Update parquet files (step 86 of 397)

spaces/0xSynapse/Segmagine/app.py

@@ -1,97 +0,0 @@
-import os
-
-import cv2
-import gradio as gr
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from PIL import Image
-
-from segment_anything import SamAutomaticMaskGenerator, SamPredictor, sam_model_registry
-
-# suppress server-side GUI windows
-matplotlib.pyplot.switch_backend('Agg') 
-
-# setup models
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-sam = sam_model_registry["vit_b"](checkpoint="./sam_vit_b_01ec64.pth")
-sam.to(device=device)
-mask_generator = SamAutomaticMaskGenerator(sam)
-predictor = SamPredictor(sam)
-
-
-# copied from: https://github.com/facebookresearch/segment-anything
-def show_anns(anns):
-    if len(anns) == 0:
-        return
-    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
-    ax = plt.gca()
-    ax.set_autoscale_on(False)
-    polygons = []
-    color = []
-    for ann in sorted_anns:
-        m = ann['segmentation']
-        img = np.ones((m.shape[0], m.shape[1], 3))
-        color_mask = np.random.random((1, 3)).tolist()[0]
-        for i in range(3):
-            img[:,:,i] = color_mask[i]
-        ax.imshow(np.dstack((img, m*0.35)))
-
-
-# demo function
-def segment_image(input_image):
-
-    if input_image is not None:
-
-        # generate masks
-        masks = mask_generator.generate(input_image)
-
-        # add masks to image
-        plt.clf()
-        ppi = 100
-        height, width, _ = input_image.shape
-        plt.figure(figsize=(width / ppi, height / ppi))  # convert pixel to inches
-        plt.imshow(input_image)
-        show_anns(masks)
-        plt.axis('off')
-
-        # save and get figure
-        plt.savefig('output_figure.png', bbox_inches='tight')
-        output_image = cv2.imread('output_figure.png')
-        return Image.fromarray(output_image)
-
-
-with gr.Blocks() as demo:
-
-    with gr.Row():
-        gr.Markdown("## Segmagine 🎨")
-    with gr.Row():
-        gr.Markdown("Gradio demo for Segment Anything Model (SAM) by Meta AI Research, produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a dataset of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks.[Learn More](https://segment-anything.com/)")
-
-    with gr.Row():
-
-        with gr.Column():
-            image_input = gr.Image()
-            segment_image_button = gr.Button('Generate Mask')
-
-        with gr.Column():
-            image_output = gr.Image()
-
-    segment_image_button.click(segment_image, inputs=[image_input], outputs=image_output)
-
-    gr.Examples(
-        examples=[
-            ['./examples/dog.jpg'],
-            ['./examples/groceries.jpg'],
-            ['./examples/truck.jpg']
-
-        ],
-        inputs=[image_input],
-        outputs=[image_output],
-        fn=segment_image,
-        #cache_examples=True
-    )
-
-demo.launch()

spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Photo Manager 12.0.342 Keys Keygen The Best Photo Editing Software.md

@@ -1,149 +0,0 @@
-
-<h1>ACDSee Photo Manager 12.0.342 Keys Keygen: How to Get and Use This Amazing Photo Software</h1>
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- <h2>What are Keys and Keygen?</h2>
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-<ul>
-<li>GPGDVH-334YC-36V54N-H463Y5B</li>
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- <ol>
-<li>Download the software from one of the sources mentioned above and save it to your computer.</li>
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-<li>When prompted, enter one of the keys provided by the source or generated by the keygen to register your software.</li>
-<li>Enjoy using ACDSee Photo Manager 12.0.342 with all its features unlocked.</li>
-</ol>
- <h2>How to Use ACDSee Photo Manager 12.0.342 to Organize, Edit, and Share Your Photos</h2>
- <p>Now that you have installed and activated ACDSee Photo Manager 12.0.342 with keys and keygen, you can start using it to manage your photos.</p>
- <p>To use ACDSee Photo Manager 12.0.342 to organize, edit, and share your photos, follow these steps:</p>
- <ol>
-<li>Launch the software and switch to Manage mode by clicking on the Manage button at the top left corner of the screen.</li>
-<li>Navigate to the folder where your photos are stored using the Folder pane on the left side of the screen.</li>
-<li>Select the photos that you want to organize by clicking on them or using Ctrl+click or Shift+click for multiple selections.</li>
-<li>Use the tools on the right side of the screen to organize your photos according to your preferences:</li>
