node_features, num_positional_embeddings=16,
-        num_rbf=16, top_k=30, augment_eps=0., num_chain_embeddings=16):
-        """ Extract protein features """
-        super(ProteinFeatures, self).__init__()
-        self.edge_features = edge_features
-        self.node_features = node_features
-        self.top_k = top_k
-        self.augment_eps = augment_eps 
-        self.num_rbf = num_rbf
-        self.num_positional_embeddings = num_positional_embeddings
-
-        self.embeddings = PositionalEncodings(num_positional_embeddings)
-        node_in, edge_in = 6, num_positional_embeddings + num_rbf*25
-        self.edge_embedding = nn.Linear(edge_in, edge_features, bias=False)
-        self.norm_edges = nn.LayerNorm(edge_features)
-
-    def _dist(self, X, mask, eps=1E-6):
-        mask_2D = torch.unsqueeze(mask,1) * torch.unsqueeze(mask,2)
-        dX = torch.unsqueeze(X,1) - torch.unsqueeze(X,2)
-        D = mask_2D * torch.sqrt(torch.sum(dX**2, 3) + eps)
-        D_max, _ = torch.max(D, -1, keepdim=True)
-        D_adjust = D + (1. - mask_2D) * D_max
-        sampled_top_k = self.top_k
-        D_neighbors, E_idx = torch.topk(D_adjust, np.minimum(self.top_k, X.shape[1]), dim=-1, largest=False)
-        return D_neighbors, E_idx
-
-    def _rbf(self, D):
-        device = D.device
-        D_min, D_max, D_count = 2., 22., self.num_rbf
-        D_mu = torch.linspace(D_min, D_max, D_count, device=device)
-        D_mu = D_mu.view([1,1,1,-1])
-        D_sigma = (D_max - D_min) / D_count
-        D_expand = torch.unsqueeze(D, -1)
-        RBF = torch.exp(-((D_expand - D_mu) / D_sigma)**2)
-        return RBF
-
-    def _get_rbf(self, A, B, E_idx):
-        D_A_B = torch.sqrt(torch.sum((A[:,:,None,:] - B[:,None,:,:])**2,-1) + 1e-6) #[B, L, L]
-        D_A_B_neighbors = gather_edges(D_A_B[:,:,:,None], E_idx)[:,:,:,0] #[B,L,K]
-        RBF_A_B = self._rbf(D_A_B_neighbors)
-        return RBF_A_B
-
-    def forward(self, X, mask, residue_idx, chain_labels):
-        if self.augment_eps !='':
-            if self.augment_eps > 0:
-                X = X + self.augment_eps * torch.randn_like(X)
-        
-        b = X[:,:,1,:] - X[:,:,0,:]
-        c = X[:,:,2,:] - X[:,:,1,:]
-        a = torch.cross(b, c, dim=-1)
-        Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + X[:,:,1,:]
-        Ca = X[:,:,1,:]
-        N = X[:,:,0,:]
-        C = X[:,:,2,:]
-        O = X[:,:,3,:]
- 
-        D_neighbors, E_idx = self._dist(Ca, mask)
-
-        RBF_all = []
-        RBF_all.append(self._rbf(D_neighbors)) #Ca-Ca
-        RBF_all.append(self._get_rbf(N, N, E_idx)) #N-N
-        RBF_all.append(self._get_rbf(C, C, E_idx)) #C-C
-        RBF_all.append(self._get_rbf(O, O, E_idx)) #O-O
-        RBF_all.append(self._get_rbf(Cb, Cb, E_idx)) #Cb-Cb
-        RBF_all.append(self._get_rbf(Ca, N, E_idx)) #Ca-N
-        RBF_all.append(self._get_rbf(Ca, C, E_idx)) #Ca-C
-        RBF_all.append(self._get_rbf(Ca, O, E_idx)) #Ca-O
-        RBF_all.append(self._get_rbf(Ca, Cb, E_idx)) #Ca-Cb
-        RBF_all.append(self._get_rbf(N, C, E_idx)) #N-C
-        RBF_all.append(self._get_rbf(N, O, E_idx)) #N-O
-        RBF_all.append(self._get_rbf(N, Cb, E_idx)) #N-Cb
-        RBF_all.append(self._get_rbf(Cb, C, E_idx)) #Cb-C
-        RBF_all.append(self._get_rbf(Cb, O, E_idx)) #Cb-O
-        RBF_all.append(self._get_rbf(O, C, E_idx)) #O-C
-        RBF_all.append(self._get_rbf(N, Ca, E_idx)) #N-Ca
-        RBF_all.append(self._get_rbf(C, Ca, E_idx)) #C-Ca
-        RBF_all.append(self._get_rbf(O, Ca, E_idx)) #O-Ca
-        RBF_all.append(self._get_rbf(Cb, Ca, E_idx)) #Cb-Ca
-        RBF_all.append(self._get_rbf(C, N, E_idx)) #C-N
-        RBF_all.append(self._get_rbf(O, N, E_idx)) #O-N
-        RBF_all.append(self._get_rbf(Cb, N, E_idx)) #Cb-N
-        RBF_all.append(self._get_rbf(C, Cb, E_idx)) #C-Cb
-        RBF_all.append(self._get_rbf(O, Cb, E_idx)) #O-Cb
-        RBF_all.append(self._get_rbf(C, O, E_idx)) #C-O
-        RBF_all = torch.cat(tuple(RBF_all), dim=-1)
-
-        offset = residue_idx[:,:,None]-residue_idx[:,None,:]
-        offset = gather_edges(offset[:,:,:,None], E_idx)[:,:,:,0] #[B, L, K]
-
-        d_chains = ((chain_labels[:, :, None] - chain_labels[:,None,:])==0).long() #find self vs non-self interaction
-        E_chains = gather_edges(d_chains[:,:,:,None], E_idx)[:,:,:,0]
-        E_positional = self.embeddings(offset.long(), E_chains)
-        E = torch.cat((E_positional, RBF_all), -1)
-        E = self.edge_embedding(E)
-        E = self.norm_edges(E)
-        return E, E_idx 
-
-
-
-class ProteinMPNN(nn.Module):
-    def __init__(self, num_letters, node_features, edge_features,
-        hidden_dim, num_encoder_layers=3, num_decoder_layers=3,
-        vocab=21, k_neighbors=64, augment_eps=0.05, dropout=0.1):
-        super(ProteinMPNN, self).__init__()
-
-        # Hyperparameters
-        self.node_features = node_features
-        self.edge_features = edge_features
-        self.hidden_dim = hidden_dim
-
-        # Featurization layers
-        self.features = ProteinFeatures(node_features, edge_features, top_k=k_neighbors, augment_eps=augment_eps)
-
-        self.W_e = nn.Linear(edge_features, hidden_dim, bias=True)
-        self.W_s = nn.Embedding(vocab, hidden_dim)
-
-        # Encoder layers
-        self.encoder_layers = nn.ModuleList([
-            EncLayer(hidden_dim, hidden_dim*2, dropout=dropout)
-            for _ in range(num_encoder_layers)
-        ])
-
-        # Decoder layers
-        self.decoder_layers = nn.ModuleList([
-            DecLayer(hidden_dim, hidden_dim*3, dropout=dropout)
-            for _ in range(num_decoder_layers)
-        ])
-        self.W_out = nn.Linear(hidden_dim, num_letters, bias=True)
-
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, use_input_decoding_order=False, decoding_order=None):
-        """ Graph-conditioned sequence model """
-        device=X.device
-        # Prepare node and edge embeddings
-        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
-        h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device)
-        h_E = self.W_e(E)
-
-        # Encoder is unmasked self-attention
-        mask_attend = gather_nodes(mask.unsqueeze(-1),  E_idx).squeeze(-1)
-        mask_attend = mask.unsqueeze(-1) * mask_attend
-        for layer in self.encoder_layers:
-            h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)
-
-        # Concatenate sequence embeddings for autoregressive decoder
-        h_S = self.W_s(S)
-        h_ES = cat_neighbors_nodes(h_S, h_E, E_idx)
-
-        # Build encoder embeddings
-        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
-        h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
-
-
-        chain_M = chain_M*mask #update chain_M to include missing regions
-        if not use_input_decoding_order:
-            decoding_order = torch.argsort((chain_M+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
-        mask_size = E_idx.shape[1]
-        permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
-        order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
-        mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
-        mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
-        mask_bw = mask_1D * mask_attend
-        mask_fw = mask_1D * (1. - mask_attend)
-
-        h_EXV_encoder_fw = mask_fw * h_EXV_encoder
-        for layer in self.decoder_layers:
-            # Masked positions attend to encoder information, unmasked see. 
-            h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx)
-            h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw
-            h_V = layer(h_V, h_ESV, mask)
-
-        logits = self.W_out(h_V)
-        log_probs = F.log_softmax(logits, dim=-1)
-        return log_probs
-
-
-
-    def sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, bias_by_res=None):
-        device = X.device
-        # Prepare node and edge embeddings
-        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
-        h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device)
-        h_E = self.W_e(E)
-
-        # Encoder is unmasked self-attention
-        mask_attend = gather_nodes(mask.unsqueeze(-1),  E_idx).squeeze(-1)
-        mask_attend = mask.unsqueeze(-1) * mask_attend
-        for layer in self.encoder_layers:
-            h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)
-
-        # Decoder uses masked self-attention
-        chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions
-        decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
-        mask_size = E_idx.shape[1]
-        permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
-        order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
-        mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
-        mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
-        mask_bw = mask_1D * mask_attend
-        mask_fw = mask_1D * (1. - mask_attend)
-
-        N_batch, N_nodes = X.size(0), X.size(1)
-        log_probs = torch.zeros((N_batch, N_nodes, 21), device=device)
-        all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32)
-        h_S = torch.zeros_like(h_V, device=device)
-        S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device)
-        h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))]
-        constant = torch.tensor(omit_AAs_np, device=device)
-        constant_bias = torch.tensor(bias_AAs_np, device=device)
-        #chain_mask_combined = chain_mask*chain_M_pos 
-        omit_AA_mask_flag = omit_AA_mask != None
-
-
-        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
-        h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
-        h_EXV_encoder_fw = mask_fw * h_EXV_encoder
-        for t_ in range(N_nodes):
-            t = decoding_order[:,t_] #[B]
-            chain_mask_gathered = torch.gather(chain_mask, 1, t[:,None]) #[B]
-            bias_by_res_gathered = torch.gather(bias_by_res, 1, t[:,None,None].repeat(1,1,21))[:,0,:] #[B, 21]
-            if (chain_mask_gathered==0).all():
-                S_t = torch.gather(S_true, 1, t[:,None])
-            else:
-                # Hidden layers
-                E_idx_t = torch.gather(E_idx, 1, t[:,None,None].repeat(1,1,E_idx.shape[-1]))
-                h_E_t = torch.gather(h_E, 1, t[:,None,None,None].repeat(1,1,h_E.shape[-2], h_E.shape[-1]))
-                h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t)
-                h_EXV_encoder_t = torch.gather(h_EXV_encoder_fw, 1, t[:,None,None,None].repeat(1,1,h_EXV_encoder_fw.shape[-2], h_EXV_encoder_fw.shape[-1]))
-                mask_t = torch.gather(mask, 1, t[:,None])
-                for l, layer in enumerate(self.decoder_layers):
-                    # Updated relational features for future states
-                    h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t)
-                    h_V_t = torch.gather(h_V_stack[l], 1, t[:,None,None].repeat(1,1,h_V_stack[l].shape[-1]))
-                    h_ESV_t = torch.gather(mask_bw, 1, t[:,None,None,None].repeat(1,1,mask_bw.shape[-2], mask_bw.shape[-1])) * h_ESV_decoder_t + h_EXV_encoder_t
-                    h_V_stack[l+1].scatter_(1, t[:,None,None].repeat(1,1,h_V.shape[-1]), layer(h_V_t, h_ESV_t, mask_V=mask_t))
-                # Sampling step
-                h_V_t = torch.gather(h_V_stack[-1], 1, t[:,None,None].repeat(1,1,h_V_stack[-1].shape[-1]))[:,0]
-                logits = self.W_out(h_V_t) / temperature
-                probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1)
-                if pssm_bias_flag:
-                    pssm_coef_gathered = torch.gather(pssm_coef, 1, t[:,None])[:,0]
-                    pssm_bias_gathered = torch.gather(pssm_bias, 1, t[:,None,None].repeat(1,1,pssm_bias.shape[-1]))[:,0]
-                    probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered
-                if pssm_log_odds_flag:
-                    pssm_log_odds_mask_gathered = torch.gather(pssm_log_odds_mask, 1, t[:,None, None].repeat(1,1,pssm_log_odds_mask.shape[-1]))[:,0] #[B, 21]
-                    probs_masked = probs*pssm_log_odds_mask_gathered
-                    probs_masked += probs * 0.001
-                    probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
-                if omit_AA_mask_flag:
-                    omit_AA_mask_gathered = torch.gather(omit_AA_mask, 1, t[:,None, None].repeat(1,1,omit_AA_mask.shape[-1]))[:,0] #[B, 21]
-                    probs_masked = probs*(1.0-omit_AA_mask_gathered)
-                    probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
-                S_t = torch.multinomial(probs, 1)
-                all_probs.scatter_(1, t[:,None,None].repeat(1,1,21), (chain_mask_gathered[:,:,None,]*probs[:,None,:]).float())
-            S_true_gathered = torch.gather(S_true, 1, t[:,None])
-            S_t = (S_t*chain_mask_gathered+S_true_gathered*(1.0-chain_mask_gathered)).long()
-            temp1 = self.W_s(S_t)
-            h_S.scatter_(1, t[:,None,None].repeat(1,1,temp1.shape[-1]), temp1)
-            S.scatter_(1, t[:,None], S_t)
-        output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order}
-        return output_dict
-
-
-    def tied_sample(self, X, randn, S_true, chain_mask, chain_encoding_all, residue_idx, mask=None, temperature=1.0, omit_AAs_np=None, bias_AAs_np=None, chain_M_pos=None, omit_AA_mask=None, pssm_coef=None, pssm_bias=None, pssm_multi=None, pssm_log_odds_flag=None, pssm_log_odds_mask=None, pssm_bias_flag=None, tied_pos=None, tied_beta=None, bias_by_res=None):
-        device = X.device
-        # Prepare node and edge embeddings
-        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
-        h_V = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=device)
-        h_E = self.W_e(E)
-        # Encoder is unmasked self-attention
-        mask_attend = gather_nodes(mask.unsqueeze(-1),  E_idx).squeeze(-1)
-        mask_attend = mask.unsqueeze(-1) * mask_attend
-        for layer in self.encoder_layers:
-            h_V, h_E = layer(h_V, h_E, E_idx, mask, mask_attend)
-
-        # Decoder uses masked self-attention
-        chain_mask = chain_mask*chain_M_pos*mask #update chain_M to include missing regions
-        decoding_order = torch.argsort((chain_mask+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
-
-        new_decoding_order = []
-        for t_dec in list(decoding_order[0,].cpu().data.numpy()):
-            if t_dec not in list(itertools.chain(*new_decoding_order)):
-                list_a = [item for item in tied_pos if t_dec in item]
-                if list_a:
-                    new_decoding_order.append(list_a[0])
-                else:
-                    new_decoding_order.append([t_dec])
-        decoding_order = torch.tensor(list(itertools.chain(*new_decoding_order)), device=device)[None,].repeat(X.shape[0],1)
-
-        mask_size = E_idx.shape[1]
-        permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
-        order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
-        mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
-        mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
-        mask_bw = mask_1D * mask_attend
-        mask_fw = mask_1D * (1. - mask_attend)
-
-        N_batch, N_nodes = X.size(0), X.size(1)
-        log_probs = torch.zeros((N_batch, N_nodes, 21), device=device)
-        all_probs = torch.zeros((N_batch, N_nodes, 21), device=device, dtype=torch.float32)
-        h_S = torch.zeros_like(h_V, device=device)
-        S = torch.zeros((N_batch, N_nodes), dtype=torch.int64, device=device)
-        h_V_stack = [h_V] + [torch.zeros_like(h_V, device=device) for _ in range(len(self.decoder_layers))]
-        constant = torch.tensor(omit_AAs_np, device=device)
-        constant_bias = torch.tensor(bias_AAs_np, device=device)
-        omit_AA_mask_flag = omit_AA_mask != None
-
-        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
-        h_EXV_encoder = cat_neighbors_nodes(h_V, h_EX_encoder, E_idx)
-        h_EXV_encoder_fw = mask_fw * h_EXV_encoder
-        for t_list in new_decoding_order:
-            logits = 0.0
-            logit_list = []
-            done_flag = False
-            for t in t_list:
-                if (chain_mask[:,t]==0).all():
-                    S_t = S_true[:,t]
-                    for t in t_list:
-                        h_S[:,t,:] = self.W_s(S_t)
-                        S[:,t] = S_t
-                    done_flag = True
-                    break
-                else:
-                    E_idx_t = E_idx[:,t:t+1,:]
-                    h_E_t = h_E[:,t:t+1,:,:]
-                    h_ES_t = cat_neighbors_nodes(h_S, h_E_t, E_idx_t)
-                    h_EXV_encoder_t = h_EXV_encoder_fw[:,t:t+1,:,:]
-                    mask_t = mask[:,t:t+1]
-                    for l, layer in enumerate(self.decoder_layers):
-                        h_ESV_decoder_t = cat_neighbors_nodes(h_V_stack[l], h_ES_t, E_idx_t)
-                        h_V_t = h_V_stack[l][:,t:t+1,:]
-                        h_ESV_t = mask_bw[:,t:t+1,:,:] * h_ESV_decoder_t + h_EXV_encoder_t
-                        h_V_stack[l+1][:,t,:] = layer(h_V_t, h_ESV_t, mask_V=mask_t).squeeze(1)
-                    h_V_t = h_V_stack[-1][:,t,:]
-                    logit_list.append((self.W_out(h_V_t) / temperature)/len(t_list))
-                    logits += tied_beta[t]*(self.W_out(h_V_t) / temperature)/len(t_list)
-            if done_flag:
-                pass
-            else:
-                bias_by_res_gathered = bias_by_res[:,t,:] #[B, 21]
-                probs = F.softmax(logits-constant[None,:]*1e8+constant_bias[None,:]/temperature+bias_by_res_gathered/temperature, dim=-1)
-                if pssm_bias_flag:
-                    pssm_coef_gathered = pssm_coef[:,t]
-                    pssm_bias_gathered = pssm_bias[:,t]
-                    probs = (1-pssm_multi*pssm_coef_gathered[:,None])*probs + pssm_multi*pssm_coef_gathered[:,None]*pssm_bias_gathered
-                if pssm_log_odds_flag:
-                    pssm_log_odds_mask_gathered = pssm_log_odds_mask[:,t]
-                    probs_masked = probs*pssm_log_odds_mask_gathered
-                    probs_masked += probs * 0.001
-                    probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
-                if omit_AA_mask_flag:
-                    omit_AA_mask_gathered = omit_AA_mask[:,t]
-                    probs_masked = probs*(1.0-omit_AA_mask_gathered)
-                    probs = probs_masked/torch.sum(probs_masked, dim=-1, keepdim=True) #[B, 21]
-                S_t_repeat = torch.multinomial(probs, 1).squeeze(-1)
-                for t in t_list:
-                    h_S[:,t,:] = self.W_s(S_t_repeat)
-                    S[:,t] = S_t_repeat
-                    all_probs[:,t,:] = probs.float()
-        output_dict = {"S": S, "probs": all_probs, "decoding_order": decoding_order}
-        return output_dict
-
-
-    def conditional_probs(self, X, S, mask, chain_M, residue_idx, chain_encoding_all, randn, backbone_only=False):
-        """ Graph-conditioned sequence model """
-        device=X.device
-        # Prepare node and edge embeddings
-        E, E_idx = self.features(X, mask, residue_idx, chain_encoding_all)
-        h_V_enc = torch.zeros((E.shape[0], E.shape[1], E.shape[-1]), device=E.device)
-        h_E = self.W_e(E)
-
-        # Encoder is unmasked self-attention
-        mask_attend = gather_nodes(mask.unsqueeze(-1),  E_idx).squeeze(-1)
-        mask_attend = mask.unsqueeze(-1) * mask_attend
-        for layer in self.encoder_layers:
-            h_V_enc, h_E = layer(h_V_enc, h_E, E_idx, mask, mask_attend)
-
-        # Concatenate sequence embeddings for autoregressive decoder
-        h_S = self.W_s(S)
-        h_ES = cat_neighbors_nodes(h_S, h_E, E_idx)
-
-        # Build encoder embeddings
-        h_EX_encoder = cat_neighbors_nodes(torch.zeros_like(h_S), h_E, E_idx)
-        h_EXV_encoder = cat_neighbors_nodes(h_V_enc, h_EX_encoder, E_idx)
-
-
-        chain_M = chain_M*mask #update chain_M to include missing regions
-  
-        chain_M_np = chain_M.cpu().numpy()
-        idx_to_loop = np.argwhere(chain_M_np[0,:]==1)[:,0]
-        log_conditional_probs = torch.zeros([X.shape[0], chain_M.shape[1], 21], device=device).float()
-
-        for idx in idx_to_loop:
-            h_V = torch.clone(h_V_enc)
-            order_mask = torch.zeros(chain_M.shape[1], device=device).float()
-            if backbone_only:
-                order_mask = torch.ones(chain_M.shape[1], device=device).float()
-                order_mask[idx] = 0.
-            else:
-                order_mask = torch.zeros(chain_M.shape[1], device=device).float()
-                order_mask[idx] = 1.
-            decoding_order = torch.argsort((order_mask[None,]+0.0001)*(torch.abs(randn))) #[numbers will be smaller for places where chain_M = 0.0 and higher for places where chain_M = 1.0]
-            mask_size = E_idx.shape[1]
-            permutation_matrix_reverse = torch.nn.functional.one_hot(decoding_order, num_classes=mask_size).float()
-            order_mask_backward = torch.einsum('ij, biq, bjp->bqp',(1-torch.triu(torch.ones(mask_size,mask_size, device=device))), permutation_matrix_reverse, permutation_matrix_reverse)
-            mask_attend = torch.gather(order_mask_backward, 2, E_idx).unsqueeze(-1)
-            mask_1D = mask.view([mask.size(0), mask.size(1), 1, 1])
-            mask_bw = mask_1D * mask_attend
-            mask_fw = mask_1D * (1. - mask_attend)
-
-            h_EXV_encoder_fw = mask_fw * h_EXV_encoder
-            for layer in self.decoder_layers:
-                # Masked positions attend to encoder information, unmasked see. 
-                h_ESV = cat_neighbors_nodes(h_V, h_ES, E_idx)
-                h_ESV = mask_bw * h_ESV + h_EXV_encoder_fw
-                h_V = layer(h_V, h_ESV, mask)
-
-            logits = self.W_out(h_V)
-            log_probs = F.log_softmax(logits, dim=-1)
-            log_conditional_probs[:,idx,:] = log_probs[:,idx,:]
-        return log_conditional_probs
-
-
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/Niemals Geht Man So Ganz Noten Pdf Free [VERIFIED].md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/Niemals Geht Man So Ganz Noten Pdf Free [VERIFIED].md
deleted file mode 100644
index 42d7895c511a28edeaaf43c5e54137afc1fe7d80..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/Niemals Geht Man So Ganz Noten Pdf Free [VERIFIED].md	
+++ /dev/null
@@ -1,66 +0,0 @@
-## Niemals Geht Man So Ganz Noten Pdf Free
-
-
-
- 
-
- ![Niemals Geht Man So Ganz Noten Pdf Free \[VERIFIED\]](https://musikverein-iggelheim.de/Bilder/Brieder_Noten.jpg)
-
- 
-
- 
-
-**CLICK HERE ✔ [https://kneedacexbrew.blogspot.com/?d=2txiFo](https://kneedacexbrew.blogspot.com/?d=2txiFo)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# How to Play Niemals Geht Man So Ganz on Guitar
-
- 
-
-Niemals Geht Man So Ganz (You Never Leave Completely) is a popular German song by Trude Herr, which was later covered by Ella Endlich, Florian Silbereisen and Patrizio Buanne. The song is about saying goodbye to someone you love, but knowing that a part of them will always stay with you. If you want to learn how to play this beautiful song on guitar, here are some tips and resources to help you.
-
- 
-
-- The song is in the key of C major, and uses the chords Dm, F, Am, G and C. You can use a capo on the 6th fret to make it easier to play along with the original recording.
-
-- The strumming pattern is simple and consistent throughout the song. You can use a down-down-up-up-down-up pattern for each chord, or experiment with different variations.
-
-- The song has four verses, a pre-chorus and a chorus. The chorus repeats twice at the end of the song, with a slight variation in the lyrics. The melody is catchy and easy to sing along with.
-
-- If you want to see the chords and lyrics for the song, you can visit [this link](https://tabs.ultimate-guitar.com/tab/trude-herr/niemals-geht-man-so-ganz-chords-706646) for the original version by Trude Herr, or [this link](https://tabs.ultimate-guitar.com/tab/ella-endlich/niemals-geht-man-so-ganz-chords-3803696) for the modern version by Ella Endlich, Florian Silbereisen and Patrizio Buanne.
-
-- If you want to hear how the song sounds like, you can watch [this video](https://www.youtube.com/watch?v=1MpSInOiMNs) of the radio version by Ella Endlich, Florian Silbereisen and Patrizio Buanne.
-
-
-
-Niemals Geht Man So Ganz is a touching and timeless song that expresses the bittersweet feeling of parting ways with someone you care about. By learning how to play it on guitar, you can share this emotion with yourself and others. Have fun and enjoy!
-
-  
-
-The song Niemals Geht Man So Ganz was written by Trude Herr, a German actress, singer and theater director, who was born in 1927 in Cologne and died in 1991 in France. She was known for her distinctive voice and her songs in both standard German and Rhineland dialect. She also had a successful career in film and theater, working with famous actors like Willy Millowitsch and Heinz Rühmann.
-
- 
-
-The song was first released in 1987 as a duet with Wolfgang Niedecken, the lead singer of the rock band BAP. It was a tribute to their mutual friend and colleague Tommy Engel, who had left the folk band Bläck Fööss. The song became a hit and reached number 20 on the German charts. It was also performed by Trude Herr, Wolfgang Niedecken and Tommy Engel together at various occasions.
-
- 
-
-In 2012, the song was covered by Ella Endlich, Florian Silbereisen and Patrizio Buanne, three popular singers of different genres. They recorded a radio version and a live version of the song, which were both well received by the fans and critics. The song was also featured on the album Niemals Geht Man So Ganz by Ella Endlich, which included other songs by Trude Herr.
-
- 1b8d091108
-
- 
-
- 
-
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download My PlayHome Plus for Free and Explore a Mini Town on PC.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download My PlayHome Plus for Free and Explore a Mini Town on PC.md
deleted file mode 100644
index 68bd4119e01beb12ec21a442abf99afe29f2dd9a..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Download My PlayHome Plus for Free and Explore a Mini Town on PC.md	
+++ /dev/null
@@ -1,102 +0,0 @@
-<br />
-<h1>How to Download My PlayHome Plus for Free</h1>
-<p>My PlayHome Plus is a popular doll house game for kids that lets them explore and use everything in the house. The game combines all the original My PlayHome apps into one giant world, where you can jump between houses, stores, school and hospital. You can also enjoy new areas like the food court, where you can relax in four different fast food shops. My PlayHome Plus is a fun and interactive game that stimulates your child's imagination and creativity.</p>
-<h2>how to download my playhome plus for free</h2><br /><p><b><b>Download</b> &#9675; <a href="https://ssurll.com/2uO0nE">https://ssurll.com/2uO0nE</a></b></p><br /><br />
-<p>However, My PlayHome Plus is not a free game. You have to pay $4.99 to download it from the Google Play Store or the App Store. If you don't want to spend money on this game, you might be wondering if there is a way to download it for free. The answer is yes, but it is not easy or safe. In this article, we will show you how to download My PlayHome Plus for free on Android and iOS devices, as well as the risks and drawbacks of doing so.</p>
-<h2>What is My PlayHome Plus?</h2>
-<p>My PlayHome Plus is a doll house game for the iGeneration. It is developed by Shimon Young, a father of three who wanted to create a game that his kids could enjoy. He launched the first My PlayHome app in 2011, and since then he has released several more apps that expand the play world. In 2020, he released My PlayHome Plus, which combines all the previous apps into one app.</p>
-<h3>Features of My PlayHome Plus</h3>
-<p>Some of the features of My PlayHome Plus are:</p>
-<ul>
-<li>Massively interactive: your kids can explore and use everything in the house, from the closets, TV and shower, to the fridge, oven and pizza. They can also interact with the characters, who eat, sleep, shower, brush their teeth and more.</li>
-<li>Realistic and detailed: the game has realistic graphics and sounds that make the play world come alive. The game also has many details that add to the fun, such as changing music CDs, blowing bubbles and turning off lights.</li>
-<li>Easy and fun: the game is designed so that even a 2 year old can use it, yet detailed enough to entertain an 8 year old. The game has no rules or goals, so your kids can play however they want.</li>
-<li>Huge and varied: the game has a huge play world that includes several houses, stores, school and hospital. You can also access new areas like the food court, where you can choose from pizza, sushi, coffee or burgers.</li>
-</ul>
-<h3>Why download My PlayHome Plus for free?</h3>
-<p>You might want to download My PlayHome Plus for free because:</p>
-<p>How to get my playhome plus app for free on android<br />
-My playhome plus free download apk for android devices<br />
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-My playhome plus premium apk download - how to get the full version for free</p>
-<ul>
-<li>You don't want to pay $4.99 for a game that your kids might get bored of soon.</li>
-<li>You want to try the game before buying it.</li>
-<li>You want to access all the content without having to buy additional apps.</li>
-</ul>
-<h2>How to download My PlayHome Plus for free on Android</h2>
-<p>If you have an Android device, you can download My PlayHome Plus for free by installing an APK file. An APK file is an Android application package that contains the code and resources of an app. You can find APK files of various apps on the internet, but you have to be careful about the source and the quality of the file. Here are the steps to download My PlayHome Plus for free on Android using an APK file:</p>
-<h3>Step 1: Enable unknown sources</h3>
-<p>Before you can install an APK file, you have to enable unknown sources on your device. This means that you allow your device to install apps from sources other than the Google Play Store. To do this, go to Settings > Security > Unknown sources and toggle it on. You might see a warning message that installing apps from unknown sources can harm your device, but you can ignore it if you trust the source of the APK file.</p>
-<h3>Step 2: Find a reliable APK file</h3>
-<p>The next step is to find a reliable APK file of My PlayHome Plus. You can search for it on Google or use a website that offers APK files, such as APKPure, APKMirror or APKMonk. However, you have to be careful about the quality and safety of the file, as some APK files might contain malware or viruses that can harm your device or steal your data. You can check the reviews, ratings and comments of the file before downloading it. You can also scan the file with an antivirus app before installing it.</p>
-<h3>Step 3: Install the APK file</h3>
-<p>Once you have downloaded the APK file, you can install it by tapping on it. You might see a prompt that asks you to confirm the installation. You might also see a list of permissions that the app requires, such as access to your storage, camera or microphone. You can review the permissions and decide whether to grant them or not. After that, the installation will begin and you will see a progress bar. When the installation is complete, you will see a message that says "App installed".</p>
-<h3>Step 4: Enjoy the game</h3>
-<p>Now you can enjoy My PlayHome Plus for free on your Android device. You can find the app icon on your home screen or in your app drawer. Tap on it to launch the game and start playing. You can explore and use everything in the game world, from the houses, stores, school and hospital, to the food court and more.</p>
-<h2>How to download My PlayHome Plus for free on iOS</h2>
-<p>If you have an iOS device, you can download My PlayHome Plus for free by installing an IPA file. An IPA file is an iOS application archive that contains the code and resources of an app. However, unlike Android devices, iOS devices do not allow you to install apps from unknown sources by default. You have to jailbreak your device first, which means that you remove the restrictions imposed by Apple on your device. Jailbreaking your device allows you to install apps from sources other than the App Store, such as Cydia or Sileo. Here are the steps to download My PlayHome Plus for free on iOS using an IPA file:</p>
-<h3>Step 1: Jailbreak your device</h3>
-<p>The first step is to jailbreak your device. Jailbreaking your device is a risky and complicated process that can void your warranty, damage your device or expose it to security threats. Therefore, we do not recommend doing it unless you know what you are doing and are willing to take the consequences. There are different methods and tools to jailbreak different iOS devices and versions, so you have to do some research before attempting it. Some of the popular jailbreaking tools are Checkra1n, Unc0ver and Chimera.</p>
-<h3>Step 2: Install Cydia or Sileo</h3>
-<p>After jailbreaking your device, you have to install Cydia or Sileo on it. Cydia and Sileo are alternative app stores that offer apps that are not available on the App Store, such as tweaks, themes and games. You can install Cydia or Sileo by following the instructions of the jailbreaking tool that you used. Once you have installed Cydia or Sileo, you can launch it and browse through its categories and repositories.</p>
-<h3>Step 3: Find a compatible IPA file</h3>
-<p>The next step is to find a compatible IPA file of My PlayHome Plus. You can search for it on Google or use a website that offers IPA files, such as Panda Helper, AppCake or iPABox. However, you have to be careful about the quality and safety of the file, as some IPA files might contain malware or viruses that can harm your device or steal your data. You can check the reviews, ratings and comments of the file before downloading it.</p>
-<h3>Step 4: Install the IPA file</h3>
-<p>Once you have downloaded the IPA file, you can install it by using a file manager app, such as Filza or iFile. You can also use a computer program, such as Cydia Impactor or AltStore. You have to locate the IPA file on your device or computer and tap or drag it to install it. You might see a prompt that asks you to confirm the installation. You might also see a list of permissions that the app requires, such as access to your storage, camera or microphone. You can review the permissions and decide whether to grant them or not. After that, the installation will begin and you will see a progress bar. When the installation is complete, you will see a message that says "App installed".</p>
-<h3>Step 5: Enjoy the game</h3>
-<p>Now you can enjoy My PlayHome Plus for free on your iOS device. You can find the app icon on your home screen or in your app drawer. Tap on it to launch the game and start playing. You can explore and use everything in the game world, from the houses, stores, school and hospital, to the food court and more.</p>
-<h2>Risks and drawbacks of downloading My PlayHome Plus for free</h2>
-<p>While downloading My PlayHome Plus for free might sound tempting, it is not without risks and drawbacks. Some of the possible consequences of downloading My PlayHome Plus for free are:</p>
-<h3>Legal issues</h3>
-<p>Downloading My PlayHome Plus for free is illegal, as it violates the intellectual property rights of the developer. You are essentially stealing their work and depriving them of their revenue. This can result in legal action from the developer or the authorities, who can track your IP address and device information. You might face fines, lawsuits or even jail time for piracy.</p>
-<h3>Security risks</h3>
-<p>Downloading My PlayHome Plus for free can expose your device to security risks, such as malware or viruses that can harm your device or steal your data. The APK or IPA file that you download might contain malicious code that can infect your device or access your personal information, such as your contacts, photos, messages or bank accounts. This can lead to identity theft, fraud or blackmail.</p>
-<h3>Performance issues</h3>
-<p>Downloading My PlayHome Plus for free can affect the performance of your device and the game itself. The APK or IPA file that you download might not be compatible with your device model or software version, which can cause crashes, glitches or errors. The game might also run slower or lag due to the lack of optimization or updates from the developer.</p>
-<h3>Missing updates and features</h3>
-<p>Downloading My PlayHome Plus for free can prevent you from accessing the latest updates and features of the game. The developer might release new content, bug fixes or improvements for the game, but you might not be able to get them if you download the game for free. You might also miss out on some features that are exclusive to the paid version of the game, such as cloud saving, parental controls or multiplayer mode.</p>
-<h2>Conclusion</h2>
-<p>My PlayHome Plus is a fun and interactive doll house game for kids that lets them explore and use everything in a huge play world. However, it is not a free game and you have to pay $4.99 to download it from the official app stores. If you want to download it for free, you have to use an APK file for Android devices or an IPA file for iOS devices. However, this is not easy or safe, as it involves jailbreaking your device, enabling unknown sources and installing files from unreliable sources. You also risk facing legal issues, security risks, performance issues and missing updates and features.</p>
-<p>Therefore, we do not recommend downloading My PlayHome Plus for free, as it is unfair to the developer and risky for you. Instead, we suggest that you support the developer by buying the game from the official app stores and enjoy it without any worries.</p>
- <h2>Frequently Asked Questions</h2>
-<ul>
-<li><b>Q: How much does My PlayHome Plus cost?</b></li>
-<li>A: My PlayHome Plus costs $4.99 on both Google Play Store and App Store.</li>
-<li><b>Q: Is My PlayHome Plus suitable for kids?</b></li>
-<li>A: Yes, My PlayHome Plus is suitable for kids aged 2 to 8 years old. It is a kid-friendly game that has no violence, ads or in-app purchases.</li>
-<li><b>Q: How many areas are there in My PlayHome Plus?</b></li>
-<li>A: There are 12 areas in My PlayHome Plus: four houses, four stores, school, hospital, food court and playground.</li>
-<li><b>Q: Can I play My PlayHome Plus offline?</b></li>
-<li>A: Yes, you can play My PlayHome Plus offline, as it does not require an internet connection to play.</li>
-<li><b>Q: Can I play My PlayHome Plus with my friends?</b></li>
-<li>A: Yes, you can play My PlayHome Plus with your friends if you have the paid version of the game. You can use the multiplayer mode to join or host a game with up to four players. You can also chat and share stickers with your friends in the game.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skaur20/AW-02-H5_AR-VR-IOT/README.md b/spaces/skaur20/AW-02-H5_AR-VR-IOT/README.md
deleted file mode 100644
index 200226730b02423a87ac6c9d062b0b6495f51f87..0000000000000000000000000000000000000000
--- a/spaces/skaur20/AW-02-H5_AR-VR-IOT/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: AW-02-H5 AR-VR-IOT
-emoji: 🏢
-colorFrom: blue
-colorTo: gray
-sdk: static
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/skf15963/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/nn.py b/spaces/skf15963/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/nn.py
deleted file mode 100644
index b28bd83cf23b4e19868afc2075b11ca1cfbd0e8d..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/disco_project/guided_diffusion/guided_diffusion/nn.py
+++ /dev/null
@@ -1,190 +0,0 @@
-"""
-Various utilities for neural networks.
-"""
-
-import math
-
-import torch as th
-import torch.nn as nn
-
-
-# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
-class SiLU(nn.Module):
-    def forward(self, x):
-        return x * th.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}")
-
-
-def update_ema(target_params, source_params, rate=0.99):
-    """
-    Update target parameters to be closer to those of source parameters using
-    an exponential moving average.
-
-    :param target_params: the target parameter sequence.
-    :param source_params: the source parameter sequence.
-    :param rate: the EMA rate (closer to 1 means slower).
-    """
-    for targ, src in zip(target_params, source_params):
-        targ.detach().mul_(rate).add_(src, alpha=1 - rate)
-
-
-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)
-
-
-def timestep_embedding(timesteps, dim, max_period=10000):
-    """
-    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.
-    """
-    half = dim // 2
-    freqs = th.exp(
-        -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half
-    ).to(device=timesteps.device)
-    args = timesteps[:, None].float() * freqs[None]
-    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
-    return embedding
-
-
-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(th.autograd.Function):
-    @staticmethod
-    @th.cuda.amp.custom_fwd
-    def forward(ctx, run_function, length, *args):
-        ctx.run_function = run_function
-        ctx.input_length = length
-        ctx.save_for_backward(*args)
-        with th.no_grad():
-            output_tensors = ctx.run_function(*args[:length])
-        return output_tensors
-
-    @staticmethod
-    @th.cuda.amp.custom_bwd
-    def backward(ctx, *output_grads):
-        args = list(ctx.saved_tensors)
-
-        # Filter for inputs that require grad. If none, exit early.
-        input_indices = [i for (i, x) in enumerate(args) if x.requires_grad]
-        if not input_indices:
-            return (None, None) + tuple(None for _ in args)
-
-        with th.enable_grad():
-            for i in input_indices:
-                if i < ctx.input_length:
-                    # Not sure why the OAI code does this little
-                    # dance. It might not be necessary.
-                    args[i] = args[i].detach().requires_grad_()
-                    args[i] = args[i].view_as(args[i])
-            output_tensors = ctx.run_function(*args[:ctx.input_length])
-
-        if isinstance(output_tensors, th.Tensor):
-            output_tensors = [output_tensors]
-
-        # Filter for outputs that require grad. If none, exit early.
-        out_and_grads = [(o, g) for (o, g) in zip(output_tensors, output_grads) if o.requires_grad]
-        if not out_and_grads:
-            return (None, None) + tuple(None for _ in args)
-
-        # Compute gradients on the filtered tensors.
-        computed_grads = th.autograd.grad(
-            [o for (o, g) in out_and_grads],
-            [args[i] for i in input_indices],
-            [g for (o, g) in out_and_grads]
-        )
-
-        # Reassemble the complete gradient tuple.
-        input_grads = [None for _ in args]
-        for (i, g) in zip(input_indices, computed_grads):
-            input_grads[i] = g
-        return (None, None) + tuple(input_grads)
diff --git a/spaces/stephenleo/stripnet/markdown/centrality.md b/spaces/stephenleo/stripnet/markdown/centrality.md
deleted file mode 100644
index c5e44e93156ae5b20237099c2e17a58229c6a777..0000000000000000000000000000000000000000
--- a/spaces/stephenleo/stripnet/markdown/centrality.md
+++ /dev/null
@@ -1,10 +0,0 @@
-- Now that we've contructed our semantic similarity network, we can use Graph Theory on this network to compute the centrality of each node using [NetworkX](https://networkx.org/).
-- The nodes with the highest centrality will be the most important nodes (papers) in our semantic network. [Centrality Wiki](https://en.wikipedia.org/wiki/Centrality)
-- There are several types of centrality depending on the problem statement [Link](https://neo4j.com/developer/graph-data-science/centrality-graph-algorithms/).
-- Since our goal is to find the most important papers that connect all the other papers in our corpus, we'll use [Betweenness Centrality](https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.centrality.betweenness_centrality.html#networkx.algorithms.centrality.betweenness_centrality) by default. Feel free to test out other centrality algorithms using the `Select Centrality Measure` dropdown.
-- The plot is generated using [Plotly](https://plotly.com/python/) and is fully interactive!
-    - Bars are ordered in descending order, so the most important paper in your corpus is the top bar.
-    - Colored by the topic the paper belongs to.
-    - Hover over any bar to view the title and truncated abstract of the paper
-    - Zoom in and out by clicking and dragging over the chart. Double click to zoom out.
-- Click on the `Download plot as png` button on the top right hand side corner to download a .png file of your plot.