-<ul>
-<li>Use the Categories tool to assign predefined or custom categories to your photos, such as People, Places, Events, etc.</li>
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-<li>Use the Tags tool to mark your best shots with a check mark for further review I'm happy to help you with your task. Here is the continuation of the article I have created based on your prompt. <p>editing or sharing.</li>
-</ul>
-<li>Switch to View mode by clicking on the View button at the top left corner of the screen.</li>
-<li>Select a photo that you want to edit by clicking on it in the File List pane at the bottom of the screen.</li>
-<li>Use the tools on the right side of the screen to edit your photo according to your needs:</li>
-<ul>
-<li>Use the Zoom tool to magnify or reduce your photo at any level of detail.</li>
-<li>Use the Pan tool to move around your photo when zoomed in.</li>
-<li>Use the Rotate tool to rotate your photo clockwise or counterclockwise by 90 degrees.</li>
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-<li>Use the Edit tab to access more advanced editing tools, such as exposure, white balance, color, detail, repair, geometry, and effects.</li>
-</ul>
-<li>Switch to Online mode by clicking on the Online button at the top left corner of the screen.</li>
-<li>Drag and drop your photos from your computer or connected devices to your own personal 2 GB of free storage space on ACDSeeOnline.com.</li>
-<li>Share your photos with your friends and family by sending them a link to your online album or posting it on Facebook or Twitter.</li>
-</ol>
- <h1>Conclusion</h1>
- <p>ACDSee Photo Manager 12.0.342 is a powerful and fast photo software that can help you organize, view, edit, and share your photos with ease and speed. However, it is not free and you need to pay $69 to get the full version of this software.</p>
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- <ol>
-<li><b>Is ACDSee Photo Manager 12.0.342 safe to use?</b></li>
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-<p>To uninstall ACDSee Photo Manager 12.0.342 from your computer, follow these steps:</p>
-<ol type="a">
-<li>Click on Start > Control Panel > Programs > Programs and Features.</li>
-<li>Select ACDSee Photo Manager 12 from the list of programs and click on Uninstall/Change.</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-</ol>
-<li><b>How do I update ACDSee Photo Manager 12.0.342?</b></li>
-<p>To update ACDSee Photo Manager 12.0.342 to the latest version, follow these steps:</p>
-<ol type="a">
-<li>Launch ACDSee Photo Manager 12 and click on Help > Check for Updates.</li>
-<li>If there is an update available, click on Download Now and follow the instructions on the screen to install it.</li>
-</ol>
-<li><b>How do I contact ACD Systems for support?</b></li>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Adata Classic CH94 Driver Windows 7 91 Troubleshooting Tips and Fixes.md

@@ -1,121 +0,0 @@
-<br />
-<h1>ADATA Classic CH94 Driver Windows 7 91: How to Download and Install</h1>
-<p>If you have an ADATA Classic CH94 external hard drive and you want to use it with your Windows 7 computer, you may need to download and install a driver for it. A driver is a software that helps your computer communicate with your device and enables its proper functioning. In this article, we will show you how to download and install ADATA Classic CH94 driver for Windows 7 91 in two easy ways.</p>
-<h2>adata classic ch94 driver windows 7 91</h2><br /><p><b><b>Download File</b> &#9881; <a href="https://byltly.com/2uKwpt">https://byltly.com/2uKwpt</a></b></p><br /><br />
-<h2>Introduction</h2>
-<h3>What is ADATA Classic CH94?</h3>
-<p>ADATA Classic CH94 is a portable external hard drive that offers up to 640GB of storage capacity. It has a sleek design and comes in various colors. It also features a wrap-around USB cable that makes it easy to carry and use. You can use it to store and backup your files, photos, videos, music, and more.</p>
-<h3>Why do you need a driver for ADATA Classic CH94?</h3>
-<p>A driver is a software that allows your computer to recognize and communicate with your device. Without a driver, your computer may not be able to detect or access your device properly. You may also encounter errors or performance issues when using your device. Therefore, it is important to have the correct and updated driver for your device.</p>
-<h3>How to check your Windows version and system type?</h3>