\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Airserver Activation Key.md b/spaces/stomexserde/gpt4-ui/Examples/Airserver Activation Key.md
deleted file mode 100644
index 2332dd965f5114c27138e2288abcdb93ff6b92a0..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Airserver Activation Key.md	
+++ /dev/null
@@ -1,104 +0,0 @@
-
-<h1>What is AirServer and why do you need an activation key?</h1>
-<p>AirServer is a powerful and versatile software that allows you to turn any screen into a wireless mirror receiver. With AirServer, you can cast your screen from any device, such as Mac, PC, iPhone, iPad, Android, Nexus, or Chromebook, to a bigger display, such as a computer monitor, HD TV, or projector. You can use AirServer for various purposes, such as education, business, gaming, entertainment, or collaboration.</p>
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-<p>To use AirServer, you need an activation key that grants you a valid license for the software. An activation key is a unique code that you enter during the installation or launch of AirServer. By activating AirServer with an activation key, you can enjoy the full features and benefits of the software, such as:</p>
-<ul>
-<li>Unlimited mirroring sessions</li>
-<li>No watermark or ads</li>
-<li>High-quality video and audio streaming</li>
-<li>Multi-device support</li>
-<li>Security and privacy options</li>
-<li>Free updates and support</li>
-</ul>
-<p>In this article, we will show you how to purchase an AirServer activation key, how to activate AirServer on your device, how to use AirServer to mirror your screen, and how to update or deactivate AirServer. By the end of this article, you will be able to enjoy the best screen mirroring experience with AirServer.</p>
- <h2>How to purchase an AirServer activation key?</h2>
-<p>If you want to use AirServer with an activation key, you need to purchase one from the official website or from a reseller. There are different license types and prices for AirServer, depending on your needs and preferences. Here are the main license types for AirServer:</p>
-<p></p>
-<ul>
-<li>AirServer Windows 10 Desktop Edition: This license is for Windows 10 users who want to use AirServer on their personal computers. It costs $19.99 per computer.</li>
-<li>AirServer Universal: This license is for Windows 7/8/8.1/10 users who want to use AirServer on their personal computers or Surface Hub devices. It costs $19.99 per computer or $79.99 per Surface Hub.</li>
-<li>AirServer for Mac: This license is for Mac users who want to use AirServer on their personal computers. It costs $19.99 per computer.</li>
-<li>AirServer Connect 4K UHD: This license is for users who want to use AirServer on a dedicated hardware device that can receive 4K UHD video streams. It costs $999 per device.</li>
-<li>AirServer Xbox Edition: This license is for Xbox One users who want to use AirServer on their gaming consoles. It costs $19.99 per console.</li>
-<li>AirServer Education/Non-Profit/Bulk License: This license is for users who want to use AirServer in educational or non-profit organizations or who want to buy multiple licenses at a discounted price. The price varies depending on the number of licenses and the type of organization.</li>
-</ul>
-<p>To purchase an AirServer activation key, you can follow these steps:</p>
-<ol>
-<li>Go to the official website of AirServer and click on the "Buy" button on the top right corner.</li>
-<li>Select the license type that suits your needs and click on the "Buy Now" button.</li>
-<li>Enter your billing information and payment method and click on the "Place Order" button.</li>
-<li>Check your email for a confirmation message that contains your activation key and a download link for AirServer.</li>
-<li>Download and install AirServer on your device using the link provided in the email.</li>
-</ol>
-<p>If you prefer to buy an AirServer activation key offline, you can contact a reseller in your region and place your order with them. You can find a list of resellers on the official website of AirServer. You will receive your activation key and download link by email or by mail, depending on the reseller's policy.</p>
- <h2>How to activate AirServer on your device?</h2>
-<p>Once you have purchased an AirServer activation key, you can activate AirServer on your device and start using it. Before you activate AirServer, make sure that your device meets the system requirements and compatibility of AirServer. Here are the main system requirements and compatibility of AirServer:</p>
-<ul>
-<li>AirServer Windows 10 Desktop Edition: You need a Windows 10 PC with a 64-bit processor, 4 GB of RAM, and DirectX 11 support. You also need a Wi-Fi or Ethernet connection and a display that supports HDCP.</li>
-<li>AirServer Universal: You need a Windows 7/8/8.1/10 PC or a Surface Hub device with a 64-bit processor, 4 GB of RAM, and DirectX 11 support. You also need a Wi-Fi or Ethernet connection and a display that supports HDCP.</li>
-<li>AirServer for Mac: You need a Mac running macOS 10.9 or later with a 64-bit processor, 4 GB of RAM, and OpenGL support. You also need a Wi-Fi or Ethernet connection and a display that supports HDCP.</li>
-<li>AirServer Connect 4K UHD: You need an HDMI cable, a power adapter, and an internet connection. You also need a display that supports 4K UHD resolution and HDCP 2.2.</li>
-<li>AirServer Xbox Edition: You need an Xbox One console with an internet connection and a display that supports HDCP.</li>
-</ul>
-<p>To activate AirServer on your device, you can follow these instructions:</p>
-<ol>
-<li>Download and install AirServer on your device using the link provided in your confirmation email or from the official website of AirServer.</li>
-<li>Launch AirServer on your device and click on the "Activate" button on the main screen.</li>
-<li>Enter your activation key in the text box and click on the "Activate" button again.</li>
-<li>Wait for a few seconds until you see a message that says "Activation Successful".</li>
-<li>Check your license status by clicking on the "About" button on the main screen. You should see your license type, expiration date, and activation code.</li>
-</ol>
- <h2>How to use AirServer to mirror your screen?</h2>
-<p>After you have activated AirServer on your device, you can use it to mirror your screen from any other device that supports screen mirroring protocols. AirServer supports various protocols, such as AirPlay, Google Cast, Miracast, MiraScreen, DLNA, and more. You can mirror your screen from devices such as iPhone, iPad, Android, Nexus, Chromebook, Mac, PC, or Xbox One.</p>
-<p>To use AirServer to mirror your screen, you can follow these tips and tricks:</p>
-<ul>
-<li>Make sure that both your device and the device that you want to mirror from are connected to the same network.</li>
-<li>On the device that you want to mirror from, open the screen mirroring app or feature that is compatible with AirServer. For example, if you want to mirror from an iPhone, you can use AirPlay; if you want to mirror from an Android phone, you can use Google Cast; if you want to mirror from a Windows PC, you can use Miracast; etc.</li>
-<li>Select your device name from the list of available receivers. Your device name should be something like "AirServer (your computer name)" or "AirServer Connect (your device serial number)".</li>
-<li>Wait for a few seconds until you see your screen mirrored on your device. You can adjust the resolution, orientation, audio output and quality settings by clicking on the "Settings" button on the main screen of AirServer.</li>
-<li>To stop mirroring, simply disconnect your device from AirServer by closing the screen mirroring app or feature on your device.</li>
-</ul>
-<p>To use AirServer to mirror your screen, you can also follow this troubleshooting guide if you encounter any issues or errors:</p>
-<ul>
-<li>If you cannot find your device name on the list of available receivers, make sure that your device and the device that you want to mirror from are connected to the same network and that AirServer is running on your device.</li>
-<li>If you see a black screen or a frozen image on your device, make sure that your network connection is stable and that your device has enough memory and CPU resources to run AirServer.</li>
-<li>If you hear no sound or a distorted sound on your device, make sure that your device's volume is not muted and that AirServer is using the correct audio output device. You can change the audio output device by clicking on the "Settings" button on the main screen of AirServer and selecting the "Audio" tab.</li>
-<li>If you experience lag or stuttering on your device, make sure that your network bandwidth is sufficient and that your device's resolution and frame rate are compatible with AirServer. You can lower the resolution and frame rate by clicking on the "Settings" button on the main screen of AirServer and selecting the "Mirroring" tab.</li>
-<li>If you see a watermark or ads on your device, make sure that you have activated AirServer with a valid activation key. You can check your activation status by clicking on the "About" button on the main screen of AirServer.</li>
-</ul>
- <h2>How to update or deactivate AirServer?</h2>
-<p>It is important to keep AirServer updated to enjoy the latest features, improvements, and bug fixes. To update AirServer, you can follow these steps:</p>
-<ol>
-<li>Go to the official website of AirServer and click on the "Download" button on the top right corner.</li>
-<li>Select the version of AirServer that matches your license type and device.</li>
-<li>Download and install the latest version of AirServer on your device. You do not need to uninstall the previous version or re-enter your activation key.</li>
-<li>Launch AirServer and enjoy the new features and improvements.</li>
-</ol>
-<p>You can also check for updates manually by clicking on the "About" button on the main screen of AirServer and selecting the "Check for Updates" option. If there is a new version available, you will see a notification and a download link.</p>
-<p>If you want to deactivate AirServer on your device or transfer your license to another device, you can follow these steps:</p>
-<ol>
-<li>Launch AirServer and click on the "About" button on the main screen.</li>
-<li>Select the "Deactivate License" option and confirm your choice.</li>
-<li>Wait for a few seconds until you see a message that says "Deactivation Successful".</li>
-<li>You can now activate AirServer on another device using the same activation key. You can activate AirServer up to five times with one activation key.</li>
-</ol>
- <h1>Conclusion</h1>
-<p>AirServer is a great software that allows you to turn any screen into a wireless mirror receiver. With AirServer, you can cast your screen from any device to a bigger display with high-quality video and audio streaming. You can use AirServer for various purposes, such as education, business, gaming, entertainment, or collaboration.</p>
-<p>To use AirServer, you need an activation key that grants you a valid license for the software. You can purchase an activation key from the official website or from a reseller. You can activate AirServer on your device by entering your activation key during the installation or launch of AirServer. You can use AirServer to mirror your screen from any device that supports screen mirroring protocols, such as AirPlay, Google Cast, Miracast, MiraScreen, DLNA, and more. You can also update or deactivate AirServer if you want to enjoy the latest features or transfer your license to another device.</p>
-<p>We hope that this article has helped you understand how to use AirServer with an activation key. If you want to try AirServer for yourself, you can purchase an activation key from <a href="">here</a>. You can also download a free trial version of AirServer from <a href="">here</a>. Thank you for reading this article and feel free to leave us feedback or contact us if you have any questions or comments.</p>
- <h2>FAQs</h2>
-<ul>
-<li><strong>Q: How long does an AirServer activation key last?</strong></li>
-<li>A: An AirServer activation key lasts for a lifetime, meaning that you can use it forever as long as you do not exceed the activation limit of five times per key. However, you may need to renew your license if you want to receive updates and support after one year.</li>
-<li><strong>Q: How can I get a free AirServer activation key?</strong></li>
-<li>A: There is no official way to get a free AirServer activation key, as it is a paid software that requires a valid license to use. However, you can download a free trial version of AirServer from the official website and use it for seven days without an activation key. The trial version has all the features of the full version, except that it has a watermark and ads on the screen.</li>
-<li><strong>Q: How can I recover my AirServer activation key if I lose it?</strong></li>
-<li>A: If you lose your AirServer activation key, you can recover it by contacting the support team of AirServer or the reseller that you bought it from. You will need to provide your order number, email address, and proof of purchase to verify your identity and ownership of the license.</li>
-<li><strong>Q: How can I share my AirServer activation key with others?</strong></li>
-<li>A: You can share your AirServer activation key with others if you have purchased a bulk license or an education/non-profit license that allows multiple users. However, you should not share your activation key with others if you have purchased a personal license or a single-user license, as this may violate the terms and conditions of AirServer and result in the deactivation of your license.</li>
-<li><strong>Q: How can I contact the support team of AirServer if I have any issues or questions?</strong></li>
-<li>A: You can contact the support team of AirServer by filling out the online form on the official website of AirServer or by sending an email to support@airserver.com. You can also check the online documentation and FAQ section on the website for more information and guidance.</li>
-</ul></p> b2dd77e56b<br />
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\ No newline at end of file
diff --git a/spaces/stomexserde/gpt4-ui/Examples/Bhaag Milkha Bhaag Movie Download Full Hd.md b/spaces/stomexserde/gpt4-ui/Examples/Bhaag Milkha Bhaag Movie Download Full Hd.md
deleted file mode 100644
index 9189359ed0e4d7f66ca78d87542ab9c7a4317249..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/Bhaag Milkha Bhaag Movie Download Full Hd.md	
+++ /dev/null
@@ -1,17 +0,0 @@
-<br />
-Here is the content I generated for you:
-
-<h1>Bhaag Milkha Bhaag Movie Download Full HD: A Tribute to the Flying Sikh</h1>
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-<p></p>
-<p>The film also showcases some of the most memorable moments of Milkha Singh's career, such as his participation in the 1956 Melbourne Olympics, where he met his idol Jesse Owens; his historic victory in the 1958 Commonwealth Games, where he became the first Indian to win a gold medal in athletics; his heartbreaking defeat in the 1960 Rome Olympics, where he missed the bronze medal by a whisker; his redemption in the 1962 Asian Games, where he won two gold medals; and his goodwill gesture in the 1964 Lahore Indo-Pak meet, where he defeated his Pakistani rival Abdul Khaliq and earned the nickname "The Flying Sikh" from General Ayub Khan.</p>
-<p>Bhaag Milkha Bhaag is a film that celebrates the spirit of Milkha Singh, who inspired millions of Indians with his passion, dedication, and determination. He was not only a great athlete, but also a great human being, who contributed to the society through various social causes. He was awarded the Padma Shri, India's fourth-highest civilian honour, in 1959 for his achievements in sports. He also donated all his medals and trophies to the nation and established the Milkha Singh Charitable Trust to help poor and needy sportspeople.</p> 7196e7f11a<br />
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diff --git a/spaces/stratussox/yolov5_inference/utils/loggers/clearml/__init__.py b/spaces/stratussox/yolov5_inference/utils/loggers/clearml/__init__.py
deleted file mode 100644
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deleted file mode 100644
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-<p>Dieses Programm funktioniert bei 95 % der Spiele von Deutschland-spielt.de[^2^], die verschiedene Genres wie Wimmelbild, 3-Gewinnt oder Klick-Management umfassen. Diese Spiele sind vor allem für zwischendurch oder für Kinder geeignet und erfreuen sich großer Beliebtheit bei vielen Nutzern[^2^].</p>
-<p>Deutschland spielt unwrapper exe ist jedoch kein offizielles Produkt von Deutschland-spielt.de und verstößt möglicherweise gegen deren Nutzungsbedingungen oder Urheberrechte. Die Verwendung dieses Programms erfolgt daher auf eigene Gefahr und Verantwortung. Es ist ratsam, die Spiele legal zu erwerben oder nur die kostenlose Testversion zu nutzen, um die Entwickler zu unterstützen und mögliche rechtliche Konsequenzen zu vermeiden.</p><p>Deutschland-spielt.de ist eine der größten Plattformen für Casual Games in Deutschland. Seit 2001 bietet die Seite über 2.500 Spiele zum Download oder Online-Spielen an. Die Spiele sind in verschiedene Kategorien wie Abenteuer, Denkspiele, Puzzle oder Sport unterteilt und können nach verschiedenen Kriterien wie Neuheiten, Beliebtheit oder Bewertung sortiert werden. Die Seite bietet außerdem einen Club an, bei dem man für eine monatliche Gebühr unbegrenzten Zugriff auf alle Spiele erhält und von exklusiven Vorteilen profitiert.</p>
-<p>Deutschland-spielt.de legt viel Wert auf die Qualität und Sicherheit seiner Spiele. Alle Spiele werden vor der Veröffentlichung auf Viren und Malware geprüft und regelmäßig aktualisiert. Die Seite verfügt über ein Trusted Shops Gütesiegel und bietet verschiedene Zahlungsmöglichkeiten wie PayPal, Kreditkarte oder Sofortüberweisung an. Die Seite hat außerdem einen Kundenservice, der per E-Mail oder Telefon erreichbar ist und bei Fragen oder Problemen hilft.</p>
-<p>Deutschland-spielt.de ist eine ideale Seite für alle, die gerne Casual Games spielen und eine große Auswahl an verschiedenen Genres und Schwierigkeitsgraden suchen. Die Seite bietet sowohl kostenlose als auch kostenpflichtige Spiele an, die man auf dem PC oder im Browser spielen kann. Die Seite ist übersichtlich gestaltet und einfach zu bedienen. Die Spiele sind unterhaltsam, abwechslungsreich und für alle Altersgruppen geeignet.</p>
-<p></p><p>Deutschland-spielt.de ist nicht die einzige Seite, die Casual Games anbietet. Es gibt viele andere Seiten, die ähnliche oder sogar die gleichen Spiele haben. Einige Beispiele sind Big Fish Games, Zylom, GameHouse oder Alawar. Diese Seiten haben ebenfalls eine große Auswahl an verschiedenen Genres und bieten sowohl kostenlose als auch kostenpflichtige Spiele an. Einige Seiten haben auch eigene Clubs oder Abonnements, die zusätzliche Vorteile bieten.</p>
-<p>Die Unterschiede zwischen den verschiedenen Seiten liegen vor allem in der Gestaltung, dem Preis, dem Service und der Verfügbarkeit der Spiele. Manche Seiten haben mehr oder weniger Spiele als andere, manche haben exklusive Spiele oder Angebote, manche haben bessere oder schlechtere Bewertungen oder Kundenmeinungen. Es kommt also darauf an, was man persönlich bevorzugt und was einem wichtig ist. Es kann sich lohnen, verschiedene Seiten zu vergleichen und auszuprobieren, um die beste Seite für sich zu finden.</p>
-<p>Deutschland-spielt.de ist jedoch eine der bekanntesten und beliebtesten Seiten für Casual Games in Deutschland. Die Seite hat viele treue Fans und Stammkunden, die die Qualität und Vielfalt der Spiele schätzen. Die Seite hat auch viele positive Bewertungen und Auszeichnungen erhalten und gilt als eine der führenden Seiten in diesem Bereich. Die Seite ist daher eine gute Wahl für alle, die gerne Casual Games spielen und eine vertrauenswürdige und zuverlässige Seite suchen.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Sony Vegas 32 Bit Free.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Sony Vegas 32 Bit Free.md
deleted file mode 100644
index ad8513002dade3ea1b53aefa7841658b241b4a71..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Sony Vegas 32 Bit Free.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-
-<h1>How to Download Sony Vegas Pro 10.0e (32-bit) for Free</h1>
-<p>Sony Vegas Pro is a professional video and audio editor software for Windows PC. It allows you to edit high-resolution formats up to 4K, apply hundreds of effects and transitions, design dynamic titles, create custom DVDs and Blu-ray discs, and more. If you are looking for a powerful and versatile video editing software, Sony Vegas Pro might be the right choice for you.</p>
-<p>However, Sony Vegas Pro is not a cheap software. The latest version, VEGAS Pro 20.0 Build 326, costs $599 for a perpetual license or $19.99 per month for a subscription. If you are on a tight budget or just want to try out the software before buying it, you might be interested in downloading Sony Vegas Pro 10.0e (32-bit) for free.</p>
-<h2>Sony vegas 32 bit free</h2><br /><p><b><b>Download</b> --->>> <a href="https://cinurl.com/2uEZ5E">https://cinurl.com/2uEZ5E</a></b></p><br /><br />
-<p>Sony Vegas Pro 10.0e (32-bit) is an older version of the software that was released in 2011. It still has many features and functions that can meet your video editing needs, such as stereoscopic 3D support, multicamera editing, GPU acceleration, color correction and grading, audio mixing and editing, and more. It also supports Windows (All Versions), so you don't have to worry about compatibility issues.</p>
-<p>So, how can you download Sony Vegas Pro 10.0e (32-bit) for free? Here are the steps:</p>
-<ol>
-<li>Go to <a href="https://www.filehorse.com/download-vegas-pro/25303/">FileHorse.com</a>, a trusted website that offers free software downloads.</li>
-<li>Search for "Sony Vegas Pro 10.0e (32-bit)" or click on this <a href="https://www.filehorse.com/download-vegas-pro/25303/">link</a>.</li>
-<li>Click on the green "Download" button on the right side of the page.</li>
-<li>Wait for the download to finish and then open the installer file.</li>
-<li>Follow the instructions on the screen to install Sony Vegas Pro 10.0e (32-bit) on your PC.</li>
-<li>Enjoy using Sony Vegas Pro 10.0e (32-bit) for free!</li>
-</ol>
-<p>Note: This is a trial version of Sony Vegas Pro 10.0e (32-bit) that will expire after 30 days of use. You can extend the trial period by registering with a valid email address on the Sony Creative Software website. However, this is not a legal way to use the software permanently. If you like Sony Vegas Pro 10.0e (32-bit) and want to continue using it after the trial period, you should buy a license from the official website or upgrade to a newer version.</p>
-<p>Alternatively, you can also download other versions of Sony Vegas Pro for free from other websites, such as <a href="https://www.techspot.com/downloads/5279-sony-vegas-pro.html">TechSpot.com</a>, <a href="https://archive.org/details/vegaspro11_32bit">Archive.org</a>, or <a href="https://archive.org/details/vegaspro90_32bit">Archive.org</a>. However, these versions may not be as stable or secure as the one from FileHorse.com, so download them at your own risk.</p>
-<p>We hope this article helped you learn how to download Sony Vegas Pro 10.0e (32-bit) for free. If you have any questions or feedback, please leave a comment below.</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Airparrot 1.0.4 Serial Number Maker.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Airparrot 1.0.4 Serial Number Maker.md
deleted file mode 100644
index a920eef0c603e199303ec00e793a108398b87d5c..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/Airparrot 1.0.4 Serial Number Maker.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Airparrot 1.0.4 Serial Number Maker</h2><br /><p><b><b>Download File</b> &middot;&middot;&middot; <a href="https://urluss.com/2uCDGn">https://urluss.com/2uCDGn</a></b></p><br /><br />
-<br />
-AirParrot Remote allows you to remotely control AirParrot with your iPhone or iPad. Easily control what's being sent from your computer to Apple TV or ... 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/bricks/non_local.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/bricks/non_local.py
deleted file mode 100644
index 92d00155ef275c1201ea66bba30470a1785cc5d7..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/bricks/non_local.py
+++ /dev/null
@@ -1,306 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from abc import ABCMeta
-
-import torch
-import torch.nn as nn
-
-from ..utils import constant_init, normal_init
-from .conv_module import ConvModule
-from .registry import PLUGIN_LAYERS
-
-
-class _NonLocalNd(nn.Module, metaclass=ABCMeta):
-    """Basic Non-local module.
-
-    This module is proposed in
-    "Non-local Neural Networks"
-    Paper reference: https://arxiv.org/abs/1711.07971
-    Code reference: https://github.com/AlexHex7/Non-local_pytorch
-
-    Args:
-        in_channels (int): Channels of the input feature map.
-        reduction (int): Channel reduction ratio. Default: 2.
-        use_scale (bool): Whether to scale pairwise_weight by
-            `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`.
-            Default: True.
-        conv_cfg (None | dict): The config dict for convolution layers.
-            If not specified, it will use `nn.Conv2d` for convolution layers.
-            Default: None.
-        norm_cfg (None | dict): The config dict for normalization layers.
-            Default: None. (This parameter is only applicable to conv_out.)
-        mode (str): Options are `gaussian`, `concatenation`,
-            `embedded_gaussian` and `dot_product`. Default: embedded_gaussian.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 reduction=2,
-                 use_scale=True,
-                 conv_cfg=None,
-                 norm_cfg=None,
-                 mode='embedded_gaussian',
-                 **kwargs):
-        super(_NonLocalNd, self).__init__()
-        self.in_channels = in_channels
-        self.reduction = reduction
-        self.use_scale = use_scale
-        self.inter_channels = max(in_channels // reduction, 1)
-        self.mode = mode
-
-        if mode not in [
-                'gaussian', 'embedded_gaussian', 'dot_product', 'concatenation'
-        ]:
-            raise ValueError("Mode should be in 'gaussian', 'concatenation', "
-                             f"'embedded_gaussian' or 'dot_product', but got "
-                             f'{mode} instead.')
-
-        # g, theta, phi are defaulted as `nn.ConvNd`.
-        # Here we use ConvModule for potential usage.
-        self.g = ConvModule(
-            self.in_channels,
-            self.inter_channels,
-            kernel_size=1,
-            conv_cfg=conv_cfg,
-            act_cfg=None)
-        self.conv_out = ConvModule(
-            self.inter_channels,
-            self.in_channels,
-            kernel_size=1,
-            conv_cfg=conv_cfg,
-            norm_cfg=norm_cfg,
-            act_cfg=None)
-
-        if self.mode != 'gaussian':
-            self.theta = ConvModule(
-                self.in_channels,
-                self.inter_channels,
-                kernel_size=1,
-                conv_cfg=conv_cfg,
-                act_cfg=None)
-            self.phi = ConvModule(
-                self.in_channels,
-                self.inter_channels,
-                kernel_size=1,
-                conv_cfg=conv_cfg,
-                act_cfg=None)
-
-        if self.mode == 'concatenation':
-            self.concat_project = ConvModule(
-                self.inter_channels * 2,
-                1,
-                kernel_size=1,
-                stride=1,
-                padding=0,
-                bias=False,
-                act_cfg=dict(type='ReLU'))
-
-        self.init_weights(**kwargs)
-
-    def init_weights(self, std=0.01, zeros_init=True):
-        if self.mode != 'gaussian':
-            for m in [self.g, self.theta, self.phi]:
-                normal_init(m.conv, std=std)
-        else:
-            normal_init(self.g.conv, std=std)
-        if zeros_init:
-            if self.conv_out.norm_cfg is None:
-                constant_init(self.conv_out.conv, 0)
-            else:
-                constant_init(self.conv_out.norm, 0)
-        else:
-            if self.conv_out.norm_cfg is None:
-                normal_init(self.conv_out.conv, std=std)
-            else:
-                normal_init(self.conv_out.norm, std=std)
-
-    def gaussian(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = torch.matmul(theta_x, phi_x)
-        pairwise_weight = pairwise_weight.softmax(dim=-1)
-        return pairwise_weight
-
-    def embedded_gaussian(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = torch.matmul(theta_x, phi_x)
-        if self.use_scale:
-            # theta_x.shape[-1] is `self.inter_channels`
-            pairwise_weight /= theta_x.shape[-1]**0.5
-        pairwise_weight = pairwise_weight.softmax(dim=-1)
-        return pairwise_weight
-
-    def dot_product(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = torch.matmul(theta_x, phi_x)
-        pairwise_weight /= pairwise_weight.shape[-1]
-        return pairwise_weight
-
-    def concatenation(self, theta_x, phi_x):
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        h = theta_x.size(2)
-        w = phi_x.size(3)
-        theta_x = theta_x.repeat(1, 1, 1, w)
-        phi_x = phi_x.repeat(1, 1, h, 1)
-
-        concat_feature = torch.cat([theta_x, phi_x], dim=1)
-        pairwise_weight = self.concat_project(concat_feature)
-        n, _, h, w = pairwise_weight.size()
-        pairwise_weight = pairwise_weight.view(n, h, w)
-        pairwise_weight /= pairwise_weight.shape[-1]
-
-        return pairwise_weight
-
-    def forward(self, x):
-        # Assume `reduction = 1`, then `inter_channels = C`
-        # or `inter_channels = C` when `mode="gaussian"`
-
-        # NonLocal1d x: [N, C, H]
-        # NonLocal2d x: [N, C, H, W]
-        # NonLocal3d x: [N, C, T, H, W]
-        n = x.size(0)
-
-        # NonLocal1d g_x: [N, H, C]
-        # NonLocal2d g_x: [N, HxW, C]
-        # NonLocal3d g_x: [N, TxHxW, C]
-        g_x = self.g(x).view(n, self.inter_channels, -1)
-        g_x = g_x.permute(0, 2, 1)
-
-        # NonLocal1d theta_x: [N, H, C], phi_x: [N, C, H]
-        # NonLocal2d theta_x: [N, HxW, C], phi_x: [N, C, HxW]
-        # NonLocal3d theta_x: [N, TxHxW, C], phi_x: [N, C, TxHxW]
-        if self.mode == 'gaussian':
-            theta_x = x.view(n, self.in_channels, -1)
-            theta_x = theta_x.permute(0, 2, 1)
-            if self.sub_sample:
-                phi_x = self.phi(x).view(n, self.in_channels, -1)
-            else:
-                phi_x = x.view(n, self.in_channels, -1)
-        elif self.mode == 'concatenation':
-            theta_x = self.theta(x).view(n, self.inter_channels, -1, 1)
-            phi_x = self.phi(x).view(n, self.inter_channels, 1, -1)
-        else:
-            theta_x = self.theta(x).view(n, self.inter_channels, -1)
-            theta_x = theta_x.permute(0, 2, 1)
-            phi_x = self.phi(x).view(n, self.inter_channels, -1)
-
-        pairwise_func = getattr(self, self.mode)
-        # NonLocal1d pairwise_weight: [N, H, H]
-        # NonLocal2d pairwise_weight: [N, HxW, HxW]
-        # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW]
-        pairwise_weight = pairwise_func(theta_x, phi_x)
-
-        # NonLocal1d y: [N, H, C]
-        # NonLocal2d y: [N, HxW, C]
-        # NonLocal3d y: [N, TxHxW, C]
-        y = torch.matmul(pairwise_weight, g_x)
-        # NonLocal1d y: [N, C, H]
-        # NonLocal2d y: [N, C, H, W]
-        # NonLocal3d y: [N, C, T, H, W]
-        y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels,
-                                                    *x.size()[2:])
-
-        output = x + self.conv_out(y)
-
-        return output
-
-
-class NonLocal1d(_NonLocalNd):
-    """1D Non-local module.
-
-    Args:
-        in_channels (int): Same as `NonLocalND`.
-        sub_sample (bool): Whether to apply max pooling after pairwise
-            function (Note that the `sub_sample` is applied on spatial only).
-            Default: False.
-        conv_cfg (None | dict): Same as `NonLocalND`.
-            Default: dict(type='Conv1d').
-    """
-
-    def __init__(self,
-                 in_channels,
-                 sub_sample=False,
-                 conv_cfg=dict(type='Conv1d'),
-                 **kwargs):
-        super(NonLocal1d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
-
-        self.sub_sample = sub_sample
-
-        if sub_sample:
-            max_pool_layer = nn.MaxPool1d(kernel_size=2)
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            if self.mode != 'gaussian':
-                self.phi = nn.Sequential(self.phi, max_pool_layer)
-            else:
-                self.phi = max_pool_layer
-
-
-@PLUGIN_LAYERS.register_module()
-class NonLocal2d(_NonLocalNd):
-    """2D Non-local module.
-
-    Args:
-        in_channels (int): Same as `NonLocalND`.
-        sub_sample (bool): Whether to apply max pooling after pairwise
-            function (Note that the `sub_sample` is applied on spatial only).
-            Default: False.
-        conv_cfg (None | dict): Same as `NonLocalND`.
-            Default: dict(type='Conv2d').
-    """
-
-    _abbr_ = 'nonlocal_block'
-
-    def __init__(self,
-                 in_channels,
-                 sub_sample=False,
-                 conv_cfg=dict(type='Conv2d'),
-                 **kwargs):
-        super(NonLocal2d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
-
-        self.sub_sample = sub_sample
-
-        if sub_sample:
-            max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2))
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            if self.mode != 'gaussian':
-                self.phi = nn.Sequential(self.phi, max_pool_layer)
-            else:
-                self.phi = max_pool_layer
-
-
-class NonLocal3d(_NonLocalNd):
-    """3D Non-local module.
-
-    Args:
-        in_channels (int): Same as `NonLocalND`.
-        sub_sample (bool): Whether to apply max pooling after pairwise
-            function (Note that the `sub_sample` is applied on spatial only).
-            Default: False.
-        conv_cfg (None | dict): Same as `NonLocalND`.
-            Default: dict(type='Conv3d').
-    """
-
-    def __init__(self,
-                 in_channels,
-                 sub_sample=False,
-                 conv_cfg=dict(type='Conv3d'),
-                 **kwargs):
-        super(NonLocal3d, self).__init__(
-            in_channels, conv_cfg=conv_cfg, **kwargs)
-        self.sub_sample = sub_sample
-
-        if sub_sample:
-            max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2))
-            self.g = nn.Sequential(self.g, max_pool_layer)
-            if self.mode != 'gaussian':
-                self.phi = nn.Sequential(self.phi, max_pool_layer)
-            else:
-                self.phi = max_pool_layer
diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/utils/weight_init.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/utils/weight_init.py
deleted file mode 100644
index 287a1d0bffe26e023029d48634d9b761deda7ba4..0000000000000000000000000000000000000000
--- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/cnn/utils/weight_init.py
+++ /dev/null
@@ -1,684 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-import math
-import warnings
-
-import numpy as np
-import torch
-import torch.nn as nn
-from torch import Tensor
-
-from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
-
-INITIALIZERS = Registry('initializer')
-
-
-def update_init_info(module, init_info):
-    """Update the `_params_init_info` in the module if the value of parameters
-    are changed.
-
-    Args:
-        module (obj:`nn.Module`): The module of PyTorch with a user-defined
-            attribute `_params_init_info` which records the initialization
-            information.
-        init_info (str): The string that describes the initialization.
-    """
-    assert hasattr(
-        module,
-        '_params_init_info'), f'Can not find `_params_init_info` in {module}'
-    for name, param in module.named_parameters():
-
-        assert param in module._params_init_info, (
-            f'Find a new :obj:`Parameter` '
-            f'named `{name}` during executing the '
-            f'`init_weights` of '
-            f'`{module.__class__.__name__}`. '
-            f'Please do not add or '
-            f'replace parameters during executing '
-            f'the `init_weights`. ')
-
-        # The parameter has been changed during executing the
-        # `init_weights` of module
-        mean_value = param.data.mean()
-        if module._params_init_info[param]['tmp_mean_value'] != mean_value:
-            module._params_init_info[param]['init_info'] = init_info
-            module._params_init_info[param]['tmp_mean_value'] = mean_value
-
-
-def constant_init(module, val, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.constant_(module.weight, val)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def xavier_init(module, gain=1, bias=0, distribution='normal'):
-    assert distribution in ['uniform', 'normal']
-    if hasattr(module, 'weight') and module.weight is not None:
-        if distribution == 'uniform':
-            nn.init.xavier_uniform_(module.weight, gain=gain)
-        else:
-            nn.init.xavier_normal_(module.weight, gain=gain)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def normal_init(module, mean=0, std=1, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.normal_(module.weight, mean, std)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def trunc_normal_init(module: nn.Module,
-                      mean: float = 0,
-                      std: float = 1,
-                      a: float = -2,
-                      b: float = 2,
-                      bias: float = 0) -> None:
-    if hasattr(module, 'weight') and module.weight is not None:
-        trunc_normal_(module.weight, mean, std, a, b)  # type: ignore
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)  # type: ignore
-
-
-def uniform_init(module, a=0, b=1, bias=0):
-    if hasattr(module, 'weight') and module.weight is not None:
-        nn.init.uniform_(module.weight, a, b)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def kaiming_init(module,
-                 a=0,
-                 mode='fan_out',
-                 nonlinearity='relu',
-                 bias=0,
-                 distribution='normal'):
-    assert distribution in ['uniform', 'normal']
-    if hasattr(module, 'weight') and module.weight is not None:
-        if distribution == 'uniform':
-            nn.init.kaiming_uniform_(
-                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
-        else:
-            nn.init.kaiming_normal_(
-                module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
-    if hasattr(module, 'bias') and module.bias is not None:
-        nn.init.constant_(module.bias, bias)
-
-
-def caffe2_xavier_init(module, bias=0):
-    # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
-    # Acknowledgment to FAIR's internal code
-    kaiming_init(
-        module,
-        a=1,
-        mode='fan_in',
-        nonlinearity='leaky_relu',
-        bias=bias,
-        distribution='uniform')
-
-
-def bias_init_with_prob(prior_prob):
-    """initialize conv/fc bias value according to a given probability value."""
-    bias_init = float(-np.log((1 - prior_prob) / prior_prob))
-    return bias_init
-
-
-def _get_bases_name(m):
-    return [b.__name__ for b in m.__class__.__bases__]
-
-
-class BaseInit(object):
-
-    def __init__(self, *, bias=0, bias_prob=None, layer=None):
-        self.wholemodule = False
-        if not isinstance(bias, (int, float)):
-            raise TypeError(f'bias must be a number, but got a {type(bias)}')
-
-        if bias_prob is not None:
-            if not isinstance(bias_prob, float):
-                raise TypeError(f'bias_prob type must be float, \
-                    but got {type(bias_prob)}')
-
-        if layer is not None:
-            if not isinstance(layer, (str, list)):
-                raise TypeError(f'layer must be a str or a list of str, \
-                    but got a {type(layer)}')
-        else:
-            layer = []
-
-        if bias_prob is not None:
-            self.bias = bias_init_with_prob(bias_prob)
-        else:
-            self.bias = bias
-        self.layer = [layer] if isinstance(layer, str) else layer
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Constant')
-class ConstantInit(BaseInit):
-    """Initialize module parameters with constant values.
-
-    Args:
-        val (int | float): the value to fill the weights in the module with
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, val, **kwargs):
-        super().__init__(**kwargs)
-        self.val = val
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                constant_init(m, self.val, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    constant_init(m, self.val, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: val={self.val}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Xavier')
-class XavierInit(BaseInit):
-    r"""Initialize module parameters with values according to the method
-    described in `Understanding the difficulty of training deep feedforward
-    neural networks - Glorot, X. & Bengio, Y. (2010).
-    <http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf>`_
-
-    Args:
-        gain (int | float): an optional scaling factor. Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        distribution (str): distribution either be ``'normal'``
-            or ``'uniform'``. Defaults to ``'normal'``.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, gain=1, distribution='normal', **kwargs):
-        super().__init__(**kwargs)
-        self.gain = gain
-        self.distribution = distribution
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                xavier_init(m, self.gain, self.bias, self.distribution)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    xavier_init(m, self.gain, self.bias, self.distribution)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: gain={self.gain}, ' \
-               f'distribution={self.distribution}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Normal')
-class NormalInit(BaseInit):
-    r"""Initialize module parameters with the values drawn from the normal
-    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.
-
-    Args:
-        mean (int | float):the mean of the normal distribution. Defaults to 0.
-        std (int | float): the standard deviation of the normal distribution.
-            Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-
-    """
-
-    def __init__(self, mean=0, std=1, **kwargs):
-        super().__init__(**kwargs)
-        self.mean = mean
-        self.std = std
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                normal_init(m, self.mean, self.std, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    normal_init(m, self.mean, self.std, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: mean={self.mean},' \
-               f' std={self.std}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='TruncNormal')
-class TruncNormalInit(BaseInit):
-    r"""Initialize module parameters with the values drawn from the normal
-    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values
-    outside :math:`[a, b]`.
-
-    Args:
-        mean (float): the mean of the normal distribution. Defaults to 0.
-        std (float):  the standard deviation of the normal distribution.
-            Defaults to 1.
-        a (float): The minimum cutoff value.
-        b ( float): The maximum cutoff value.
-        bias (float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-
-    """
-
-    def __init__(self,
-                 mean: float = 0,
-                 std: float = 1,
-                 a: float = -2,
-                 b: float = 2,
-                 **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.mean = mean
-        self.std = std
-        self.a = a
-        self.b = b
-
-    def __call__(self, module: nn.Module) -> None:
-
-        def init(m):
-            if self.wholemodule:
-                trunc_normal_init(m, self.mean, self.std, self.a, self.b,
-                                  self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    trunc_normal_init(m, self.mean, self.std, self.a, self.b,
-                                      self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a}, b={self.b},' \
-               f' mean={self.mean}, std={self.std}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Uniform')
-class UniformInit(BaseInit):
-    r"""Initialize module parameters with values drawn from the uniform
-    distribution :math:`\mathcal{U}(a, b)`.
-
-    Args:
-        a (int | float): the lower bound of the uniform distribution.
-            Defaults to 0.
-        b (int | float): the upper bound of the uniform distribution.
-            Defaults to 1.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self, a=0, b=1, **kwargs):
-        super().__init__(**kwargs)
-        self.a = a
-        self.b = b
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                uniform_init(m, self.a, self.b, self.bias)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    uniform_init(m, self.a, self.b, self.bias)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a},' \
-               f' b={self.b}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Kaiming')
-class KaimingInit(BaseInit):
-    r"""Initialize module parameters with the values according to the method
-    described in `Delving deep into rectifiers: Surpassing human-level
-    performance on ImageNet classification - He, K. et al. (2015).
-    <https://www.cv-foundation.org/openaccess/content_iccv_2015/
-    papers/He_Delving_Deep_into_ICCV_2015_paper.pdf>`_
-
-    Args:
-        a (int | float): the negative slope of the rectifier used after this
-            layer (only used with ``'leaky_relu'``). Defaults to 0.
-        mode (str):  either ``'fan_in'`` or ``'fan_out'``. Choosing
-            ``'fan_in'`` preserves the magnitude of the variance of the weights
-            in the forward pass. Choosing ``'fan_out'`` preserves the
-            magnitudes in the backwards pass. Defaults to ``'fan_out'``.
-        nonlinearity (str): the non-linear function (`nn.functional` name),
-            recommended to use only with ``'relu'`` or ``'leaky_relu'`` .
-            Defaults to 'relu'.
-        bias (int | float): the value to fill the bias. Defaults to 0.
-        bias_prob (float, optional): the probability for bias initialization.
-            Defaults to None.
-        distribution (str): distribution either be ``'normal'`` or
-            ``'uniform'``. Defaults to ``'normal'``.
-        layer (str | list[str], optional): the layer will be initialized.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 a=0,
-                 mode='fan_out',
-                 nonlinearity='relu',
-                 distribution='normal',
-                 **kwargs):
-        super().__init__(**kwargs)
-        self.a = a
-        self.mode = mode
-        self.nonlinearity = nonlinearity
-        self.distribution = distribution
-
-    def __call__(self, module):
-
-        def init(m):
-            if self.wholemodule:
-                kaiming_init(m, self.a, self.mode, self.nonlinearity,
-                             self.bias, self.distribution)
-            else:
-                layername = m.__class__.__name__
-                basesname = _get_bases_name(m)
-                if len(set(self.layer) & set([layername] + basesname)):
-                    kaiming_init(m, self.a, self.mode, self.nonlinearity,
-                                 self.bias, self.distribution)
-
-        module.apply(init)
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: a={self.a}, mode={self.mode}, ' \
-               f'nonlinearity={self.nonlinearity}, ' \
-               f'distribution ={self.distribution}, bias={self.bias}'
-        return info
-
-
-@INITIALIZERS.register_module(name='Caffe2Xavier')
-class Caffe2XavierInit(KaimingInit):
-    # `XavierFill` in Caffe2 corresponds to `kaiming_uniform_` in PyTorch
-    # Acknowledgment to FAIR's internal code
-    def __init__(self, **kwargs):
-        super().__init__(
-            a=1,
-            mode='fan_in',
-            nonlinearity='leaky_relu',
-            distribution='uniform',
-            **kwargs)
-
-    def __call__(self, module):
-        super().__call__(module)
-
-
-@INITIALIZERS.register_module(name='Pretrained')
-class PretrainedInit(object):
-    """Initialize module by loading a pretrained model.
-
-    Args:
-        checkpoint (str): the checkpoint file of the pretrained model should
-            be load.
-        prefix (str, optional): the prefix of a sub-module in the pretrained
-            model. it is for loading a part of the pretrained model to
-            initialize. For example, if we would like to only load the
-            backbone of a detector model, we can set ``prefix='backbone.'``.
-            Defaults to None.
-        map_location (str): map tensors into proper locations.
-    """
-
-    def __init__(self, checkpoint, prefix=None, map_location=None):
-        self.checkpoint = checkpoint
-        self.prefix = prefix
-        self.map_location = map_location
-
-    def __call__(self, module):
-        from annotator.uniformer.mmcv.runner import (_load_checkpoint_with_prefix, load_checkpoint,
-                                 load_state_dict)
-        logger = get_logger('mmcv')
-        if self.prefix is None:
-            print_log(f'load model from: {self.checkpoint}', logger=logger)
-            load_checkpoint(
-                module,
-                self.checkpoint,
-                map_location=self.map_location,
-                strict=False,
-                logger=logger)
-        else:
-            print_log(
-                f'load {self.prefix} in model from: {self.checkpoint}',
-                logger=logger)
-            state_dict = _load_checkpoint_with_prefix(
-                self.prefix, self.checkpoint, map_location=self.map_location)
-            load_state_dict(module, state_dict, strict=False, logger=logger)
-
-        if hasattr(module, '_params_init_info'):
-            update_init_info(module, init_info=self._get_init_info())
-
-    def _get_init_info(self):
-        info = f'{self.__class__.__name__}: load from {self.checkpoint}'
-        return info
-
-
-def _initialize(module, cfg, wholemodule=False):
-    func = build_from_cfg(cfg, INITIALIZERS)
-    # wholemodule flag is for override mode, there is no layer key in override
-    # and initializer will give init values for the whole module with the name
-    # in override.
-    func.wholemodule = wholemodule
-    func(module)
-
-
-def _initialize_override(module, override, cfg):
-    if not isinstance(override, (dict, list)):
-        raise TypeError(f'override must be a dict or a list of dict, \
-                but got {type(override)}')
-
-    override = [override] if isinstance(override, dict) else override
-
-    for override_ in override:
-
-        cp_override = copy.deepcopy(override_)
-        name = cp_override.pop('name', None)
-        if name is None:
-            raise ValueError('`override` must contain the key "name",'
-                             f'but got {cp_override}')
-        # if override only has name key, it means use args in init_cfg
-        if not cp_override:
-            cp_override.update(cfg)
-        # if override has name key and other args except type key, it will
-        # raise error
-        elif 'type' not in cp_override.keys():
-            raise ValueError(
-                f'`override` need "type" key, but got {cp_override}')
-
-        if hasattr(module, name):
-            _initialize(getattr(module, name), cp_override, wholemodule=True)
-        else:
-            raise RuntimeError(f'module did not have attribute {name}, '
-                               f'but init_cfg is {cp_override}.')
-
-
-def initialize(module, init_cfg):
-    """Initialize a module.
-
-    Args:
-        module (``torch.nn.Module``): the module will be initialized.
-        init_cfg (dict | list[dict]): initialization configuration dict to
-            define initializer. OpenMMLab has implemented 6 initializers
-            including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
-            ``Kaiming``, and ``Pretrained``.
-    Example:
-        >>> module = nn.Linear(2, 3, bias=True)
-        >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)
-        >>> initialize(module, init_cfg)
-
-        >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
-        >>> # define key ``'layer'`` for initializing layer with different
-        >>> # configuration
-        >>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
-                dict(type='Constant', layer='Linear', val=2)]
-        >>> initialize(module, init_cfg)
-
-        >>> # define key``'override'`` to initialize some specific part in
-        >>> # module
-        >>> class FooNet(nn.Module):
-        >>>     def __init__(self):
-        >>>         super().__init__()
-        >>>         self.feat = nn.Conv2d(3, 16, 3)
-        >>>         self.reg = nn.Conv2d(16, 10, 3)
-        >>>         self.cls = nn.Conv2d(16, 5, 3)
-        >>> model = FooNet()
-        >>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d',
-        >>>     override=dict(type='Constant', name='reg', val=3, bias=4))
-        >>> initialize(model, init_cfg)
-
-        >>> model = ResNet(depth=50)
-        >>> # Initialize weights with the pretrained model.
-        >>> init_cfg = dict(type='Pretrained',
-                checkpoint='torchvision://resnet50')
-        >>> initialize(model, init_cfg)
-
-        >>> # Initialize weights of a sub-module with the specific part of
-        >>> # a pretrained model by using "prefix".
-        >>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\
-        >>>     'retinanet_r50_fpn_1x_coco/'\
-        >>>     'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
-        >>> init_cfg = dict(type='Pretrained',
-                checkpoint=url, prefix='backbone.')
-    """
-    if not isinstance(init_cfg, (dict, list)):
-        raise TypeError(f'init_cfg must be a dict or a list of dict, \
-                but got {type(init_cfg)}')
-
-    if isinstance(init_cfg, dict):
-        init_cfg = [init_cfg]
-
-    for cfg in init_cfg:
-        # should deeply copy the original config because cfg may be used by
-        # other modules, e.g., one init_cfg shared by multiple bottleneck
-        # blocks, the expected cfg will be changed after pop and will change
-        # the initialization behavior of other modules
-        cp_cfg = copy.deepcopy(cfg)
-        override = cp_cfg.pop('override', None)
-        _initialize(module, cp_cfg)
-
-        if override is not None:
-            cp_cfg.pop('layer', None)
-            _initialize_override(module, override, cp_cfg)
-        else:
-            # All attributes in module have same initialization.
-            pass
-
-
-def _no_grad_trunc_normal_(tensor: Tensor, mean: float, std: float, a: float,
-                           b: float) -> Tensor:
-    # Method based on
-    # https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    # Modified from
-    # https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1. + math.erf(x / math.sqrt(2.))) / 2.
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
-            'The distribution of values may be incorrect.',
-            stacklevel=2)
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        lower = norm_cdf((a - mean) / std)
-        upper = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [lower, upper], then translate
-        # to [2lower-1, 2upper-1].
-        tensor.uniform_(2 * lower - 1, 2 * upper - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(tensor: Tensor,
-                  mean: float = 0.,
-                  std: float = 1.,
-                  a: float = -2.,
-                  b: float = 2.) -> Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    Modified from
-    https://github.com/pytorch/pytorch/blob/master/torch/nn/init.py
-
-    Args:
-        tensor (``torch.Tensor``): an n-dimensional `torch.Tensor`.
-        mean (float): the mean of the normal distribution.
-        std (float): the standard deviation of the normal distribution.
-        a (float): the minimum cutoff value.
-        b (float): the maximum cutoff value.
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
diff --git a/spaces/taesiri/ViTPose/model.py b/spaces/taesiri/ViTPose/model.py
deleted file mode 100644
index 35a2ec62568425fbe95a61ed0d59466b33100cfb..0000000000000000000000000000000000000000
--- a/spaces/taesiri/ViTPose/model.py
+++ /dev/null
@@ -1,249 +0,0 @@
-from __future__ import annotations
-
-import os
-import pathlib
-import shlex
-import subprocess
-import sys
-import json
-
-if os.getenv("SYSTEM") == "spaces":
-    import mim
-
-    mim.uninstall("mmcv-full", confirm_yes=True)
-    mim.install("mmcv-full==1.5.0", is_yes=True)
-
-    subprocess.run(shlex.split("pip uninstall -y opencv-python"))
-    subprocess.run(shlex.split("pip uninstall -y opencv-python-headless"))
-    subprocess.run(shlex.split("pip install opencv-python-headless==4.5.5.64"))
-
-import huggingface_hub
-import numpy as np
-import torch
-import torch.nn as nn
-
-app_dir = pathlib.Path(__file__).parent
-submodule_dir = app_dir / "ViTPose"
-sys.path.insert(0, submodule_dir.as_posix())
-
-from mmdet.apis import inference_detector, init_detector
-from mmpose.apis import (
-    inference_top_down_pose_model,
-    init_pose_model,
-    process_mmdet_results,
-    vis_pose_result,
-)
-
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-
-class DetModel:
-    MODEL_DICT = {
-        "YOLOX-tiny": {
-            "config": "mmdet_configs/configs/yolox/yolox_tiny_8x8_300e_coco.py",
-            "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth",
-        },
-        "YOLOX-s": {
-            "config": "mmdet_configs/configs/yolox/yolox_s_8x8_300e_coco.py",
-            "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth",
-        },
-        "YOLOX-l": {
-            "config": "mmdet_configs/configs/yolox/yolox_l_8x8_300e_coco.py",
-            "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth",
-        },
-        "YOLOX-x": {
-            "config": "mmdet_configs/configs/yolox/yolox_x_8x8_300e_coco.py",
-            "model": "https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth",
-        },
-    }
-
-    def __init__(self):
-        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        self._load_all_models_once()
-        self.model_name = "YOLOX-l"
-        self.model = self._load_model(self.model_name)
-
-    def _load_all_models_once(self) -> None:
-        for name in self.MODEL_DICT:
-            self._load_model(name)
-
-    def _load_model(self, name: str) -> nn.Module:
-        dic = self.MODEL_DICT[name]
-        return init_detector(dic["config"], dic["model"], device=self.device)
-
-    def set_model(self, name: str) -> None:
-        if name == self.model_name:
-            return
-        self.model_name = name
-        self.model = self._load_model(name)
-
-    def detect_and_visualize(
-        self, image: np.ndarray, score_threshold: float
-    ) -> tuple[list[np.ndarray], np.ndarray]:
-        out, outjson = self.detect(image)
-        vis = self.visualize_detection_results(image, out, score_threshold)
-        return out, vis, outjson
-
-    def detect(self, image: np.ndarray) -> list[np.ndarray]:
-        image = image[:, :, ::-1]  # RGB -> BGR
-        out = inference_detector(self.model, image)
-        # Convert numpy arrays to lists
-        out2 = [arr.tolist() for arr in out]
-        # Convert output to JSON
-        out_json = json.dumps(out2)
-        return out, out_json
-
-    def visualize_detection_results(
-        self,
-        image: np.ndarray,
-        detection_results: list[np.ndarray],
-        score_threshold: float = 0.3,
-    ) -> np.ndarray:
-        person_det = [detection_results[0]] + [np.array([]).reshape(0, 5)] * 79
-
-        image = image[:, :, ::-1]  # RGB -> BGR
-        vis = self.model.show_result(
-            image,
-            person_det,
-            score_thr=score_threshold,
-            bbox_color=None,
-            text_color=(200, 200, 200),
-            mask_color=None,
-        )
-        return vis[:, :, ::-1]  # BGR -> RGB
-
-
-class AppDetModel(DetModel):
-    def run(
-        self, model_name: str, image: np.ndarray, score_threshold: float
-    ) -> tuple[list[np.ndarray], np.ndarray]:
-        self.set_model(model_name)
-        return self.detect_and_visualize(image, score_threshold)
-
-
-class PoseModel:
-    MODEL_DICT = {
-        "ViTPose-B (single-task train)": {
-            "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py",
-            "model": "vitpose-b.pth",
-        },
-        "ViTPose-L (single-task train)": {
-            "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py",
-            "model": "vitpose-l.pth",
-        },
-        "ViTPose-B (multi-task train, COCO)": {
-            "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py",
-            "model": "vitpose-b-multi-coco.pth",
-        },
-        "ViTPose-L (multi-task train, COCO)": {
-            "config": "ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py",
-            "model": "vitpose-l-multi-coco.pth",
-        },
-    }
-
-    def __init__(self):
-        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        self.model_name = "ViTPose-B (multi-task train, COCO)"
-        self.model = self._load_model(self.model_name)
-
-    def _load_all_models_once(self) -> None:
-        for name in self.MODEL_DICT:
-            self._load_model(name)
-
-    def _load_model(self, name: str) -> nn.Module:
-        dic = self.MODEL_DICT[name]
-        ckpt_path = huggingface_hub.hf_hub_download(
-            "taesiri/ViTPose", dic["model"], use_auth_token=HF_TOKEN
-        )
-        model = init_pose_model(dic["config"], ckpt_path, device=self.device)
-        return model
-
-    def set_model(self, name: str) -> None:
-        if name == self.model_name:
-            return
-        self.model_name = name
-        self.model = self._load_model(name)
-
-    def predict_pose_and_visualize(
-        self,
-        image: np.ndarray,
-        det_results: list[np.ndarray],
-        box_score_threshold: float,
-        kpt_score_threshold: float,
-        vis_dot_radius: int,
-        vis_line_thickness: int,
-    ) -> tuple[list[dict[str, np.ndarray]], np.ndarray]:
-        out, outjson = self.predict_pose(image, det_results, box_score_threshold)
-        vis = self.visualize_pose_results(
-            image, out, kpt_score_threshold, vis_dot_radius, vis_line_thickness
-        )
-        return out, vis, outjson
-
-    def predict_pose(
-        self,
-        image: np.ndarray,
-        det_results: list[np.ndarray],
-        box_score_threshold: float = 0.5,
-    ) -> list[dict[str, np.ndarray]]:
-        image = image[:, :, ::-1]  # RGB -> BGR
-        person_results = process_mmdet_results(det_results, 1)
-        out, _ = inference_top_down_pose_model(
-            self.model,
-            image,
-            person_results=person_results,
-            bbox_thr=box_score_threshold,
-            format="xyxy",
-        )
-        # return out
-        out_for_json = [
-            {
-                k: (v.tolist() if isinstance(v, np.ndarray) else v)
-                for k, v in person.items()
-            }
-            for person in out
-        ]
-        # Convert output to JSON
-        outjson = json.dumps(out_for_json)
-
-        return out, outjson
-
-    def visualize_pose_results(
-        self,
-        image: np.ndarray,
-        pose_results: list[np.ndarray],
-        kpt_score_threshold: float = 0.3,
-        vis_dot_radius: int = 4,
-        vis_line_thickness: int = 1,
-    ) -> np.ndarray:
-        image = image[:, :, ::-1]  # RGB -> BGR
-        vis = vis_pose_result(
-            self.model,
-            image,
-            pose_results,
-            kpt_score_thr=kpt_score_threshold,
-            radius=vis_dot_radius,
-            thickness=vis_line_thickness,
-        )
-        return vis[:, :, ::-1]  # BGR -> RGB
-
-
-class AppPoseModel(PoseModel):
-    def run(
-        self,
-        model_name: str,
-        image: np.ndarray,
-        det_results: list[np.ndarray],
-        box_score_threshold: float,
-        kpt_score_threshold: float,
-        vis_dot_radius: int,
-        vis_line_thickness: int,
-    ) -> tuple[list[dict[str, np.ndarray]], np.ndarray]:
-        self.set_model(model_name)
-        return self.predict_pose_and_visualize(
-            image,
-            det_results,
-            box_score_threshold,
-            kpt_score_threshold,
-            vis_dot_radius,
-            vis_line_thickness,
-        )
diff --git a/spaces/tarunika-03/PersonalityPrediction_Psychology/README.md b/spaces/tarunika-03/PersonalityPrediction_Psychology/README.md
deleted file mode 100644
index 1922526f87837b66d3acf7abaca25dd3ae0afdd8..0000000000000000000000000000000000000000
--- a/spaces/tarunika-03/PersonalityPrediction_Psychology/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PersonalityPrediction Psychology
-emoji: 🌖
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tenslai/mianhuatang/upcunet_v3.py b/spaces/tenslai/mianhuatang/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/tenslai/mianhuatang/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)
diff --git a/spaces/terfces0erbo/CollegeProjectV2/Free Download Overloud Th2 Full Crack __FULL__.md b/spaces/terfces0erbo/CollegeProjectV2/Free Download Overloud Th2 Full Crack __FULL__.md
deleted file mode 100644
index 8e4feaaf53f0c658bbcfe8be625f08126e837141..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Free Download Overloud Th2 Full Crack __FULL__.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Free Download Overloud Th2 Full Crack</h2><br /><p><b><b>Download Zip</b> &#9734;&#9734;&#9734; <a href="https://bytlly.com/2uGm0f">https://bytlly.com/2uGm0f</a></b></p><br /><br />
-<br />
-Overloud TH-U Complete v1.0.13 Incl Keygen (WIN OSX)-R2R. scene. Overloud TH-U ... Full compatibility with TH3 user presets ... download from free file storage ... download from Usenet - 14 days free access +300GB ... I have run TH2 and TH3 as well as Amplitube 4 on my machine without any issues even at 96000Hz. 4d29de3e1b<br />
-<br />
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diff --git a/spaces/teticio/audio-diffusion/audiodiffusion/pipeline_audio_diffusion.py b/spaces/teticio/audio-diffusion/audiodiffusion/pipeline_audio_diffusion.py
deleted file mode 100644
index 42e92123f23d493e2d3aa5a61ef21d62821f79a6..0000000000000000000000000000000000000000
--- a/spaces/teticio/audio-diffusion/audiodiffusion/pipeline_audio_diffusion.py
+++ /dev/null
@@ -1,274 +0,0 @@
-# This code has been migrated to diffusers but can be run locally with
-# pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256", custom_pipeline="audio-diffusion/audiodiffusion/pipeline_audio_diffusion.py")
-
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from math import acos, sin
-from typing import List, Tuple, Union
-
-import numpy as np
-import torch
-from diffusers import (
-    AudioPipelineOutput,
-    AutoencoderKL,
-    DDIMScheduler,
-    DDPMScheduler,
-    DiffusionPipeline,
-    ImagePipelineOutput,
-    UNet2DConditionModel,
-)
-from diffusers.utils import BaseOutput
-from PIL import Image
-
-from .mel import Mel
-
-class AudioDiffusionPipeline(DiffusionPipeline):
-    """
-    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.)
-
-    Parameters:
-        vqae ([`AutoencoderKL`]): Variational AutoEncoder for Latent Audio Diffusion or None
-        unet ([`UNet2DConditionModel`]): UNET model
-        mel ([`Mel`]): transform audio <-> spectrogram
-        scheduler ([`DDIMScheduler` or `DDPMScheduler`]): de-noising scheduler
-    """
-
-    _optional_components = ["vqvae"]
-
-    def __init__(
-        self,
-        vqvae: AutoencoderKL,
-        unet: UNet2DConditionModel,
-        mel: Mel,
-        scheduler: Union[DDIMScheduler, DDPMScheduler],
-    ):
-        super().__init__()
-        self.register_modules(unet=unet, scheduler=scheduler, mel=mel, vqvae=vqvae)
-
-    def get_input_dims(self) -> Tuple:
-        """Returns dimension of input image
-
-        Returns:
-            `Tuple`: (height, width)
-        """
-        input_module = self.vqvae if self.vqvae is not None else self.unet
-        # For backwards compatibility
-        sample_size = (
-            (input_module.sample_size, input_module.sample_size)
-            if type(input_module.sample_size) == int
-            else input_module.sample_size
-        )
-        return sample_size
-
-    def get_default_steps(self) -> int:
-        """Returns default number of steps recommended for inference
-
-        Returns:
-            `int`: number of steps
-        """
-        return 50 if isinstance(self.scheduler, DDIMScheduler) else 1000
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        batch_size: int = 1,
-        audio_file: str = None,
-        raw_audio: np.ndarray = None,
-        slice: int = 0,
-        start_step: int = 0,
-        steps: int = None,
-        generator: torch.Generator = None,
-        mask_start_secs: float = 0,
-        mask_end_secs: float = 0,
-        step_generator: torch.Generator = None,
-        eta: float = 0,
-        noise: torch.Tensor = None,
-        encoding: torch.Tensor = None,
-        return_dict=True,
-    ) -> Union[
-        Union[AudioPipelineOutput, ImagePipelineOutput],
-        Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
-    ]:
-        """Generate random mel spectrogram from audio input and convert to audio.
-
-        Args:
-            batch_size (`int`): number of samples to generate
-            audio_file (`str`): must be a file on disk due to Librosa limitation or
-            raw_audio (`np.ndarray`): audio as numpy array
-            slice (`int`): slice number of audio to convert
-            start_step (int): step to start from
-            steps (`int`): number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)
-            generator (`torch.Generator`): random number generator or None
-            mask_start_secs (`float`): number of seconds of audio to mask (not generate) at start
-            mask_end_secs (`float`): number of seconds of audio to mask (not generate) at end
-            step_generator (`torch.Generator`): random number generator used to de-noise or None
-            eta (`float`): parameter between 0 and 1 used with DDIM scheduler
-            noise (`torch.Tensor`): noise tensor of shape (batch_size, 1, height, width) or None
-            encoding (`torch.Tensor`): for UNet2DConditionModel shape (batch_size, seq_length, cross_attention_dim)
-            return_dict (`bool`): if True return AudioPipelineOutput, ImagePipelineOutput else Tuple
-
-        Returns:
-            `List[PIL Image]`: mel spectrograms (`float`, `List[np.ndarray]`): sample rate and raw audios
-        """
-
-        steps = steps or self.get_default_steps()
-        self.scheduler.set_timesteps(steps)
-        step_generator = step_generator or generator
-        # For backwards compatibility
-        if type(self.unet.sample_size) == int:
-            self.unet.sample_size = (self.unet.sample_size, self.unet.sample_size)
-        input_dims = self.get_input_dims()
-        self.mel.set_resolution(x_res=input_dims[1], y_res=input_dims[0])
-        if noise is None:
-            noise = torch.randn(
-                (
-                    batch_size,
-                    self.unet.in_channels,
-                    self.unet.sample_size[0],
-                    self.unet.sample_size[1],
-                ),
-                generator=generator,
-                device=self.device,
-            )
-        images = noise
-        mask = None
-
-        if audio_file is not None or raw_audio is not None:
-            self.mel.load_audio(audio_file, raw_audio)
-            input_image = self.mel.audio_slice_to_image(slice)
-            input_image = np.frombuffer(input_image.tobytes(), dtype="uint8").reshape(
-                (input_image.height, input_image.width)
-            )
-            input_image = (input_image / 255) * 2 - 1
-            input_images = torch.tensor(input_image[np.newaxis, :, :], dtype=torch.float).to(self.device)
-
-            if self.vqvae is not None:
-                input_images = self.vqvae.encode(torch.unsqueeze(input_images, 0)).latent_dist.sample(
-                    generator=generator
-                )[0]
-                input_images = 0.18215 * input_images
-
-            if start_step > 0:
-                images[0, 0] = self.scheduler.add_noise(input_images, noise, self.scheduler.timesteps[start_step - 1])
-
-            pixels_per_second = (
-                self.unet.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
-            )
-            mask_start = int(mask_start_secs * pixels_per_second)
-            mask_end = int(mask_end_secs * pixels_per_second)
-            mask = self.scheduler.add_noise(input_images, noise, torch.tensor(self.scheduler.timesteps[start_step:]))
-
-        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])):
-            if isinstance(self.unet, UNet2DConditionModel):
-                model_output = self.unet(images, t, encoding)["sample"]
-            else:
-                model_output = self.unet(images, t)["sample"]
-
-            if isinstance(self.scheduler, DDIMScheduler):
-                images = self.scheduler.step(
-                    model_output=model_output,
-                    timestep=t,
-                    sample=images,
-                    eta=eta,
-                    generator=step_generator,
-                )["prev_sample"]
-            else:
-                images = self.scheduler.step(
-                    model_output=model_output,
-                    timestep=t,
-                    sample=images,
-                    generator=step_generator,
-                )["prev_sample"]
-
-            if mask is not None:
-                if mask_start > 0:
-                    images[:, :, :, :mask_start] = mask[:, step, :, :mask_start]
-                if mask_end > 0:
-                    images[:, :, :, -mask_end:] = mask[:, step, :, -mask_end:]
-
-        if self.vqvae is not None:
-            # 0.18215 was scaling factor used in training to ensure unit variance
-            images = 1 / 0.18215 * images
-            images = self.vqvae.decode(images)["sample"]
-
-        images = (images / 2 + 0.5).clamp(0, 1)
-        images = images.cpu().permute(0, 2, 3, 1).numpy()
-        images = (images * 255).round().astype("uint8")
-        images = list(
-            map(lambda _: Image.fromarray(_[:, :, 0]), images)
-            if images.shape[3] == 1
-            else map(lambda _: Image.fromarray(_, mode="RGB").convert("L"), images)
-        )
-
-        audios = list(map(lambda _: self.mel.image_to_audio(_), images))
-        if not return_dict:
-            return images, (self.mel.get_sample_rate(), audios)
-
-        return BaseOutput(**AudioPipelineOutput(np.array(audios)[:, np.newaxis, :]), **ImagePipelineOutput(images))
-
-    @torch.no_grad()
-    def encode(self, images: List[Image.Image], steps: int = 50) -> np.ndarray:
-        """Reverse step process: recover noisy image from generated image.
-
-        Args:
-            images (`List[PIL Image]`): list of images to encode
-            steps (`int`): number of encoding steps to perform (defaults to 50)
-
-        Returns:
-            `np.ndarray`: noise tensor of shape (batch_size, 1, height, width)
-        """
-
-        # Only works with DDIM as this method is deterministic
-        assert isinstance(self.scheduler, DDIMScheduler)
-        self.scheduler.set_timesteps(steps)
-        sample = np.array(
-            [np.frombuffer(image.tobytes(), dtype="uint8").reshape((1, image.height, image.width)) for image in images]
-        )
-        sample = (sample / 255) * 2 - 1
-        sample = torch.Tensor(sample).to(self.device)
-
-        for t in self.progress_bar(torch.flip(self.scheduler.timesteps, (0,))):
-            prev_timestep = t - self.scheduler.num_train_timesteps // self.scheduler.num_inference_steps
-            alpha_prod_t = self.scheduler.alphas_cumprod[t]
-            alpha_prod_t_prev = (
-                self.scheduler.alphas_cumprod[prev_timestep]
-                if prev_timestep >= 0
-                else self.scheduler.final_alpha_cumprod
-            )
-            beta_prod_t = 1 - alpha_prod_t
-            model_output = self.unet(sample, t)["sample"]
-            pred_sample_direction = (1 - alpha_prod_t_prev) ** (0.5) * model_output
-            sample = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
-            sample = sample * alpha_prod_t ** (0.5) + beta_prod_t ** (0.5) * model_output
-
-        return sample
-
-    @staticmethod
-    def slerp(x0: torch.Tensor, x1: torch.Tensor, alpha: float) -> torch.Tensor:
-        """Spherical Linear intERPolation
-
-        Args:
-            x0 (`torch.Tensor`): first tensor to interpolate between
-            x1 (`torch.Tensor`): seconds tensor to interpolate between
-            alpha (`float`): interpolation between 0 and 1
-
-        Returns:
-            `torch.Tensor`: interpolated tensor
-        """
-
-        theta = acos(torch.dot(torch.flatten(x0), torch.flatten(x1)) / torch.norm(x0) / torch.norm(x1))
-        return sin((1 - alpha) * theta) * x0 / sin(theta) + sin(alpha * theta) * x1 / sin(theta)
diff --git a/spaces/thejagstudio/procom/main/migrations/0004_alter_products_category.py b/spaces/thejagstudio/procom/main/migrations/0004_alter_products_category.py
deleted file mode 100644
index 13e56b2d735705e292ca5f099ad796153d67b083..0000000000000000000000000000000000000000
--- a/spaces/thejagstudio/procom/main/migrations/0004_alter_products_category.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# Generated by Django 4.1.5 on 2023-01-28 05:43
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    dependencies = [
-        ("main", "0003_products_category"),
-    ]
-
-    operations = [
-        migrations.AlterField(
-            model_name="products",
-            name="category",
-            field=models.CharField(max_length=500),
-        ),
-    ]
diff --git a/spaces/thelou1s/sleep_data/analysis_db.py b/spaces/thelou1s/sleep_data/analysis_db.py
deleted file mode 100644
index bb9df2f79d1a7d536c5304d132c27a849e51d811..0000000000000000000000000000000000000000
--- a/spaces/thelou1s/sleep_data/analysis_db.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import sqlite3
-import sys
-
-from util.debug import debug_print
-from draw_db import draw_lists, min_max_list, up_down_list
-from util.date_util import format_date
-
-
-def get_filename(file_obj):
-    return file_obj.name
-
-
-def read_db(db_file):
-    db_name = get_filename(db_file)
-    debug_print('read_db, db_name = ', db_name)
-    return read_section(db_name, 'tb_section', '')
-
-
-def read_section(db_name, tb_name='tb_section', section_id=''):
-    res_fig = None
-    debug_print('read_section, db_name = ', db_name, ', tb_name = ', tb_name, ', section_id = ', section_id)
-    conn = sqlite3.connect(db_name)
-
-    if not tb_name:
-        table_cursor = conn.cursor()
-        table_cursor.execute(' SELECT name FROM sqlite_master WHERE type=\'table\'; ')
-        table_names = table_cursor.fetchall()
-        print('table_names = ', str(table_names))
-        return
-
-    if not section_id:
-        section_cursor = conn.execute(' SELECT section_id, section_date, section_end_date FROM ' + tb_name)
-        for section_row in section_cursor:
-            section_id = section_row[0]
-            section_date = section_row[1]
-            section_end_date = section_row[2]
-            print('section_id = ', section_id, ', section_date = ', format_date(section_date), ', section_end_date = ',
-                  format_date(section_end_date))
-            res_fig = read_sample(conn, section_id, tb_name)
-    else:
-        res_fig = read_sample(conn, section_id, tb_name)
-
-    conn.close()
-    return res_fig
-
-
-def read_sample(conn, section_id, tb_name):
-    sql_str = ' SELECT section_id, _sample_id, _sample_end_id, section_date, section_end_date FROM ' + str(
-        tb_name) + ' WHERE section_id == ' + str(section_id)
-    section_cursor = conn.execute(sql_str)
-    for section_row in section_cursor:
-        section_id = section_row[0]
-        _sample_id = section_row[1]
-        _sample_end_id = section_row[2]
-        section_date = section_row[3]
-        section_end_date = section_row[4]
-        debug_print('section_id = ', section_id, ', _sample_id = ', _sample_id, ', _sample_end_id = ',
-                    _sample_end_id)
-
-        date_list = []
-        max_list = []
-        min_list = []
-        acc_list = []
-        light_list = []
-        screen_list = []
-        sample_sql = "SELECT _id, date, max, min, acc_max_dx, acc_max_dy, acc_max_dz, avg, max_snoring FROM sample_table WHERE _id >= " + str(
-            _sample_id) + ' AND ' + ' _id <= ' + str(_sample_end_id)
-        sample_cursor = conn.execute(sample_sql)
-        for sample_row in sample_cursor:
-            _id = sample_row[0]
-            date = sample_row[1]
-            _max = sample_row[2]
-            _min = sample_row[3]
-            acc_max = sample_row[4] + sample_row[5] + sample_row[6]
-            light = sample_row[7]
-            screen = sample_row[8]
-            debug_print('_id = ', _id, 'date = ', format_date(date))
-
-            date_list.append(format_date(date))
-            max_list.append(_max)
-            min_list.append(_min)
-            acc_list.append(acc_max)
-            light_list.append(light)
-            screen_list.append(screen)
-
-        title_str = str(section_id) + ', ' + str(format_date(section_date)) + ', ' + str(format_date(section_end_date))
-
-        if len(light_list) > 0:
-            light_min = min(light_list)
-            light_max = max(light_list)
-            screen_list = min_max_list(screen_list, light_min, light_max)
-            screen_list = up_down_list(screen_list)
-        return draw_lists(title_str, date_list, max=max_list, min=min_list, acc=acc_list, screen=screen_list,
-                          light=light_list)
-
-
-if __name__ == '__main__':
-    argv = sys.argv[1:]
-    argc = len(sys.argv[1:])
-    # print('sys.argv[1:] = ', argv, ', ', str(len(argv)))
-
-    if argc < 1:
-        print('USAGE: python analysis_db.py xxx.db')
-        exit(1)
-
-    db_uri = argv[0] if argc >= 1 else ''
-    tb_name = argv[1] if argc >= 2 else ''
-    section_id = argv[2] if argc >= 3 else ''
-    read_section(db_uri, tb_name, section_id)
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/All Windows 10 VCL Styles From DelphiStyles.com BETTER.md b/spaces/tioseFevbu/cartoon-converter/scripts/All Windows 10 VCL Styles From DelphiStyles.com BETTER.md
deleted file mode 100644
index 926c3ba0c7e596294070792259dd92abf8911f20..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/All Windows 10 VCL Styles From DelphiStyles.com BETTER.md	
+++ /dev/null
@@ -1,49 +0,0 @@
-<br />
-<h1>All Windows 10 VCL styles from DelphiStyles.com: How to make your VCL applications look modern and professional</h1>
-
-<p>If you are a Delphi or C++ Builder developer who wants to create stunning Windows 10 applications with ease, you should check out the VCL styles from DelphiStyles.com. VCL styles are similar to Windows themes, but they allow you to customize the appearance of every control and element in your VCL forms, dialogs, and menus. You can choose from a variety of predefined styles, or create your own with the Bitmap Style Designer tool.</p>
-
-<p>One of the advantages of using VCL styles is that they support per-control styling, which means you can apply different styles to different parts of your application. For example, you can use a light style for the main form, and a dark style for a pop-up dialog. This gives you more flexibility and control over the look and feel of your application.</p>
-<h2>All Windows 10 VCL styles from DelphiStyles.com</h2><br /><p><b><b>DOWNLOAD</b> &#10084;&#10084;&#10084; <a href="https://urlcod.com/2uHvHw">https://urlcod.com/2uHvHw</a></b></p><br /><br />
-
-<p>Another benefit of using VCL styles is that they are compatible with high-DPI monitors and scaling settings. Your application will look sharp and crisp on any screen resolution and size, without any extra coding or tweaking.</p>
-
-<p>DelphiStyles.com offers a collection of high-quality VCL styles that are designed to match the latest trends and standards of Windows 10. You can find styles that mimic the native Windows 11 appearance, such as Windows11 Mirage Light and Dark, Windows11 Office Light and Dark, and Windows11 Manatee. You can also find styles that are inspired by popular web design frameworks, such as Light Vector and Dark Vector, Chambray Light and Dark, Mint Cream Light and Dark, and Waikawa Sky Blue.</p>
-
-<p>To use these styles in your VCL applications, you need to have RAD Studio XE3 - 11 Alexandria installed. You can download the styles from DelphiStyles.com[^1^] or from GetIt[^2^], and then add them to your project using the Project Options dialog. You can then select the style you want to apply using the TStyleManager component or the StyleBook property of your form.</p>
-
-<p>If you want to learn more about how to use VCL styles effectively, you can watch this webinar replay[^3^] by Alexey Sharagin, the creative genius behind DelphiStyles.com. He will show you how to use the properties of TStyleManager, how to create and customize your own styles with the Bitmap Style Designer tool, and how to use styles from GetIt and DelphiStyles.com.</p>
-
-<p>VCL styles are a powerful feature that can help you create beautiful modern apps in Windows 10 with minimal effort. With DelphiStyles.com, you can access a wide range of professional and attractive styles that will make your applications stand out from the crowd.</p>
-<p></p>
-
-<p>One of the challenges of using VCL styles is that some third-party controls or custom controls may not support them by default. This means that they may look out of place or inconsistent with the rest of your application. To solve this problem, you need to add some code to your controls to make them compatible with VCL styles.</p>
-
-<p>The basic steps to add VCL style support to a custom control are:</p>
-
-<ul>
-<li>Override the StyleChanged method of the control and call Invalidate or Repaint to redraw the control when the style changes.</li>
-<li>Use the StyleServices class to access the current style and its elements, such as colors, fonts, and bitmaps.</li>
-<li>Use the StyleServices.DrawElement method to draw standard elements, such as buttons, checkboxes, and scrollbars, using the style resources.</li>
-<li>Use the StyleServices.GetElementDetails method to get the details of a specific element state, such as pressed, focused, or disabled.</li>
-<li>Use the StyleServices.GetSystemColor method to get the color of a system element, such as clWindow or clHighlight, according to the style.</li>
-</ul>
-
-<p>For more information and examples on how to add VCL style support to custom controls, you can read this article by Remy Lebeau.</p>
-
-<p>If you want to create your own VCL style from scratch, or modify an existing one, you can use the Bitmap Style Designer tool that comes with RAD Studio. This tool allows you to edit every aspect of a VCL style, such as colors, fonts, margins, borders, gradients, and images. You can also preview your style in different resolutions and scenarios.</p>
-
-<p>To use the Bitmap Style Designer tool, you need to follow these steps:</p>
-
-<ul>
-<li>Launch the tool from the Tools menu in RAD Studio.</li>
-<li>Open an existing VCL style file (.vsf) or create a new one.</li>
-<li>Select an element category from the left panel, such as Buttons or Edits.</li>
-<li>Select an element from the top panel, such as Button or CheckBox.</li>
-<li>Edit the properties of the element in the right panel, such as Color, Font, or Image.</li>
-<li>Save your changes and test your style in your application.</li>
-</ul>
-
-<p>For more information and tutorials on how to use the Bitmap Style Designer tool, you can read this documentation by Embarcadero.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Cs 1.6 Zombie Ammo Hack.md b/spaces/tioseFevbu/cartoon-converter/scripts/Cs 1.6 Zombie Ammo Hack.md
deleted file mode 100644
index a931c6c439f906f54358d7f4d1b5a4862f1f857a..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Cs 1.6 Zombie Ammo Hack.md	
+++ /dev/null
@@ -1,50 +0,0 @@
-<br />
-<h1>How to Get Unlimited Ammo Packs in Cs 1.6 Zombie Mod</h1>
-<p>If you are a fan of Counter-Strike 1.6 and love playing the zombie mod, you might be wondering how to get more ammo packs to buy weapons and items. Ammo packs are the currency of the zombie mod, and they are earned by killing zombies, surviving rounds, or completing objectives. However, sometimes you might run out of ammo packs or want to have more fun with unlimited ammo packs. In this article, we will show you how to get unlimited ammo packs in Cs 1.6 zombie mod using a simple hack.</p>
-<h2>Cs 1.6 Zombie Ammo Hack</h2><br /><p><b><b>Download Zip</b> &#9989; <a href="https://urlcod.com/2uHvq6">https://urlcod.com/2uHvq6</a></b></p><br /><br />
-<h2>What is Cs 1.6 Zombie Mod?</h2>
-<p>Cs 1.6 zombie mod is a popular modification of the classic first-person shooter game Counter-Strike 1.6. In this mod, one or more players are randomly chosen to become zombies at the start of each round, while the rest are humans. The zombies have to infect all the humans by attacking them with their claws, while the humans have to defend themselves with guns and items. The humans can buy weapons and items using ammo packs, which are earned by killing zombies, surviving rounds, or completing objectives. The zombie mod has many different modes and maps, such as plague, escape, biohazard, and more.</p>
-<h2>How to Get Unlimited Ammo Packs in Cs 1.6 Zombie Mod?</h2>
-<p>There are several ways to get unlimited ammo packs in Cs 1.6 zombie mod, but one of the easiest and most effective ways is to use a hack menu that allows you to add as many ammo packs as you want. Here are the steps to use this hack:</p>
-<ol>
-<li>Download the hack menu from this link: <a href="https://www.mediafire.com/file/y084we...">https://www.mediafire.com/file/y084we...</a></li>
-<li>Extract the file and copy the cstrike folder to your Cs 1.6 game directory.</li>
-<li>Launch Cs 1.6 and join any zombie server.</li>
-<li>Press F12 to open the hack menu.</li>
-<li>Select "Add Ammo Packs" and enter the amount of ammo packs you want to add.</li>
-<li>Press F12 again to close the hack menu.</li>
-<li>Enjoy your unlimited ammo packs!</li>
-</ol>
-<h2>Conclusion</h2>
-<p>Cs 1.6 zombie mod is a fun and exciting way to play Counter-Strike 1.6 with your friends or online players. However, sometimes you might want to have more ammo packs to buy weapons and items or just have more fun. In this article, we showed you how to get unlimited ammo packs in Cs 1.6 zombie mod using a simple hack menu that allows you to add as many ammo packs as you want. We hope you enjoyed this article and found it useful. Happy gaming!</p>
-
-<h2>What are the Benefits of Using Cs 1.6 Zombie Ammo Hack?</h2>
-<p>Using Cs 1.6 zombie ammo hack can have many benefits for your gaming experience. Some of the benefits are:</p>
-<ul>
-<li>You can buy any weapon or item you want without worrying about running out of ammo packs.</li>
-<li>You can experiment with different weapons and items and see what works best for you.</li>
-<li>You can have more fun and challenge yourself by using different weapons and items in different situations.</li>
-<li>You can impress your friends or other players with your skills and arsenal.</li>
-<li>You can dominate the zombie server and become the best player.</li>
-</ul>
-<h2>What are the Risks of Using Cs 1.6 Zombie Ammo Hack?</h2>
-<p>Using Cs 1.6 zombie ammo hack can also have some risks for your gaming experience. Some of the risks are:</p>
-<ul>
-<li>You might get banned from the zombie server or from Cs 1.6 altogether if the hack is detected by the anti-cheat system.</li>
-<li>You might lose the thrill and challenge of playing the zombie mod if you have unlimited ammo packs.</li>
-<li>You might get bored of playing the zombie mod if you have unlimited ammo packs.</li>
-<li>You might annoy or anger other players who are playing fair and without hacks.</li>
-<li>You might ruin the balance and fun of the zombie mod for yourself and others.</li>
-</ul>
-<h2>How to Use Cs 1.6 Zombie Ammo Hack Responsibly?</h2>
-<p>Using Cs 1.6 zombie ammo hack can be tempting and fun, but it can also be risky and unfair. Therefore, we recommend that you use this hack responsibly and with moderation. Here are some tips on how to use this hack responsibly:</p>
-<ul>
-<li>Use this hack only for personal use and not for commercial or malicious purposes.</li>
-<li>Use this hack only on servers that allow hacks or do not have anti-cheat systems.</li>
-<li>Use this hack only occasionally and not all the time.</li>
-<li>Use this hack only to have fun and not to ruin the fun for others.</li>
-<li>Use this hack only with respect and not with arrogance or rudeness.</li>
-</ul></p>
-<p></p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Fiat EPER V84 05.2014 Multilanguage High Quality.md b/spaces/tioseFevbu/cartoon-converter/scripts/Fiat EPER V84 05.2014 Multilanguage High Quality.md
deleted file mode 100644
index ad8cce135ebfc6887dd873048d3fe5b7d6a08c0d..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Fiat EPER V84 05.2014 Multilanguage High Quality.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<br />
-Here is a possible title and article with HTML formatting for the keyword "Fiat ePER v84 05.2014 Multilanguage":
-
-<h1>Fiat ePER v84: The Latest Spare Parts Catalog for Fiat Vehicles</h1>
-<p>If you own a Fiat vehicle or work as a mechanic, you might be interested in the latest version of Fiat ePER, the official spare parts catalog for Fiat cars and vans. Fiat ePER v84 [05.2014] Multilanguage is a comprehensive and updated database that contains information on all the components of Fiat vehicles, including models from 1985 to today. You can browse the catalog by brand (Fiat, Alfa Romeo, Lancia, Fiat Commercial, Abarth), model, year, engine, chassis number, or part number. You can also view diagrams, photos, prices, and availability of the parts you need.</p>
-<h2>Fiat ePER v84 05.2014 Multilanguage</h2><br /><p><b><b>Download</b> &mdash; <a href="https://urlcod.com/2uHwmj">https://urlcod.com/2uHwmj</a></b></p><br /><br />
-<p>Fiat ePER v84 [05.2014] Multilanguage is available on one DVD or can be installed on your HDD or server. It supports multiple languages, such as English, Italian, German, French, Spanish, Portuguese, Polish, and more. You can order the DVD online from various websites or download it from torrent sites[^1^]. However, be careful of viruses and malware that might harm your computer. Alternatively, you can access the online version of Fiat ePER from the official website[^2^], but you will need to register and pay a fee to use it.</p>
-<p>Fiat ePER v84 [05.2014] Multilanguage is a useful tool for anyone who wants to maintain or repair their Fiat vehicle with original and quality parts. It is also a valuable resource for learning more about the history and features of Fiat vehicles. With Fiat ePER v84 [05.2014] Multilanguage, you can find the right part for your Fiat vehicle in no time.</p>Here is a possible continuation of the article with HTML formatting:
-
-<p>Fiat ePER v84 [05.2014] Multilanguage is not only a catalog of spare parts, but also a web application that offers various features and functions for Fiat vehicle owners and dealers. Some of the features of Fiat ePER v84 [05.2014] Multilanguage are:</p>
-<ul>
-<li>Accessories and Merchandise: You can browse and order accessories and merchandise for your Fiat vehicle, such as mats, roof racks, stickers, clothing, and more[^1^] [^2^]. You can also view the compatibility and installation instructions of the accessories.</li>
-<li>Service Packages: You can find out the recommended service intervals and maintenance operations for your Fiat vehicle, as well as the costs and benefits of each service package[^1^] [^2^]. You can also book a service appointment online with your preferred dealer.</li>
-<li>Tools: You can access various tools that help you diagnose and repair your Fiat vehicle, such as wiring diagrams, fault codes, technical bulletins, and more[^1^] [^2^]. You can also download software updates and manuals for your Fiat vehicle.</li>
-<li>Remanufactured Products: You can purchase remanufactured products for your Fiat vehicle, such as engines, transmissions, alternators, and more[^1^] [^2^]. These products are restored to original specifications and quality standards, and come with a warranty and a lower environmental impact.</li>
-</ul>
-<p>Fiat ePER v84 [05.2014] Multilanguage is a web application that can be used with any common Internet browser and is available as an Internet/Intranet web application[^1^] [^2^]. You can access it from any device and location, as long as you have an Internet connection. However, you will need to register and pay a fee to use some of the features of Fiat ePER v84 [05.2014] Multilanguage.</p>
-<p></p> 7196e7f11a<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Lecciones Ampliadas De Derecho Romano Rinaldi Pdf.md b/spaces/tioseFevbu/cartoon-converter/scripts/Lecciones Ampliadas De Derecho Romano Rinaldi Pdf.md
deleted file mode 100644
index 37ded1f71e6f9374a333d998d0894dd82f7591bc..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Lecciones Ampliadas De Derecho Romano Rinaldi Pdf.md	
+++ /dev/null
@@ -1,10 +0,0 @@
-
-<h1>Lecciones Ampliadas De Derecho Romano Rinaldi Pdf</h1>
-<p>Lecciones Ampliadas De Derecho Romano Rinaldi Pdf es un libro de texto escrito por el profesor Norberto Rinaldi, que aborda los principales temas del derecho romano desde una perspectiva histórica y jurídica. El libro está dividido en cuatro partes: la primera se ocupa de la introducción, las fuentes, el derecho procesal y el derecho penal; la segunda trata del derecho privado, el derecho de las personas y el derecho de familia; la tercera se dedica al derecho de cosas, el derecho de obligaciones y el derecho de sucesiones; y la cuarta se refiere al derecho público, el derecho constitucional y el derecho administrativo.</p>
-<p>El libro tiene como objetivo ofrecer una visión amplia y actualizada del derecho romano, que sirva como base para el estudio de otras ramas del derecho y para la comprensión de la cultura jurídica occidental. El autor combina el rigor científico con una exposición clara y didáctica, apoyada en numerosos ejemplos, citas y referencias bibliográficas. El libro también incluye ejercicios prácticos, cuadros sinópticos y mapas conceptuales para facilitar el aprendizaje y la evaluación de los estudiantes.</p>
-<h2>Lecciones Ampliadas De Derecho Romano Rinaldi Pdf</h2><br /><p><b><b>Download</b> &#9658;&#9658;&#9658;&#9658;&#9658; <a href="https://urlcod.com/2uHwzX">https://urlcod.com/2uHwzX</a></b></p><br /><br />
-<p>Lecciones Ampliadas De Derecho Romano Rinaldi Pdf es un libro recomendado para los estudiantes de derecho, especialmente para los que cursan la asignatura de derecho romano o historia del derecho. También puede ser de interés para los profesionales del derecho, los historiadores y los aficionados al mundo romano. El libro se puede descargar gratuitamente desde la plataforma Scribd[^1^] [^3^] o desde otros sitios web que lo alojan.</p><p>El derecho romano es el conjunto de normas jurídicas que rigieron la vida de los romanos desde la fundación de Roma (siglo VIII a.C.) hasta la caída del Imperio Romano de Occidente (siglo V d.C.). El derecho romano se caracteriza por su flexibilidad, su adaptación a las circunstancias históricas y sociales, y su influencia en el desarrollo del derecho europeo y latinoamericano.</p>
-<p>El derecho romano se puede clasificar en dos grandes etapas: el derecho romano arcaico y el derecho romano clásico. El derecho romano arcaico abarca desde los orígenes de Roma hasta el final de la República (siglo I a.C.). En esta etapa, el derecho romano se basa en las costumbres de los antepasados (mos maiorum) y en las leyes escritas (leges). El derecho romano clásico comprende desde el inicio del Imperio (siglo I d.C.) hasta la época de Justiniano (siglo VI d.C.). En esta etapa, el derecho romano se enriquece con las opiniones de los juristas (iurisprudentia) y con las decisiones de los emperadores (constitutiones).</p>
-<p>El derecho romano se divide en dos grandes ramas: el derecho privado y el derecho público. El derecho privado regula las relaciones entre los particulares, tanto en el ámbito patrimonial como en el personal. El derecho público regula las relaciones entre los ciudadanos y el Estado, tanto en el ámbito político como en el administrativo. El derecho privado se subdivide en tres partes: el derecho de las personas, el derecho de cosas y el derecho de obligaciones. El derecho público se subdivide en dos partes: el derecho constitucional y el derecho administrativo.</p> e93f5a0c3f<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Lianne La Havas Blood __TOP__.md b/spaces/tioseFevbu/cartoon-converter/scripts/Lianne La Havas Blood __TOP__.md
deleted file mode 100644
index 0c55ddf79bd3cbb50df30ad0aea4104831d749a3..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Lianne La Havas Blood __TOP__.md	
+++ /dev/null
@@ -1,21 +0,0 @@
-<br />
-<h1>Lianne La Havas Blood: A Soulful Journey of Self-Discovery</h1>
-<p>Lianne La Havas is a British singer-songwriter and guitarist who has captivated audiences with her blend of neo soul, jazz, R&B, doo-wop, reggae and gospel music. Her second studio album, <em>Blood</em>, released in 2015, is a testament to her musical versatility and personal growth.</p>
-<p><em>Blood</em> was inspired by La Havas' trip to Jamaica, where she reconnected with her roots and explored her Jamaican and Greek heritage. She collaborated with reggae producer Stephen McGregor, as well as other artists such as Mark Batson, Paul Epworth, Matt Hales, Jamie Lidell and more. The album features songs that reflect La Havas' identity, love, relationships and emotions.</p>
-<h2>Lianne La Havas Blood</h2><br /><p><b><b>DOWNLOAD</b> &#10027;&#10027;&#10027; <a href="https://urlcod.com/2uHwhW">https://urlcod.com/2uHwhW</a></b></p><br /><br />
-<p>The album received critical acclaim from music critics, who praised La Havas' vocal performance, songwriting skills and musical transition from her debut album <em>Is Your Love Big Enough?</em> (2012). It also achieved commercial success, peaking at number two in the UK, number one in the Netherlands and Norway, and receiving a nomination for Best Urban Contemporary Album at the 2016 Grammy Awards.</p>
-<p>Some of the standout tracks from <em>Blood</em> include:</p>
-<ul>
-<li><strong>Unstoppable</strong>: The lead single from the album, a soulful and uplifting anthem about overcoming obstacles and being confident in oneself.</li>
-<li><strong>What You Don't Do</strong>: The second single from the album, a catchy and upbeat song about trusting in one's partner and not needing constant reassurance.</li>
-<li><strong>Green & Gold</strong>: The third single from the album, a nostalgic and introspective song about La Havas' childhood memories and family history.</li>
-<li><strong>Midnight</strong>: A powerful and dramatic song about finding strength and freedom in the dark hours of the night.</li>
-<li><strong>Tokyo</strong>: A smooth and groovy song about longing for someone who is far away.</li>
-</ul>
-<p>If you are looking for a soulful and diverse album that showcases La Havas' talent and personality, <em>Blood</em> is a must-listen. You can stream or buy the album on various platforms such as Spotify, Apple Music, YouTube Music, Amazon Music and more.</p>
-
-<p>Not only did <em>Blood</em> receive praise from critics, who highlighted La Havas' wonderfully expressive vocals, adventurous musical choices and honest lyrics, but it also earned her recognition from some of her musical idols. Prince, who had invited La Havas to perform with him at his secret London gigs in 2014, featured her on his 2015 album <em>HITnRUN Phase One</em>. Coldplay's Chris Martin, who had collaborated with La Havas on a song for the film <em>Unbroken</em>, called her \"the best singer in the world right now\". La Havas also supported Coldplay on their A Head Full of Dreams Tour in 2016.</p>
-<p><em>Blood</em> is a remarkable album that showcases La Havas' growth as an artist and a person. It is a soulful journey of self-discovery that celebrates her heritage, her influences and her voice. If you are a fan of Lianne La Havas, or if you are looking for a new artist to discover, you should definitely check out <em>Blood</em>.</p>
-<p></p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/compat.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/compat.py
deleted file mode 100644
index 3f4d300cef077e698989245562375a9444d983fa..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/utils/compat.py
+++ /dev/null
@@ -1,63 +0,0 @@
-"""Stuff that differs in different Python versions and platform
-distributions."""