-<p>Before you download and install a driver for your device, you need to check your Windows version and system type. This will help you find the compatible driver for your device. To check your Windows version and system type, follow these steps:</p>
-<p>How to install adata classic ch94 driver on windows 7 91<br />
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-<ol>
-<li>Click on the Start button and type "system" in the search box.</li>
-<li>Select System from the list of results.</li>
-<li>In the System window, you will see your Windows edition, service pack, and system type.</li>
-</ol>
-<p>For example, if you see "Windows 7 Service Pack 1 (SP1) - 64-bit", it means you have Windows 7 with SP1 installed and your system type is 64-bit.</p>
-<h2>How to download ADATA Classic CH94 driver for Windows 7 91</h2>
-<h3>Option 1: Download from ADATA official website</h3>
-<p>The first option is to download the driver from ADATA official website. This is the recommended option as you can get the latest and official driver for your device. To download the driver from ADATA official website, follow these steps:</p>
-<h4>Step 1: Go to ADATA support page</h4>
-<p>Open your web browser and go to <a href="https://www.adata.com/us/support/driver?tab=downloads">https://www.adata.com/us/support/driver?tab=downloads</a>. This is the support page of ADATA where you can find drivers, manuals, firmware, software, and more for various products.</p>
-<h4>Step 2: Select your product category and model</h4>
-<p>On the support page, select "External HDD" from the product category dropdown menu. Then select "Classic Series" from the product series dropdown menu. Finally, select "CH94" from the product model dropdown menu. You will see a list of drivers available for download.</p>
-<h4>Step 3: Download the driver file</h4>
-<p>Find the driver that matches your Windows version and system type. For example, if you have Windows 7 SP1 -64-bit, look for "CH94 Driver (Windows Vista/7/8/10) -64bit". Click on the download icon next to the driver name. Save the file to a location where you can easily find it later.</p>
-<h3>Option 2: Download from DriverDouble website</h3>
-<p>The second option is to download the driver from DriverDouble website. This is an alternative option if you cannot find or access the driver from ADATA official website. DriverDouble is a website that provides drivers for various devices from different manufacturers. To download the driver from DriverDouble website, follow these steps:</p>
-<h4>Step 1: Go to DriverDouble website</h4>
-<p>Open your web browser and go to <a href="https://driverdouble.com/drivers/adata-ch94-classic.html">https://driverdouble.com/drivers/adata-ch94-classic.html</a>. This is the page where you can find drivers for ADATA CH94 Classic device.</p>
-<h4>Step 2: Search for ADATA CH94 Classic driver</h4>
-<p>On the page, scroll down until you see a list of drivers available for download. Find the driver that matches your Windows version and system type. For example, if you have Windows 7 SP1 -64-bit, look for "ADATA CH94 Classic - windows vista-7-8-10 drivers". Click on "Download Now" button next to the driver name.</p>
-<h4>Step 3: Download the driver file</h4>
-<p>You will be redirected to another page where you can download the driver file. Click on "Download Now" button again and save the file to a location where you can easily find it later.</p>
-<h2>How to install ADATA Classic CH94 driver for Windows 7 91</h2>
-<h3>Option 1: Install using the downloaded file</h3><p>The first option is to install the driver using the downloaded file. This is the easiest and fastest way to install the driver. To install the driver using the downloaded file, follow these steps:</p><h4>Step 1: Locate the downloaded file and double-click on it</h4><p>Navigate to the location where you saved the downloaded file. The file name should be something like "CH94_Driver_Win_Vista_7_8_10_64bit.zip" or "ADATA_CH94_Classic_driver.zip". Double-click on the file to open it. You will see a folder containing the driver files.</p><h4>Step 2: Follow the on-screen instructions to complete the installation</h4><p>In some cases, you may need to extract the folder first before running the installation. To extract the folder, right-click on it and select "Extract All". Then choose a destination where you want to extract it. After extracting, open  the folder  and look  for an executable file  such as "setup.exe" or "install.exe". Double-click on  the executable file  to run  the installation. Follow  the on-screen instructions  to complete  <h4>Step 3: Restart your computer if prompted</h4>