-
-import logging
-import os
-import sys
-
-__all__ = ["get_path_uid", "stdlib_pkgs", "WINDOWS"]
-
-
-logger = logging.getLogger(__name__)
-
-
-def has_tls() -> bool:
-    try:
-        import _ssl  # noqa: F401  # ignore unused
-
-        return True
-    except ImportError:
-        pass
-
-    from pip._vendor.urllib3.util import IS_PYOPENSSL
-
-    return IS_PYOPENSSL
-
-
-def get_path_uid(path: str) -> int:
-    """
-    Return path's uid.
-
-    Does not follow symlinks:
-        https://github.com/pypa/pip/pull/935#discussion_r5307003
-
-    Placed this function in compat due to differences on AIX and
-    Jython, that should eventually go away.
-
-    :raises OSError: When path is a symlink or can't be read.
-    """
-    if hasattr(os, "O_NOFOLLOW"):
-        fd = os.open(path, os.O_RDONLY | os.O_NOFOLLOW)
-        file_uid = os.fstat(fd).st_uid
-        os.close(fd)
-    else:  # AIX and Jython
-        # WARNING: time of check vulnerability, but best we can do w/o NOFOLLOW
-        if not os.path.islink(path):
-            # older versions of Jython don't have `os.fstat`
-            file_uid = os.stat(path).st_uid
-        else:
-            # raise OSError for parity with os.O_NOFOLLOW above
-            raise OSError(f"{path} is a symlink; Will not return uid for symlinks")
-    return file_uid
-
-
-# packages in the stdlib that may have installation metadata, but should not be
-# considered 'installed'.  this theoretically could be determined based on
-# dist.location (py27:`sysconfig.get_paths()['stdlib']`,
-# py26:sysconfig.get_config_vars('LIBDEST')), but fear platform variation may
-# make this ineffective, so hard-coding
-stdlib_pkgs = {"python", "wsgiref", "argparse"}
-
-
-# windows detection, covers cpython and ironpython
-WINDOWS = sys.platform.startswith("win") or (sys.platform == "cli" and os.name == "nt")
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/index.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/index.py
deleted file mode 100644
index 9b6d129ed690361770738bec73f44ba7e10a21c5..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/distlib/index.py
+++ /dev/null
@@ -1,508 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (C) 2013 Vinay Sajip.
-# Licensed to the Python Software Foundation under a contributor agreement.
-# See LICENSE.txt and CONTRIBUTORS.txt.
-#
-import hashlib
-import logging
-import os
-import shutil
-import subprocess
-import tempfile
-try:
-    from threading import Thread
-except ImportError:  # pragma: no cover
-    from dummy_threading import Thread
-
-from . import DistlibException
-from .compat import (HTTPBasicAuthHandler, Request, HTTPPasswordMgr,
-                     urlparse, build_opener, string_types)
-from .util import zip_dir, ServerProxy
-
-logger = logging.getLogger(__name__)
-
-DEFAULT_INDEX = 'https://pypi.org/pypi'
-DEFAULT_REALM = 'pypi'
-
-class PackageIndex(object):
-    """
-    This class represents a package index compatible with PyPI, the Python
-    Package Index.
-    """
-
-    boundary = b'----------ThIs_Is_tHe_distlib_index_bouNdaRY_$'
-
-    def __init__(self, url=None):
-        """
-        Initialise an instance.
-
-        :param url: The URL of the index. If not specified, the URL for PyPI is
-                    used.
-        """
-        self.url = url or DEFAULT_INDEX
-        self.read_configuration()
-        scheme, netloc, path, params, query, frag = urlparse(self.url)
-        if params or query or frag or scheme not in ('http', 'https'):
-            raise DistlibException('invalid repository: %s' % self.url)
-        self.password_handler = None
-        self.ssl_verifier = None
-        self.gpg = None
-        self.gpg_home = None
-        with open(os.devnull, 'w') as sink:
-            # Use gpg by default rather than gpg2, as gpg2 insists on
-            # prompting for passwords
-            for s in ('gpg', 'gpg2'):
-                try:
-                    rc = subprocess.check_call([s, '--version'], stdout=sink,
-                                               stderr=sink)
-                    if rc == 0:
-                        self.gpg = s
-                        break
-                except OSError:
-                    pass
-
-    def _get_pypirc_command(self):
-        """
-        Get the distutils command for interacting with PyPI configurations.
-        :return: the command.
-        """
-        from .util import _get_pypirc_command as cmd
-        return cmd()
-
-    def read_configuration(self):
-        """
-        Read the PyPI access configuration as supported by distutils. This populates
-        ``username``, ``password``, ``realm`` and ``url`` attributes from the
-        configuration.
-        """
-        from .util import _load_pypirc
-        cfg = _load_pypirc(self)
-        self.username = cfg.get('username')
-        self.password = cfg.get('password')
-        self.realm = cfg.get('realm', 'pypi')
-        self.url = cfg.get('repository', self.url)
-
-    def save_configuration(self):
-        """
-        Save the PyPI access configuration. You must have set ``username`` and
-        ``password`` attributes before calling this method.
-        """
-        self.check_credentials()
-        from .util import _store_pypirc
-        _store_pypirc(self)
-
-    def check_credentials(self):
-        """
-        Check that ``username`` and ``password`` have been set, and raise an
-        exception if not.
-        """
-        if self.username is None or self.password is None:
-            raise DistlibException('username and password must be set')
-        pm = HTTPPasswordMgr()
-        _, netloc, _, _, _, _ = urlparse(self.url)
-        pm.add_password(self.realm, netloc, self.username, self.password)
-        self.password_handler = HTTPBasicAuthHandler(pm)
-
-    def register(self, metadata):  # pragma: no cover
-        """
-        Register a distribution on PyPI, using the provided metadata.
-
-        :param metadata: A :class:`Metadata` instance defining at least a name
-                         and version number for the distribution to be
-                         registered.
-        :return: The HTTP response received from PyPI upon submission of the
-                request.
-        """
-        self.check_credentials()
-        metadata.validate()
-        d = metadata.todict()
-        d[':action'] = 'verify'
-        request = self.encode_request(d.items(), [])
-        response = self.send_request(request)
-        d[':action'] = 'submit'
-        request = self.encode_request(d.items(), [])
-        return self.send_request(request)
-
-    def _reader(self, name, stream, outbuf):
-        """
-        Thread runner for reading lines of from a subprocess into a buffer.
-
-        :param name: The logical name of the stream (used for logging only).
-        :param stream: The stream to read from. This will typically a pipe
-                       connected to the output stream of a subprocess.
-        :param outbuf: The list to append the read lines to.
-        """
-        while True:
-            s = stream.readline()
-            if not s:
-                break
-            s = s.decode('utf-8').rstrip()
-            outbuf.append(s)
-            logger.debug('%s: %s' % (name, s))
-        stream.close()
-
-    def get_sign_command(self, filename, signer, sign_password, keystore=None):  # pragma: no cover
-        """
-        Return a suitable command for signing a file.
-
-        :param filename: The pathname to the file to be signed.
-        :param signer: The identifier of the signer of the file.
-        :param sign_password: The passphrase for the signer's
-                              private key used for signing.
-        :param keystore: The path to a directory which contains the keys
-                         used in verification. If not specified, the
-                         instance's ``gpg_home`` attribute is used instead.
-        :return: The signing command as a list suitable to be
-                 passed to :class:`subprocess.Popen`.
-        """
-        cmd = [self.gpg, '--status-fd', '2', '--no-tty']
-        if keystore is None:
-            keystore = self.gpg_home
-        if keystore:
-            cmd.extend(['--homedir', keystore])
-        if sign_password is not None:
-            cmd.extend(['--batch', '--passphrase-fd', '0'])
-        td = tempfile.mkdtemp()
-        sf = os.path.join(td, os.path.basename(filename) + '.asc')
-        cmd.extend(['--detach-sign', '--armor', '--local-user',
-                    signer, '--output', sf, filename])
-        logger.debug('invoking: %s', ' '.join(cmd))
-        return cmd, sf
-
-    def run_command(self, cmd, input_data=None):
-        """
-        Run a command in a child process , passing it any input data specified.
-
-        :param cmd: The command to run.
-        :param input_data: If specified, this must be a byte string containing
-                           data to be sent to the child process.
-        :return: A tuple consisting of the subprocess' exit code, a list of
-                 lines read from the subprocess' ``stdout``, and a list of
-                 lines read from the subprocess' ``stderr``.
-        """
-        kwargs = {
-            'stdout': subprocess.PIPE,
-            'stderr': subprocess.PIPE,
-        }
-        if input_data is not None:
-            kwargs['stdin'] = subprocess.PIPE
-        stdout = []
-        stderr = []
-        p = subprocess.Popen(cmd, **kwargs)
-        # We don't use communicate() here because we may need to
-        # get clever with interacting with the command
-        t1 = Thread(target=self._reader, args=('stdout', p.stdout, stdout))
-        t1.start()
-        t2 = Thread(target=self._reader, args=('stderr', p.stderr, stderr))
-        t2.start()
-        if input_data is not None:
-            p.stdin.write(input_data)
-            p.stdin.close()
-
-        p.wait()
-        t1.join()
-        t2.join()
-        return p.returncode, stdout, stderr
-
-    def sign_file(self, filename, signer, sign_password, keystore=None):  # pragma: no cover
-        """
-        Sign a file.
-
-        :param filename: The pathname to the file to be signed.
-        :param signer: The identifier of the signer of the file.
-        :param sign_password: The passphrase for the signer's
-                              private key used for signing.
-        :param keystore: The path to a directory which contains the keys
-                         used in signing. If not specified, the instance's
-                         ``gpg_home`` attribute is used instead.
-        :return: The absolute pathname of the file where the signature is
-                 stored.
-        """
-        cmd, sig_file = self.get_sign_command(filename, signer, sign_password,
-                                              keystore)
-        rc, stdout, stderr = self.run_command(cmd,
-                                              sign_password.encode('utf-8'))
-        if rc != 0:
-            raise DistlibException('sign command failed with error '
-                                   'code %s' % rc)
-        return sig_file
-
-    def upload_file(self, metadata, filename, signer=None, sign_password=None,
-                    filetype='sdist', pyversion='source', keystore=None):
-        """
-        Upload a release file to the index.
-
-        :param metadata: A :class:`Metadata` instance defining at least a name
-                         and version number for the file to be uploaded.
-        :param filename: The pathname of the file to be uploaded.
-        :param signer: The identifier of the signer of the file.
-        :param sign_password: The passphrase for the signer's
-                              private key used for signing.
-        :param filetype: The type of the file being uploaded. This is the
-                        distutils command which produced that file, e.g.
-                        ``sdist`` or ``bdist_wheel``.
-        :param pyversion: The version of Python which the release relates
-                          to. For code compatible with any Python, this would
-                          be ``source``, otherwise it would be e.g. ``3.2``.
-        :param keystore: The path to a directory which contains the keys
-                         used in signing. If not specified, the instance's
-                         ``gpg_home`` attribute is used instead.
-        :return: The HTTP response received from PyPI upon submission of the
-                request.
-        """
-        self.check_credentials()
-        if not os.path.exists(filename):
-            raise DistlibException('not found: %s' % filename)
-        metadata.validate()
-        d = metadata.todict()
-        sig_file = None
-        if signer:
-            if not self.gpg:
-                logger.warning('no signing program available - not signed')
-            else:
-                sig_file = self.sign_file(filename, signer, sign_password,
-                                          keystore)
-        with open(filename, 'rb') as f:
-            file_data = f.read()
-        md5_digest = hashlib.md5(file_data).hexdigest()
-        sha256_digest = hashlib.sha256(file_data).hexdigest()
-        d.update({
-            ':action': 'file_upload',
-            'protocol_version': '1',
-            'filetype': filetype,
-            'pyversion': pyversion,
-            'md5_digest': md5_digest,
-            'sha256_digest': sha256_digest,
-        })
-        files = [('content', os.path.basename(filename), file_data)]
-        if sig_file:
-            with open(sig_file, 'rb') as f:
-                sig_data = f.read()
-            files.append(('gpg_signature', os.path.basename(sig_file),
-                         sig_data))
-            shutil.rmtree(os.path.dirname(sig_file))
-        request = self.encode_request(d.items(), files)
-        return self.send_request(request)
-
-    def upload_documentation(self, metadata, doc_dir):  # pragma: no cover
-        """
-        Upload documentation to the index.
-
-        :param metadata: A :class:`Metadata` instance defining at least a name
-                         and version number for the documentation to be
-                         uploaded.
-        :param doc_dir: The pathname of the directory which contains the
-                        documentation. This should be the directory that
-                        contains the ``index.html`` for the documentation.
-        :return: The HTTP response received from PyPI upon submission of the
-                request.
-        """
-        self.check_credentials()
-        if not os.path.isdir(doc_dir):
-            raise DistlibException('not a directory: %r' % doc_dir)
-        fn = os.path.join(doc_dir, 'index.html')
-        if not os.path.exists(fn):
-            raise DistlibException('not found: %r' % fn)
-        metadata.validate()
-        name, version = metadata.name, metadata.version
-        zip_data = zip_dir(doc_dir).getvalue()
-        fields = [(':action', 'doc_upload'),
-                  ('name', name), ('version', version)]
-        files = [('content', name, zip_data)]
-        request = self.encode_request(fields, files)
-        return self.send_request(request)
-
-    def get_verify_command(self, signature_filename, data_filename,
-                           keystore=None):
-        """
-        Return a suitable command for verifying a file.
-
-        :param signature_filename: The pathname to the file containing the
-                                   signature.
-        :param data_filename: The pathname to the file containing the
-                              signed data.
-        :param keystore: The path to a directory which contains the keys
-                         used in verification. If not specified, the
-                         instance's ``gpg_home`` attribute is used instead.
-        :return: The verifying command as a list suitable to be
-                 passed to :class:`subprocess.Popen`.
-        """
-        cmd = [self.gpg, '--status-fd', '2', '--no-tty']
-        if keystore is None:
-            keystore = self.gpg_home
-        if keystore:
-            cmd.extend(['--homedir', keystore])
-        cmd.extend(['--verify', signature_filename, data_filename])
-        logger.debug('invoking: %s', ' '.join(cmd))
-        return cmd
-
-    def verify_signature(self, signature_filename, data_filename,
-                         keystore=None):
-        """
-        Verify a signature for a file.
-
-        :param signature_filename: The pathname to the file containing the
-                                   signature.
-        :param data_filename: The pathname to the file containing the
-                              signed data.
-        :param keystore: The path to a directory which contains the keys
-                         used in verification. If not specified, the
-                         instance's ``gpg_home`` attribute is used instead.
-        :return: True if the signature was verified, else False.
-        """
-        if not self.gpg:
-            raise DistlibException('verification unavailable because gpg '
-                                   'unavailable')
-        cmd = self.get_verify_command(signature_filename, data_filename,
-                                      keystore)
-        rc, stdout, stderr = self.run_command(cmd)
-        if rc not in (0, 1):
-            raise DistlibException('verify command failed with error '
-                             'code %s' % rc)
-        return rc == 0
-
-    def download_file(self, url, destfile, digest=None, reporthook=None):
-        """
-        This is a convenience method for downloading a file from an URL.
-        Normally, this will be a file from the index, though currently
-        no check is made for this (i.e. a file can be downloaded from
-        anywhere).
-
-        The method is just like the :func:`urlretrieve` function in the
-        standard library, except that it allows digest computation to be
-        done during download and checking that the downloaded data
-        matched any expected value.
-
-        :param url: The URL of the file to be downloaded (assumed to be
-                    available via an HTTP GET request).
-        :param destfile: The pathname where the downloaded file is to be
-                         saved.
-        :param digest: If specified, this must be a (hasher, value)
-                       tuple, where hasher is the algorithm used (e.g.
-                       ``'md5'``) and ``value`` is the expected value.
-        :param reporthook: The same as for :func:`urlretrieve` in the
-                           standard library.
-        """
-        if digest is None:
-            digester = None
-            logger.debug('No digest specified')
-        else:
-            if isinstance(digest, (list, tuple)):
-                hasher, digest = digest
-            else:
-                hasher = 'md5'
-            digester = getattr(hashlib, hasher)()
-            logger.debug('Digest specified: %s' % digest)
-        # The following code is equivalent to urlretrieve.
-        # We need to do it this way so that we can compute the
-        # digest of the file as we go.
-        with open(destfile, 'wb') as dfp:
-            # addinfourl is not a context manager on 2.x
-            # so we have to use try/finally
-            sfp = self.send_request(Request(url))
-            try:
-                headers = sfp.info()
-                blocksize = 8192
-                size = -1
-                read = 0
-                blocknum = 0
-                if "content-length" in headers:
-                    size = int(headers["Content-Length"])
-                if reporthook:
-                    reporthook(blocknum, blocksize, size)
-                while True:
-                    block = sfp.read(blocksize)
-                    if not block:
-                        break
-                    read += len(block)
-                    dfp.write(block)
-                    if digester:
-                        digester.update(block)
-                    blocknum += 1
-                    if reporthook:
-                        reporthook(blocknum, blocksize, size)
-            finally:
-                sfp.close()
-
-        # check that we got the whole file, if we can
-        if size >= 0 and read < size:
-            raise DistlibException(
-                'retrieval incomplete: got only %d out of %d bytes'
-                % (read, size))
-        # if we have a digest, it must match.
-        if digester:
-            actual = digester.hexdigest()
-            if digest != actual:
-                raise DistlibException('%s digest mismatch for %s: expected '
-                                       '%s, got %s' % (hasher, destfile,
-                                                       digest, actual))
-            logger.debug('Digest verified: %s', digest)
-
-    def send_request(self, req):
-        """
-        Send a standard library :class:`Request` to PyPI and return its
-        response.
-
-        :param req: The request to send.
-        :return: The HTTP response from PyPI (a standard library HTTPResponse).
-        """
-        handlers = []
-        if self.password_handler:
-            handlers.append(self.password_handler)
-        if self.ssl_verifier:
-            handlers.append(self.ssl_verifier)
-        opener = build_opener(*handlers)
-        return opener.open(req)
-
-    def encode_request(self, fields, files):
-        """
-        Encode fields and files for posting to an HTTP server.
-
-        :param fields: The fields to send as a list of (fieldname, value)
-                       tuples.
-        :param files: The files to send as a list of (fieldname, filename,
-                      file_bytes) tuple.
-        """
-        # Adapted from packaging, which in turn was adapted from
-        # http://code.activestate.com/recipes/146306
-
-        parts = []
-        boundary = self.boundary
-        for k, values in fields:
-            if not isinstance(values, (list, tuple)):
-                values = [values]
-
-            for v in values:
-                parts.extend((
-                    b'--' + boundary,
-                    ('Content-Disposition: form-data; name="%s"' %
-                     k).encode('utf-8'),
-                    b'',
-                    v.encode('utf-8')))
-        for key, filename, value in files:
-            parts.extend((
-                b'--' + boundary,
-                ('Content-Disposition: form-data; name="%s"; filename="%s"' %
-                 (key, filename)).encode('utf-8'),
-                b'',
-                value))
-
-        parts.extend((b'--' + boundary + b'--', b''))
-
-        body = b'\r\n'.join(parts)
-        ct = b'multipart/form-data; boundary=' + boundary
-        headers = {
-            'Content-type': ct,
-            'Content-length': str(len(body))
-        }
-        return Request(self.url, body, headers)
-
-    def search(self, terms, operator=None):  # pragma: no cover
-        if isinstance(terms, string_types):
-            terms = {'name': terms}
-        rpc_proxy = ServerProxy(self.url, timeout=3.0)
-        try:
-            return rpc_proxy.search(terms, operator or 'and')
-        finally:
-            rpc_proxy('close')()
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/resolvelib/resolvers.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/resolvelib/resolvers.py
deleted file mode 100644
index 787681b03e9ec2fd4490de10cdc95e58c893c8b5..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/resolvelib/resolvers.py
+++ /dev/null
@@ -1,482 +0,0 @@
-import collections
-import operator
-
-from .providers import AbstractResolver
-from .structs import DirectedGraph, IteratorMapping, build_iter_view
-
-RequirementInformation = collections.namedtuple(
-    "RequirementInformation", ["requirement", "parent"]
-)
-
-
-class ResolverException(Exception):
-    """A base class for all exceptions raised by this module.
-
-    Exceptions derived by this class should all be handled in this module. Any
-    bubbling pass the resolver should be treated as a bug.
-    """
-
-
-class RequirementsConflicted(ResolverException):
-    def __init__(self, criterion):
-        super(RequirementsConflicted, self).__init__(criterion)
-        self.criterion = criterion
-
-    def __str__(self):
-        return "Requirements conflict: {}".format(
-            ", ".join(repr(r) for r in self.criterion.iter_requirement()),
-        )
-
-
-class InconsistentCandidate(ResolverException):
-    def __init__(self, candidate, criterion):
-        super(InconsistentCandidate, self).__init__(candidate, criterion)
-        self.candidate = candidate
-        self.criterion = criterion
-
-    def __str__(self):
-        return "Provided candidate {!r} does not satisfy {}".format(
-            self.candidate,
-            ", ".join(repr(r) for r in self.criterion.iter_requirement()),
-        )
-
-
-class Criterion(object):
-    """Representation of possible resolution results of a package.
-
-    This holds three attributes:
-
-    * `information` is a collection of `RequirementInformation` pairs.
-      Each pair is a requirement contributing to this criterion, and the
-      candidate that provides the requirement.
-    * `incompatibilities` is a collection of all known not-to-work candidates
-      to exclude from consideration.
-    * `candidates` is a collection containing all possible candidates deducted
-      from the union of contributing requirements and known incompatibilities.
-      It should never be empty, except when the criterion is an attribute of a
-      raised `RequirementsConflicted` (in which case it is always empty).
-
-    .. note::
-        This class is intended to be externally immutable. **Do not** mutate
-        any of its attribute containers.
-    """
-
-    def __init__(self, candidates, information, incompatibilities):
-        self.candidates = candidates
-        self.information = information
-        self.incompatibilities = incompatibilities
-
-    def __repr__(self):
-        requirements = ", ".join(
-            "({!r}, via={!r})".format(req, parent)
-            for req, parent in self.information
-        )
-        return "Criterion({})".format(requirements)
-
-    def iter_requirement(self):
-        return (i.requirement for i in self.information)
-
-    def iter_parent(self):
-        return (i.parent for i in self.information)
-
-
-class ResolutionError(ResolverException):
-    pass
-
-
-class ResolutionImpossible(ResolutionError):
-    def __init__(self, causes):
-        super(ResolutionImpossible, self).__init__(causes)
-        # causes is a list of RequirementInformation objects
-        self.causes = causes
-
-
-class ResolutionTooDeep(ResolutionError):
-    def __init__(self, round_count):
-        super(ResolutionTooDeep, self).__init__(round_count)
-        self.round_count = round_count
-
-
-# Resolution state in a round.
-State = collections.namedtuple("State", "mapping criteria backtrack_causes")
-
-
-class Resolution(object):
-    """Stateful resolution object.
-
-    This is designed as a one-off object that holds information to kick start
-    the resolution process, and holds the results afterwards.
-    """
-
-    def __init__(self, provider, reporter):
-        self._p = provider
-        self._r = reporter
-        self._states = []
-
-    @property
-    def state(self):
-        try:
-            return self._states[-1]
-        except IndexError:
-            raise AttributeError("state")
-
-    def _push_new_state(self):
-        """Push a new state into history.
-
-        This new state will be used to hold resolution results of the next
-        coming round.
-        """
-        base = self._states[-1]
-        state = State(
-            mapping=base.mapping.copy(),
-            criteria=base.criteria.copy(),
-            backtrack_causes=base.backtrack_causes[:],
-        )
-        self._states.append(state)
-
-    def _add_to_criteria(self, criteria, requirement, parent):
-        self._r.adding_requirement(requirement=requirement, parent=parent)
-
-        identifier = self._p.identify(requirement_or_candidate=requirement)
-        criterion = criteria.get(identifier)
-        if criterion:
-            incompatibilities = list(criterion.incompatibilities)
-        else:
-            incompatibilities = []
-
-        matches = self._p.find_matches(
-            identifier=identifier,
-            requirements=IteratorMapping(
-                criteria,
-                operator.methodcaller("iter_requirement"),
-                {identifier: [requirement]},
-            ),
-            incompatibilities=IteratorMapping(
-                criteria,
-                operator.attrgetter("incompatibilities"),
-                {identifier: incompatibilities},
-            ),
-        )
-
-        if criterion:
-            information = list(criterion.information)
-            information.append(RequirementInformation(requirement, parent))
-        else:
-            information = [RequirementInformation(requirement, parent)]
-
-        criterion = Criterion(
-            candidates=build_iter_view(matches),
-            information=information,
-            incompatibilities=incompatibilities,
-        )
-        if not criterion.candidates:
-            raise RequirementsConflicted(criterion)
-        criteria[identifier] = criterion
-
-    def _get_preference(self, name):
-        return self._p.get_preference(
-            identifier=name,
-            resolutions=self.state.mapping,
-            candidates=IteratorMapping(
-                self.state.criteria,
-                operator.attrgetter("candidates"),
-            ),
-            information=IteratorMapping(
-                self.state.criteria,
-                operator.attrgetter("information"),
-            ),
-            backtrack_causes=self.state.backtrack_causes,
-        )
-
-    def _is_current_pin_satisfying(self, name, criterion):
-        try:
-            current_pin = self.state.mapping[name]
-        except KeyError:
-            return False
-        return all(
-            self._p.is_satisfied_by(requirement=r, candidate=current_pin)
-            for r in criterion.iter_requirement()
-        )
-
-    def _get_updated_criteria(self, candidate):
-        criteria = self.state.criteria.copy()
-        for requirement in self._p.get_dependencies(candidate=candidate):
-            self._add_to_criteria(criteria, requirement, parent=candidate)
-        return criteria
-
-    def _attempt_to_pin_criterion(self, name):
-        criterion = self.state.criteria[name]
-
-        causes = []
-        for candidate in criterion.candidates:
-            try:
-                criteria = self._get_updated_criteria(candidate)
-            except RequirementsConflicted as e:
-                causes.append(e.criterion)
-                continue
-
-            # Check the newly-pinned candidate actually works. This should
-            # always pass under normal circumstances, but in the case of a
-            # faulty provider, we will raise an error to notify the implementer
-            # to fix find_matches() and/or is_satisfied_by().
-            satisfied = all(
-                self._p.is_satisfied_by(requirement=r, candidate=candidate)
-                for r in criterion.iter_requirement()
-            )
-            if not satisfied:
-                raise InconsistentCandidate(candidate, criterion)
-
-            self._r.pinning(candidate=candidate)
-            self.state.criteria.update(criteria)
-
-            # Put newly-pinned candidate at the end. This is essential because
-            # backtracking looks at this mapping to get the last pin.
-            self.state.mapping.pop(name, None)
-            self.state.mapping[name] = candidate
-
-            return []
-
-        # All candidates tried, nothing works. This criterion is a dead
-        # end, signal for backtracking.
-        return causes
-
-    def _backtrack(self):
-        """Perform backtracking.
-
-        When we enter here, the stack is like this::
-
-            [ state Z ]
-            [ state Y ]
-            [ state X ]
-            .... earlier states are irrelevant.
-
-        1. No pins worked for Z, so it does not have a pin.
-        2. We want to reset state Y to unpinned, and pin another candidate.
-        3. State X holds what state Y was before the pin, but does not
-           have the incompatibility information gathered in state Y.
-
-        Each iteration of the loop will:
-
-        1.  Discard Z.
-        2.  Discard Y but remember its incompatibility information gathered
-            previously, and the failure we're dealing with right now.
-        3.  Push a new state Y' based on X, and apply the incompatibility
-            information from Y to Y'.
-        4a. If this causes Y' to conflict, we need to backtrack again. Make Y'
-            the new Z and go back to step 2.
-        4b. If the incompatibilities apply cleanly, end backtracking.
-        """
-        while len(self._states) >= 3:
-            # Remove the state that triggered backtracking.
-            del self._states[-1]
-
-            # Retrieve the last candidate pin and known incompatibilities.
-            broken_state = self._states.pop()
-            name, candidate = broken_state.mapping.popitem()
-            incompatibilities_from_broken = [
-                (k, list(v.incompatibilities))
-                for k, v in broken_state.criteria.items()
-            ]
-
-            # Also mark the newly known incompatibility.
-            incompatibilities_from_broken.append((name, [candidate]))
-
-            self._r.backtracking(candidate=candidate)
-
-            # Create a new state from the last known-to-work one, and apply
-            # the previously gathered incompatibility information.
-            def _patch_criteria():
-                for k, incompatibilities in incompatibilities_from_broken:
-                    if not incompatibilities:
-                        continue
-                    try:
-                        criterion = self.state.criteria[k]
-                    except KeyError:
-                        continue
-                    matches = self._p.find_matches(
-                        identifier=k,
-                        requirements=IteratorMapping(
-                            self.state.criteria,
-                            operator.methodcaller("iter_requirement"),
-                        ),
-                        incompatibilities=IteratorMapping(
-                            self.state.criteria,
-                            operator.attrgetter("incompatibilities"),
-                            {k: incompatibilities},
-                        ),
-                    )
-                    candidates = build_iter_view(matches)
-                    if not candidates:
-                        return False
-                    incompatibilities.extend(criterion.incompatibilities)
-                    self.state.criteria[k] = Criterion(
-                        candidates=candidates,
-                        information=list(criterion.information),
-                        incompatibilities=incompatibilities,
-                    )
-                return True
-
-            self._push_new_state()
-            success = _patch_criteria()
-
-            # It works! Let's work on this new state.
-            if success:
-                return True
-
-            # State does not work after applying known incompatibilities.
-            # Try the still previous state.
-
-        # No way to backtrack anymore.
-        return False
-
-    def resolve(self, requirements, max_rounds):
-        if self._states:
-            raise RuntimeError("already resolved")
-
-        self._r.starting()
-
-        # Initialize the root state.
-        self._states = [
-            State(
-                mapping=collections.OrderedDict(),
-                criteria={},
-                backtrack_causes=[],
-            )
-        ]
-        for r in requirements:
-            try:
-                self._add_to_criteria(self.state.criteria, r, parent=None)
-            except RequirementsConflicted as e:
-                raise ResolutionImpossible(e.criterion.information)
-
-        # The root state is saved as a sentinel so the first ever pin can have
-        # something to backtrack to if it fails. The root state is basically
-        # pinning the virtual "root" package in the graph.
-        self._push_new_state()
-
-        for round_index in range(max_rounds):
-            self._r.starting_round(index=round_index)
-
-            unsatisfied_names = [
-                key
-                for key, criterion in self.state.criteria.items()
-                if not self._is_current_pin_satisfying(key, criterion)
-            ]
-
-            # All criteria are accounted for. Nothing more to pin, we are done!
-            if not unsatisfied_names:
-                self._r.ending(state=self.state)
-                return self.state
-
-            # Choose the most preferred unpinned criterion to try.
-            name = min(unsatisfied_names, key=self._get_preference)
-            failure_causes = self._attempt_to_pin_criterion(name)
-
-            if failure_causes:
-                causes = [i for c in failure_causes for i in c.information]
-                # Backtrack if pinning fails. The backtrack process puts us in
-                # an unpinned state, so we can work on it in the next round.
-                self._r.resolving_conflicts(causes=causes)
-                success = self._backtrack()
-                self.state.backtrack_causes[:] = causes
-
-                # Dead ends everywhere. Give up.
-                if not success:
-                    raise ResolutionImpossible(self.state.backtrack_causes)
-            else:
-                # Pinning was successful. Push a new state to do another pin.
-                self._push_new_state()
-
-            self._r.ending_round(index=round_index, state=self.state)
-
-        raise ResolutionTooDeep(max_rounds)
-
-
-def _has_route_to_root(criteria, key, all_keys, connected):
-    if key in connected:
-        return True
-    if key not in criteria:
-        return False
-    for p in criteria[key].iter_parent():
-        try:
-            pkey = all_keys[id(p)]
-        except KeyError:
-            continue
-        if pkey in connected:
-            connected.add(key)
-            return True
-        if _has_route_to_root(criteria, pkey, all_keys, connected):
-            connected.add(key)
-            return True
-    return False
-
-
-Result = collections.namedtuple("Result", "mapping graph criteria")
-
-
-def _build_result(state):
-    mapping = state.mapping
-    all_keys = {id(v): k for k, v in mapping.items()}
-    all_keys[id(None)] = None
-
-    graph = DirectedGraph()
-    graph.add(None)  # Sentinel as root dependencies' parent.
-
-    connected = {None}
-    for key, criterion in state.criteria.items():
-        if not _has_route_to_root(state.criteria, key, all_keys, connected):
-            continue
-        if key not in graph:
-            graph.add(key)
-        for p in criterion.iter_parent():
-            try:
-                pkey = all_keys[id(p)]
-            except KeyError:
-                continue
-            if pkey not in graph:
-                graph.add(pkey)
-            graph.connect(pkey, key)
-
-    return Result(
-        mapping={k: v for k, v in mapping.items() if k in connected},
-        graph=graph,
-        criteria=state.criteria,
-    )
-
-
-class Resolver(AbstractResolver):
-    """The thing that performs the actual resolution work."""
-
-    base_exception = ResolverException
-
-    def resolve(self, requirements, max_rounds=100):
-        """Take a collection of constraints, spit out the resolution result.
-
-        The return value is a representation to the final resolution result. It
-        is a tuple subclass with three public members:
-
-        * `mapping`: A dict of resolved candidates. Each key is an identifier
-            of a requirement (as returned by the provider's `identify` method),
-            and the value is the resolved candidate.
-        * `graph`: A `DirectedGraph` instance representing the dependency tree.
-            The vertices are keys of `mapping`, and each edge represents *why*
-            a particular package is included. A special vertex `None` is
-            included to represent parents of user-supplied requirements.
-        * `criteria`: A dict of "criteria" that hold detailed information on
-            how edges in the graph are derived. Each key is an identifier of a
-            requirement, and the value is a `Criterion` instance.
-
-        The following exceptions may be raised if a resolution cannot be found:
-
-        * `ResolutionImpossible`: A resolution cannot be found for the given
-            combination of requirements. The `causes` attribute of the
-            exception is a list of (requirement, parent), giving the
-            requirements that could not be satisfied.
-        * `ResolutionTooDeep`: The dependency tree is too deeply nested and
-            the resolver gave up. This is usually caused by a circular
-            dependency, but you can try to resolve this by increasing the
-            `max_rounds` argument.
-        """
-        resolution = Resolution(self.provider, self.reporter)
-        state = resolution.resolve(requirements, max_rounds=max_rounds)
-        return _build_result(state)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/align.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/align.py
deleted file mode 100644
index d5abb5947325728d0645cb4b7c701bb0b1c06d48..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/rich/align.py
+++ /dev/null
@@ -1,311 +0,0 @@
-import sys
-from itertools import chain
-from typing import TYPE_CHECKING, Iterable, Optional
-
-if sys.version_info >= (3, 8):
-    from typing import Literal
-else:
-    from pip._vendor.typing_extensions import Literal  # pragma: no cover
-
-from .constrain import Constrain
-from .jupyter import JupyterMixin
-from .measure import Measurement
-from .segment import Segment
-from .style import StyleType
-
-if TYPE_CHECKING:
-    from .console import Console, ConsoleOptions, RenderableType, RenderResult
-
-AlignMethod = Literal["left", "center", "right"]
-VerticalAlignMethod = Literal["top", "middle", "bottom"]
-
-
-class Align(JupyterMixin):
-    """Align a renderable by adding spaces if necessary.
-
-    Args:
-        renderable (RenderableType): A console renderable.
-        align (AlignMethod): One of "left", "center", or "right""
-        style (StyleType, optional): An optional style to apply to the background.
-        vertical (Optional[VerticalAlginMethod], optional): Optional vertical align, one of "top", "middle", or "bottom". Defaults to None.
-        pad (bool, optional): Pad the right with spaces. Defaults to True.
-        width (int, optional): Restrict contents to given width, or None to use default width. Defaults to None.
-        height (int, optional): Set height of align renderable, or None to fit to contents. Defaults to None.
-
-    Raises:
-        ValueError: if ``align`` is not one of the expected values.
-    """
-
-    def __init__(
-        self,
-        renderable: "RenderableType",
-        align: AlignMethod = "left",
-        style: Optional[StyleType] = None,
-        *,
-        vertical: Optional[VerticalAlignMethod] = None,
-        pad: bool = True,
-        width: Optional[int] = None,
-        height: Optional[int] = None,
-    ) -> None:
-        if align not in ("left", "center", "right"):
-            raise ValueError(
-                f'invalid value for align, expected "left", "center", or "right" (not {align!r})'
-            )
-        if vertical is not None and vertical not in ("top", "middle", "bottom"):
-            raise ValueError(
-                f'invalid value for vertical, expected "top", "middle", or "bottom" (not {vertical!r})'
-            )
-        self.renderable = renderable
-        self.align = align
-        self.style = style
-        self.vertical = vertical
-        self.pad = pad
-        self.width = width
-        self.height = height
-
-    def __repr__(self) -> str:
-        return f"Align({self.renderable!r}, {self.align!r})"
-
-    @classmethod
-    def left(
-        cls,
-        renderable: "RenderableType",
-        style: Optional[StyleType] = None,
-        *,
-        vertical: Optional[VerticalAlignMethod] = None,
-        pad: bool = True,
-        width: Optional[int] = None,
-        height: Optional[int] = None,
-    ) -> "Align":
-        """Align a renderable to the left."""
-        return cls(
-            renderable,
-            "left",
-            style=style,
-            vertical=vertical,
-            pad=pad,
-            width=width,
-            height=height,
-        )
-
-    @classmethod
-    def center(
-        cls,
-        renderable: "RenderableType",
-        style: Optional[StyleType] = None,
-        *,
-        vertical: Optional[VerticalAlignMethod] = None,
-        pad: bool = True,
-        width: Optional[int] = None,
-        height: Optional[int] = None,
-    ) -> "Align":
-        """Align a renderable to the center."""
-        return cls(
-            renderable,
-            "center",
-            style=style,
-            vertical=vertical,
-            pad=pad,
-            width=width,
-            height=height,
-        )
-
-    @classmethod
-    def right(
-        cls,
-        renderable: "RenderableType",
-        style: Optional[StyleType] = None,
-        *,
-        vertical: Optional[VerticalAlignMethod] = None,
-        pad: bool = True,
-        width: Optional[int] = None,
-        height: Optional[int] = None,
-    ) -> "Align":
-        """Align a renderable to the right."""
-        return cls(
-            renderable,
-            "right",
-            style=style,
-            vertical=vertical,
-            pad=pad,
-            width=width,
-            height=height,
-        )
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        align = self.align
-        width = console.measure(self.renderable, options=options).maximum
-        rendered = console.render(
-            Constrain(
-                self.renderable, width if self.width is None else min(width, self.width)
-            ),
-            options.update(height=None),
-        )
-        lines = list(Segment.split_lines(rendered))
-        width, height = Segment.get_shape(lines)
-        lines = Segment.set_shape(lines, width, height)
-        new_line = Segment.line()
-        excess_space = options.max_width - width
-        style = console.get_style(self.style) if self.style is not None else None
-
-        def generate_segments() -> Iterable[Segment]:
-            if excess_space <= 0:
-                # Exact fit
-                for line in lines:
-                    yield from line
-                    yield new_line
-
-            elif align == "left":
-                # Pad on the right
-                pad = Segment(" " * excess_space, style) if self.pad else None
-                for line in lines:
-                    yield from line
-                    if pad:
-                        yield pad
-                    yield new_line
-
-            elif align == "center":
-                # Pad left and right
-                left = excess_space // 2
-                pad = Segment(" " * left, style)
-                pad_right = (
-                    Segment(" " * (excess_space - left), style) if self.pad else None
-                )
-                for line in lines:
-                    if left:
-                        yield pad
-                    yield from line
-                    if pad_right:
-                        yield pad_right
-                    yield new_line
-
-            elif align == "right":
-                # Padding on left
-                pad = Segment(" " * excess_space, style)
-                for line in lines:
-                    yield pad
-                    yield from line
-                    yield new_line
-
-        blank_line = (
-            Segment(f"{' ' * (self.width or options.max_width)}\n", style)
-            if self.pad
-            else Segment("\n")
-        )
-
-        def blank_lines(count: int) -> Iterable[Segment]:
-            if count > 0:
-                for _ in range(count):
-                    yield blank_line
-
-        vertical_height = self.height or options.height
-        iter_segments: Iterable[Segment]
-        if self.vertical and vertical_height is not None:
-            if self.vertical == "top":
-                bottom_space = vertical_height - height
-                iter_segments = chain(generate_segments(), blank_lines(bottom_space))
-            elif self.vertical == "middle":
-                top_space = (vertical_height - height) // 2
-                bottom_space = vertical_height - top_space - height
-                iter_segments = chain(
-                    blank_lines(top_space),
-                    generate_segments(),
-                    blank_lines(bottom_space),
-                )
-            else:  #  self.vertical == "bottom":
-                top_space = vertical_height - height
-                iter_segments = chain(blank_lines(top_space), generate_segments())
-        else:
-            iter_segments = generate_segments()
-        if self.style:
-            style = console.get_style(self.style)
-            iter_segments = Segment.apply_style(iter_segments, style)
-        yield from iter_segments
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> Measurement:
-        measurement = Measurement.get(console, options, self.renderable)
-        return measurement
-
-
-class VerticalCenter(JupyterMixin):
-    """Vertically aligns a renderable.
-
-    Warn:
-        This class is deprecated and may be removed in a future version. Use Align class with
-        `vertical="middle"`.
-
-    Args:
-        renderable (RenderableType): A renderable object.
-    """
-
-    def __init__(
-        self,
-        renderable: "RenderableType",
-        style: Optional[StyleType] = None,
-    ) -> None:
-        self.renderable = renderable
-        self.style = style
-
-    def __repr__(self) -> str:
-        return f"VerticalCenter({self.renderable!r})"
-
-    def __rich_console__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> "RenderResult":
-        style = console.get_style(self.style) if self.style is not None else None
-        lines = console.render_lines(
-            self.renderable, options.update(height=None), pad=False
-        )
-        width, _height = Segment.get_shape(lines)
-        new_line = Segment.line()
-        height = options.height or options.size.height
-        top_space = (height - len(lines)) // 2
-        bottom_space = height - top_space - len(lines)
-        blank_line = Segment(f"{' ' * width}", style)
-
-        def blank_lines(count: int) -> Iterable[Segment]:
-            for _ in range(count):
-                yield blank_line
-                yield new_line
-
-        if top_space > 0:
-            yield from blank_lines(top_space)
-        for line in lines:
-            yield from line
-            yield new_line
-        if bottom_space > 0:
-            yield from blank_lines(bottom_space)
-
-    def __rich_measure__(
-        self, console: "Console", options: "ConsoleOptions"
-    ) -> Measurement:
-        measurement = Measurement.get(console, options, self.renderable)
-        return measurement
-
-
-if __name__ == "__main__":  # pragma: no cover
-    from pip._vendor.rich.console import Console, Group
-    from pip._vendor.rich.highlighter import ReprHighlighter
-    from pip._vendor.rich.panel import Panel
-
-    highlighter = ReprHighlighter()
-    console = Console()
-
-    panel = Panel(
-        Group(
-            Align.left(highlighter("align='left'")),
-            Align.center(highlighter("align='center'")),
-            Align.right(highlighter("align='right'")),
-        ),
-        width=60,
-        style="on dark_blue",
-        title="Algin",
-    )
-
-    console.print(
-        Align.center(panel, vertical="middle", style="on red", height=console.height)
-    )
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
deleted file mode 100644
index 9367a3c83aeb1e05f38f4db9fb0110e731dd859c..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './faster_rcnn_r50_fpn_2x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py
deleted file mode 100644
index 8c766f05f4ee61273670ce74ed60c91c89beb50e..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py
+++ /dev/null
@@ -1,13 +0,0 @@
-_base_ = './rpn_r50_fpn_2x_coco.py'
-model = dict(
-    pretrained='open-mmlab://resnext101_64x4d',
-    backbone=dict(
-        type='ResNeXt',
-        depth=101,
-        groups=64,
-        base_width=4,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        style='pytorch'))
diff --git a/spaces/touchscale/White-box-Cartoonization/wbc/cartoonize.py b/spaces/touchscale/White-box-Cartoonization/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/touchscale/White-box-Cartoonization/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
-    h, w, c = np.shape(image)
-    if min(h, w) > 720:
-        if h > w:
-            h, w = int(720 * h / w), 720
-        else:
-            h, w = 720, int(720 * w / h)
-    image = cv2.resize(image, (w, h),
-                       interpolation=cv2.INTER_AREA)
-    h, w = (h // 8) * 8, (w // 8) * 8
-    image = image[:h, :w, :]
-    return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
-    print(model_path)
-    input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-    network_out = network.unet_generator(input_photo)
-    final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
-    all_vars = tf.trainable_variables()
-    gene_vars = [var for var in all_vars if 'generator' in var.name]
-    saver = tf.train.Saver(var_list=gene_vars)
-
-    config = tf.ConfigProto()
-    config.gpu_options.allow_growth = True
-    sess = tf.Session(config=config)
-
-    sess.run(tf.global_variables_initializer())
-    saver.restore(sess, tf.train.latest_checkpoint(model_path))
-    name_list = os.listdir(load_folder)
-    for name in tqdm(name_list):
-        try:
-            load_path = os.path.join(load_folder, name)
-            save_path = os.path.join(save_folder, name)
-            image = cv2.imread(load_path)
-            image = resize_crop(image)
-            batch_image = image.astype(np.float32) / 127.5 - 1
-            batch_image = np.expand_dims(batch_image, axis=0)
-            output = sess.run(final_out, feed_dict={input_photo: batch_image})
-            output = (np.squeeze(output) + 1) * 127.5
-            output = np.clip(output, 0, 255).astype(np.uint8)
-            cv2.imwrite(save_path, output)
-        except:
-            print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
-    def __init__(self, model_path):
-        print(model_path)
-        self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
-        network_out = network.unet_generator(self.input_photo)
-        self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
-        all_vars = tf.trainable_variables()
-        gene_vars = [var for var in all_vars if 'generator' in var.name]
-        saver = tf.train.Saver(var_list=gene_vars)
-
-        config = tf.ConfigProto()
-        config.gpu_options.allow_growth = True
-        self.sess = tf.Session(config=config)
-
-        self.sess.run(tf.global_variables_initializer())
-        saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
-    def run(self, load_folder, save_folder):
-        name_list = os.listdir(load_folder)
-        for name in tqdm(name_list):
-            try:
-                load_path = os.path.join(load_folder, name)
-                save_path = os.path.join(save_folder, name)
-                image = cv2.imread(load_path)
-                image = resize_crop(image)
-                batch_image = image.astype(np.float32) / 127.5 - 1
-                batch_image = np.expand_dims(batch_image, axis=0)
-                output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
-                output = (np.squeeze(output) + 1) * 127.5
-                output = np.clip(output, 0, 255).astype(np.uint8)
-                cv2.imwrite(save_path, output)
-            except:
-                print('cartoonize {} failed'.format(load_path))
-
-    def run_sigle(self, load_path, save_path):
-        try:
-            image = cv2.imread(load_path)
-            image = resize_crop(image)
-            batch_image = image.astype(np.float32) / 127.5 - 1
-            batch_image = np.expand_dims(batch_image, axis=0)
-            output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
-            output = (np.squeeze(output) + 1) * 127.5
-            output = np.clip(output, 0, 255).astype(np.uint8)
-            cv2.imwrite(save_path, output)
-        except:
-            print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
-    model_path = 'saved_models'
-    load_folder = 'test_images'
-    save_folder = 'cartoonized_images'
-    if not os.path.exists(save_folder):
-        os.mkdir(save_folder)
-    cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/ulysses115/diffsvc_test/modules/commons/common_layers.py b/spaces/ulysses115/diffsvc_test/modules/commons/common_layers.py
deleted file mode 100644
index 192997cee5b265525b244e1f421b41dab9874b99..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/diffsvc_test/modules/commons/common_layers.py
+++ /dev/null
@@ -1,671 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import Parameter
-import torch.onnx.operators
-import torch.nn.functional as F
-import utils
-
-
-class Reshape(nn.Module):
-    def __init__(self, *args):
-        super(Reshape, self).__init__()
-        self.shape = args
-
-    def forward(self, x):
-        return x.view(self.shape)
-
-
-class Permute(nn.Module):
-    def __init__(self, *args):
-        super(Permute, self).__init__()
-        self.args = args
-
-    def forward(self, x):
-        return x.permute(self.args)
-
-
-class LinearNorm(torch.nn.Module):
-    def __init__(self, in_dim, out_dim, bias=True, w_init_gain='linear'):
-        super(LinearNorm, self).__init__()
-        self.linear_layer = torch.nn.Linear(in_dim, out_dim, bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.linear_layer.weight,
-            gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, x):
-        return self.linear_layer(x)
-
-
-class ConvNorm(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
-                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
-        super(ConvNorm, self).__init__()
-        if padding is None:
-            assert (kernel_size % 2 == 1)
-            padding = int(dilation * (kernel_size - 1) / 2)
-
-        self.conv = torch.nn.Conv1d(in_channels, out_channels,
-                                    kernel_size=kernel_size, stride=stride,
-                                    padding=padding, dilation=dilation,
-                                    bias=bias)
-
-        torch.nn.init.xavier_uniform_(
-            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
-
-    def forward(self, signal):
-        conv_signal = self.conv(signal)
-        return conv_signal
-
-
-def Embedding(num_embeddings, embedding_dim, padding_idx=None):
-    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
-    nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
-    if padding_idx is not None:
-        nn.init.constant_(m.weight[padding_idx], 0)
-    return m
-
-
-def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
-    if not export and torch.cuda.is_available():
-        try:
-            from apex.normalization import FusedLayerNorm
-            return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
-        except ImportError:
-            pass
-    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
-
-
-def Linear(in_features, out_features, bias=True):
-    m = nn.Linear(in_features, out_features, bias)
-    nn.init.xavier_uniform_(m.weight)
-    if bias:
-        nn.init.constant_(m.bias, 0.)
-    return m
-
-
-class SinusoidalPositionalEmbedding(nn.Module):
-    """This module produces sinusoidal positional embeddings of any length.
-
-    Padding symbols are ignored.
-    """
-
-    def __init__(self, embedding_dim, padding_idx, init_size=1024):
-        super().__init__()
-        self.embedding_dim = embedding_dim
-        self.padding_idx = padding_idx
-        self.weights = SinusoidalPositionalEmbedding.get_embedding(
-            init_size,
-            embedding_dim,
-            padding_idx,
-        )
-        self.register_buffer('_float_tensor', torch.FloatTensor(1))
-
-    @staticmethod
-    def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
-        """Build sinusoidal embeddings.
-
-        This matches the implementation in tensor2tensor, but differs slightly
-        from the description in Section 3.5 of "Attention Is All You Need".
-        """
-        half_dim = embedding_dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
-        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
-        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
-        if embedding_dim % 2 == 1:
-            # zero pad
-            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
-        if padding_idx is not None:
-            emb[padding_idx, :] = 0
-        return emb
-
-    def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs):
-        """Input is expected to be of size [bsz x seqlen]."""
-        bsz, seq_len = input.shape[:2]
-        max_pos = self.padding_idx + 1 + seq_len
-        if self.weights is None or max_pos > self.weights.size(0):
-            # recompute/expand embeddings if needed
-            self.weights = SinusoidalPositionalEmbedding.get_embedding(
-                max_pos,
-                self.embedding_dim,
-                self.padding_idx,
-            )
-        self.weights = self.weights.to(self._float_tensor)
-
-        if incremental_state is not None:
-            # positions is the same for every token when decoding a single step
-            pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
-            return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
-
-        positions = utils.make_positions(input, self.padding_idx) if positions is None else positions
-        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
-
-    def max_positions(self):
-        """Maximum number of supported positions."""
-        return int(1e5)  # an arbitrary large number
-
-
-class ConvTBC(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, padding=0):
-        super(ConvTBC, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.padding = padding
-
-        self.weight = torch.nn.Parameter(torch.Tensor(
-            self.kernel_size, in_channels, out_channels))
-        self.bias = torch.nn.Parameter(torch.Tensor(out_channels))
-
-    def forward(self, input):
-        return torch.conv_tbc(input.contiguous(), self.weight, self.bias, self.padding)
-
-
-class MultiheadAttention(nn.Module):
-    def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
-                 add_bias_kv=False, add_zero_attn=False, self_attention=False,
-                 encoder_decoder_attention=False):
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.kdim = kdim if kdim is not None else embed_dim
-        self.vdim = vdim if vdim is not None else embed_dim
-        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
-
-        self.num_heads = num_heads
-        self.dropout = dropout
-        self.head_dim = embed_dim // num_heads
-        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
-        self.scaling = self.head_dim ** -0.5
-
-        self.self_attention = self_attention
-        self.encoder_decoder_attention = encoder_decoder_attention
-
-        assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \
-                                                             'value to be of the same size'
-
-        if self.qkv_same_dim:
-            self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
-        else:
-            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
-            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
-            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
-
-        if bias:
-            self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
-        else:
-            self.register_parameter('in_proj_bias', None)
-
-        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
-
-        if add_bias_kv:
-            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
-            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
-        else:
-            self.bias_k = self.bias_v = None
-
-        self.add_zero_attn = add_zero_attn
-
-        self.reset_parameters()
-
-        self.enable_torch_version = False
-        if hasattr(F, "multi_head_attention_forward"):
-            self.enable_torch_version = True
-        else:
-            self.enable_torch_version = False
-        self.last_attn_probs = None
-
-    def reset_parameters(self):
-        if self.qkv_same_dim:
-            nn.init.xavier_uniform_(self.in_proj_weight)
-        else:
-            nn.init.xavier_uniform_(self.k_proj_weight)
-            nn.init.xavier_uniform_(self.v_proj_weight)
-            nn.init.xavier_uniform_(self.q_proj_weight)
-
-        nn.init.xavier_uniform_(self.out_proj.weight)
-        if self.in_proj_bias is not None:
-            nn.init.constant_(self.in_proj_bias, 0.)
-            nn.init.constant_(self.out_proj.bias, 0.)
-        if self.bias_k is not None:
-            nn.init.xavier_normal_(self.bias_k)
-        if self.bias_v is not None:
-            nn.init.xavier_normal_(self.bias_v)
-
-    def forward(
-            self,
-            query, key, value,
-            key_padding_mask=None,
-            incremental_state=None,
-            need_weights=True,
-            static_kv=False,
-            attn_mask=None,
-            before_softmax=False,
-            need_head_weights=False,
-            enc_dec_attn_constraint_mask=None,
-            reset_attn_weight=None
-    ):
-        """Input shape: Time x Batch x Channel
-
-        Args:
-            key_padding_mask (ByteTensor, optional): mask to exclude
-                keys that are pads, of shape `(batch, src_len)`, where
-                padding elements are indicated by 1s.
-            need_weights (bool, optional): return the attention weights,
-                averaged over heads (default: False).
-            attn_mask (ByteTensor, optional): typically used to
-                implement causal attention, where the mask prevents the
-                attention from looking forward in time (default: None).
-            before_softmax (bool, optional): return the raw attention
-                weights and values before the attention softmax.
-            need_head_weights (bool, optional): return the attention
-                weights for each head. Implies *need_weights*. Default:
-                return the average attention weights over all heads.
-        """
-        if need_head_weights:
-            need_weights = True
-
-        tgt_len, bsz, embed_dim = query.size()
-        assert embed_dim == self.embed_dim
-        assert list(query.size()) == [tgt_len, bsz, embed_dim]
-
-        if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None:
-            if self.qkv_same_dim:
-                return F.multi_head_attention_forward(query, key, value,
-                                                      self.embed_dim, self.num_heads,
-                                                      self.in_proj_weight,
-                                                      self.in_proj_bias, self.bias_k, self.bias_v,
-                                                      self.add_zero_attn, self.dropout,
-                                                      self.out_proj.weight, self.out_proj.bias,
-                                                      self.training, key_padding_mask, need_weights,
-                                                      attn_mask)
-            else:
-                return F.multi_head_attention_forward(query, key, value,
-                                                      self.embed_dim, self.num_heads,
-                                                      torch.empty([0]),
-                                                      self.in_proj_bias, self.bias_k, self.bias_v,
-                                                      self.add_zero_attn, self.dropout,
-                                                      self.out_proj.weight, self.out_proj.bias,
-                                                      self.training, key_padding_mask, need_weights,
-                                                      attn_mask, use_separate_proj_weight=True,
-                                                      q_proj_weight=self.q_proj_weight,
-                                                      k_proj_weight=self.k_proj_weight,
-                                                      v_proj_weight=self.v_proj_weight)
-
-        if incremental_state is not None:
-            print('Not implemented error.')
-            exit()
-        else:
-            saved_state = None
-
-        if self.self_attention:
-            # self-attention
-            q, k, v = self.in_proj_qkv(query)
-        elif self.encoder_decoder_attention:
-            # encoder-decoder attention
-            q = self.in_proj_q(query)
-            if key is None:
-                assert value is None
-                k = v = None
-            else:
-                k = self.in_proj_k(key)
-                v = self.in_proj_v(key)
-
-        else:
-            q = self.in_proj_q(query)
-            k = self.in_proj_k(key)
-            v = self.in_proj_v(value)
-        q *= self.scaling
-
-        if self.bias_k is not None:
-            assert self.bias_v is not None
-            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
-            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
-            if attn_mask is not None:
-                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
-            if key_padding_mask is not None:
-                key_padding_mask = torch.cat(
-                    [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
-
-        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-        if k is not None:
-            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-        if v is not None:
-            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
-
-        if saved_state is not None:
-            print('Not implemented error.')
-            exit()
-
-        src_len = k.size(1)
-
-        # This is part of a workaround to get around fork/join parallelism
-        # not supporting Optional types.
-        if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]):
-            key_padding_mask = None
-
-        if key_padding_mask is not None:
-            assert key_padding_mask.size(0) == bsz
-            assert key_padding_mask.size(1) == src_len
-
-        if self.add_zero_attn:
-            src_len += 1
-            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
-            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
-            if attn_mask is not None:
-                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
-            if key_padding_mask is not None:
-                key_padding_mask = torch.cat(
-                    [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
-
-        attn_weights = torch.bmm(q, k.transpose(1, 2))
-        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
-
-        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
-
-        if attn_mask is not None:
-            if len(attn_mask.shape) == 2:
-                attn_mask = attn_mask.unsqueeze(0)
-            elif len(attn_mask.shape) == 3:
-                attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape(
-                    bsz * self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights + attn_mask
-
-        if enc_dec_attn_constraint_mask is not None:  # bs x head x L_kv
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.masked_fill(
-                enc_dec_attn_constraint_mask.unsqueeze(2).bool(),
-                -1e9,
-            )
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        if key_padding_mask is not None:
-            # don't attend to padding symbols
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights.masked_fill(
-                key_padding_mask.unsqueeze(1).unsqueeze(2),
-                -1e9,
-            )
-            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
-        attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-
-        if before_softmax:
-            return attn_weights, v
-
-        attn_weights_float = utils.softmax(attn_weights, dim=-1)
-        attn_weights = attn_weights_float.type_as(attn_weights)
-        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
-
-        if reset_attn_weight is not None:
-            if reset_attn_weight:
-                self.last_attn_probs = attn_probs.detach()
-            else:
-                assert self.last_attn_probs is not None
-                attn_probs = self.last_attn_probs
-        attn = torch.bmm(attn_probs, v)
-        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
-        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
-        attn = self.out_proj(attn)
-
-        if need_weights:
-            attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
-            if not need_head_weights:
-                # average attention weights over heads
-                attn_weights = attn_weights.mean(dim=0)
-        else:
-            attn_weights = None
-
-        return attn, (attn_weights, attn_logits)
-
-    def in_proj_qkv(self, query):
-        return self._in_proj(query).chunk(3, dim=-1)
-
-    def in_proj_q(self, query):
-        if self.qkv_same_dim:
-            return self._in_proj(query, end=self.embed_dim)
-        else:
-            bias = self.in_proj_bias
-            if bias is not None:
-                bias = bias[:self.embed_dim]
-            return F.linear(query, self.q_proj_weight, bias)
-
-    def in_proj_k(self, key):
-        if self.qkv_same_dim:
-            return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
-        else:
-            weight = self.k_proj_weight
-            bias = self.in_proj_bias
-            if bias is not None:
-                bias = bias[self.embed_dim:2 * self.embed_dim]
-            return F.linear(key, weight, bias)
-
-    def in_proj_v(self, value):
-        if self.qkv_same_dim:
-            return self._in_proj(value, start=2 * self.embed_dim)
-        else:
-            weight = self.v_proj_weight
-            bias = self.in_proj_bias
-            if bias is not None:
-                bias = bias[2 * self.embed_dim:]
-            return F.linear(value, weight, bias)
-
-    def _in_proj(self, input, start=0, end=None):
-        weight = self.in_proj_weight
-        bias = self.in_proj_bias
-        weight = weight[start:end, :]
-        if bias is not None:
-            bias = bias[start:end]
-        return F.linear(input, weight, bias)
-
-
-    def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
-        return attn_weights
-
-
-class Swish(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, i):
-        result = i * torch.sigmoid(i)
-        ctx.save_for_backward(i)
-        return result
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        i = ctx.saved_variables[0]
-        sigmoid_i = torch.sigmoid(i)
-        return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))
-
-
-class CustomSwish(nn.Module):
-    def forward(self, input_tensor):
-        return Swish.apply(input_tensor)
-        
-class Mish(nn.Module):
-    def forward(self, x):
-        return x * torch.tanh(F.softplus(x))
-
-class TransformerFFNLayer(nn.Module):
-    def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'):
-        super().__init__()
-        self.kernel_size = kernel_size
-        self.dropout = dropout
-        self.act = act
-        if padding == 'SAME':
-            self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2)
-        elif padding == 'LEFT':
-            self.ffn_1 = nn.Sequential(
-                nn.ConstantPad1d((kernel_size - 1, 0), 0.0),
-                nn.Conv1d(hidden_size, filter_size, kernel_size)
-            )
-        self.ffn_2 = Linear(filter_size, hidden_size)
-        if self.act == 'swish':
-            self.swish_fn = CustomSwish()
-
-    def forward(self, x, incremental_state=None):
-        # x: T x B x C
-        if incremental_state is not None:
-            assert incremental_state is None, 'Nar-generation does not allow this.'
-            exit(1)
-
-        x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1)
-        x = x * self.kernel_size ** -0.5
-
-        if incremental_state is not None:
-            x = x[-1:]
-        if self.act == 'gelu':
-            x = F.gelu(x)
-        if self.act == 'relu':
-            x = F.relu(x)
-        if self.act == 'swish':
-            x = self.swish_fn(x)
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = self.ffn_2(x)
-        return x
-
-
-class BatchNorm1dTBC(nn.Module):
-    def __init__(self, c):
-        super(BatchNorm1dTBC, self).__init__()
-        self.bn = nn.BatchNorm1d(c)
-
-    def forward(self, x):
-        """
-
-        :param x: [T, B, C]
-        :return: [T, B, C]
-        """
-        x = x.permute(1, 2, 0)  # [B, C, T]
-        x = self.bn(x)  # [B, C, T]
-        x = x.permute(2, 0, 1)  # [T, B, C]
-        return x
-
-
-class EncSALayer(nn.Module):
-    def __init__(self, c, num_heads, dropout, attention_dropout=0.1,
-                 relu_dropout=0.1, kernel_size=9, padding='SAME', norm='ln', act='gelu'):
-        super().__init__()
-        self.c = c
-        self.dropout = dropout
-        self.num_heads = num_heads
-        if num_heads > 0:
-            if norm == 'ln':
-                self.layer_norm1 = LayerNorm(c)
-            elif norm == 'bn':
-                self.layer_norm1 = BatchNorm1dTBC(c)
-            self.self_attn = MultiheadAttention(
-                self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False,
-            )
-        if norm == 'ln':
-            self.layer_norm2 = LayerNorm(c)
-        elif norm == 'bn':
-            self.layer_norm2 = BatchNorm1dTBC(c)
-        self.ffn = TransformerFFNLayer(
-            c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act)
-
-    def forward(self, x, encoder_padding_mask=None, **kwargs):
-        layer_norm_training = kwargs.get('layer_norm_training', None)
-        if layer_norm_training is not None:
-            self.layer_norm1.training = layer_norm_training
-            self.layer_norm2.training = layer_norm_training
-        if self.num_heads > 0:
-            residual = x
-            x = self.layer_norm1(x)
-            x, _, = self.self_attn(
-                query=x,
-                key=x,
-                value=x,
-                key_padding_mask=encoder_padding_mask
-            )
-            x = F.dropout(x, self.dropout, training=self.training)
-            x = residual + x
-            x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
-
-        residual = x
-        x = self.layer_norm2(x)
-        x = self.ffn(x)
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-        x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
-        return x
-
-
-class DecSALayer(nn.Module):
-    def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1, kernel_size=9, act='gelu'):
-        super().__init__()
-        self.c = c
-        self.dropout = dropout
-        self.layer_norm1 = LayerNorm(c)
-        self.self_attn = MultiheadAttention(
-            c, num_heads, self_attention=True, dropout=attention_dropout, bias=False
-        )
-        self.layer_norm2 = LayerNorm(c)
-        self.encoder_attn = MultiheadAttention(
-            c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False,
-        )
-        self.layer_norm3 = LayerNorm(c)
-        self.ffn = TransformerFFNLayer(
-            c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act)
-
-    def forward(
-            self,
-            x,
-            encoder_out=None,
-            encoder_padding_mask=None,
-            incremental_state=None,
-            self_attn_mask=None,
-            self_attn_padding_mask=None,
-            attn_out=None,
-            reset_attn_weight=None,
-            **kwargs,
-    ):
-        layer_norm_training = kwargs.get('layer_norm_training', None)
-        if layer_norm_training is not None:
-            self.layer_norm1.training = layer_norm_training
-            self.layer_norm2.training = layer_norm_training
-            self.layer_norm3.training = layer_norm_training
-        residual = x
-        x = self.layer_norm1(x)
-        x, _ = self.self_attn(
-            query=x,
-            key=x,
-            value=x,
-            key_padding_mask=self_attn_padding_mask,
-            incremental_state=incremental_state,
-            attn_mask=self_attn_mask
-        )
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-
-        residual = x
-        x = self.layer_norm2(x)
-        if encoder_out is not None:
-            x, attn = self.encoder_attn(
-                query=x,
-                key=encoder_out,
-                value=encoder_out,
-                key_padding_mask=encoder_padding_mask,
-                incremental_state=incremental_state,
-                static_kv=True,
-                enc_dec_attn_constraint_mask=None, #utils.get_incremental_state(self, incremental_state, 'enc_dec_attn_constraint_mask'),
-                reset_attn_weight=reset_attn_weight
-            )
-            attn_logits = attn[1]
-        else:
-            assert attn_out is not None
-            x = self.encoder_attn.in_proj_v(attn_out.transpose(0, 1))
-            attn_logits = None
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-
-        residual = x
-        x = self.layer_norm3(x)
-        x = self.ffn(x, incremental_state=incremental_state)
-        x = F.dropout(x, self.dropout, training=self.training)
-        x = residual + x
-        # if len(attn_logits.size()) > 3:
-        #    indices = attn_logits.softmax(-1).max(-1).values.sum(-1).argmax(-1)
-        #    attn_logits = attn_logits.gather(1,
-        #        indices[:, None, None, None].repeat(1, 1, attn_logits.size(-2), attn_logits.size(-1))).squeeze(1)
-        return x, attn_logits
diff --git a/spaces/ulysses115/diffsvc_test/utils/indexed_datasets.py b/spaces/ulysses115/diffsvc_test/utils/indexed_datasets.py
deleted file mode 100644
index e15632be30d6296a3c9aa80a1f351058003698b3..0000000000000000000000000000000000000000
--- a/spaces/ulysses115/diffsvc_test/utils/indexed_datasets.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import pickle
-from copy import deepcopy
-
-import numpy as np
-
-
-class IndexedDataset:
-    def __init__(self, path, num_cache=1):
-        super().__init__()
-        self.path = path
-        self.data_file = None
-        self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets']
-        self.data_file = open(f"{path}.data", 'rb', buffering=-1)
-        self.cache = []
-        self.num_cache = num_cache
-
-    def check_index(self, i):
-        if i < 0 or i >= len(self.data_offsets) - 1:
-            raise IndexError('index out of range')
-
-    def __del__(self):
-        if self.data_file:
-            self.data_file.close()
-
-    def __getitem__(self, i):
-        self.check_index(i)
-        if self.num_cache > 0:
-            for c in self.cache:
-                if c[0] == i:
-                    return c[1]
-        self.data_file.seek(self.data_offsets[i])
-        b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i])
-        item = pickle.loads(b)
-        if self.num_cache > 0:
-            self.cache = [(i, deepcopy(item))] + self.cache[:-1]
-        return item
-
-    def __len__(self):
-        return len(self.data_offsets) - 1
-
-class IndexedDatasetBuilder:
-    def __init__(self, path):
-        self.path = path
-        self.out_file = open(f"{path}.data", 'wb')
-        self.byte_offsets = [0]
-
-    def add_item(self, item):
-        s = pickle.dumps(item)
-        bytes = self.out_file.write(s)
-        self.byte_offsets.append(self.byte_offsets[-1] + bytes)
-
-    def finalize(self):
-        self.out_file.close()
-        np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets})
-
-
-if __name__ == "__main__":
-    import random
-    from tqdm import tqdm
-    ds_path = '/tmp/indexed_ds_example'
-    size = 100
-    items = [{"a": np.random.normal(size=[10000, 10]),
-              "b": np.random.normal(size=[10000, 10])} for i in range(size)]
-    builder = IndexedDatasetBuilder(ds_path)
-    for i in tqdm(range(size)):
-        builder.add_item(items[i])
-    builder.finalize()
-    ds = IndexedDataset(ds_path)
-    for i in tqdm(range(10000)):
-        idx = random.randint(0, size - 1)
-        assert (ds[idx]['a'] == items[idx]['a']).all()
diff --git a/spaces/unity/ML-Agents-Walker/TemplateData/style.css b/spaces/unity/ML-Agents-Walker/TemplateData/style.css
deleted file mode 100644
index cdc3477fb8c1c824db96f451631bca7cde305923..0000000000000000000000000000000000000000
--- a/spaces/unity/ML-Agents-Walker/TemplateData/style.css
+++ /dev/null
@@ -1,105 +0,0 @@
-html {
-    box-sizing: border-box;
-}
-*, *:before, *:after {
-    box-sizing: inherit;
-}
-html, body {
-    height: 100%;
-}
-canvas {
-    display: block;
-}
-body {
-    margin: 0;
-}
-#unity-container {
-    width: 100%;
-    height: 100%;
-}
-#unity-canvas {
-    width: 100%;
-    height: 100%;
-    background: #231F20;
-}
-#loading-cover {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-}
-#unity-loading-bar {
-    flex: 1 1 auto;
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    align-items: center;
-}
-#unity-logo {
-    text-align: center;
-}
-#unity-logo img {
-    max-width: 80%;
-}
-#unity-progress-bar-empty {
-    width: 80%;
-    height: 24px;
-    margin: 10px 20px 20px 10px;
-    text-align: left;
-    border: 1px solid white;
-    padding: 2px;
-}
-#unity-progress-bar-full {
-    width: 0%;
-    height: 100%;
-    background: #ffd21e;
-}
-.light #unity-progress-bar-empty {
-    border-color: black;
-}
-.light #unity-progress-bar-full {
-    background: black;
-}
-
-#unity-fullscreen-button {
-    position: absolute;
-    right: 10px;
-    bottom: 10px;
-    width: 38px;
-    height: 38px;
-    background: url('fullscreen-button.png') no-repeat center;
-    background-size: contain;
-}
-
-.spinner,
-.spinner:after {
-  border-radius: 50%;
-  width: 5em;
-  height: 5em;
-}
-.spinner {
-  margin: 10px;
-  font-size: 10px;
-  position: relative;
-  text-indent: -9999em;
-  border-top: 1.1em solid rgba(255, 255, 255, 0.2);
-  border-right: 1.1em solid rgba(255, 255, 255, 0.2);
-  border-bottom: 1.1em solid rgba(255, 255, 255, 0.2);
-  border-left: 1.1em solid #ffffff;
-  transform: translateZ(0);
-  animation: spinner-spin 1.1s infinite linear;
-}
-@keyframes spinner-spin {
-  0% {
-    transform: rotate(0deg);
-  }
-  100% {
-    transform: rotate(360deg);
-  }
-}
-
-
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/?luvu?ker ???n E????k?.md b/spaces/usbethFlerru/sovits-modelsV2/example/?luvu?ker ???n E????k?.md
deleted file mode 100644
index 0353af1fe0ef6a81a823983f861111153657455a..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/?luvu?ker ???n E????k?.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>?luvu?ker ???n e????k?</h2><br /><p><b><b>Download</b> &rArr;&rArr;&rArr; <a href="https://urlcod.com/2uyWzR">https://urlcod.com/2uyWzR</a></b></p><br /><br />
-
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/esrgan_model_arch.py b/spaces/user238921933/stable-diffusion-webui/modules/esrgan_model_arch.py
deleted file mode 100644
index bec5962b595cfc0e6c52d916275538c9c4252068..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/esrgan_model_arch.py
+++ /dev/null
@@ -1,464 +0,0 @@
-# this file is adapted from https://github.com/victorca25/iNNfer
-
-from collections import OrderedDict
-import math
-import functools
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-####################
-# RRDBNet Generator
-####################
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
-            act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
-            finalact=None, gaussian_noise=False, plus=False):
-        super(RRDBNet, self).__init__()
-        n_upscale = int(math.log(upscale, 2))
-        if upscale == 3:
-            n_upscale = 1
-
-        self.resrgan_scale = 0
-        if in_nc % 16 == 0:
-            self.resrgan_scale = 1
-        elif in_nc != 4 and in_nc % 4 == 0:
-            self.resrgan_scale = 2
-
-        fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
-        rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
-            norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
-            gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
-        LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
-
-        if upsample_mode == 'upconv':
-            upsample_block = upconv_block
-        elif upsample_mode == 'pixelshuffle':
-            upsample_block = pixelshuffle_block
-        else:
-            raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
-        if upscale == 3:
-            upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
-        else:
-            upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
-        HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
-        HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
-
-        outact = act(finalact) if finalact else None
-
-        self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
-            *upsampler, HR_conv0, HR_conv1, outact)
-
-    def forward(self, x, outm=None):
-        if self.resrgan_scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        elif self.resrgan_scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        else:
-            feat = x
-
-        return self.model(feat)
-
-
-class RRDB(nn.Module):
-    """
-    Residual in Residual Dense Block
-    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
-    """
-
-    def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
-            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
-            spectral_norm=False, gaussian_noise=False, plus=False):
-        super(RRDB, self).__init__()
-        # This is for backwards compatibility with existing models
-        if nr == 3:
-            self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                    gaussian_noise=gaussian_noise, plus=plus)
-            self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                    gaussian_noise=gaussian_noise, plus=plus)
-            self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                    gaussian_noise=gaussian_noise, plus=plus)
-        else:
-            RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                                              norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                                              gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
-            self.RDBs = nn.Sequential(*RDB_list)
-
-    def forward(self, x):
-        if hasattr(self, 'RDB1'):
-            out = self.RDB1(x)
-            out = self.RDB2(out)
-            out = self.RDB3(out)
-        else:
-            out = self.RDBs(x)
-        return out * 0.2 + x
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    """
-    Residual Dense Block
-    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
-    Modified options that can be used:
-        - "Partial Convolution based Padding" arXiv:1811.11718
-        - "Spectral normalization" arXiv:1802.05957
-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 
-            {Rakotonirina} and A. {Rasoanaivo}
-    """
-
-    def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
-            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
-            spectral_norm=False, gaussian_noise=False, plus=False):
-        super(ResidualDenseBlock_5C, self).__init__()
-
-        self.noise = GaussianNoise() if gaussian_noise else None
-        self.conv1x1 = conv1x1(nf, gc) if plus else None
-
-        self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        if mode == 'CNA':
-            last_act = None
-        else:
-            last_act = act_type
-        self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv2(torch.cat((x, x1), 1))
-        if self.conv1x1:
-            x2 = x2 + self.conv1x1(x)
-        x3 = self.conv3(torch.cat((x, x1, x2), 1))
-        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
-        if self.conv1x1:
-            x4 = x4 + x2
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        if self.noise:
-            return self.noise(x5.mul(0.2) + x)
-        else:
-            return x5 * 0.2 + x
-
-
-####################
-# ESRGANplus
-####################
-
-class GaussianNoise(nn.Module):
-    def __init__(self, sigma=0.1, is_relative_detach=False):
-        super().__init__()
-        self.sigma = sigma
-        self.is_relative_detach = is_relative_detach
-        self.noise = torch.tensor(0, dtype=torch.float)
-
-    def forward(self, x):
-        if self.training and self.sigma != 0:
-            self.noise = self.noise.to(x.device)
-            scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
-            sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
-            x = x + sampled_noise
-        return x 
-
-def conv1x1(in_planes, out_planes, stride=1):
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-####################
-# SRVGGNetCompact
-####################
-
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-    This class is copied from https://github.com/xinntao/Real-ESRGAN
-    """
-
-    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
-        super(SRVGGNetCompact, self).__init__()
-        self.num_in_ch = num_in_ch
-        self.num_out_ch = num_out_ch
-        self.num_feat = num_feat
-        self.num_conv = num_conv
-        self.upscale = upscale
-        self.act_type = act_type
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == 'relu':
-            activation = nn.ReLU(inplace=True)
-        elif act_type == 'prelu':
-            activation = nn.PReLU(num_parameters=num_feat)
-        elif act_type == 'leakyrelu':
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)
-
-        # the body structure
-        for _ in range(num_conv):
-            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
-            # activation
-            if act_type == 'relu':
-                activation = nn.ReLU(inplace=True)
-            elif act_type == 'prelu':
-                activation = nn.PReLU(num_parameters=num_feat)
-            elif act_type == 'leakyrelu':
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)
-
-        # the last conv
-        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
-        # upsample
-        self.upsampler = nn.PixelShuffle(upscale)
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
-        out += base
-        return out
-
-
-####################
-# Upsampler
-####################
-
-class Upsample(nn.Module):
-    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
-    The input data is assumed to be of the form
-    `minibatch x channels x [optional depth] x [optional height] x width`.
-    """
-
-    def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
-        super(Upsample, self).__init__()
-        if isinstance(scale_factor, tuple):
-            self.scale_factor = tuple(float(factor) for factor in scale_factor)
-        else:
-            self.scale_factor = float(scale_factor) if scale_factor else None
-        self.mode = mode
-        self.size = size
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
-
-    def extra_repr(self):
-        if self.scale_factor is not None:
-            info = 'scale_factor=' + str(self.scale_factor)
-        else:
-            info = 'size=' + str(self.size)
-        info += ', mode=' + self.mode
-        return info
-
-
-def pixel_unshuffle(x, scale):
-    """ Pixel unshuffle.
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-
-def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
-                        pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
-    """
-    Pixel shuffle layer
-    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
-    Neural Network, CVPR17)
-    """
-    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
-                        pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
-    pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
-    n = norm(norm_type, out_nc) if norm_type else None
-    a = act(act_type) if act_type else None
-    return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
-                pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
-    """ Upconv layer """
-    upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
-    upsample = Upsample(scale_factor=upscale_factor, mode=mode)
-    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
-                        pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
-    return sequential(upsample, conv)
-
-
-
-
-
-
-
-
-####################
-# Basic blocks
-####################
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
-    """Make layers by stacking the same blocks.
-    Args:
-        basic_block (nn.module): nn.module class for basic block. (block)
-        num_basic_block (int): number of blocks. (n_layers)
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
-    """ activation helper """
-    act_type = act_type.lower()
-    if act_type == 'relu':
-        layer = nn.ReLU(inplace)
-    elif act_type in ('leakyrelu', 'lrelu'):
-        layer = nn.LeakyReLU(neg_slope, inplace)
-    elif act_type == 'prelu':
-        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
-    elif act_type == 'tanh':  # [-1, 1] range output
-        layer = nn.Tanh()
-    elif act_type == 'sigmoid':  # [0, 1] range output
-        layer = nn.Sigmoid()
-    else:
-        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
-    return layer
-
-
-class Identity(nn.Module):
-    def __init__(self, *kwargs):
-        super(Identity, self).__init__()
-
-    def forward(self, x, *kwargs):
-        return x
-
-
-def norm(norm_type, nc):
-    """ Return a normalization layer """
-    norm_type = norm_type.lower()
-    if norm_type == 'batch':
-        layer = nn.BatchNorm2d(nc, affine=True)
-    elif norm_type == 'instance':
-        layer = nn.InstanceNorm2d(nc, affine=False)
-    elif norm_type == 'none':
-        def norm_layer(x): return Identity()
-    else:
-        raise NotImplementedError('normalization layer [{:s}] is not found'.format(norm_type))
-    return layer
-
-
-def pad(pad_type, padding):
-    """ padding layer helper """
-    pad_type = pad_type.lower()
-    if padding == 0:
-        return None
-    if pad_type == 'reflect':
-        layer = nn.ReflectionPad2d(padding)
-    elif pad_type == 'replicate':
-        layer = nn.ReplicationPad2d(padding)
-    elif pad_type == 'zero':
-        layer = nn.ZeroPad2d(padding)
-    else:
-        raise NotImplementedError('padding layer [{:s}] is not implemented'.format(pad_type))
-    return layer
-
-
-def get_valid_padding(kernel_size, dilation):
-    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
-    padding = (kernel_size - 1) // 2
-    return padding
-
-
-class ShortcutBlock(nn.Module):
-    """ Elementwise sum the output of a submodule to its input """
-    def __init__(self, submodule):
-        super(ShortcutBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = x + self.sub(x)
-        return output
-
-    def __repr__(self):
-        return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
-
-
-def sequential(*args):
-    """ Flatten Sequential. It unwraps nn.Sequential. """
-    if len(args) == 1:
-        if isinstance(args[0], OrderedDict):
-            raise NotImplementedError('sequential does not support OrderedDict input.')