-<p>Some drivers may require you to restart your computer after the installation. If you see a message asking you to restart your computer, click on "Yes" or "Restart Now". This will ensure that the driver is properly installed and activated.</p>
-<h3>Option 2: Install using Device Manager</h3>
-<p>The second option is to install the driver using Device Manager. This is an alternative option if you encounter any problems or errors when installing the driver using the downloaded file. Device Manager is a tool that allows you to manage and update the devices connected to your computer. To install the driver using Device Manager, follow these steps:</p>
-<h4>Step 1: Connect your ADATA Classic CH94 to your computer</h4>
-<p>Plug your ADATA Classic CH94 into a USB port on your computer. Make sure that the device is securely connected and powered on.</p>
-<h4>Step 2: Open Device Manager and find your device</h4>
-<p>Click on the Start button and type "device manager" in the search box. Select Device Manager from the list of results. In the Device Manager window, look for your device under "Disk drives" or "Other devices". It may be labeled as "ADATA CH94 Classic" or "Unknown device". If you see a yellow exclamation mark or a red cross next to your device, it means that there is a problem with the driver.</p>
-<h4>Step 3: Right-click on your device and select Update driver software</h4>
-<p>Right-click on your device and select "Update driver software" from the menu. This will open a new window where you can choose how to update your driver.</p>
-<h4>Step 4: Choose Browse my computer for driver software and locate the downloaded file</h4>
-<p>In the new window, choose "Browse my computer for driver software". This will allow you to manually select the driver file that you downloaded earlier. Click on "Browse" and navigate to the location where you saved the downloaded file. The file name should be something like "CH94_Driver_Win_Vista_7_8_10_64bit.zip" or "ADATA_CH94_Classic_driver.zip". Select the file and click on "Open". Then click on "Next".</p>
-<h4>Step 5: Follow the on-screen instructions to complete the installation</h4>
-<p>The system will start installing the driver for your device. Follow the on-screen instructions to complete the installation. You may see a warning message saying that the driver is not digitally signed or verified. This is normal and you can ignore it. Just click on "Install this driver software anyway" or "Continue anyway".</p>
-<h4>Step 6: Restart your computer if prompted</h4>
-<p>Some drivers may require you to restart your computer after the installation. If you see a message asking you to restart your computer, click on "Yes" or "Restart Now". This will ensure that the driver is properly installed and activated.</p>
-<h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In this article, we have shown you how to download and install ADATA Classic CH94 driver for Windows 7 91 in two easy ways. You can either download the driver from ADATA official website or from DriverDouble website. Then you can install the driver using the downloaded file or using Device Manager. Both methods are simple and effective.</p>
-<h3>Benefits of using ADATA Classic CH94 driver for Windows 7 91</h3>
-<p>By using ADATA Classic CH94 driver for Windows 7 91, you can enjoy several benefits such as:</p>
-<ul>
-<li>Improved performance and compatibility of your device with your computer.</li>
-<li>Reduced errors and issues when using your device.</li>
-<li>Enhanced features and functions of your device.</li>
-<li>Better security and protection of your data.</li>
-</ul>
-<p>We hope this article has helped you download and install ADATA Classic CH94 driver for Windows 7 91 successfully. If you have any questions or feedback, please feel free to leave a comment below.</p>
- **FAQs** Q: How do I know if my ADATA Classic CH94 driver is up to date? A: You can check if your ADATA Classic CH94 driver is up to date by using Device Manager. To do this, follow these steps: <ol>
-<li>Connect your ADATA Classic CH94 to your computer.</li>
-<li>Open Device Manager and find your device under "Disk drives" or "Other devices".</li>
-<li>Right-click on your device and select "Properties".</li>
-<li>In the Properties window, click on the "Driver" tab.</li>
-<li>You will see the driver version, date, provider, and other information.</li>
-<li>If you see a newer version of the driver available on ADATA official website or DriverDouble website, you can download and install it following the steps in this article.</li>
-</ol>
- Q: How do I uninstall ADATA Classic CH94 driver from my computer? A: You can uninstall ADATA Classic CH94 driver from your computer by using Device Manager. To do this, follow these steps: <ol>
-<li>Connect your ADATA Classic CH94 to your computer.</li>
-<li>Open Device Manager and find your device under "Disk drives" or "Other devices".</li>
-<li>Right-click on your device and select "Uninstall".</li>
-<li>In the confirmation window, check the box that says "Delete the driver software for this device" and click on "OK".</li>
-<li>Restart your computer if prompted.</li>
-</ol>
- Q: How do I format my ADATA Classic CH94 external hard drive? A: You can format your ADATA Classic CH94 external hard drive by using Disk Management. To do this, follow these steps: <ol>
-<li>Connect your ADATA Classic CH94 to your computer.</li>
-<li>Click on the Start button and type "disk management" in the search box.</li><li>Select Disk Management from the list of results.</li><li>In the Disk Management window, look for your external hard drive under the volume list. It may be labeled as "ADATA CH94" or something similar.</li><li>Right-click on your external hard drive and select "Format".</li><li>In the Format window, choose the file system, allocation unit size, volume label, and format options that you want. You can use the default settings or customize them according to your preference.</li><li>Click on "OK" to start the formatting process. Be aware that this will erase all the data on your external hard drive, so make sure you have backed up any important files beforehand.</li><li>Wait until the formatting process is completed. You will see a message saying that the format was successful.</li></ol>
- Q: How do I troubleshoot ADATA Classic CH94 external hard drive problems? A: If you encounter any problems or errors when using your ADATA Classic CH94 external hard drive, you can try some of these troubleshooting tips: <ul><li>Make sure that your external hard drive is securely connected and powered on.</li><li>Make sure that you have installed the correct and updated driver for your external hard drive.</li><li>Make sure that your external hard drive is compatible with your Windows version and system type.</li><li>Make sure that your external hard drive has enough free space and is not corrupted or damaged.</li><li>Try using a different USB port or cable for your external hard drive.</li><li>Try using a different computer for your external hard drive.</li><li>Contact ADATA customer service or technical support for further assistance.</li></ul>
- Q: How do I contact ADATA customer service or technical support? A: You can contact ADATA customer service or technical support by using one of these methods: <ul><li>Email: [email protected]</li><li>Tel: +886-2-82280886 ext.3510~3519 (Monday-Friday 9am-6pm GMT+8)</li><li>Fax: +886-2-82271616 ext.3510~3519 (Monday-Friday 9am-6pm GMT+8)</li><li>WeChat: adatasupport (Monday-Friday 9am-6pm GMT+8)</li></ul>
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- <p>If you are looking for a powerful and versatile 3D software that can help you create stunning animations, models, simulations, and renderings, you might want to check out Maya 2017. Maya 2017 is one of the most popular products of Autodesk, a leading company in the field of design and engineering software. However, to use Maya 2017, you need to have a valid product key that can activate the software. This is where xforce keygen comes in handy. Xforce keygen is a software that can generate product keys for any Autodesk product of 2017 version, including Maya 2017. In this article, we will show you how to download and use xforce keygen to activate Maya 2017 xforce keygen x64 x86.</p>
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- <h2>What is Maya 2017 and what are its features?</h2>
- <h3>Maya 2017 is a 3D animation, modeling, simulation, and rendering software</h3>
- <p>Maya 2017 is a comprehensive software that can help you create amazing 3D content for various purposes, such as games, films, TV shows, advertisements, and more. With Maya 2017, you can create realistic characters, environments, effects, and animations using a range of tools and workflows. You can also import and export data from other software, such as Photoshop, After Effects, Mudbox, MotionBuilder, and more.</p>
- <h3>Maya 2017 has new features such as motion graphics, time editor, Arnold renderer, and more</h3>
- <p>Maya 2017 has introduced some new features that can enhance your creativity and productivity. Some of these features are:</p>