-        return args[0]  # No sequential is needed.
-    modules = []
-    for module in args:
-        if isinstance(module, nn.Sequential):
-            for submodule in module.children():
-                modules.append(submodule)
-        elif isinstance(module, nn.Module):
-            modules.append(module)
-    return nn.Sequential(*modules)
-
-
-def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
-               pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
-               spectral_norm=False):
-    """ Conv layer with padding, normalization, activation """
-    assert mode in ['CNA', 'NAC', 'CNAC'], 'Wrong conv mode [{:s}]'.format(mode)
-    padding = get_valid_padding(kernel_size, dilation)
-    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
-    padding = padding if pad_type == 'zero' else 0
-
-    if convtype=='PartialConv2D':
-        c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-               dilation=dilation, bias=bias, groups=groups)
-    elif convtype=='DeformConv2D':
-        c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-               dilation=dilation, bias=bias, groups=groups)
-    elif convtype=='Conv3D':
-        c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-                dilation=dilation, bias=bias, groups=groups)
-    else:
-        c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-                dilation=dilation, bias=bias, groups=groups)
-
-    if spectral_norm:
-        c = nn.utils.spectral_norm(c)
-
-    a = act(act_type) if act_type else None
-    if 'CNA' in mode:
-        n = norm(norm_type, out_nc) if norm_type else None
-        return sequential(p, c, n, a)
-    elif mode == 'NAC':
-        if norm_type is None and act_type is not None:
-            a = act(act_type, inplace=False)
-        n = norm(norm_type, in_nc) if norm_type else None
-        return sequential(n, a, p, c)
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/modes/benchmark.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/modes/benchmark.md
deleted file mode 100644
index c1c159e6bed97519b2a830ba70068068d3875da5..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/modes/benchmark.md
+++ /dev/null
@@ -1,74 +0,0 @@
----
-comments: true
-description: Benchmark mode compares speed and accuracy of various YOLOv8 export formats like ONNX or OpenVINO. Optimize formats for speed or accuracy.
-keywords: YOLOv8, Benchmark Mode, Export Formats, ONNX, OpenVINO, TensorRT, Ultralytics Docs
----
-
-<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
-
-**Benchmark mode** is used to profile the speed and accuracy of various export formats for YOLOv8. The benchmarks
-provide information on the size of the exported format, its `mAP50-95` metrics (for object detection, segmentation and pose)
-or `accuracy_top5` metrics (for classification), and the inference time in milliseconds per image across various export
-formats like ONNX, OpenVINO, TensorRT and others. This information can help users choose the optimal export format for
-their specific use case based on their requirements for speed and accuracy.
-
-!!! tip "Tip"
-
-    * Export to ONNX or OpenVINO for up to 3x CPU speedup.
-    * Export to TensorRT for up to 5x GPU speedup.
-
-## Usage Examples
-
-Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT etc. See Arguments section below for a
-full list of export arguments.
-
-!!! example ""
-
-    === "Python"
-    
-        ```python
-        from ultralytics.yolo.utils.benchmarks import benchmark
-        
-        # Benchmark on GPU
-        benchmark(model='yolov8n.pt', imgsz=640, half=False, device=0)
-        ```
-    === "CLI"
-    
-        ```bash
-        yolo benchmark model=yolov8n.pt imgsz=640 half=False device=0
-        ```
-
-## Arguments
-
-Arguments such as `model`, `imgsz`, `half`, `device`, and `hard_fail` provide users with the flexibility to fine-tune
-the benchmarks to their specific needs and compare the performance of different export formats with ease.
-
-| Key         | Value   | Description                                                          |
-|-------------|---------|----------------------------------------------------------------------|
-| `model`     | `None`  | path to model file, i.e. yolov8n.pt, yolov8n.yaml                    |
-| `imgsz`     | `640`   | image size as scalar or (h, w) list, i.e. (640, 480)                 |
-| `half`      | `False` | FP16 quantization                                                    |
-| `int8`      | `False` | INT8 quantization                                                    |
-| `device`    | `None`  | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
-| `hard_fail` | `False` | do not continue on error (bool), or val floor threshold (float)      |
-
-## Export Formats
-
-Benchmarks will attempt to run automatically on all possible export formats below.
-
-| Format                                                             | `format` Argument | Model                     | Metadata |
-|--------------------------------------------------------------------|-------------------|---------------------------|----------|
-| [PyTorch](https://pytorch.org/)                                    | -                 | `yolov8n.pt`              | ✅        |
-| [TorchScript](https://pytorch.org/docs/stable/jit.html)            | `torchscript`     | `yolov8n.torchscript`     | ✅        |
-| [ONNX](https://onnx.ai/)                                           | `onnx`            | `yolov8n.onnx`            | ✅        |
-| [OpenVINO](https://docs.openvino.ai/latest/index.html)             | `openvino`        | `yolov8n_openvino_model/` | ✅        |
-| [TensorRT](https://developer.nvidia.com/tensorrt)                  | `engine`          | `yolov8n.engine`          | ✅        |
-| [CoreML](https://github.com/apple/coremltools)                     | `coreml`          | `yolov8n.mlmodel`         | ✅        |
-| [TF SavedModel](https://www.tensorflow.org/guide/saved_model)      | `saved_model`     | `yolov8n_saved_model/`    | ✅        |
-| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb`              | `yolov8n.pb`              | ❌        |
-| [TF Lite](https://www.tensorflow.org/lite)                         | `tflite`          | `yolov8n.tflite`          | ✅        |
-| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/)         | `edgetpu`         | `yolov8n_edgetpu.tflite`  | ✅        |
-| [TF.js](https://www.tensorflow.org/js)                             | `tfjs`            | `yolov8n_web_model/`      | ✅        |
-| [PaddlePaddle](https://github.com/PaddlePaddle)                    | `paddle`          | `yolov8n_paddle_model/`   | ✅        |
-
-See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.
\ No newline at end of file
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/vit/rtdetr/train.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/vit/rtdetr/train.py
deleted file mode 100644
index 54eeaf4aa79a9f22fd5d04d60482d52d7155cfc8..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/vit/rtdetr/train.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# Ultralytics YOLO 🚀, AGPL-3.0 license
-
-from copy import copy
-
-import torch
-
-from ultralytics.nn.tasks import RTDETRDetectionModel
-from ultralytics.yolo.utils import DEFAULT_CFG, RANK, colorstr
-from ultralytics.yolo.v8.detect import DetectionTrainer
-
-from .val import RTDETRDataset, RTDETRValidator
-
-
-class RTDETRTrainer(DetectionTrainer):
-
-    def get_model(self, cfg=None, weights=None, verbose=True):
-        """Return a YOLO detection model."""
-        model = RTDETRDetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
-        if weights:
-            model.load(weights)
-        return model
-
-    def build_dataset(self, img_path, mode='val', batch=None):
-        """Build RTDETR Dataset
-
-        Args:
-            img_path (str): Path to the folder containing images.
-            mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
-            batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
-        """
-        return RTDETRDataset(
-            img_path=img_path,
-            imgsz=self.args.imgsz,
-            batch_size=batch,
-            augment=mode == 'train',  # no augmentation
-            hyp=self.args,
-            rect=False,  # no rect
-            cache=self.args.cache or None,
-            prefix=colorstr(f'{mode}: '),
-            data=self.data)
-
-    def get_validator(self):
-        """Returns a DetectionValidator for RTDETR model validation."""
-        self.loss_names = 'giou_loss', 'cls_loss', 'l1_loss'
-        return RTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
-
-    def preprocess_batch(self, batch):
-        """Preprocesses a batch of images by scaling and converting to float."""
-        batch = super().preprocess_batch(batch)
-        bs = len(batch['img'])
-        batch_idx = batch['batch_idx']
-        gt_bbox, gt_class = [], []
-        for i in range(bs):
-            gt_bbox.append(batch['bboxes'][batch_idx == i].to(batch_idx.device))
-            gt_class.append(batch['cls'][batch_idx == i].to(device=batch_idx.device, dtype=torch.long))
-        return batch
-
-
-def train(cfg=DEFAULT_CFG, use_python=False):
-    """Train and optimize RTDETR model given training data and device."""
-    model = 'rtdetr-l.yaml'
-    data = cfg.data or 'coco128.yaml'  # or yolo.ClassificationDataset("mnist")
-    device = cfg.device if cfg.device is not None else ''
-
-    # NOTE: F.grid_sample which is in rt-detr does not support deterministic=True
-    # NOTE: amp training causes nan outputs and end with error while doing bipartite graph matching
-    args = dict(model=model,
-                data=data,
-                device=device,
-                imgsz=640,
-                exist_ok=True,
-                batch=4,
-                deterministic=False,
-                amp=False)
-    trainer = RTDETRTrainer(overrides=args)
-    trainer.train()
-
-
-if __name__ == '__main__':
-    train()
diff --git a/spaces/wall-e-zz/stable-diffusion-logo-fine-tuned/README.md b/spaces/wall-e-zz/stable-diffusion-logo-fine-tuned/README.md
deleted file mode 100644
index 880962a2f4f928dd1d9e3ff0511b3f980dec2941..0000000000000000000000000000000000000000
--- a/spaces/wall-e-zz/stable-diffusion-logo-fine-tuned/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Stable Diffusion Logo Fine Tuned
-emoji: 🏃
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/utils/evolve.sh b/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/utils/evolve.sh
deleted file mode 100644
index bd3344042fefa586aff053b344ed5d7f9e92a0bc..0000000000000000000000000000000000000000
--- a/spaces/weiren119/AudiogramDigitization/src/digitizer/yolov5/utils/evolve.sh
+++ /dev/null
@@ -1,14 +0,0 @@
-#!/bin/bash
-# Hyperparameter evolution commands (avoids CUDA memory leakage issues)
-# Replaces train.py python generations 'for' loop with a bash 'for' loop
-
-# Start on 4-GPU machine
-#for i in 0 1 2 3; do
-#  t=ultralytics/yolov5:test && sudo docker pull $t && sudo docker run -d --ipc=host --gpus all -v "$(pwd)"/VOC:/usr/src/VOC $t bash utils/evolve.sh $i
-#  sleep 60 # avoid simultaneous evolve.txt read/write
-#done
-
-# Hyperparameter evolution commands
-while true; do
-  python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 --evolve --bucket ult/voc --device $1
-done
diff --git a/spaces/wliu88/StructDiffusionDemo/src/StructDiffusion/data/semantic_arrangement.py b/spaces/wliu88/StructDiffusionDemo/src/StructDiffusion/data/semantic_arrangement.py
deleted file mode 100644
index 9d6db8193927c52bd5478d8385477f8d0b4c0eee..0000000000000000000000000000000000000000
--- a/spaces/wliu88/StructDiffusionDemo/src/StructDiffusion/data/semantic_arrangement.py
+++ /dev/null
@@ -1,536 +0,0 @@
-import copy
-import cv2
-import h5py
-import numpy as np
-import os
-import trimesh
-import torch
-from tqdm import tqdm
-import json
-import random
-
-from torch.utils.data import DataLoader
-
-# Local imports
-from StructDiffusion.utils.rearrangement import show_pcs, get_pts, combine_and_sample_xyzs
-from StructDiffusion.language.tokenizer import Tokenizer
-
-import StructDiffusion.utils.brain2.camera as cam
-import StructDiffusion.utils.brain2.image as img
-import StructDiffusion.utils.transformations as tra
-
-
-class SemanticArrangementDataset(torch.utils.data.Dataset):
-
-    def __init__(self, data_roots, index_roots, split, tokenizer,
-                 max_num_target_objects=11, max_num_distractor_objects=5,
-                 max_num_shape_parameters=7, max_num_rearrange_features=1, max_num_anchor_features=3,
-                 num_pts=1024,
-                 use_virtual_structure_frame=True, ignore_distractor_objects=True, ignore_rgb=True,
-                 filter_num_moved_objects_range=None, shuffle_object_index=False,
-                 data_augmentation=True, debug=False, **kwargs):
-        """
-
-        Note: setting filter_num_moved_objects_range=[k, k] and max_num_objects=k will create no padding for target objs
-
-        :param data_root:
-        :param split: train, valid, or test
-        :param shuffle_object_index: whether to shuffle the positions of target objects and other objects in the sequence
-        :param debug:
-        :param max_num_shape_parameters:
-        :param max_num_objects:
-        :param max_num_rearrange_features:
-        :param max_num_anchor_features:
-        :param num_pts:
-        :param use_stored_arrangement_indices:
-        :param kwargs:
-        """
-
-        self.use_virtual_structure_frame = use_virtual_structure_frame
-        self.ignore_distractor_objects = ignore_distractor_objects
-        self.ignore_rgb = ignore_rgb and not debug
-
-        self.num_pts = num_pts
-        self.debug = debug
-
-        self.max_num_objects = max_num_target_objects
-        self.max_num_other_objects = max_num_distractor_objects
-        self.max_num_shape_parameters = max_num_shape_parameters
-        self.max_num_rearrange_features = max_num_rearrange_features
-        self.max_num_anchor_features = max_num_anchor_features
-        self.shuffle_object_index = shuffle_object_index
-
-        # used to tokenize the language part
-        self.tokenizer = tokenizer
-
-        # retrieve data
-        self.data_roots = data_roots
-        self.arrangement_data = []
-        arrangement_steps = []
-        for ddx in range(len(data_roots)):
-            data_root = data_roots[ddx]
-            index_root = index_roots[ddx]
-            arrangement_indices_file = os.path.join(data_root, index_root, "{}_arrangement_indices_file_all.txt".format(split))
-            if os.path.exists(arrangement_indices_file):
-                with open(arrangement_indices_file, "r") as fh:
-                    arrangement_steps.extend([(os.path.join(data_root, f[0]), f[1]) for f in eval(fh.readline().strip())])
-            else:
-                print("{} does not exist".format(arrangement_indices_file))
-        # only keep the goal, ignore the intermediate steps
-        for filename, step_t in arrangement_steps:
-            if step_t == 0:
-                if "data00026058" in filename or "data00011415" in filename or "data00026061" in filename or "data00700565" in filename:
-                    continue
-                self.arrangement_data.append((filename, step_t))
-        # if specified, filter data
-        if filter_num_moved_objects_range is not None:
-            self.arrangement_data = self.filter_based_on_number_of_moved_objects(filter_num_moved_objects_range)
-        print("{} valid sequences".format(len(self.arrangement_data)))
-
-        # Data Aug
-        self.data_augmentation = data_augmentation
-        # additive noise
-        self.gp_rescale_factor_range = [12, 20]
-        self.gaussian_scale_range = [0., 0.003]
-        # multiplicative noise
-        self.gamma_shape = 1000.
-        self.gamma_scale = 0.001
-
-    def filter_based_on_number_of_moved_objects(self, filter_num_moved_objects_range):
-        assert len(list(filter_num_moved_objects_range)) == 2
-        min_num, max_num = filter_num_moved_objects_range
-        print("Remove scenes that have less than {} or more than {} objects being moved".format(min_num, max_num))
-        ok_data = []
-        for filename, step_t in self.arrangement_data:
-            h5 = h5py.File(filename, 'r')
-            moved_objs = h5['moved_objs'][()].split(',')
-            if min_num <= len(moved_objs) <= max_num:
-                ok_data.append((filename, step_t))
-        print("{} valid sequences left".format(len(ok_data)))
-        return ok_data
-
-    def get_data_idx(self, idx):
-        # Create the datum to return
-        file_idx = np.argmax(idx < self.file_to_count)
-        data = h5py.File(self.data_files[file_idx], 'r')
-        if file_idx > 0:
-            # for lang2sym, idx is always 0
-            idx = idx - self.file_to_count[file_idx - 1]
-        return data, idx, file_idx
-
-    def add_noise_to_depth(self, depth_img):
-        """ add depth noise """
-        multiplicative_noise = np.random.gamma(self.gamma_shape, self.gamma_scale)
-        depth_img = multiplicative_noise * depth_img
-        return depth_img
-
-    def add_noise_to_xyz(self, xyz_img, depth_img):
-        """ TODO: remove this code or at least celean it up"""
-        xyz_img = xyz_img.copy()
-        H, W, C = xyz_img.shape
-        gp_rescale_factor = np.random.randint(self.gp_rescale_factor_range[0],
-                                              self.gp_rescale_factor_range[1])
-        gp_scale = np.random.uniform(self.gaussian_scale_range[0],
-                                     self.gaussian_scale_range[1])
-        small_H, small_W = (np.array([H, W]) / gp_rescale_factor).astype(int)
-        additive_noise = np.random.normal(loc=0.0, scale=gp_scale, size=(small_H, small_W, C))
-        additive_noise = cv2.resize(additive_noise, (W, H), interpolation=cv2.INTER_CUBIC)
-        xyz_img[depth_img > 0, :] += additive_noise[depth_img > 0, :]
-        return xyz_img
-
-    def random_index(self):
-        return self[np.random.randint(len(self))]
-
-    def _get_rgb(self, h5, idx, ee=True):
-        RGB = "ee_rgb" if ee else "rgb"
-        rgb1 = img.PNGToNumpy(h5[RGB][idx])[:, :, :3] / 255.  # remove alpha
-        return rgb1
-
-    def _get_depth(self, h5, idx, ee=True):
-        DEPTH = "ee_depth" if ee else "depth"
-
-    def _get_images(self, h5, idx, ee=True):
-        if ee:
-            RGB, DEPTH, SEG = "ee_rgb", "ee_depth", "ee_seg"
-            DMIN, DMAX = "ee_depth_min", "ee_depth_max"
-        else:
-            RGB, DEPTH, SEG = "rgb", "depth", "seg"
-            DMIN, DMAX = "depth_min", "depth_max"
-        dmin = h5[DMIN][idx]
-        dmax = h5[DMAX][idx]
-        rgb1 = img.PNGToNumpy(h5[RGB][idx])[:, :, :3] / 255.  # remove alpha
-        depth1 = h5[DEPTH][idx] / 20000. * (dmax - dmin) + dmin
-        seg1 = img.PNGToNumpy(h5[SEG][idx])
-
-        valid1 = np.logical_and(depth1 > 0.1, depth1 < 2.)
-
-        # proj_matrix = h5['proj_matrix'][()]
-        camera = cam.get_camera_from_h5(h5)
-        if self.data_augmentation:
-            depth1 = self.add_noise_to_depth(depth1)
-
-        xyz1 = cam.compute_xyz(depth1, camera)
-        if self.data_augmentation:
-            xyz1 = self.add_noise_to_xyz(xyz1, depth1)
-
-        # Transform the point cloud
-        # Here it is...
-        # CAM_POSE = "ee_cam_pose" if ee else "cam_pose"
-        CAM_POSE = "ee_camera_view" if ee else "camera_view"
-        cam_pose = h5[CAM_POSE][idx]
-        if ee:
-            # ee_camera_view has 0s for x, y, z
-            cam_pos = h5["ee_cam_pose"][:][:3, 3]
-            cam_pose[:3, 3] = cam_pos
-
-        # Get transformed point cloud
-        h, w, d = xyz1.shape
-        xyz1 = xyz1.reshape(h * w, -1)
-        xyz1 = trimesh.transform_points(xyz1, cam_pose)
-        xyz1 = xyz1.reshape(h, w, -1)
-
-        scene1 = rgb1, depth1, seg1, valid1, xyz1
-
-        return scene1
-
-    def __len__(self):
-        return len(self.arrangement_data)
-
-    def _get_ids(self, h5):
-        """
-        get object ids
-
-        @param h5:
-        @return:
-        """
-        ids = {}
-        for k in h5.keys():
-            if k.startswith("id_"):
-                ids[k[3:]] = h5[k][()]
-        return ids
-
-    def get_positive_ratio(self):
-        num_pos = 0
-        for d in self.arrangement_data:
-            filename, step_t = d
-            if step_t == 0:
-                num_pos += 1
-        return (len(self.arrangement_data) - num_pos) * 1.0 / num_pos
-
-    def get_object_position_vocab_sizes(self):
-        return self.tokenizer.get_object_position_vocab_sizes()
-
-    def get_vocab_size(self):
-        return self.tokenizer.get_vocab_size()
-
-    def get_data_index(self, idx):
-        filename = self.arrangement_data[idx]
-        return filename
-
-    def get_raw_data(self, idx, inference_mode=False, shuffle_object_index=False):
-        """
-
-        :param idx:
-        :param inference_mode:
-        :param shuffle_object_index: used to test different orders of objects
-        :return:
-        """
-
-        filename, _ = self.arrangement_data[idx]
-
-        h5 = h5py.File(filename, 'r')
-        ids = self._get_ids(h5)
-        all_objs = sorted([o for o in ids.keys() if "object_" in o])
-        goal_specification = json.loads(str(np.array(h5["goal_specification"])))
-        num_rearrange_objs = len(goal_specification["rearrange"]["objects"])
-        num_other_objs = len(goal_specification["anchor"]["objects"] + goal_specification["distract"]["objects"])
-        assert len(all_objs) == num_rearrange_objs + num_other_objs, "{}, {}".format(len(all_objs), num_rearrange_objs + num_other_objs)
-        assert num_rearrange_objs <= self.max_num_objects
-        assert num_other_objs <= self.max_num_other_objects
-
-        # important: only using the last step
-        step_t = num_rearrange_objs
-
-        target_objs = all_objs[:num_rearrange_objs]
-        other_objs = all_objs[num_rearrange_objs:]
-
-        structure_parameters = goal_specification["shape"]
-
-        # Important: ensure the order is correct
-        if structure_parameters["type"] == "circle" or structure_parameters["type"] == "line":
-            target_objs = target_objs[::-1]
-        elif structure_parameters["type"] == "tower" or structure_parameters["type"] == "dinner":
-            target_objs = target_objs
-        else:
-            raise KeyError("{} structure is not recognized".format(structure_parameters["type"]))
-        all_objs = target_objs + other_objs
-
-        ###################################
-        # getting scene images and point clouds
-        scene = self._get_images(h5, step_t, ee=True)
-        rgb, depth, seg, valid, xyz = scene
-        if inference_mode:
-            initial_scene = scene
-
-        # getting object point clouds
-        obj_pcs = []
-        obj_pad_mask = []
-        current_pc_poses = []
-        other_obj_pcs = []
-        other_obj_pad_mask = []
-        for obj in all_objs:
-            obj_mask = np.logical_and(seg == ids[obj], valid)
-            if np.sum(obj_mask) <= 0:
-                raise Exception
-            ok, obj_xyz, obj_rgb, _ = get_pts(xyz, rgb, obj_mask, num_pts=self.num_pts)
-            if not ok:
-                raise Exception
-
-            if obj in target_objs:
-                if self.ignore_rgb:
-                    obj_pcs.append(obj_xyz)
-                else:
-                    obj_pcs.append(torch.concat([obj_xyz, obj_rgb], dim=-1))
-                obj_pad_mask.append(0)
-                pc_pose = np.eye(4)
-                pc_pose[:3, 3] = torch.mean(obj_xyz, dim=0).numpy()
-                current_pc_poses.append(pc_pose)
-            elif obj in other_objs:
-                if self.ignore_rgb:
-                    other_obj_pcs.append(obj_xyz)
-                else:
-                    other_obj_pcs.append(torch.concat([obj_xyz, obj_rgb], dim=-1))
-                other_obj_pad_mask.append(0)
-            else:
-                raise Exception
-
-        ###################################
-        # computes goal positions for objects
-        # Important: because of the noises we added to point clouds, the rearranged point clouds will not be perfect
-        if self.use_virtual_structure_frame:
-            goal_structure_pose = tra.euler_matrix(structure_parameters["rotation"][0], structure_parameters["rotation"][1],
-                                              structure_parameters["rotation"][2])
-            goal_structure_pose[:3, 3] = [structure_parameters["position"][0], structure_parameters["position"][1],
-                                     structure_parameters["position"][2]]
-            goal_structure_pose_inv = np.linalg.inv(goal_structure_pose)
-
-        goal_obj_poses = []
-        current_obj_poses = []
-        goal_pc_poses = []
-        for obj, current_pc_pose in zip(target_objs, current_pc_poses):
-            goal_pose = h5[obj][0]
-            current_pose = h5[obj][step_t]
-            if inference_mode:
-                goal_obj_poses.append(goal_pose)
-                current_obj_poses.append(current_pose)
-
-            goal_pc_pose = goal_pose @ np.linalg.inv(current_pose) @ current_pc_pose
-            if self.use_virtual_structure_frame:
-                goal_pc_pose = goal_structure_pose_inv @ goal_pc_pose
-            goal_pc_poses.append(goal_pc_pose)
-
-        # transform current object point cloud to the goal point cloud in the world frame
-        if self.debug:
-            new_obj_pcs = [copy.deepcopy(pc.numpy()) for pc in obj_pcs]
-            for i, obj_pc in enumerate(new_obj_pcs):
-
-                current_pc_pose = current_pc_poses[i]
-                goal_pc_pose = goal_pc_poses[i]
-                if self.use_virtual_structure_frame:
-                    goal_pc_pose = goal_structure_pose @ goal_pc_pose
-                print("current pc pose", current_pc_pose)
-                print("goal pc pose", goal_pc_pose)
-
-                goal_pc_transform = goal_pc_pose @ np.linalg.inv(current_pc_pose)
-                print("transform", goal_pc_transform)
-                new_obj_pc = copy.deepcopy(obj_pc)
-                new_obj_pc[:, :3] = trimesh.transform_points(obj_pc[:, :3], goal_pc_transform)
-                print(new_obj_pc.shape)
-
-                # visualize rearrangement sequence (new_obj_xyzs), the current object before moving (obj_xyz), and other objects
-                new_obj_pcs[i] = new_obj_pc
-                new_obj_pcs[i][:, 3:] = np.tile(np.array([1, 0, 0], dtype=np.float), (new_obj_pc.shape[0], 1))
-                new_obj_rgb_current = np.tile(np.array([0, 1, 0], dtype=np.float), (new_obj_pc.shape[0], 1))
-                show_pcs([pc[:, :3] for pc in new_obj_pcs] + [pc[:, :3] for pc in other_obj_pcs] + [obj_pc[:, :3]],
-                         [pc[:, 3:] for pc in new_obj_pcs] + [pc[:, 3:] for pc in other_obj_pcs] + [new_obj_rgb_current],
-                         add_coordinate_frame=True)
-            show_pcs([pc[:, :3] for pc in new_obj_pcs], [pc[:, 3:] for pc in new_obj_pcs], add_coordinate_frame=True)
-
-        # pad data
-        for i in range(self.max_num_objects - len(target_objs)):
-            obj_pcs.append(torch.zeros_like(obj_pcs[0], dtype=torch.float32))
-            obj_pad_mask.append(1)
-        for i in range(self.max_num_other_objects - len(other_objs)):
-            other_obj_pcs.append(torch.zeros_like(obj_pcs[0], dtype=torch.float32))
-            other_obj_pad_mask.append(1)
-
-        ###################################
-        # preparing sentence
-        sentence = []
-        sentence_pad_mask = []
-
-        # structure parameters
-        # 5 parameters
-        structure_parameters = goal_specification["shape"]
-        if structure_parameters["type"] == "circle" or structure_parameters["type"] == "line":
-            sentence.append((structure_parameters["type"], "shape"))
-            sentence.append((structure_parameters["rotation"][2], "rotation"))
-            sentence.append((structure_parameters["position"][0], "position_x"))
-            sentence.append((structure_parameters["position"][1], "position_y"))
-            if structure_parameters["type"] == "circle":
-                sentence.append((structure_parameters["radius"], "radius"))
-            elif structure_parameters["type"] == "line":
-                sentence.append((structure_parameters["length"] / 2.0, "radius"))
-            for _ in range(5):
-                sentence_pad_mask.append(0)
-        else:
-            sentence.append((structure_parameters["type"], "shape"))
-            sentence.append((structure_parameters["rotation"][2], "rotation"))
-            sentence.append((structure_parameters["position"][0], "position_x"))
-            sentence.append((structure_parameters["position"][1], "position_y"))
-            for _ in range(4):
-                sentence_pad_mask.append(0)
-            sentence.append(("PAD", None))
-            sentence_pad_mask.append(1)
-
-        ###################################
-        # paddings
-        for i in range(self.max_num_objects - len(target_objs)):
-            goal_pc_poses.append(np.eye(4))
-
-        ###################################
-        if self.debug:
-            print("---")
-            print("all objects:", all_objs)
-            print("target objects:", target_objs)
-            print("other objects:", other_objs)
-            print("goal specification:", goal_specification)
-            print("sentence:", sentence)
-            show_pcs([pc[:, :3] for pc in obj_pcs + other_obj_pcs], [pc[:, 3:] for pc in obj_pcs + other_obj_pcs], add_coordinate_frame=True)
-
-        assert len(obj_pcs) == len(goal_pc_poses)
-        ###################################
-
-        # shuffle the position of objects
-        if shuffle_object_index:
-            shuffle_target_object_indices = list(range(len(target_objs)))
-            random.shuffle(shuffle_target_object_indices)
-            shuffle_object_indices = shuffle_target_object_indices + list(range(len(target_objs), self.max_num_objects))
-            obj_pcs = [obj_pcs[i] for i in shuffle_object_indices]
-            goal_pc_poses = [goal_pc_poses[i] for i in shuffle_object_indices]
-            if inference_mode:
-                goal_obj_poses = [goal_obj_poses[i] for i in shuffle_object_indices]
-                current_obj_poses = [current_obj_poses[i] for i in shuffle_object_indices]
-                target_objs = [target_objs[i] for i in shuffle_target_object_indices]
-                current_pc_poses = [current_pc_poses[i] for i in shuffle_object_indices]
-
-        ###################################
-        if self.use_virtual_structure_frame:
-            if self.ignore_distractor_objects:
-                # language, structure virtual frame, target objects
-                pcs = obj_pcs
-                type_index = [0] * self.max_num_shape_parameters + [2] + [3] * self.max_num_objects
-                position_index = list(range(self.max_num_shape_parameters)) + [0] + list(range(self.max_num_objects))
-                pad_mask = sentence_pad_mask + [0] + obj_pad_mask
-            else:
-                # language, distractor objects, structure virtual frame, target objects
-                pcs = other_obj_pcs + obj_pcs
-                type_index = [0] * self.max_num_shape_parameters + [1] * self.max_num_other_objects + [2] + [3] * self.max_num_objects
-                position_index = list(range(self.max_num_shape_parameters)) + list(range(self.max_num_other_objects)) + [0] + list(range(self.max_num_objects))
-                pad_mask = sentence_pad_mask + other_obj_pad_mask + [0] + obj_pad_mask
-            goal_poses = [goal_structure_pose] + goal_pc_poses
-        else:
-            if self.ignore_distractor_objects:
-                # language, target objects
-                pcs = obj_pcs
-                type_index = [0] * self.max_num_shape_parameters + [3] * self.max_num_objects
-                position_index = list(range(self.max_num_shape_parameters)) + list(range(self.max_num_objects))
-                pad_mask = sentence_pad_mask + obj_pad_mask
-            else:
-                # language, distractor objects, target objects
-                pcs = other_obj_pcs + obj_pcs
-                type_index = [0] * self.max_num_shape_parameters + [1] * self.max_num_other_objects + [3] * self.max_num_objects
-                position_index = list(range(self.max_num_shape_parameters)) + list(range(self.max_num_other_objects)) + list(range(self.max_num_objects))
-                pad_mask = sentence_pad_mask + other_obj_pad_mask + obj_pad_mask
-            goal_poses = goal_pc_poses
-
-        datum = {
-            "pcs": pcs,
-            "sentence": sentence,
-            "goal_poses": goal_poses,
-            "type_index": type_index,
-            "position_index": position_index,
-            "pad_mask": pad_mask,
-            "t": step_t,
-            "filename": filename
-        }
-
-        if inference_mode:
-            datum["rgb"] = rgb
-            datum["goal_obj_poses"] = goal_obj_poses
-            datum["current_obj_poses"] = current_obj_poses
-            datum["target_objs"] = target_objs
-            datum["initial_scene"] = initial_scene
-            datum["ids"] = ids
-            datum["goal_specification"] = goal_specification
-            datum["current_pc_poses"] = current_pc_poses
-
-        return datum
-
-    @staticmethod
-    def convert_to_tensors(datum, tokenizer):
-        tensors = {
-            "pcs": torch.stack(datum["pcs"], dim=0),
-            "sentence": torch.LongTensor(np.array([tokenizer.tokenize(*i) for i in datum["sentence"]])),
-            "goal_poses": torch.FloatTensor(np.array(datum["goal_poses"])),
-            "type_index": torch.LongTensor(np.array(datum["type_index"])),
-            "position_index": torch.LongTensor(np.array(datum["position_index"])),
-            "pad_mask": torch.LongTensor(np.array(datum["pad_mask"])),
-            "t": datum["t"],
-            "filename": datum["filename"]
-        }
-        return tensors
-
-    def __getitem__(self, idx):
-
-        datum = self.convert_to_tensors(self.get_raw_data(idx, shuffle_object_index=self.shuffle_object_index),
-                                        self.tokenizer)
-
-        return datum
-
-    def single_datum_to_batch(self, x, num_samples, device, inference_mode=True):
-        tensor_x = {}
-
-        tensor_x["pcs"] = x["pcs"].to(device)[None, :, :, :].repeat(num_samples, 1, 1, 1)
-        tensor_x["sentence"] = x["sentence"].to(device)[None, :].repeat(num_samples, 1)
-        if not inference_mode:
-            tensor_x["goal_poses"] = x["goal_poses"].to(device)[None, :, :, :].repeat(num_samples, 1, 1, 1)
-
-        tensor_x["type_index"] = x["type_index"].to(device)[None, :].repeat(num_samples, 1)
-        tensor_x["position_index"] = x["position_index"].to(device)[None, :].repeat(num_samples, 1)
-        tensor_x["pad_mask"] = x["pad_mask"].to(device)[None, :].repeat(num_samples, 1)
-
-        return tensor_x
-
-
-def compute_min_max(dataloader):
-
-    # tensor([-0.3557, -0.3847,  0.0000, -1.0000, -1.0000, -0.4759, -1.0000, -1.0000,
-    #         -0.9079, -0.8668, -0.9105, -0.4186])
-    # tensor([0.3915, 0.3494, 0.3267, 1.0000, 1.0000, 0.8961, 1.0000, 1.0000, 0.8194,
-    #         0.4787, 0.6421, 1.0000])
-    # tensor([0.0918, -0.3758, 0.0000, -1.0000, -1.0000, 0.0000, -1.0000, -1.0000,
-    #         -0.0000, 0.0000, 0.0000, 1.0000])
-    # tensor([0.9199, 0.3710, 0.0000, 1.0000, 1.0000, 0.0000, 1.0000, 1.0000, -0.0000,
-    #         0.0000, 0.0000, 1.0000])
-
-    min_value = torch.ones(16) * 10000
-    max_value = torch.ones(16) * -10000
-    for d in tqdm(dataloader):
-        goal_poses = d["goal_poses"]
-        goal_poses = goal_poses.reshape(-1, 16)
-        current_max, _ = torch.max(goal_poses, dim=0)
-        current_min, _ = torch.min(goal_poses, dim=0)
-        max_value[max_value < current_max] = current_max[max_value < current_max]
-        max_value[max_value > current_min] = current_min[max_value > current_min]
-    print(f"{min_value} - {max_value}")
\ No newline at end of file
diff --git a/spaces/wwwwwwww2/bingo/cloudflare/worker.js b/spaces/wwwwwwww2/bingo/cloudflare/worker.js
deleted file mode 100644
index e0debd750615f1329b2c72fbce73e1b9291f7137..0000000000000000000000000000000000000000
--- a/spaces/wwwwwwww2/bingo/cloudflare/worker.js
+++ /dev/null
@@ -1,18 +0,0 @@
-const TRAGET_HOST='hf4all-bingo.hf.space' // 请将此域名改成你自己的，域名信息在设置》站点域名查看。
-
-export default {
-  async fetch(request) {
-    const uri = new URL(request.url);
-    if (uri.protocol === 'http:') {
-      uri.protocol = 'https:';
-      return new Response('', {
-        status: 301,
-        headers: {
-          location: uri.toString(),
-        },
-      })
-    }
-    uri.host = TRAGET_HOST
-    return fetch(new Request(uri.toString(), request));
-  },
-};
diff --git a/spaces/xiangdy/chatGPT/readme/README_ja.md b/spaces/xiangdy/chatGPT/readme/README_ja.md
deleted file mode 100644
index fc56eec0b81c22ff0a49e3960aa52ffd7d6dc5cb..0000000000000000000000000000000000000000
--- a/spaces/xiangdy/chatGPT/readme/README_ja.md
+++ /dev/null
@@ -1,126 +0,0 @@
-<div align="right">
-  <!-- Language: -->
-  <a title="Chinese" href="../README.md">简体中文</a> | <a title="English" href="README_en.md">English</a> | 日本語
-</div>
-
-<h1 align="center">川虎 Chat 🐯 Chuanhu Chat</h1>
-<div align="center">
-  <a href="https://github.com/GaiZhenBiao/ChuanhuChatGPT">
-    <img src="https://user-images.githubusercontent.com/70903329/227087087-93b37d64-7dc3-4738-a518-c1cf05591c8a.png" alt="Logo" height="156">
-  </a>
-
-<p align="center">
-    <h3>ChatGPT/ChatGLM/LLaMAなどのLLMのための軽量でユーザーフレンドリーなWeb-UI</h3>
-    <p align="center">
-      <a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/blob/main/LICENSE">
-        <img alt="Tests Passing" src="https://img.shields.io/github/license/GaiZhenbiao/ChuanhuChatGPT" />
-      </a>
-      <a href="https://gradio.app/">
-        <img alt="GitHub Contributors" src="https://img.shields.io/badge/Base-Gradio-fb7d1a?style=flat" />
-      </a>
-      <a href="https://t.me/tkdifferent">
-        <img alt="GitHub pull requests" src="https://img.shields.io/badge/Telegram-Group-blue.svg?logo=telegram" />
-      </a>
-      <p>
-        ストリーム出力／会話回数無制限／履歴保存／プリセットプロンプト／ファイルへの質問チャット<br>
-        ウェブ検索／LaTeXレンダリング／表レンダリング／コードハイライト<br>
-        オートダークモード／アダプティブ・ウェブ・インターフェイス／WeChatライク・テーマ<br />
-        マルチパラメーターチューニング／マルチAPI-Key対応／マルチユーザー対応<br>
-        GPT-4対応／LLMのローカルデプロイ可能。
-      </p>
-      <a href="https://www.youtube.com/watch?v=MtxS4XZWbJE"><strong>動画チュートリアル</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=77nw7iimYDE"><strong>2.0 イントロダクション</strong></a>
-        ·
-      <a href="https://www.youtube.com/watch?v=x-O1jjBqgu4"><strong>3.0 イントロダクション & チュートリアル</strong></a>
-	||
-      <a href="https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT"><strong>オンライントライアル</strong></a>
-      	·
-      <a href="https://huggingface.co/login?next=%2Fspaces%2FJohnSmith9982%2FChuanhuChatGPT%3Fduplicate%3Dtrue"><strong>ワンクリックデプロイ</strong></a>
-    </p>
-    <p align="center">
-      <img alt="Animation Demo" src="https://user-images.githubusercontent.com/51039745/226255695-6b17ff1f-ea8d-464f-b69b-a7b6b68fffe8.gif" />
-    </p>
-  </p>
-</div>
-
-## 使う上でのTips
-
-- ChatGPTをより適切に制御するために、システムプロンプトを使用できます。
-- プロンプトテンプレートを使用するには、プロンプトテンプレートコレクションを選択し、ドロップダウンメニューから特定のプロンプトを選択。回答が不十分な場合は、`🔄再生成`ボタンを使って再試行します。
-- 入力ボックスで改行するには、<kbd>Shift</kbd> + <kbd>Enter</kbd>キーを押してください。
-- 入力履歴を素早く切り替えるには、入力ボックスで <kbd>↑</kbd>と<kbd>↓</kbd>キーを押す。
-- プログラムをサーバにデプロイするには、プログラムの最終行を `demo.launch(server_name="0.0.0.0", server_port=<your port number>)`に変更します。
-- 共有リンクを取得するには、プログラムの最後の行を `demo.launch(share=True)` に変更してください。なお、公開リンクでアクセスするためには、プログラムが実行されている必要があることに注意してください。
-- Hugging Face Spacesで使用する場合： より速く、より安全に利用するために、**Duplicate Space**を使用し、自分のスペースでプログラムを実行することをお勧めします。
-
-## インストール
-
-```shell
-git clone https://github.com/GaiZhenbiao/ChuanhuChatGPT.git
-cd ChuanhuChatGPT
-pip install -r requirements.txt
-```
-
-次に `config_example.json`をコピーして `config.json`にリネームし、そのファイルにAPI-Keyなどの設定を記入する。
-
-```shell
-python ChuanhuChatbot.py
-```
-
-ブラウザのウィンドウが開き、ChatGPTとチャットできるようになります。
-
-> **Note**
->
-> 詳しい手順は[wikiページ](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用教程)をご確認ください。
-
-## トラブルシューティング
-
-問題が発生した場合は、まずこのプロジェクトの最新の変更点を手動で引っ張ってみるのがよいでしょう。その手順は以下の通りです：
-
-1. ウェブページの `Download ZIP` をクリックして最新のコードアーカイブをダウンロードするか、または
-   ```shell
-   git pull https://github.com/GaiZhenbiao/ChuanhuChatGPT.git main -f
-   ```
-2. 新しい依存関係が導入されている可能性があるため、依存関係を再度インストールしてみてください。
-   ```
-   pip install -r requirements.txt
-   ```
-3. Gradioを更新
-   ```
-   pip install gradio --upgrade --force-reinstall
-   ```
-
-一般的に、以下の手順でほとんどの問題を解決することができます。
-
-それでも問題が解決しない場合は、こちらのページをご参照ください： [よくある質問（FAQ）](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/常见问题)
-
-このページでは、考えられるほぼすべての問題点と解決策を掲載しています。よくお読みください。
-
-## More Information
-
-より詳細な情報は、[wiki](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki) をご覧ください。:
-
-- [How to contribute a translation](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/Localization)
-- [How to make a contribution](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/贡献指南)
-- [How to cite the project](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可#如何引用该项目)
-- [Project changelog](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/更新日志)
-- [Project license](https://github.com/GaiZhenbiao/ChuanhuChatGPT/wiki/使用许可)
-
-## Starchart
-
-[![Star History Chart](https://api.star-history.com/svg?repos=GaiZhenbiao/ChuanhuChatGPT&type=Date)](https://star-history.com/#GaiZhenbiao/ChuanhuChatGPT&Date)
-
-## Contributors
-
-<a href="https://github.com/GaiZhenbiao/ChuanhuChatGPT/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=GaiZhenbiao/ChuanhuChatGPT" />
-</a>
-
-## Sponsor
-
-🐯 この企画が役に立ったら、遠慮なくコーラかコーヒーでもおごってください〜。
-
-<a href="https://www.buymeacoffee.com/ChuanhuChat" ><img src="https://img.buymeacoffee.com/button-api/?text=Buy me a coffee&emoji=&slug=ChuanhuChat&button_colour=219d53&font_colour=ffffff&font_family=Poppins&outline_colour=ffffff&coffee_colour=FFDD00" alt="Buy Me A Coffee" width="250"></a>
-
-<img width="250" alt="image" src="https://user-images.githubusercontent.com/51039745/226920291-e8ec0b0a-400f-4c20-ac13-dafac0c3aeeb.JPG">
diff --git a/spaces/xiaoxicc/susu/modules/overwrites.py b/spaces/xiaoxicc/susu/modules/overwrites.py
deleted file mode 100644
index bfcd4d01b7d7bec1184a8d09113933bca860530b..0000000000000000000000000000000000000000
--- a/spaces/xiaoxicc/susu/modules/overwrites.py
+++ /dev/null
@@ -1,56 +0,0 @@
-from __future__ import annotations
-import logging
-
-from llama_index import Prompt
-from typing import List, Tuple
-import mdtex2html
-
-from modules.presets import *
-from modules.llama_func import *
-
-
-def compact_text_chunks(self, prompt: Prompt, text_chunks: List[str]) -> List[str]:
-    logging.debug("Compacting text chunks...🚀🚀🚀")
-    combined_str = [c.strip() for c in text_chunks if c.strip()]
-    combined_str = [f"[{index+1}] {c}" for index, c in enumerate(combined_str)]
-    combined_str = "\n\n".join(combined_str)
-    # resplit based on self.max_chunk_overlap
-    text_splitter = self.get_text_splitter_given_prompt(prompt, 1, padding=1)
-    return text_splitter.split_text(combined_str)
-
-
-def postprocess(
-    self, y: List[Tuple[str | None, str | None]]
-) -> List[Tuple[str | None, str | None]]:
-    """
-    Parameters:
-        y: List of tuples representing the message and response pairs. Each message and response should be a string, which may be in Markdown format.