- <ul>
-<li><b>Motion graphics:</b> You can create stunning motion graphics using the new MASH toolset that allows you to generate complex procedural animations with nodes. You can also use the new 3D Type tool to create text and logos with extrusion, beveling, animation, and shading.</li>
-<li><b>Time editor:</b> You can edit your animations in a non-linear way using the new Time Editor that lets you clip, blend, and loop animation tracks. You can also use the Time Editor to retarget animation from one character to another.</li>
-<li><b>Arnold renderer:</b> You can render your scenes with high quality and realism using the new Arnold renderer that is integrated with Maya 2017. Arnold renderer supports features such as global illumination, depth of field, motion blur, subsurface scattering, hair and fur shading, and more.</li>
-<li><b>And more:</b> You can also enjoy other features such as Bifrost Ocean Simulation System that lets you create realistic ocean surfaces with waves, ripples, and foam; XGen Interactive Grooming that lets you style hair and fur with brushes; Live Link with Stingray that lets you preview your scenes in real time with the Stingray game engine; and more.</li>
-</ul>
- <h2>What is xforce keygen and how does it work?</h2>
- <h3>Xforce keygen is a software that generates product keys for Autodesk products</h3>
- <p>Xforce keygen is a software that can help you activate any Autodesk product of 2017 version without paying for a license. Xforce keygen works by generating a unique product key for each Autodesk product based on its serial number and request code. The product key can then be used to activate the software online or offline.</p>
- <h3>Xforce keygen can activate any Autodesk product of 2017 version</h3>
- <p>Xforce keygen can generate product keys for any Autodesk product of 2017 version, such as AutoCAD, Revit, Inventor, Fusion 360, Civil 3D, 3ds Max, <h2>How to download and install Maya 2017 xforce keygen x64 x86?</h2>
- <h3>Download xforce keygen from a reliable source</h3>
- <p>To download xforce keygen for Maya 2017, you need to find a reliable source that offers the software for free. You can search online for xforce keygen 2017 and choose a website that has positive reviews and ratings. You can also use the links provided by some of the web search results   . However, you need to be careful and avoid downloading any malware or virus that might harm your computer. You should also scan the downloaded file with an antivirus software before opening it.</p>
- <h3>Install xforce keygen on your computer</h3>
- <p>To install xforce keygen on your computer, you need to follow these steps:</p>
- <ol>
-<li>Extract the downloaded file using a software such as WinRAR or 7-Zip.</li>
-<li>Open the extracted folder and find the file named xf-adsk2017_x64.exe or xf-adsk2017_x86.exe depending on your system architecture.</li>
-<li>Right-click on the file and choose Run as administrator.</li>
-<li>Click on Yes if prompted by User Account Control.</li>
-<li>Wait for the installation to complete and close the window.</li>
-</ol>
- <h2>How to use xforce keygen to activate Maya 2017?</h2>
- <h3>Run xforce keygen as administrator</h3>
- <p>To run xforce keygen as administrator, you need to follow these steps:</p>
- <ol>
-<li>Go to the folder where you installed xforce keygen and find the file named xf-adsk2017_x64.exe or xf-adsk2017_x86.exe depending on your system architecture.</li>
-<li>Right-click on the file and choose Run as administrator.</li>
-<li>Click on Yes if prompted by User Account Control.</li>
-<li>You will see a window with a list of Autodesk products and a button named Patch.</li>
-</ol>
- <h3>Select Maya 2017 from the product list and copy the product key</h3>
- <p>To select Maya 2017 from the product list and copy the product key, you need to follow these steps:</p>
- <ol>
-<li>In the window of xforce keygen, scroll down and find Maya 2017 from the product list. The product key for Maya 2017 is 657I1.</li>
-<li>Click on the product name and copy the product key by pressing Ctrl + C or right-clicking and choosing Copy.</li>
-<li>You will need this product key later when you activate Maya 2017.</li>
-</ol>
- <h3>Finish the installation of Maya 2017 and restart it</h3>
- <p>To finish the installation of Maya 2017 and restart it, you need to follow these steps:</p>
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- <ol>
-<li>If you have not installed Maya 2017 yet, you can download it from the official website of Autodesk or from other sources. You can also use a trial version of Maya 2017 if you do not have a license.</li>
-<li>Run the installer of Maya 2017 and follow the instructions on the screen. When asked to enter a serial number, enter anything you want or use this one: 666-69696969.</li>
-<li>When asked to enter a product key, paste the product key that you copied from xforce keygen by pressing Ctrl + V or right-clicking and choosing Paste. The product key for Maya 2017 is 657I1.</li>
-<li>Complete the installation process and restart Maya 2017.</li>
-</ol>
- <h3>Click on activate and paste the product key generated by xforce keygen</h3>
- <p>To click on activate and paste the product key generated by xforce keygen, you need to follow these steps:</p>
- <ol>
-<li>When you restart Maya 2017, you will see a window that asks you to activate your product. Click on Activate.</li>
-<li>You will see another window that asks you to connect to the internet or enter an activation code. Choose I have an activation code from Autodesk.</li>
-<li>Go back to the window of xforce keygen and click on Patch. You will see a message that says Successfully patched. If not, make sure you run xforce keygen as administrator and try again.</li>
-<li>In the window of xforce keygen, click on Generate. You will see a long code in the Activation field. Copy this code by pressing Ctrl + C or right-clicking and choosing Copy.</li>
-<li>Go back to the window of Maya 2017 activation and paste the code in the Activation field by pressing Ctrl + V or right-clicking and choosing Paste. Make sure you fill all the boxes with the code.</li>
-<li>Click on Next. You will see a message that says Thank you for activating your Autodesk product. Click on Finish.</li>
-</ol>
- <h2>Conclusion and FAQs</h2>
- <p>In this article, we have shown you how to download and use xforce keygen to activate Maya 2017 xforce keygen x64 x86. Xforce keygen is a software that can generate product keys for any Autodesk product of 2017 version, including Maya 2017. Maya 2017 is a powerful and versatile 3D software that can help you create stunning animations, models, simulations, and renderings. However, using xforce keygen is illegal and unethical, as it violates the terms and conditions of Autodesk. Therefore, we do not recommend using xforce keygen for any purpose other than educational or testing purposes. If you want to use Maya 2017 legally and ethically, you should buy a license from Autodesk or use other alternatives such as Blender or SketchUp.</p>
- <p>If you have any questions about using xforce keygen or Maya 2017, here are some FAQs that might help you:</p>
- <ul>
-<li><b>Q: Is xforce keygen safe to use?</b></li>
-<li>A: Xforce keygen is not safe to use, as it might contain malware or virus that can harm your computer or steal your personal information. You should also scan any file that you download from online sources with an antivirus software before opening it.</li>
-<li><b>Q: Is xforce keygen legal to use?</b></li>
-<li>A: Xforce keygen is not legal to use, as it violates the terms and conditions of Autodesk. Using xforce keygen can result in legal consequences such as fines or lawsuits. You should also respect the intellectual property rights of Autodesk and its developers.</li>
-<li><b>Q: Is there any alternative to xforce keygen?</b></li>
-the terms and conditions of Autodesk. Some of these alternatives are:</li>
-<ul>
-<li>Universal Xforce Keygen: This is a software that can generate product keys for any Autodesk product of any version. However, this software is also risky and unreliable, as it might not work properly or contain malware.</li>
-<li>KMSpico: This is a software that can activate Windows and Office products, as well as some Autodesk products. However, this software is also dangerous and illegal, as it might damage your system or expose you to cyberattacks.</li>
-<li>Crack files: These are files that can modify or replace the original files of Autodesk products to bypass the activation process. However, these files are also harmful and unlawful, as they might corrupt your data or infect your computer with virus.</li>
-</ul>
-<li><b>Q: What are some alternatives to Maya 2017?</b></li>
-<li>A: There are some alternatives to Maya 2017 that can help you create 3D content without paying for a license. However, these alternatives might not have the same features or quality as Maya 2017. Some of these alternatives are:</li>
-<ul>
-<li>Blender: This is a free and open source 3D software that can help you create animations, models, simulations, renderings, games, and more. Blender has a large and active community that supports and develops the software.</li>
-<li>SketchUp: This is a free and easy to use 3D software that can help you create models, designs, drawings, and more. SketchUp has a simple and intuitive interface that allows you to create 3D content quickly and efficiently.</li>
-<li>Cinema 4D: This is a paid but affordable 3D software that can help you create animations, models, simulations, renderings, motion graphics, and more. Cinema 4D has a user-friendly and flexible interface that allows you to customize and optimize your workflow.</li>
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-<h1>Fight Night Round 4 PC Game Free Download</h1>
- <h2>Introduction</h2>
- <p>Do you love boxing games? Do you want to experience the thrill of fighting as some of the greatest boxers of all time? If you answered yes, then you might be interested in playing Fight Night Round 4 on your PC.</p>
- <h3>What is Fight Night Round 4?</h3>
- <p>Fight Night Round 4 is a boxing video game developed by EA Sports and released in 2009 for PlayStation 3 and Xbox 360. It is the sequel to Fight Night Round 3, which was released in 2006. It features a mix of today's best and the greatest of all time, with over 40 licensed boxers to choose from. You can leave your mark on the sport by moving up the rankings as an unknown fighter, pumping up your popularity, and dominating fighters in multiple weight classes on the way to becoming a legend. You can also challenge other players online or offline in various modes, such as exhibition, career, legacy, or world championship.</p>
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- <h3>Why play Fight Night Round 4 on PC?</h3>
- <p>While Fight Night Round 4 is a great game on its own, playing it on PC can offer some advantages over playing it on consoles. For example, you can enjoy better graphics, faster loading times, smoother gameplay, and more customization options. You can also use your preferred controller or keyboard and mouse to play the game. Moreover, you can access mods and cheats that can enhance your gaming experience or make it more fun.</p>
- <h2>How to download Fight Night Round 4 for PC?</h2>
- <p>Unfortunately, Fight Night Round 4 is not officially available for PC. However, there are still ways to play it on your computer. Here are two options that you can try:</p>
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- <h3>Option 1: Use an emulator</h3>
- <h4>What is an emulator?</h4>
- <p>An emulator is a software that allows you to run games or applications designed for one system on another system. For example, you can use an emulator to run PlayStation 3 games on your PC.</p>
- <h4>How to use an emulator to play Fight Night Round 4?</h4>
- <p>One of the most popular emulators for PlayStation 3 games is RPCS3. It is a free and open-source emulator that can run many PS3 games with high compatibility and performance. To use RPCS3 to play Fight Night Round 4, you need to follow these steps:</p>
- <ol>
-<li>Download and install RPCS3 from its official website: https://rpcs3.net/</li>
-<li>Download and install the PS3 firmware from the same website: https://rpcs3.net/quickstart</li>
-<li>Download or rip the ISO file of Fight Night Round 4 from your PS3 disc or online source.</li>
-<li>Launch RPCS3 and click on File > Boot Game.</li>
-<li>Select the ISO file of Fight Night Round 4 and click on Open.</li>
-<li>Wait for the game to load and enjoy!</li>
-</ol>
- <p>Note: You may need to adjust some settings in RPCS3 to optimize the game's performance and compatibility. You can check the official wiki for more information: https://wiki.rpcs3.net/index.php?title=Help:Game_Patches</p>
- <h3>Option 2: Use a torrent site</h3>
- <h4>What is a torrent site?</h4>
- <p>A torrent site is a website that hosts torrent files, which are small files that contain information about larger files that can be downloaded from other users through a peer-to-peer network. For example, you can use a torrent site to download movies, music, games, or software.</p>
- <h4>How to use a torrent site to download Fight Night Round 4?</h4>
- <p>To use a torrent site to download Fight Night Round 4 for PC, you need to follow these steps:</p>
- <ol>
-<li>Download and install a torrent client, such as uTorrent or BitTorrent: https://www.utorrent.com/ or https://www.bittorrent.com/</li>
-<li>Go to a torrent site that has Fight Night Round 4 for PC, such as