-    Returns:
-        List of tuples representing the message and response. Each message and response will be a string of HTML.
-    """
-    if y is None or y == []:
-        return []
-    user, bot = y[-1]
-    if not detect_converted_mark(user):
-        user = convert_asis(user)
-    if not detect_converted_mark(bot):
-        bot = convert_mdtext(bot)
-    y[-1] = (user, bot)
-    return y
-
-with open("./assets/custom.js", "r", encoding="utf-8") as f, open("./assets/Kelpy-Codos.js", "r", encoding="utf-8") as f2:
-    customJS = f.read()
-    kelpyCodos = f2.read()
-
-def reload_javascript():
-    print("Reloading javascript...")
-    js = f'<script>{customJS}</script><script>{kelpyCodos}</script>'
-    def template_response(*args, **kwargs):
-        res = GradioTemplateResponseOriginal(*args, **kwargs)
-        res.body = res.body.replace(b'</html>', f'{js}</html>'.encode("utf8"))
-        res.init_headers()
-        return res
-
-    gr.routes.templates.TemplateResponse = template_response
-
-GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse
\ No newline at end of file
diff --git a/spaces/xiaoyi233/xiaoyi/upcunet_v3.py b/spaces/xiaoyi233/xiaoyi/upcunet_v3.py
deleted file mode 100644
index f7919a6cc9efe3b8af73a73e30825a4c7d7d76da..0000000000000000000000000000000000000000
--- a/spaces/xiaoyi233/xiaoyi/upcunet_v3.py
+++ /dev/null
@@ -1,714 +0,0 @@
-import torch
-from torch import nn as nn
-from torch.nn import functional as F
-import os, sys
-import numpy as np
-
-root_path = os.path.abspath('.')
-sys.path.append(root_path)
-
-
-class SEBlock(nn.Module):
-    def __init__(self, in_channels, reduction=8, bias=False):
-        super(SEBlock, self).__init__()
-        self.conv1 = nn.Conv2d(in_channels, in_channels // reduction, 1, 1, 0, bias=bias)
-        self.conv2 = nn.Conv2d(in_channels // reduction, in_channels, 1, 1, 0, bias=bias)
-
-    def forward(self, x):
-        if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-            x0 = torch.mean(x.float(), dim=(2, 3), keepdim=True).half()
-        else:
-            x0 = torch.mean(x, dim=(2, 3), keepdim=True)
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-    def forward_mean(self, x, x0):
-        x0 = self.conv1(x0)
-        x0 = F.relu(x0, inplace=True)
-        x0 = self.conv2(x0)
-        x0 = torch.sigmoid(x0)
-        x = torch.mul(x, x0)
-        return x
-
-
-class UNetConv(nn.Module):
-    def __init__(self, in_channels, mid_channels, out_channels, se):
-        super(UNetConv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(in_channels, mid_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-            nn.Conv2d(mid_channels, out_channels, 3, 1, 0),
-            nn.LeakyReLU(0.1, inplace=True),
-        )
-        if se:
-            self.seblock = SEBlock(out_channels, reduction=8, bias=True)
-        else:
-            self.seblock = None
-
-    def forward(self, x):
-        z = self.conv(x)
-        if self.seblock is not None:
-            z = self.seblock(z)
-        return z
-
-
-class UNet1(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet1x3(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet1x3, self).__init__()
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 128, 64, se=True)
-        self.conv2_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv3 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 5, 3, 2)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-    def forward_a(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x1, x2):
-        x2 = self.conv2_up(x2)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-4, -4, -4, -4))
-        x3 = self.conv3(x1 + x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        z = self.conv_bottom(x3)
-        return z
-
-
-class UNet2(nn.Module):
-    def __init__(self, in_channels, out_channels, deconv):
-        super(UNet2, self).__init__()
-
-        self.conv1 = UNetConv(in_channels, 32, 64, se=False)
-        self.conv1_down = nn.Conv2d(64, 64, 2, 2, 0)
-        self.conv2 = UNetConv(64, 64, 128, se=True)
-        self.conv2_down = nn.Conv2d(128, 128, 2, 2, 0)
-        self.conv3 = UNetConv(128, 256, 128, se=True)
-        self.conv3_up = nn.ConvTranspose2d(128, 128, 2, 2, 0)
-        self.conv4 = UNetConv(128, 64, 64, se=True)
-        self.conv4_up = nn.ConvTranspose2d(64, 64, 2, 2, 0)
-        self.conv5 = nn.Conv2d(64, 64, 3, 1, 0)
-
-        if deconv:
-            self.conv_bottom = nn.ConvTranspose2d(64, out_channels, 4, 2, 3)
-        else:
-            self.conv_bottom = nn.Conv2d(64, out_channels, 3, 1, 0)
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-            elif isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, 0, 0.01)
-                if m.bias is not None:
-                    nn.init.constant_(m.bias, 0)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2(x2)
-
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3(x3)
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4(x2 + x3)
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-    def forward_a(self, x):  # conv234结尾有se
-        x1 = self.conv1(x)
-        x2 = self.conv1_down(x1)
-        x2 = F.leaky_relu(x2, 0.1, inplace=True)
-        x2 = self.conv2.conv(x2)
-        return x1, x2
-
-    def forward_b(self, x2):  # conv234结尾有se
-        x3 = self.conv2_down(x2)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-        x3 = self.conv3.conv(x3)
-        return x3
-
-    def forward_c(self, x2, x3):  # conv234结尾有se
-        x3 = self.conv3_up(x3)
-        x3 = F.leaky_relu(x3, 0.1, inplace=True)
-
-        x2 = F.pad(x2, (-4, -4, -4, -4))
-        x4 = self.conv4.conv(x2 + x3)
-        return x4
-
-    def forward_d(self, x1, x4):  # conv234结尾有se
-        x4 = self.conv4_up(x4)
-        x4 = F.leaky_relu(x4, 0.1, inplace=True)
-
-        x1 = F.pad(x1, (-16, -16, -16, -16))
-        x5 = self.conv5(x1 + x4)
-        x5 = F.leaky_relu(x5, 0.1, inplace=True)
-
-        z = self.conv_bottom(x5)
-        return z
-
-
-class UpCunet2x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet2x, self).__init__()
-        self.unet1 = UNet1(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 2, :w0 * 2]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (18, 18 + pw - w0, 18, 18 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 36, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 36, j:j + crop_size[1] + 36]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 36, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 36, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 2 - 72, w * 2 - 72)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 36, crop_size[0]):
-            for j in range(0, w - 36, crop_size[1]):
-                res[:, :, i * 2:i * 2 + h1 * 2 - 72, j * 2:j * 2 + w1 * 2 - 72] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 2, :w0 * 2]
-        return res  #
-
-
-class UpCunet3x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet3x, self).__init__()
-        self.unet1 = UNet1x3(in_channels, out_channels, deconv=True)
-        self.unet2 = UNet2(in_channels, out_channels, deconv=False)
-
-    def forward(self, x, tile_mode):  # 1.7G
-        n, c, h0, w0 = x.shape
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 4 + 1) * 4
-            pw = ((w0 - 1) // 4 + 1) * 4
-            x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 3, :w0 * 3]
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_h = (h0 - 1) // 4 * 4 + 4  # 能被4整除
-            else:
-                crop_size_h = ((h0 - 1) // 8 * 8 + 8) // 2  # 减半后能被4整除，所以要先被8整除
-                crop_size_w = (w0 - 1) // 4 * 4 + 4  # 能被4整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 2, ((w0 - 1) // 8 * 8 + 8) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 12 * 12 + 12) // 3, ((w0 - 1) // 12 * 12 + 12) // 3)  # 4.2G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 16 * 16 + 16) // 4, ((w0 - 1) // 16 * 16 + 16) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (14, 14 + pw - w0, 14, 14 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 28, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 28, j:j + crop_size[1] + 28]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 28, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 28, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                opt_res_dict[i][j] = x_crop  #
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 3 - 84, w * 3 - 84)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 28, crop_size[0]):
-            for j in range(0, w - 28, crop_size[1]):
-                res[:, :, i * 3:i * 3 + h1 * 3 - 84, j * 3:j * 3 + w1 * 3 - 84] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 3, :w0 * 3]
-        return res
-
-
-class UpCunet4x(nn.Module):  # 完美tile，全程无损
-    def __init__(self, in_channels=3, out_channels=3):
-        super(UpCunet4x, self).__init__()
-        self.unet1 = UNet1(in_channels, 64, deconv=True)
-        self.unet2 = UNet2(64, 64, deconv=False)
-        self.ps = nn.PixelShuffle(2)
-        self.conv_final = nn.Conv2d(64, 12, 3, 1, padding=0, bias=True)
-
-    def forward(self, x, tile_mode):
-        n, c, h0, w0 = x.shape
-        x00 = x
-        if (tile_mode == 0):  # 不tile
-            ph = ((h0 - 1) // 2 + 1) * 2
-            pw = ((w0 - 1) // 2 + 1) * 2
-            x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')  # 需要保证被2整除
-            x = self.unet1.forward(x)
-            x0 = self.unet2.forward(x)
-            x1 = F.pad(x, (-20, -20, -20, -20))
-            x = torch.add(x0, x1)
-            x = self.conv_final(x)
-            x = F.pad(x, (-1, -1, -1, -1))
-            x = self.ps(x)
-            if (w0 != pw or h0 != ph): x = x[:, :, :h0 * 4, :w0 * 4]
-            x += F.interpolate(x00, scale_factor=4, mode='nearest')
-            return x
-        elif (tile_mode == 1):  # 对长边减半
-            if (w0 >= h0):
-                crop_size_w = ((w0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_h = (h0 - 1) // 2 * 2 + 2  # 能被2整除
-            else:
-                crop_size_h = ((h0 - 1) // 4 * 4 + 4) // 2  # 减半后能被2整除，所以要先被4整除
-                crop_size_w = (w0 - 1) // 2 * 2 + 2  # 能被2整除
-            crop_size = (crop_size_h, crop_size_w)  # 6.6G
-        elif (tile_mode == 2):  # hw都减半
-            crop_size = (((h0 - 1) // 4 * 4 + 4) // 2, ((w0 - 1) // 4 * 4 + 4) // 2)  # 5.6G
-        elif (tile_mode == 3):  # hw都三分之一
-            crop_size = (((h0 - 1) // 6 * 6 + 6) // 3, ((w0 - 1) // 6 * 6 + 6) // 3)  # 4.1G
-        elif (tile_mode == 4):  # hw都四分之一
-            crop_size = (((h0 - 1) // 8 * 8 + 8) // 4, ((w0 - 1) // 8 * 8 + 8) // 4)  # 3.7G
-        ph = ((h0 - 1) // crop_size[0] + 1) * crop_size[0]
-        pw = ((w0 - 1) // crop_size[1] + 1) * crop_size[1]
-        x = F.pad(x, (19, 19 + pw - w0, 19, 19 + ph - h0), 'reflect')
-        n, c, h, w = x.shape
-        se_mean0 = torch.zeros((n, 64, 1, 1)).to(x.device)
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        n_patch = 0
-        tmp_dict = {}
-        opt_res_dict = {}
-        for i in range(0, h - 38, crop_size[0]):
-            tmp_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                x_crop = x[:, :, i:i + crop_size[0] + 38, j:j + crop_size[1] + 38]
-                n, c1, h1, w1 = x_crop.shape
-                tmp0, x_crop = self.unet1.forward_a(x_crop)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(x_crop.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(x_crop, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                n_patch += 1
-                tmp_dict[i][j] = (tmp0, x_crop)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                tmp0, x_crop = tmp_dict[i][j]
-                x_crop = self.unet1.conv2.seblock.forward_mean(x_crop, se_mean0)
-                opt_unet1 = self.unet1.forward_b(tmp0, x_crop)
-                tmp_x1, tmp_x2 = self.unet2.forward_a(opt_unet1)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x2.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x2, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2)
-        se_mean1 /= n_patch
-        se_mean0 = torch.zeros((n, 128, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean0 = se_mean0.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2 = tmp_dict[i][j]
-                tmp_x2 = self.unet2.conv2.seblock.forward_mean(tmp_x2, se_mean1)
-                tmp_x3 = self.unet2.forward_b(tmp_x2)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x3.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x3, dim=(2, 3), keepdim=True)
-                se_mean0 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x2, tmp_x3)
-        se_mean0 /= n_patch
-        se_mean1 = torch.zeros((n, 64, 1, 1)).to(x.device)  # 64#128#128#64
-        if ("Half" in x.type()):
-            se_mean1 = se_mean1.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x2, tmp_x3 = tmp_dict[i][j]
-                tmp_x3 = self.unet2.conv3.seblock.forward_mean(tmp_x3, se_mean0)
-                tmp_x4 = self.unet2.forward_c(tmp_x2, tmp_x3)
-                if ("Half" in x.type()):  # torch.HalfTensor/torch.cuda.HalfTensor
-                    tmp_se_mean = torch.mean(tmp_x4.float(), dim=(2, 3), keepdim=True).half()
-                else:
-                    tmp_se_mean = torch.mean(tmp_x4, dim=(2, 3), keepdim=True)
-                se_mean1 += tmp_se_mean
-                tmp_dict[i][j] = (opt_unet1, tmp_x1, tmp_x4)
-        se_mean1 /= n_patch
-        for i in range(0, h - 38, crop_size[0]):
-            opt_res_dict[i] = {}
-            for j in range(0, w - 38, crop_size[1]):
-                opt_unet1, tmp_x1, tmp_x4 = tmp_dict[i][j]
-                tmp_x4 = self.unet2.conv4.seblock.forward_mean(tmp_x4, se_mean1)
-                x0 = self.unet2.forward_d(tmp_x1, tmp_x4)
-                x1 = F.pad(opt_unet1, (-20, -20, -20, -20))
-                x_crop = torch.add(x0, x1)  # x0是unet2的最终输出
-                x_crop = self.conv_final(x_crop)
-                x_crop = F.pad(x_crop, (-1, -1, -1, -1))
-                x_crop = self.ps(x_crop)
-                opt_res_dict[i][j] = x_crop
-        del tmp_dict
-        torch.cuda.empty_cache()
-        res = torch.zeros((n, c, h * 4 - 152, w * 4 - 152)).to(x.device)
-        if ("Half" in x.type()):
-            res = res.half()
-        for i in range(0, h - 38, crop_size[0]):
-            for j in range(0, w - 38, crop_size[1]):
-                # print(opt_res_dict[i][j].shape,res[:, :, i * 4:i * 4 + h1 * 4 - 144, j * 4:j * 4 + w1 * 4 - 144].shape)
-                res[:, :, i * 4:i * 4 + h1 * 4 - 152, j * 4:j * 4 + w1 * 4 - 152] = opt_res_dict[i][j]
-        del opt_res_dict
-        torch.cuda.empty_cache()
-        if (w0 != pw or h0 != ph): res = res[:, :, :h0 * 4, :w0 * 4]
-        res += F.interpolate(x00, scale_factor=4, mode='nearest')
-        return res  #
-
-
-class RealWaifuUpScaler(object):
-    def __init__(self, scale, weight_path, half, device):
-        weight = torch.load(weight_path, map_location="cpu")
-        self.model = eval("UpCunet%sx" % scale)()
-        if (half == True):
-            self.model = self.model.half().to(device)
-        else:
-            self.model = self.model.to(device)
-        self.model.load_state_dict(weight, strict=True)
-        self.model.eval()
-        self.half = half
-        self.device = device
-
-    def np2tensor(self, np_frame):
-        if (self.half == False):
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).float() / 255
-        else:
-            return torch.from_numpy(np.transpose(np_frame, (2, 0, 1))).unsqueeze(0).to(self.device).half() / 255
-
-    def tensor2np(self, tensor):
-        if (self.half == False):
-            return (
-                np.transpose((tensor.data.squeeze() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(), (1, 2, 0)))
-        else:
-            return (np.transpose((tensor.data.squeeze().float() * 255.0).round().clamp_(0, 255).byte().cpu().numpy(),
-                                 (1, 2, 0)))
-
-    def __call__(self, frame, tile_mode):
-        with torch.no_grad():
-            tensor = self.np2tensor(frame)
-            result = self.tensor2np(self.model(tensor, tile_mode))
-        return result
-
-
-if __name__ == "__main__":
-    ###########inference_img
-    import time, cv2, sys
-    from time import time as ttime
-
-    for weight_path, scale in [("weights_v3/up2x-latest-denoise3x.pth", 2), ("weights_v3/up3x-latest-denoise3x.pth", 3),
-                               ("weights_v3/up4x-latest-denoise3x.pth", 4)]:
-        for tile_mode in [0, 1, 2, 3, 4]:
-            upscaler2x = RealWaifuUpScaler(scale, weight_path, half=True, device="cuda:0")
-            input_dir = "%s/input_dir1" % root_path
-            output_dir = "%s/opt-dir-all-test" % root_path
-            os.makedirs(output_dir, exist_ok=True)
-            for name in os.listdir(input_dir):
-                print(name)
-                tmp = name.split(".")
-                inp_path = os.path.join(input_dir, name)
-                suffix = tmp[-1]
-                prefix = ".".join(tmp[:-1])
-                tmp_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                print(inp_path, tmp_path)
-                # 支持中文路径
-                # os.link(inp_path, tmp_path)#win用硬链接
-                os.symlink(inp_path, tmp_path)  # linux用软链接
-                frame = cv2.imread(tmp_path)[:, :, [2, 1, 0]]
-                t0 = ttime()
-                result = upscaler2x(frame, tile_mode=tile_mode)[:, :, ::-1]
-                t1 = ttime()
-                print(prefix, "done", t1 - t0)
-                tmp_opt_path = os.path.join(root_path, "tmp", "%s.%s" % (int(time.time() * 1000000), suffix))
-                cv2.imwrite(tmp_opt_path, result)
-                n = 0
-                while (1):
-                    if (n == 0):
-                        suffix = "_%sx_tile%s.png" % (scale, tile_mode)
-                    else:
-                        suffix = "_%sx_tile%s_%s.png" % (scale, tile_mode, n)  #
-                    if (os.path.exists(os.path.join(output_dir, prefix + suffix)) == False):
-                        break
-                    else:
-                        n += 1
-                final_opt_path = os.path.join(output_dir, prefix + suffix)
-                os.rename(tmp_opt_path, final_opt_path)
-                os.remove(tmp_path)
diff --git a/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/options/base_options.py b/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/options/base_options.py
deleted file mode 100644
index af67450092b2428c71f2a40941efe612378ea0bd..0000000000000000000000000000000000000000
--- a/spaces/xp3857/Image_Restoration_Colorization/Face_Enhancement/options/base_options.py
+++ /dev/null
@@ -1,294 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import sys
-import argparse
-import os
-from util import util
-import torch
-import models
-import data
-import pickle
-
-
-class BaseOptions:
-    def __init__(self):
-        self.initialized = False
-
-    def initialize(self, parser):
-        # experiment specifics
-        parser.add_argument(
-            "--name",
-            type=str,
-            default="label2coco",
-            help="name of the experiment. It decides where to store samples and models",
-        )
-
-        parser.add_argument(
-            "--gpu_ids", type=str, default="0", help="gpu ids: e.g. 0  0,1,2, 0,2. use -1 for CPU"
-        )
-        parser.add_argument(
-            "--checkpoints_dir", type=str, default="./checkpoints", help="models are saved here"
-        )
-        parser.add_argument("--model", type=str, default="pix2pix", help="which model to use")
-        parser.add_argument(
-            "--norm_G",
-            type=str,
-            default="spectralinstance",
-            help="instance normalization or batch normalization",
-        )
-        parser.add_argument(
-            "--norm_D",
-            type=str,
-            default="spectralinstance",
-            help="instance normalization or batch normalization",
-        )
-        parser.add_argument(
-            "--norm_E",
-            type=str,
-            default="spectralinstance",
-            help="instance normalization or batch normalization",
-        )
-        parser.add_argument("--phase", type=str, default="train", help="train, val, test, etc")
-
-        # input/output sizes
-        parser.add_argument("--batchSize", type=int, default=1, help="input batch size")
-        parser.add_argument(
-            "--preprocess_mode",
-            type=str,
-            default="scale_width_and_crop",
-            help="scaling and cropping of images at load time.",
-            choices=(
-                "resize_and_crop",
-                "crop",
-                "scale_width",
-                "scale_width_and_crop",
-                "scale_shortside",
-                "scale_shortside_and_crop",
-                "fixed",
-                "none",
-                "resize",
-            ),
-        )
-        parser.add_argument(
-            "--load_size",
-            type=int,
-            default=1024,
-            help="Scale images to this size. The final image will be cropped to --crop_size.",
-        )
-        parser.add_argument(
-            "--crop_size",
-            type=int,
-            default=512,
-            help="Crop to the width of crop_size (after initially scaling the images to load_size.)",
-        )
-        parser.add_argument(
-            "--aspect_ratio",
-            type=float,
-            default=1.0,
-            help="The ratio width/height. The final height of the load image will be crop_size/aspect_ratio",
-        )
-        parser.add_argument(
-            "--label_nc",
-            type=int,
-            default=182,
-            help="# of input label classes without unknown class. If you have unknown class as class label, specify --contain_dopntcare_label.",
-        )
-        parser.add_argument(
-            "--contain_dontcare_label",
-            action="store_true",
-            help="if the label map contains dontcare label (dontcare=255)",
-        )
-        parser.add_argument("--output_nc", type=int, default=3, help="# of output image channels")
-
-        # for setting inputs
-        parser.add_argument("--dataroot", type=str, default="./datasets/cityscapes/")
-        parser.add_argument("--dataset_mode", type=str, default="coco")
-        parser.add_argument(
-            "--serial_batches",
-            action="store_true",
-            help="if true, takes images in order to make batches, otherwise takes them randomly",
-        )
-        parser.add_argument(
-            "--no_flip",
-            action="store_true",
-            help="if specified, do not flip the images for data argumentation",
-        )
-        parser.add_argument("--nThreads", default=0, type=int, help="# threads for loading data")
-        parser.add_argument(
-            "--max_dataset_size",
-            type=int,
-            default=sys.maxsize,
-            help="Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.",
-        )
-        parser.add_argument(
-            "--load_from_opt_file",
-            action="store_true",
-            help="load the options from checkpoints and use that as default",
-        )
-        parser.add_argument(
-            "--cache_filelist_write",
-            action="store_true",
-            help="saves the current filelist into a text file, so that it loads faster",
-        )
-        parser.add_argument(
-            "--cache_filelist_read", action="store_true", help="reads from the file list cache"
-        )
-
-        # for displays
-        parser.add_argument("--display_winsize", type=int, default=400, help="display window size")
-
-        # for generator
-        parser.add_argument(
-            "--netG", type=str, default="spade", help="selects model to use for netG (pix2pixhd | spade)"
-        )
-        parser.add_argument("--ngf", type=int, default=64, help="# of gen filters in first conv layer")
-        parser.add_argument(
-            "--init_type",
-            type=str,
-            default="xavier",
-            help="network initialization [normal|xavier|kaiming|orthogonal]",
-        )
-        parser.add_argument(
-            "--init_variance", type=float, default=0.02, help="variance of the initialization distribution"
-        )
-        parser.add_argument("--z_dim", type=int, default=256, help="dimension of the latent z vector")
-        parser.add_argument(
-            "--no_parsing_map", action="store_true", help="During training, we do not use the parsing map"
-        )
-
-        # for instance-wise features
-        parser.add_argument(
-            "--no_instance", action="store_true", help="if specified, do *not* add instance map as input"
-        )
-        parser.add_argument(
-            "--nef", type=int, default=16, help="# of encoder filters in the first conv layer"
-        )
-        parser.add_argument("--use_vae", action="store_true", help="enable training with an image encoder.")
-        parser.add_argument(
-            "--tensorboard_log", action="store_true", help="use tensorboard to record the resutls"
-        )
-
-        # parser.add_argument('--img_dir',)
-        parser.add_argument(
-            "--old_face_folder", type=str, default="", help="The folder name of input old face"
-        )
-        parser.add_argument(
-            "--old_face_label_folder", type=str, default="", help="The folder name of input old face label"
-        )
-
-        parser.add_argument("--injection_layer", type=str, default="all", help="")
-
-        self.initialized = True
-        return parser
-
-    def gather_options(self):
-        # initialize parser with basic options
-        if not self.initialized:
-            parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-            parser = self.initialize(parser)
-
-        # get the basic options
-        opt, unknown = parser.parse_known_args()
-
-        # modify model-related parser options
-        model_name = opt.model
-        model_option_setter = models.get_option_setter(model_name)
-        parser = model_option_setter(parser, self.isTrain)
-
-        # modify dataset-related parser options
-        # dataset_mode = opt.dataset_mode
-        # dataset_option_setter = data.get_option_setter(dataset_mode)
-        # parser = dataset_option_setter(parser, self.isTrain)
-
-        opt, unknown = parser.parse_known_args()
-
-        # if there is opt_file, load it.
-        # The previous default options will be overwritten
-        if opt.load_from_opt_file:
-            parser = self.update_options_from_file(parser, opt)
-
-        opt = parser.parse_args()
-        self.parser = parser
-        return opt
-
-    def print_options(self, opt):
-        message = ""
-        message += "----------------- Options ---------------\n"
-        for k, v in sorted(vars(opt).items()):
-            comment = ""
-            default = self.parser.get_default(k)
-            if v != default:
-                comment = "\t[default: %s]" % str(default)
-            message += "{:>25}: {:<30}{}\n".format(str(k), str(v), comment)
-        message += "----------------- End -------------------"
-        # print(message)
-
-    def option_file_path(self, opt, makedir=False):
-        expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
-        if makedir:
-            util.mkdirs(expr_dir)
-        file_name = os.path.join(expr_dir, "opt")
-        return file_name
-
-    def save_options(self, opt):
-        file_name = self.option_file_path(opt, makedir=True)
-        with open(file_name + ".txt", "wt") as opt_file:
-            for k, v in sorted(vars(opt).items()):
-                comment = ""
-                default = self.parser.get_default(k)
-                if v != default:
-                    comment = "\t[default: %s]" % str(default)
-                opt_file.write("{:>25}: {:<30}{}\n".format(str(k), str(v), comment))
-
-        with open(file_name + ".pkl", "wb") as opt_file:
-            pickle.dump(opt, opt_file)
-
-    def update_options_from_file(self, parser, opt):
-        new_opt = self.load_options(opt)
-        for k, v in sorted(vars(opt).items()):
-            if hasattr(new_opt, k) and v != getattr(new_opt, k):
-                new_val = getattr(new_opt, k)
-                parser.set_defaults(**{k: new_val})
-        return parser
-
-    def load_options(self, opt):
-        file_name = self.option_file_path(opt, makedir=False)
-        new_opt = pickle.load(open(file_name + ".pkl", "rb"))
-        return new_opt
-
-    def parse(self, save=False):
-
-        opt = self.gather_options()
-        opt.isTrain = self.isTrain  # train or test
-        opt.contain_dontcare_label = False
-
-        self.print_options(opt)
-        if opt.isTrain:
-            self.save_options(opt)
-
-        # Set semantic_nc based on the option.
-        # This will be convenient in many places
-        opt.semantic_nc = (
-            opt.label_nc + (1 if opt.contain_dontcare_label else 0) + (0 if opt.no_instance else 1)
-        )
-
-        # set gpu ids
-        str_ids = opt.gpu_ids.split(",")
-        opt.gpu_ids = []
-        for str_id in str_ids:
-            int_id = int(str_id)
-            if int_id >= 0:
-                opt.gpu_ids.append(int_id)
-
-        if len(opt.gpu_ids) > 0:
-            print("The main GPU is ")
-            print(opt.gpu_ids[0])
-            torch.cuda.set_device(opt.gpu_ids[0])
-
-        assert (
-            len(opt.gpu_ids) == 0 or opt.batchSize % len(opt.gpu_ids) == 0
-        ), "Batch size %d is wrong. It must be a multiple of # GPUs %d." % (opt.batchSize, len(opt.gpu_ids))
-
-        self.opt = opt
-        return self.opt
diff --git a/spaces/xwsm/gpt/docs/waifu_plugin/jquery.min.js b/spaces/xwsm/gpt/docs/waifu_plugin/jquery.min.js
deleted file mode 100644
index ab28a24729b320bffd3d2f60302af949db39ab85..0000000000000000000000000000000000000000
--- a/spaces/xwsm/gpt/docs/waifu_plugin/jquery.min.js
+++ /dev/null
@@ -1,4 +0,0 @@
-/*! jQuery v1.11.1 | (c) 2005, 2014 jQuery Foundation, Inc. | jquery.org/license */
-!function(a,b){"object"==typeof module&&"object"==typeof module.exports?module.exports=a.document?b(a,!0):function(a){if(!a.document)throw new Error("jQuery requires a window with a document");return b(a)}:b(a)}("undefined"!=typeof window?window:this,function(a,b){var c=[],d=c.slice,e=c.concat,f=c.push,g=c.indexOf,h={},i=h.toString,j=h.hasOwnProperty,k={},l="1.11.1",m=function(a,b){return new m.fn.init(a,b)},n=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g,o=/^-ms-/,p=/-([\da-z])/gi,q=function(a,b){return b.toUpperCase()};m.fn=m.prototype={jquery:l,constructor:m,selector:"",length:0,toArray:function(){return d.call(this)},get:function(a){return null!=a?0>a?this[a+this.length]:this[a]:d.call(this)},pushStack:function(a){var b=m.merge(this.constructor(),a);return b.prevObject=this,b.context=this.context,b},each:function(a,b){return m.each(this,a,b)},map:function(a){return this.pushStack(m.map(this,function(b,c){return a.call(b,c,b)}))},slice:function(){return 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0})}),m.each({Height:"height",Width:"width"},function(a,b){m.each({padding:"inner"+a,content:b,"":"outer"+a},function(c,d){m.fn[d]=function(d,e){var f=arguments.length&&(c||"boolean"!=typeof d),g=c||(d===!0||e===!0?"margin":"border");return V(this,function(b,c,d){var e;return m.isWindow(b)?b.document.documentElement["client"+a]:9===b.nodeType?(e=b.documentElement,Math.max(b.body["scroll"+a],e["scroll"+a],b.body["offset"+a],e["offset"+a],e["client"+a])):void 0===d?m.css(b,c,g):m.style(b,c,d,g)},b,f?d:void 0,f,null)}})}),m.fn.size=function(){return this.length},m.fn.andSelf=m.fn.addBack,"function"==typeof define&&define.amd&&define("jquery",[],function(){return m});var ed=a.jQuery,fd=a.$;return m.noConflict=function(b){return a.$===m&&(a.$=fd),b&&a.jQuery===m&&(a.jQuery=ed),m},typeof b===K&&(a.jQuery=a.$=m),m});
diff --git a/spaces/yaklion/youtube/README.md b/spaces/yaklion/youtube/README.md
deleted file mode 100644
index 76bc3df21d9212802f71ce7a4ff6e5c94cd1758a..0000000000000000000000000000000000000000
--- a/spaces/yaklion/youtube/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Youtube
-emoji: 🦀
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yderre-aubay/midi-player-demo/src/community/components/BottomPlayer.tsx b/spaces/yderre-aubay/midi-player-demo/src/community/components/BottomPlayer.tsx
deleted file mode 100644
index 678efd6ef2b96b4d50bb46577faf1438220676fa..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/community/components/BottomPlayer.tsx
+++ /dev/null
@@ -1,61 +0,0 @@
-import styled from "@emotion/styled"
-import SkipNext from "mdi-react/SkipNextIcon"
-import SkipPrevious from "mdi-react/SkipPreviousIcon"
-import { FC } from "react"
-import { Slider } from "../../components/Slider"
-import { CircleButton } from "../../main/components/TransportPanel/CircleButton"
-import { PlayButton } from "../../main/components/TransportPanel/PlayButton"
-import { BottomPlayerSong } from "./BottomPlayerSong"
-
-const Wrapper = styled.div`
-  border-top: 1px solid ${({ theme }) => theme.dividerColor};
-  padding: 1rem 0;
-`
-
-const Inner = styled.div`
-  width: 80%;
-  max-width: 60rem;
-  margin: 0 auto;
-  display: flex;
-  align-items: center;
-`
-
-const PlaybackSlider = styled(Slider)`
-  color: ${({ theme }) => theme.textColor};
-  margin-left: 1rem;
-`
-
-export const BottomPlayer: FC = () => {
-  const song = {
-    song: {
-      id: 2,
-      name: "Swan, black",
-    },
-    user: {
-      name: "blegassick1",
-      created_at: new Date("2022-05-02T22:43:31Z"),
-      photoURL:
-        "https://robohash.org/autvoluptatemcumque.png?size=50x50&set=set1",
-    },
-  }
-
-  return (
-    <Wrapper>
-      <Inner>
-        <CircleButton>
-          <SkipPrevious />
-        </CircleButton>
-        <PlayButton isPlaying={false} />
-        <CircleButton>
-          <SkipNext />
-        </CircleButton>
-        <PlaybackSlider
-          value={0}
-          onChange={() => {}}
-          style={{ marginRight: "2rem" }}
-        />
-        <BottomPlayerSong song={song.song} user={song.user} />
-      </Inner>
-    </Wrapper>
-  )
-}
diff --git a/spaces/ygangang/VToonify/vtoonify/model/stylegan/lpips/networks_basic.py b/spaces/ygangang/VToonify/vtoonify/model/stylegan/lpips/networks_basic.py
deleted file mode 100644
index 201359c4e743aed285694668e13da6dd5a40b621..0000000000000000000000000000000000000000
--- a/spaces/ygangang/VToonify/vtoonify/model/stylegan/lpips/networks_basic.py
+++ /dev/null
@@ -1,187 +0,0 @@
-
-from __future__ import absolute_import
-
-import sys
-import torch
-import torch.nn as nn
-import torch.nn.init as init
-from torch.autograd import Variable
-import numpy as np
-from pdb import set_trace as st
-from skimage import color
-from IPython import embed
-from model.stylegan.lpips import pretrained_networks as pn
-
-import model.stylegan.lpips as util
-
-def spatial_average(in_tens, keepdim=True):
-    return in_tens.mean([2,3],keepdim=keepdim)
-
-def upsample(in_tens, out_H=64): # assumes scale factor is same for H and W
-    in_H = in_tens.shape[2]
-    scale_factor = 1.*out_H/in_H
-
-    return nn.Upsample(scale_factor=scale_factor, mode='bilinear', align_corners=False)(in_tens)
-
-# Learned perceptual metric
-class PNetLin(nn.Module):
-    def __init__(self, pnet_type='vgg', pnet_rand=False, pnet_tune=False, use_dropout=True, spatial=False, version='0.1', lpips=True):
-        super(PNetLin, self).__init__()
-
-        self.pnet_type = pnet_type
-        self.pnet_tune = pnet_tune
-        self.pnet_rand = pnet_rand
-        self.spatial = spatial
-        self.lpips = lpips
-        self.version = version
-        self.scaling_layer = ScalingLayer()
-
-        if(self.pnet_type in ['vgg','vgg16']):
-            net_type = pn.vgg16
-            self.chns = [64,128,256,512,512]
-        elif(self.pnet_type=='alex'):
-            net_type = pn.alexnet
-            self.chns = [64,192,384,256,256]
-        elif(self.pnet_type=='squeeze'):
-            net_type = pn.squeezenet
-            self.chns = [64,128,256,384,384,512,512]
-        self.L = len(self.chns)
-
-        self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)
-
-        if(lpips):
-            self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
-            self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
-            self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
-            self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
-            self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
-            self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]
-            if(self.pnet_type=='squeeze'): # 7 layers for squeezenet
-                self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)
-                self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)
-                self.lins+=[self.lin5,self.lin6]
-
-    def forward(self, in0, in1, retPerLayer=False):
-        # v0.0 - original release had a bug, where input was not scaled
-        in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version=='0.1' else (in0, in1)
-        outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input)
-        feats0, feats1, diffs = {}, {}, {}
-
-        for kk in range(self.L):
-            feats0[kk], feats1[kk] = util.normalize_tensor(outs0[kk]), util.normalize_tensor(outs1[kk])
-            diffs[kk] = (feats0[kk]-feats1[kk])**2
-
-        if(self.lpips):
-            if(self.spatial):
-                res = [upsample(self.lins[kk].model(diffs[kk]), out_H=in0.shape[2]) for kk in range(self.L)]
-            else:
-                res = [spatial_average(self.lins[kk].model(diffs[kk]), keepdim=True) for kk in range(self.L)]
-        else:
-            if(self.spatial):
-                res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_H=in0.shape[2]) for kk in range(self.L)]
-            else:
-                res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)]
-
-        val = res[0]
-        for l in range(1,self.L):
-            val += res[l]
-        
-        if(retPerLayer):
-            return (val, res)
-        else:
-            return val
-
-class ScalingLayer(nn.Module):
-    def __init__(self):
-        super(ScalingLayer, self).__init__()
-        self.register_buffer('shift', torch.Tensor([-.030,-.088,-.188])[None,:,None,None])
-        self.register_buffer('scale', torch.Tensor([.458,.448,.450])[None,:,None,None])
-
-    def forward(self, inp):
-        return (inp - self.shift) / self.scale
-
-
-class NetLinLayer(nn.Module):
-    ''' A single linear layer which does a 1x1 conv '''
-    def __init__(self, chn_in, chn_out=1, use_dropout=False):
-        super(NetLinLayer, self).__init__()
-
-        layers = [nn.Dropout(),] if(use_dropout) else []
-        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),]
-        self.model = nn.Sequential(*layers)
-
-
-class Dist2LogitLayer(nn.Module):
-    ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''
-    def __init__(self, chn_mid=32, use_sigmoid=True):
-        super(Dist2LogitLayer, self).__init__()
-
-        layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),]
-        layers += [nn.LeakyReLU(0.2,True),]
-        layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),]
-        layers += [nn.LeakyReLU(0.2,True),]
-        layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),]
-        if(use_sigmoid):
-            layers += [nn.Sigmoid(),]
-        self.model = nn.Sequential(*layers)
-
-    def forward(self,d0,d1,eps=0.1):
-        return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1))
-
-class BCERankingLoss(nn.Module):
-    def __init__(self, chn_mid=32):
-        super(BCERankingLoss, self).__init__()
-        self.net = Dist2LogitLayer(chn_mid=chn_mid)
-        # self.parameters = list(self.net.parameters())
-        self.loss = torch.nn.BCELoss()
-
-    def forward(self, d0, d1, judge):
-        per = (judge+1.)/2.
-        self.logit = self.net.forward(d0,d1)
-        return self.loss(self.logit, per)
-
-# L2, DSSIM metrics
-class FakeNet(nn.Module):
-    def __init__(self, use_gpu=True, colorspace='Lab'):
-        super(FakeNet, self).__init__()
-        self.use_gpu = use_gpu
-        self.colorspace=colorspace
-
-class L2(FakeNet):
-
-    def forward(self, in0, in1, retPerLayer=None):
-        assert(in0.size()[0]==1) # currently only supports batchSize 1
-
-        if(self.colorspace=='RGB'):
-            (N,C,X,Y) = in0.size()
-            value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
-            return value
-        elif(self.colorspace=='Lab'):
-            value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), 
-                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
-            ret_var = Variable( torch.Tensor((value,) ) )
-            if(self.use_gpu):
-                ret_var = ret_var.cuda()
-            return ret_var
-
-class DSSIM(FakeNet):
-
-    def forward(self, in0, in1, retPerLayer=None):
-        assert(in0.size()[0]==1) # currently only supports batchSize 1
-
-        if(self.colorspace=='RGB'):
-            value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float')
-        elif(self.colorspace=='Lab'):
-            value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)), 
-                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
-        ret_var = Variable( torch.Tensor((value,) ) )
-        if(self.use_gpu):
-            ret_var = ret_var.cuda()
-        return ret_var
-
-def print_network(net):
-    num_params = 0
-    for param in net.parameters():
-        num_params += param.numel()
-    print('Network',net)
-    print('Total number of parameters: %d' % num_params)
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/integrations/peft.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/integrations/peft.py
deleted file mode 100644
index de68e01c5fde9928c329826d214913f711801c48..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/integrations/peft.py
+++ /dev/null
@@ -1,474 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import inspect
-import warnings
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
-
-from ..utils import (
-    check_peft_version,
-    find_adapter_config_file,
-    is_accelerate_available,
-    is_peft_available,
-    is_torch_available,
-    logging,
-)
-
-
-if is_accelerate_available():
-    from accelerate import dispatch_model
-    from accelerate.utils import get_balanced_memory, infer_auto_device_map
-
-# Minimum PEFT version supported for the integration
-MIN_PEFT_VERSION = "0.5.0"
-
-if TYPE_CHECKING:
-    if is_torch_available():
-        import torch
-
-
-logger = logging.get_logger(__name__)
-
-
-class PeftAdapterMixin:
-    """
-    A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
-    more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT
-    library: https://huggingface.co/docs/peft/index
-
-    Currently supported PEFT methods are all non-prefix tuning methods. Below is the list of supported PEFT methods
-    that anyone can load, train and run with this mixin class:
-    - Low Rank Adapters (LoRA): https://huggingface.co/docs/peft/conceptual_guides/lora
-    - IA3: https://huggingface.co/docs/peft/conceptual_guides/ia3
-    - AdaLora: https://arxiv.org/abs/2303.10512
-
-    Other PEFT models such as prompt tuning, prompt learning are out of scope as these adapters are not "injectable"
-    into a torch module. For using these methods, please refer to the usage guide of PEFT library.
-
-    With this mixin, if the correct PEFT version is installed, it is possible to:
-
-    - Load an adapter stored on a local path or in a remote Hub repository, and inject it in the model
-    - Attach new adapters in the model and train them with Trainer or by your own.
-    - Attach multiple adapters and iteratively activate / deactivate them
-    - Activate / deactivate all adapters from the model.
-    - Get the `state_dict` of the active adapter.
-    """
-
-    _hf_peft_config_loaded = False
-
-    def load_adapter(
-        self,
-        peft_model_id: Optional[str] = None,
-        adapter_name: Optional[str] = None,
-        revision: Optional[str] = None,
-        token: Optional[str] = None,
-        device_map: Optional[str] = "auto",
-        max_memory: Optional[str] = None,
-        offload_folder: Optional[str] = None,
-        offload_index: Optional[int] = None,
-        peft_config: Dict[str, Any] = None,
-        adapter_state_dict: Optional[Dict[str, "torch.Tensor"]] = None,
-        adapter_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> None:
-        """
-        Load adapter weights from file or remote Hub folder. If you are not familiar with adapters and PEFT methods, we
-        invite you to read more about them on PEFT official documentation: https://huggingface.co/docs/peft
-
-        Requires peft as a backend to load the adapter weights.
-
-        Args:
-            peft_model_id (`str`, *optional*):
-                The identifier of the model to look for on the Hub, or a local path to the saved adapter config file
-                and adapter weights.
-            adapter_name (`str`, *optional*):
-                The adapter name to use. If not set, will use the default adapter.
-            revision (`str`, *optional*, defaults to `"main"`):
-                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
-                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
-                identifier allowed by git.
-
-                <Tip>
-
-                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".
-
-                </Tip>
-
-            token (`str`, `optional`):
-                Whether to use authentication token to load the remote folder. Userful to load private repositories
-                that are on HuggingFace Hub. You might need to call `huggingface-cli login` and paste your tokens to
-                cache it.
-            device_map (`str` or `Dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*):
-                A map that specifies where each submodule should go. It doesn't need to be refined to each
-                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
-                same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank
-                like `1`) on which the model will be allocated, the device map will map the entire model to this
-                device. Passing `device_map = 0` means put the whole model on GPU 0.
-
-                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
-                more information about each option see [designing a device
-                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
-            max_memory (`Dict`, *optional*):
-                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
-                GPU and the available CPU RAM if unset.
-            offload_folder (`str` or `os.PathLike`, `optional`):
-                If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
-            offload_index (`int`, `optional`):
-                `offload_index` argument to be passed to `accelerate.dispatch_model` method.
-            peft_config (`Dict[str, Any]`, *optional*):
-                The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts
-                methods. This argument is used in case users directly pass PEFT state dicts
-            adapter_state_dict (`Dict[str, torch.Tensor]`, *optional*):
-                The state dict of the adapter to load. This argument is used in case users directly pass PEFT state
-                dicts
-            adapter_kwargs (`Dict[str, Any]`, *optional*):
-                Additional keyword arguments passed along to the `from_pretrained` method of the adapter config and
-                `find_adapter_config_file` method.
-        """
-        check_peft_version(min_version=MIN_PEFT_VERSION)
-
-        adapter_name = adapter_name if adapter_name is not None else "default"
-        if adapter_kwargs is None:
-            adapter_kwargs = {}
-
-        from peft import PeftConfig, inject_adapter_in_model, load_peft_weights
-        from peft.utils import set_peft_model_state_dict
-
-        if self._hf_peft_config_loaded and adapter_name in self.peft_config:
-            raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
-
-        if peft_model_id is None and (adapter_state_dict is None and peft_config is None):
-            raise ValueError(
-                "You should either pass a `peft_model_id` or a `peft_config` and `adapter_state_dict` to load an adapter."
-            )
-
-        # We keep `revision` in the signature for backward compatibility
-        if revision is not None and "revision" not in adapter_kwargs:
-            adapter_kwargs["revision"] = revision
-        elif revision is not None and "revision" in adapter_kwargs and revision != adapter_kwargs["revision"]:
-            logger.error(
-                "You passed a `revision` argument both in `adapter_kwargs` and as a standalone argument. "
-                "The one in `adapter_kwargs` will be used."
-            )
-
-        # Override token with adapter_kwargs' token
-        if "token" in adapter_kwargs:
-            token = adapter_kwargs.pop("token")
-
-        if peft_config is None:
-            adapter_config_file = find_adapter_config_file(
-                peft_model_id,
-                token=token,
-                **adapter_kwargs,
-            )
-
-            if adapter_config_file is None:
-                raise ValueError(
-                    f"adapter model file not found in {peft_model_id}. Make sure you are passing the correct path to the "
-                    "adapter model."
-                )
-
-            peft_config = PeftConfig.from_pretrained(
-                peft_model_id,
-                use_auth_token=token,
-                **adapter_kwargs,
-            )
-
-        # Create and add fresh new adapters into the model.
-        inject_adapter_in_model(peft_config, self, adapter_name)
-
-        if not self._hf_peft_config_loaded:
-            self._hf_peft_config_loaded = True
-
-        if peft_model_id is not None:
-            adapter_state_dict = load_peft_weights(peft_model_id, use_auth_token=token, **adapter_kwargs)
-
-        # We need to pre-process the state dict to remove unneeded prefixes - for backward compatibility
-        processed_adapter_state_dict = {}
-        prefix = "base_model.model."
-        for key, value in adapter_state_dict.items():
-            if key.startswith(prefix):
-                new_key = key[len(prefix) :]
-            else:
-                new_key = key
-            processed_adapter_state_dict[new_key] = value
-
-        # Load state dict
-        incompatible_keys = set_peft_model_state_dict(self, processed_adapter_state_dict, adapter_name)
-
-        if incompatible_keys is not None:
-            # check only for unexpected keys
-            if hasattr(incompatible_keys, "unexpected_keys") and len(incompatible_keys.unexpected_keys) > 0:
-                logger.warning(
-                    f"Loading adapter weights from {peft_model_id} led to unexpected keys not found in the model: "
-                    f" {incompatible_keys.unexpected_keys}. "
-                )
-
-        # Re-dispatch model and hooks in case the model is offloaded to CPU / Disk.
-        if (
-            (getattr(self, "hf_device_map", None) is not None)
-            and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
-            and len(self.peft_config) == 1
-        ):
-            self._dispatch_accelerate_model(
-                device_map=device_map,
-                max_memory=max_memory,
-                offload_folder=offload_folder,
-                offload_index=offload_index,
-            )
-
-    def add_adapter(self, adapter_config, adapter_name: Optional[str] = None) -> None:
-        r"""
-        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
-        official documentation: https://huggingface.co/docs/peft
-
-        Adds a fresh new adapter to the current model for training purpose. If no adapter name is passed, a default
-        name is assigned to the adapter to follow the convention of PEFT library (in PEFT we use "default" as the
-        default adapter name).
-
-        Args:
-            adapter_config (`~peft.PeftConfig`):
-                The configuration of the adapter to add, supported adapters are non-prefix tuning and adaption prompts
-                methods
-            adapter_name (`str`, *optional*, defaults to `"default"`):
-                The name of the adapter to add. If no name is passed, a default name is assigned to the adapter.
-        """
-        check_peft_version(min_version=MIN_PEFT_VERSION)
-
-        from peft import PeftConfig, inject_adapter_in_model
-
-        adapter_name = adapter_name or "default"
-
-        if not self._hf_peft_config_loaded:
-            self._hf_peft_config_loaded = True
-        elif adapter_name in self.peft_config:
-            raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
-
-        if not isinstance(adapter_config, PeftConfig):
-            raise ValueError(
-                f"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead."
-            )
-
-        # Retrieve the name or path of the model, one could also use self.config._name_or_path
-        # but to be consistent with what we do in PEFT: https://github.com/huggingface/peft/blob/6e783780ca9df3a623992cc4d1d665001232eae0/src/peft/mapping.py#L100
-        adapter_config.base_model_name_or_path = self.__dict__.get("name_or_path", None)
-        inject_adapter_in_model(adapter_config, self, adapter_name)
-
-        self.set_adapter(adapter_name)
-
-    def set_adapter(self, adapter_name: Union[List[str], str]) -> None:
-        """
-        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
-        official documentation: https://huggingface.co/docs/peft
-
-        Sets a specific adapter by forcing the model to use a that adapter and disable the other adapters.
-
-        Args:
-            adapter_name (`Union[List[str], str]`):
-                The name of the adapter to set. Can be also a list of strings to set multiple adapters.
-        """
-        check_peft_version(min_version=MIN_PEFT_VERSION)
-        if not self._hf_peft_config_loaded:
-            raise ValueError("No adapter loaded. Please load an adapter first.")
-        elif isinstance(adapter_name, list):
-            missing = set(adapter_name) - set(self.peft_config)
-            if len(missing) > 0:
-                raise ValueError(
-                    f"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s)."
-                    f" current loaded adapters are: {list(self.peft_config.keys())}"
-                )
-        elif adapter_name not in self.peft_config:
-            raise ValueError(
-                f"Adapter with name {adapter_name} not found. Please pass the correct adapter name among {list(self.peft_config.keys())}"
-            )
-
-        from peft.tuners.tuners_utils import BaseTunerLayer
-
-        _adapters_has_been_set = False
-
-        for _, module in self.named_modules():
-            if isinstance(module, BaseTunerLayer):
-                # For backward compatbility with previous PEFT versions
-                if hasattr(module, "set_adapter"):
-                    module.set_adapter(adapter_name)
-                else:
-                    module.active_adapter = adapter_name
-                _adapters_has_been_set = True
-
-        if not _adapters_has_been_set:
-            raise ValueError(
-                "Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters."
-            )
-
-    def disable_adapters(self) -> None:
-        r"""
-        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
-        official documentation: https://huggingface.co/docs/peft
-
-        Disable all adapters that are attached to the model. This leads to inferring with the base model only.
-        """
-        check_peft_version(min_version=MIN_PEFT_VERSION)
-
-        if not self._hf_peft_config_loaded:
-            raise ValueError("No adapter loaded. Please load an adapter first.")
-
-        from peft.tuners.tuners_utils import BaseTunerLayer
-
-        for _, module in self.named_modules():
-            if isinstance(module, BaseTunerLayer):
-                # The recent version of PEFT need to call `enable_adapters` instead
-                if hasattr(module, "enable_adapters"):
-                    module.enable_adapters(enabled=False)
-                else:
-                    module.disable_adapters = True
-
-    def enable_adapters(self) -> None:
-        """
-        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
-        official documentation: https://huggingface.co/docs/peft
-
-        Enable adapters that are attached to the model. The model will use `self.active_adapter()`
-        """
-        check_peft_version(min_version=MIN_PEFT_VERSION)
-
-        if not self._hf_peft_config_loaded:
-            raise ValueError("No adapter loaded. Please load an adapter first.")
-
-        from peft.tuners.tuners_utils import BaseTunerLayer
-
-        for _, module in self.named_modules():
-            if isinstance(module, BaseTunerLayer):
-                # The recent version of PEFT need to call `enable_adapters` instead
-                if hasattr(module, "enable_adapters"):
-                    module.enable_adapters(enabled=True)
-                else:
-                    module.disable_adapters = False
-
-    def active_adapters(self) -> List[str]:
-        """
-        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
-        official documentation: https://huggingface.co/docs/peft
-
-        Gets the current active adapters of the model. In case of multi-adapter inference (combining multiple adapters
-        for inference) returns the list of all active adapters so that users can deal with them accordingly.
-
-        For previous PEFT versions (that does not support multi-adapter inference), `module.active_adapter` will return
-        a single string.
-        """
-        check_peft_version(min_version=MIN_PEFT_VERSION)
-
-        if not is_peft_available():
-            raise ImportError("PEFT is not available. Please install PEFT to use this function: `pip install peft`.")
-
-        if not self._hf_peft_config_loaded:
-            raise ValueError("No adapter loaded. Please load an adapter first.")
-
-        from peft.tuners.tuners_utils import BaseTunerLayer
-
-        for _, module in self.named_modules():
-            if isinstance(module, BaseTunerLayer):
-                active_adapters = module.active_adapter
-                break
-
-        # For previous PEFT versions
-        if isinstance(active_adapters, str):
-            active_adapters = [active_adapters]
-
-        return active_adapters
-
-    def active_adapter(self) -> str:
-        warnings.warn(
-            "The `active_adapter` method is deprecated and will be removed in a future version.", FutureWarning
-        )
-
-        return self.active_adapters()[0]
-
-    def get_adapter_state_dict(self, adapter_name: Optional[str] = None) -> dict:
-        """
-        If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
-        official documentation: https://huggingface.co/docs/peft
-
-        Gets the adapter state dict that should only contain the weights tensors of the specified adapter_name adapter.
-        If no adapter_name is passed, the active adapter is used.
-
-        Args:
-            adapter_name (`str`, *optional*):
-                The name of the adapter to get the state dict from. If no name is passed, the active adapter is used.
-        """
-        check_peft_version(min_version=MIN_PEFT_VERSION)
-
-        if not self._hf_peft_config_loaded:
-            raise ValueError("No adapter loaded. Please load an adapter first.")
-
-        from peft import get_peft_model_state_dict
-
-        if adapter_name is None:
-            adapter_name = self.active_adapter()
-
-        adapter_state_dict = get_peft_model_state_dict(self, adapter_name=adapter_name)
-        return adapter_state_dict
-
-    def _dispatch_accelerate_model(
-        self,
-        device_map: str,
-        max_memory: Optional[int] = None,
-        offload_folder: Optional[str] = None,
-        offload_index: Optional[int] = None,
-    ) -> None:
-        """
-        Optional re-dispatch the model and attach new hooks to the model in case the model has been loaded with
-        accelerate (i.e. with `device_map=xxx`)
-
-        Args:
-            device_map (`str` or `Dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*):
-                A map that specifies where each submodule should go. It doesn't need to be refined to each
-                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
-                same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank
-                like `1`) on which the model will be allocated, the device map will map the entire model to this
-                device. Passing `device_map = 0` means put the whole model on GPU 0.
-
-                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
-                more information about each option see [designing a device
-                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
-            max_memory (`Dict`, *optional*):
-                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
-                GPU and the available CPU RAM if unset.
-            offload_folder (`str` or `os.PathLike`, *optional*):
-                If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
-            offload_index (`int`, *optional*):
-                The offload_index argument to be passed to `accelerate.dispatch_model` method.
-        """
-        dispatch_model_kwargs = {}
-        # Safety checker for previous `accelerate` versions
-        # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
-        if "offload_index" in inspect.signature(dispatch_model).parameters:
-            dispatch_model_kwargs["offload_index"] = offload_index
-
-        no_split_module_classes = self._no_split_modules
-
-        if device_map != "sequential":
-            max_memory = get_balanced_memory(
-                self,
-                max_memory=max_memory,
-                no_split_module_classes=no_split_module_classes,
-                low_zero=(device_map == "balanced_low_0"),
-            )
-        if isinstance(device_map, str):
-            device_map = infer_auto_device_map(
-                self, max_memory=max_memory, no_split_module_classes=no_split_module_classes
-            )
-        dispatch_model(
-            self,
-            device_map=device_map,
-            offload_dir=offload_folder,
-            **dispatch_model_kwargs,
-        )
diff --git a/spaces/ykilcher/apes/training/loss.py b/spaces/ykilcher/apes/training/loss.py
deleted file mode 100644
index b87de446c32d22d1d5104e6ddab2310fabb77bfd..0000000000000000000000000000000000000000
--- a/spaces/ykilcher/apes/training/loss.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import numpy as np
-import torch
-from torch_utils import training_stats
-from torch_utils import misc
-from torch_utils.ops import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-
-class Loss:
-    def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, sync, gain): # to be overridden by subclass
-        raise NotImplementedError()
-
-#----------------------------------------------------------------------------
-
-class StyleGAN2Loss(Loss):
-    def __init__(self, device, G_mapping, G_synthesis, D, augment_pipe=None, style_mixing_prob=0.9, r1_gamma=10, pl_batch_shrink=2, pl_decay=0.01, pl_weight=2):
-        super().__init__()
-        self.device = device
-        self.G_mapping = G_mapping
-        self.G_synthesis = G_synthesis
-        self.D = D
-        self.augment_pipe = augment_pipe
-        self.style_mixing_prob = style_mixing_prob
-        self.r1_gamma = r1_gamma
-        self.pl_batch_shrink = pl_batch_shrink
-        self.pl_decay = pl_decay
-        self.pl_weight = pl_weight
-        self.pl_mean = torch.zeros([], device=device)
-
-    def run_G(self, z, c, sync):
-        with misc.ddp_sync(self.G_mapping, sync):
-            ws = self.G_mapping(z, c)
-            if self.style_mixing_prob > 0:
-                with torch.autograd.profiler.record_function('style_mixing'):
-                    cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1])
-                    cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1]))
-                    ws[:, cutoff:] = self.G_mapping(torch.randn_like(z), c, skip_w_avg_update=True)[:, cutoff:]
-        with misc.ddp_sync(self.G_synthesis, sync):
-            img = self.G_synthesis(ws)
-        return img, ws
-
-    def run_D(self, img, c, sync):
-        if self.augment_pipe is not None:
-            img = self.augment_pipe(img)
-        with misc.ddp_sync(self.D, sync):
-            logits = self.D(img, c)
-        return logits
-
-    def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, sync, gain):
-        assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth']
-        do_Gmain = (phase in ['Gmain', 'Gboth'])
-        do_Dmain = (phase in ['Dmain', 'Dboth'])
-        do_Gpl   = (phase in ['Greg', 'Gboth']) and (self.pl_weight != 0)
-        do_Dr1   = (phase in ['Dreg', 'Dboth']) and (self.r1_gamma != 0)
-
-        # Gmain: Maximize logits for generated images.
-        if do_Gmain:
-            with torch.autograd.profiler.record_function('Gmain_forward'):
-                gen_img, _gen_ws = self.run_G(gen_z, gen_c, sync=(sync and not do_Gpl)) # May get synced by Gpl.
-                gen_logits = self.run_D(gen_img, gen_c, sync=False)
-                training_stats.report('Loss/scores/fake', gen_logits)
-                training_stats.report('Loss/signs/fake', gen_logits.sign())
-                loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits))
-                training_stats.report('Loss/G/loss', loss_Gmain)
-            with torch.autograd.profiler.record_function('Gmain_backward'):
-                loss_Gmain.mean().mul(gain).backward()
-
-        # Gpl: Apply path length regularization.
-        if do_Gpl:
-            with torch.autograd.profiler.record_function('Gpl_forward'):
-                batch_size = gen_z.shape[0] // self.pl_batch_shrink
-                gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size], sync=sync)
-                pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3])
-                with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients():
-                    pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0]
-                pl_lengths = pl_grads.square().sum(2).mean(1).sqrt()
-                pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay)
-                self.pl_mean.copy_(pl_mean.detach())
-                pl_penalty = (pl_lengths - pl_mean).square()
-                training_stats.report('Loss/pl_penalty', pl_penalty)
-                loss_Gpl = pl_penalty * self.pl_weight
-                training_stats.report('Loss/G/reg', loss_Gpl)
-            with torch.autograd.profiler.record_function('Gpl_backward'):
-                (gen_img[:, 0, 0, 0] * 0 + loss_Gpl).mean().mul(gain).backward()
-
-        # Dmain: Minimize logits for generated images.
-        loss_Dgen = 0
-        if do_Dmain:
-            with torch.autograd.profiler.record_function('Dgen_forward'):
-                gen_img, _gen_ws = self.run_G(gen_z, gen_c, sync=False)
-                gen_logits = self.run_D(gen_img, gen_c, sync=False) # Gets synced by loss_Dreal.
-                training_stats.report('Loss/scores/fake', gen_logits)
-                training_stats.report('Loss/signs/fake', gen_logits.sign())
-                loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits))
-            with torch.autograd.profiler.record_function('Dgen_backward'):
-                loss_Dgen.mean().mul(gain).backward()
-
-        # Dmain: Maximize logits for real images.
-        # Dr1: Apply R1 regularization.
-        if do_Dmain or do_Dr1:
-            name = 'Dreal_Dr1' if do_Dmain and do_Dr1 else 'Dreal' if do_Dmain else 'Dr1'
-            with torch.autograd.profiler.record_function(name + '_forward'):
-                real_img_tmp = real_img.detach().requires_grad_(do_Dr1)
-                real_logits = self.run_D(real_img_tmp, real_c, sync=sync)
-                training_stats.report('Loss/scores/real', real_logits)
-                training_stats.report('Loss/signs/real', real_logits.sign())
-
-                loss_Dreal = 0
-                if do_Dmain:
-                    loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits))
-                    training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal)
-
-                loss_Dr1 = 0
-                if do_Dr1:
-                    with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients():
-                        r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0]
-                    r1_penalty = r1_grads.square().sum([1,2,3])
-                    loss_Dr1 = r1_penalty * (self.r1_gamma / 2)
-                    training_stats.report('Loss/r1_penalty', r1_penalty)
-                    training_stats.report('Loss/D/reg', loss_Dr1)
-
-            with torch.autograd.profiler.record_function(name + '_backward'):
-                (real_logits * 0 + loss_Dreal + loss_Dr1).mean().mul(gain).backward()
-
-#----------------------------------------------------------------------------
diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/train_diff.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/train_diff.py
deleted file mode 100644
index aa24cfbc5a7a09c2fae8e6897b5689bba8cfae00..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/train_diff.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import os
-import argparse
-import torch
-from torch.optim import lr_scheduler
-from diffusion.logger import utils
-from diffusion.data_loaders import get_data_loaders
-from diffusion.solver import train
-from diffusion.unit2mel import Unit2Mel
-from diffusion.vocoder import Vocoder
-
-
-def parse_args(args=None, namespace=None):
-    """Parse command-line arguments."""
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-c",
-        "--config",
-        type=str,
-        required=True,
-        help="path to the config file")
-    return parser.parse_args(args=args, namespace=namespace)
-
-
-if __name__ == '__main__':
-    # parse commands
-    cmd = parse_args()
-    
-    # load config
-    args = utils.load_config(cmd.config)
-    print(' > config:', cmd.config)
-    print(' >    exp:', args.env.expdir)
-    
-    # load vocoder
-    vocoder = Vocoder(args.vocoder.type, args.vocoder.ckpt, device=args.device)
-    
-    # load model
-    model = Unit2Mel(
-                args.data.encoder_out_channels, 
-                args.model.n_spk,
-                args.model.use_pitch_aug,
-                vocoder.dimension,
-                args.model.n_layers,
-                args.model.n_chans,
-                args.model.n_hidden)
-    
-    
-    # load parameters
-    optimizer = torch.optim.AdamW(model.parameters())
-    initial_global_step, model, optimizer = utils.load_model(args.env.expdir, model, optimizer, device=args.device)
-    for param_group in optimizer.param_groups:
-        param_group['initial_lr'] = args.train.lr
-        param_group['lr'] = args.train.lr * (args.train.gamma ** max(((initial_global_step-2)//args.train.decay_step),0) )
-        param_group['weight_decay'] = args.train.weight_decay
-    scheduler = lr_scheduler.StepLR(optimizer, step_size=args.train.decay_step, gamma=args.train.gamma,last_epoch=initial_global_step-2)
-    
-    # device
-    if args.device == 'cuda':
-        torch.cuda.set_device(args.env.gpu_id)
-    model.to(args.device)
-    
-    for state in optimizer.state.values():
-        for k, v in state.items():
-            if torch.is_tensor(v):
-                state[k] = v.to(args.device)
-                    
-    # datas
-    loader_train, loader_valid = get_data_loaders(args, whole_audio=False)
-    
-    # run
-    train(args, initial_global_step, model, optimizer, scheduler, vocoder, loader_train, loader_valid)
-    
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/build.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/build.py
deleted file mode 100644
index 34eb12d00d94ff905b796e75e2c4c5845257c8e9..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/modeling/proposal_generator/build.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.utils.registry import Registry
-
-PROPOSAL_GENERATOR_REGISTRY = Registry("PROPOSAL_GENERATOR")
-PROPOSAL_GENERATOR_REGISTRY.__doc__ = """
-Registry for proposal generator, which produces object proposals from feature maps.
-
-The registered object will be called with `obj(cfg, input_shape)`.
-The call should return a `nn.Module` object.
-"""
-
-from . import rpn, rrpn  # noqa F401 isort:skip
-
-
-def build_proposal_generator(cfg, input_shape):
-    """
-    Build a proposal generator from `cfg.MODEL.PROPOSAL_GENERATOR.NAME`.
-    The name can be "PrecomputedProposals" to use no proposal generator.
-    """
-    name = cfg.MODEL.PROPOSAL_GENERATOR.NAME
-    if name == "PrecomputedProposals":
-        return None
-
-    return PROPOSAL_GENERATOR_REGISTRY.get(name)(cfg, input_shape)
diff --git a/spaces/yuchenlin/Rebiber/app.py b/spaces/yuchenlin/Rebiber/app.py
deleted file mode 100644
index e125abe4a6183d0522915ed71040cd503eb70e19..0000000000000000000000000000000000000000
--- a/spaces/yuchenlin/Rebiber/app.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import gradio as gr
-import rebiber 
-import os
-import uuid
- 
-
-# Load Bib Database 
-filepath = os.path.abspath(rebiber.__file__).replace("__init__.py","")
-bib_list_path = os.path.join(filepath, "bib_list.txt")
-abbr_tsv_path = "abbr.tsv"
-
-bib_db = rebiber.construct_bib_db(bib_list_path, start_dir=filepath)
-
-abbr_dict = rebiber.normalize.load_abbr_tsv(abbr_tsv_path)
-
-
-def process(input_bib, shorten, remove_keys, deduplicate, sort):
-    if "@" not in input_bib:
-        return "N/A"
-    global abbr_dict
-    # print(f"remove_keys={remove_keys}")
-    random_id = uuid.uuid4().hex
-    with open(f"input_{random_id}.bib", "w") as f:
-        f.write(input_bib.replace("\t", "    "))
-    all_bib_entries = rebiber.load_bib_file(f"input_{random_id}.bib")
-    print("# Input Bib Entries:", len(all_bib_entries))
-    abbr_dict_pass = []
-    if shorten:
-        abbr_dict_pass = abbr_dict
-    rebiber.normalize_bib(bib_db, all_bib_entries, f"output_{random_id}.bib",
-                          abbr_dict=abbr_dict_pass,
-                          deduplicate=deduplicate,
-                          sort=sort,
-                          removed_value_names=remove_keys)
-    with open(f"output_{random_id}.bib") as f:
-        output_bib = f.read().replace("\n ", "\n    ")
-    # delete both files
-    # print(output_bib)
-    return output_bib, random_id, gr.update(visible=True)
-
-
-example_input = """
-@article{lin2020birds,
-    title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
-    author={Lin, Bill Yuchen and Lee, Seyeon and Khanna, Rahul and Ren, Xiang},
-    journal={arXiv preprint arXiv:2005.00683},
-    year={2020}
-} 
-@inproceedings{Lin2020CommonGenAC,
-  title={CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning},
-  author={Bill Yuchen Lin and Minghan Shen and Wangchunshu Zhou and Pei Zhou and Chandra Bhagavatula and Yejin Choi and Xiang Ren},
-  booktitle={Findings},
-  year={2020}
-}
-""" 
-
-examples = [[example_input]]
-
-
-# iface = gr.Interface(fn=process,
-#     inputs=gr.inputs.Textbox(lines=30, label="Input BIB"),
-#     outputs=gr.outputs.Textbox(label="Output BIB").style(show_copy_button=True),
-#     examples=examples,
-#     allow_flagging="never"
-#     )
-
-
-
-
-
-with gr.Blocks() as demo:
-    
-    gr.Markdown(
-            '''# Rebiber: A tool for normalizing bibtex with official info.
-                <table> 
-                <tr>
-                <td>
-                <a href="https://yuchenlin.xyz/">
-                    <img src="https://img.shields.io/badge/Yuchen%20Lin-🐼-blue?style=social">
-                </a>
-                </td>
-                <td>
-                <a href="https://github.com/yuchenlin/rebiber">
-                    <img src="https://img.shields.io/badge/Github--blue?style=social&logo=github">
-                </a>
-                </td>
-                <td>
-                <a href="https://twitter.com/billyuchenlin/status/1353850378438070272?s=20">
-                    <img src="https://img.shields.io/badge/Tweet--blue?style=social&logo=twitter">
-                </a>
-                </td>
-                </tr>
-                </table>
-                <span style="font-size:13pt">
-                
-                We often cite papers using their arXiv versions without noting that they are already __PUBLISHED__ in some conferences. These unofficial bib entries might violate rules about submissions or camera-ready versions for some conferences. 
-                We introduce __Rebiber__, a simple tool in Python to fix them automatically. It is based on the official conference information from the [DBLP](https://dblp.org/) or [the ACL anthology](https://www.aclweb.org/anthology/) (for NLP conferences)!
-                Apart from handling outdated arXiv citations, __Rebiber__ also normalizes citations in a unified way (DBLP-style), supporting abbreviation and value selection.  
-                
-                </span>
-            '''
-    )
-    
-    with gr.Row():
-        with gr.Column(scale=3):
-            input_bib = gr.Textbox(lines=15, label="Input BIB", value=example_input, interactive=True)
-            removekeys = gr.CheckboxGroup(["url", "biburl", "address", "publisher", "pages", "doi", "volume", "bibsource"], 
-                                value=[False, False, False, False, False, False, False, False],
-                                label="Remove Keys", info="Which keys to remove?")
-            shorten = gr.Checkbox(label="Abbreviation", info="Shorten the conference/journal names (e.g., `Proceedings of the 2020 International Conference of ...` --> `Proc. of ICML')", value=False)            
-            dedup = gr.Checkbox(label="Deduplicate entries.", value=False)
-            sort = gr.Checkbox(label="Sort alphabetically by ID.", value=False)
-            with gr.Row():
-                clr_button = gr.Button("Clear")
-                button = gr.Button("Submit")
-            ex_uuid = gr.Text(label="UUID")
-            ex_uuid.visible = False
-        with gr.Column(scale=3):
-            output=gr.Textbox(label="Output BIB (Note that you can copy the output bib file by clicking the top-right button.)").style(show_copy_button=True, interactive=False)        
-            download_btn = gr.Button("Generate Bib File")
-            download_btn.visible = False
-            download_content = gr.outputs.File()
-            download_content.visible = False 
-    def download_file(ex_uuid):
-        global download_content
-        # Replace this with your code to generate/download the file
-        file_path = f"output_{ex_uuid}.bib"
-        download_content.update(visible=False)
-        return file_path, gr.update(visible=True)
-    download_btn.click(download_file, inputs=ex_uuid, outputs=[download_content,download_content])  
-    button.click(process, inputs=[input_bib, shorten, removekeys, dedup, sort], outputs=[output, ex_uuid, download_btn], api_name = "process")
-    def clean(text):
-        return ""
-    clr_button.click(clean, input_bib, input_bib)
-    # gr.Interface(fn=process,
-    # outputs=gr.outputs.Textbox(label="Output BIB").style(show_copy_button=True),
-    # examples=examples,
-    # allow_flagging="never",
-    # scroll_to_output=True,
-    # show_progress=True,
-    # )
-
-
-if __name__ == "__main__":
-    demo.launch()
-
-
-"""
-@article{lin2020birds,
-    title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
-    author={Lin, Bill Yuchen and Lee, Seyeon and Khanna, Rahul and Ren, Xiang},
-    journal={arXiv preprint arXiv:2005.00683},
-    year={2020}
-} 
-
-@inproceedings{lin2020birds,
- address = {Online},
- author = {Lin, Bill Yuchen  and
-Lee, Seyeon  and
-Khanna, Rahul  and
-Ren, Xiang},
- booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
- doi = {10.18653/v1/2020.emnlp-main.557},
- pages = {6862--6868},
- publisher = {Association for Computational Linguistics},
- title = {{B}irds have four legs?! {N}umer{S}ense: {P}robing {N}umerical {C}ommonsense {K}nowledge of {P}re-{T}rained {L}anguage {M}odels},
- url = {https://aclanthology.org/2020.emnlp-main.557},
- year = {2020}
-}   
-"""
\ No newline at end of file
diff --git a/spaces/yufiofficial/MusicGenQ/audiocraft/modules/__init__.py b/spaces/yufiofficial/MusicGenQ/audiocraft/modules/__init__.py
deleted file mode 100644
index 81ba30f6466ff91b90490a4fb92f7d3d0d00144d..0000000000000000000000000000000000000000
--- a/spaces/yufiofficial/MusicGenQ/audiocraft/modules/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .conv import (
-    NormConv1d,
-    NormConv2d,
-    NormConvTranspose1d,
-    NormConvTranspose2d,
-    StreamableConv1d,
-    StreamableConvTranspose1d,
-    pad_for_conv1d,
-    pad1d,
-    unpad1d,
-)
-from .lstm import StreamableLSTM
-from .seanet import SEANetEncoder, SEANetDecoder
diff --git a/spaces/zeykz/rvc-mlbb-v2zey/lib/infer_pack/models_onnx.py b/spaces/zeykz/rvc-mlbb-v2zey/lib/infer_pack/models_onnx.py
deleted file mode 100644
index 963e67b29f828e9fdd096397952054fe77cf3d10..0000000000000000000000000000000000000000
--- a/spaces/zeykz/rvc-mlbb-v2zey/lib/infer_pack/models_onnx.py
+++ /dev/null
@@ -1,819 +0,0 @@
-import math, pdb, os
-from time import time as ttime
-import torch
-from torch import nn
-from torch.nn import functional as F
-from lib.infer_pack import modules
-from lib.infer_pack import attentions
-from lib.infer_pack import commons
-from lib.infer_pack.commons import init_weights, get_padding
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from lib.infer_pack.commons import init_weights
-import numpy as np
-from lib.infer_pack import commons
-
-
-class TextEncoder256(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(256, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class TextEncoder768(nn.Module):
-    def __init__(
-        self,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.emb_phone = nn.Linear(768, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, phone, pitch, lengths):
-        if pitch == None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        n_flows=4,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(
-                    channels,
-                    hidden_channels,
-                    kernel_size,
-                    dilation_rate,
-                    n_layers,
-                    gin_channels=gin_channels,
-                    mean_only=True,
-                )
-            )
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-    def remove_weight_norm(self):
-        for i in range(self.n_flows):
-            self.flows[i * 2].remove_weight_norm()
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            gin_channels=gin_channels,
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(
-            x.dtype
-        )
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class Generator(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels=0,
-    ):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class SineGen(torch.nn.Module):
-    """Definition of sine generator
-    SineGen(samp_rate, harmonic_num = 0,
-            sine_amp = 0.1, noise_std = 0.003,
-            voiced_threshold = 0,
-            flag_for_pulse=False)
-    samp_rate: sampling rate in Hz
-    harmonic_num: number of harmonic overtones (default 0)
-    sine_amp: amplitude of sine-wavefrom (default 0.1)
-    noise_std: std of Gaussian noise (default 0.003)
-    voiced_thoreshold: F0 threshold for U/V classification (default 0)
-    flag_for_pulse: this SinGen is used inside PulseGen (default False)
-    Note: when flag_for_pulse is True, the first time step of a voiced
-        segment is always sin(np.pi) or cos(0)
-    """
-
-    def __init__(
-        self,
-        samp_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        noise_std=0.003,
-        voiced_threshold=0,
-        flag_for_pulse=False,
-    ):
-        super(SineGen, self).__init__()
-        self.sine_amp = sine_amp
-        self.noise_std = noise_std
-        self.harmonic_num = harmonic_num
-        self.dim = self.harmonic_num + 1
-        self.sampling_rate = samp_rate
-        self.voiced_threshold = voiced_threshold
-
-    def _f02uv(self, f0):
-        # generate uv signal
-        uv = torch.ones_like(f0)
-        uv = uv * (f0 > self.voiced_threshold)
-        return uv
-
-    def forward(self, f0, upp):
-        """sine_tensor, uv = forward(f0)
-        input F0: tensor(batchsize=1, length, dim=1)
-                  f0 for unvoiced steps should be 0
-        output sine_tensor: tensor(batchsize=1, length, dim)
-        output uv: tensor(batchsize=1, length, 1)
-        """
-        with torch.no_grad():
-            f0 = f0[:, None].transpose(1, 2)
-            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)
-            # fundamental component
-            f0_buf[:, :, 0] = f0[:, :, 0]
-            for idx in np.arange(self.harmonic_num):
-                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
-                    idx + 2
-                )  # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
-            rad_values = (f0_buf / self.sampling_rate) % 1  ###%1意味着n_har的乘积无法后处理优化
-            rand_ini = torch.rand(
-                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
-            )
-            rand_ini[:, 0] = 0
-            rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
-            tmp_over_one = torch.cumsum(rad_values, 1)  # % 1  #####%1意味着后面的cumsum无法再优化
-            tmp_over_one *= upp
-            tmp_over_one = F.interpolate(
-                tmp_over_one.transpose(2, 1),
-                scale_factor=upp,
-                mode="linear",
-                align_corners=True,
-            ).transpose(2, 1)
-            rad_values = F.interpolate(
-                rad_values.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(
-                2, 1
-            )  #######
-            tmp_over_one %= 1
-            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0
-            cumsum_shift = torch.zeros_like(rad_values)
-            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
-            sine_waves = torch.sin(
-                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi
-            )
-            sine_waves = sine_waves * self.sine_amp
-            uv = self._f02uv(f0)
-            uv = F.interpolate(
-                uv.transpose(2, 1), scale_factor=upp, mode="nearest"
-            ).transpose(2, 1)
-            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
-            noise = noise_amp * torch.randn_like(sine_waves)
-            sine_waves = sine_waves * uv + noise
-        return sine_waves, uv, noise
-
-
-class SourceModuleHnNSF(torch.nn.Module):
-    """SourceModule for hn-nsf
-    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
-                 add_noise_std=0.003, voiced_threshod=0)
-    sampling_rate: sampling_rate in Hz
-    harmonic_num: number of harmonic above F0 (default: 0)
-    sine_amp: amplitude of sine source signal (default: 0.1)
-    add_noise_std: std of additive Gaussian noise (default: 0.003)
-        note that amplitude of noise in unvoiced is decided
-        by sine_amp
-    voiced_threshold: threhold to set U/V given F0 (default: 0)
-    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
-    F0_sampled (batchsize, length, 1)
-    Sine_source (batchsize, length, 1)
-    noise_source (batchsize, length 1)
-    uv (batchsize, length, 1)
-    """
-
-    def __init__(
-        self,
-        sampling_rate,
-        harmonic_num=0,
-        sine_amp=0.1,
-        add_noise_std=0.003,
-        voiced_threshod=0,
-        is_half=True,
-    ):
-        super(SourceModuleHnNSF, self).__init__()
-
-        self.sine_amp = sine_amp
-        self.noise_std = add_noise_std
-        self.is_half = is_half
-        # to produce sine waveforms
-        self.l_sin_gen = SineGen(
-            sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod
-        )
-
-        # to merge source harmonics into a single excitation
-        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
-        self.l_tanh = torch.nn.Tanh()
-
-    def forward(self, x, upp=None):
-        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
-        if self.is_half:
-            sine_wavs = sine_wavs.half()
-        sine_merge = self.l_tanh(self.l_linear(sine_wavs))
-        return sine_merge, None, None  # noise, uv
-
-
-class GeneratorNSF(torch.nn.Module):
-    def __init__(
-        self,
-        initial_channel,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        gin_channels,
-        sr,
-        is_half=False,
-    ):
-        super(GeneratorNSF, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-
-        self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(
-            sampling_rate=sr, harmonic_num=0, is_half=is_half
-        )
-        self.noise_convs = nn.ModuleList()
-        self.conv_pre = Conv1d(
-            initial_channel, upsample_initial_channel, 7, 1, padding=3
-        )
-        resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            c_cur = upsample_initial_channel // (2 ** (i + 1))
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-            if i + 1 < len(upsample_rates):
-                stride_f0 = np.prod(upsample_rates[i + 1 :])
-                self.noise_convs.append(
-                    Conv1d(
-                        1,
-                        c_cur,
-                        kernel_size=stride_f0 * 2,
-                        stride=stride_f0,
-                        padding=stride_f0 // 2,
-                    )
-                )
-            else:
-                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(
-                zip(resblock_kernel_sizes, resblock_dilation_sizes)
-            ):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-        self.upp = np.prod(upsample_rates)
-
-    def forward(self, x, f0, g=None):
-        har_source, noi_source, uv = self.m_source(f0, self.upp)
-        har_source = har_source.transpose(1, 2)
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            x_source = self.noise_convs[i](har_source)
-            x = x + x_source
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-sr2sr = {
-    "32k": 32000,
-    "40k": 40000,
-    "48k": 48000,
-}
-
-
-class SynthesizerTrnMsNSFsidM(nn.Module):
-    def __init__(
-        self,
-        spec_channels,
-        segment_size,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        resblock,
-        resblock_kernel_sizes,
-        resblock_dilation_sizes,
-        upsample_rates,
-        upsample_initial_channel,
-        upsample_kernel_sizes,
-        spk_embed_dim,
-        gin_channels,
-        sr,
-        version,
-        **kwargs
-    ):
-        super().__init__()
-        if type(sr) == type("strr"):
-            sr = sr2sr[sr]
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.gin_channels = gin_channels
-        # self.hop_length = hop_length#
-        self.spk_embed_dim = spk_embed_dim
-        if version == "v1":
-            self.enc_p = TextEncoder256(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        else:
-            self.enc_p = TextEncoder768(
-                inter_channels,
-                hidden_channels,
-                filter_channels,
-                n_heads,
-                n_layers,
-                kernel_size,
-                p_dropout,
-            )
-        self.dec = GeneratorNSF(
-            inter_channels,
-            resblock,
-            resblock_kernel_sizes,
-            resblock_dilation_sizes,
-            upsample_rates,
-            upsample_initial_channel,
-            upsample_kernel_sizes,
-            gin_channels=gin_channels,
-            sr=sr,
-            is_half=kwargs["is_half"],
-        )
-        self.enc_q = PosteriorEncoder(
-            spec_channels,
-            inter_channels,
-            hidden_channels,
-            5,
-            1,
-            16,
-            gin_channels=gin_channels,
-        )
-        self.flow = ResidualCouplingBlock(
-            inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
-        )
-        self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        self.speaker_map = None
-        print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim)
-
-    def remove_weight_norm(self):
-        self.dec.remove_weight_norm()
-        self.flow.remove_weight_norm()
-        self.enc_q.remove_weight_norm()
-
-    def construct_spkmixmap(self, n_speaker):
-        self.speaker_map = torch.zeros((n_speaker, 1, 1, self.gin_channels))
-        for i in range(n_speaker):
-            self.speaker_map[i] = self.emb_g(torch.LongTensor([[i]]))
-        self.speaker_map = self.speaker_map.unsqueeze(0)
-
-    def forward(self, phone, phone_lengths, pitch, nsff0, g, rnd, max_len=None):
-        if self.speaker_map is not None:  # [N, S]  *  [S, B, 1, H]
-            g = g.reshape((g.shape[0], g.shape[1], 1, 1, 1))  # [N, S, B, 1, 1]
-            g = g * self.speaker_map  # [N, S, B, 1, H]
-            g = torch.sum(g, dim=1)  # [N, 1, B, 1, H]
-            g = g.transpose(0, -1).transpose(0, -2).squeeze(0)  # [B, H, N]
-        else:
-            g = g.unsqueeze(0)
-            g = self.emb_g(g).transpose(1, 2)
-
-        m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
-        z_p = (m_p + torch.exp(logs_p) * rnd) * x_mask
-        z = self.flow(z_p, x_mask, g=g, reverse=True)
-        o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
-        return o
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11, 17]
-        # periods = [3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class MultiPeriodDiscriminatorV2(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminatorV2, self).__init__()
-        # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2, 3, 5, 7, 11, 17, 23, 37]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [
-            DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods
-        ]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []  #
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            # for j in range(len(fmap_r)):
-            #     print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-                norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-                norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-                norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-                norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-                norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-            ]
-        )
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList(
-            [
-                norm_f(
-                    Conv2d(
-                        1,
-                        32,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        32,
-                        128,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        128,
-                        512,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        512,
-                        1024,
-                        (kernel_size, 1),
-                        (stride, 1),
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-                norm_f(
-                    Conv2d(
-                        1024,
-                        1024,
-                        (kernel_size, 1),
-                        1,
-                        padding=(get_padding(kernel_size, 1), 0),
-                    )
-                ),
-            ]
-        )
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
diff --git a/spaces/ziyadsuper2017/Biochemistry3.0/README.md b/spaces/ziyadsuper2017/Biochemistry3.0/README.md
deleted file mode 100644
index 0f74725b3200eabd371c18110a5ac63cb9fe78a2..0000000000000000000000000000000000000000
--- a/spaces/ziyadsuper2017/Biochemistry3.0/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Demo
-emoji: ⚡
-colorFrom: gray
-colorTo: gray
-sdk: gradio
-sdk_version: 3.46.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference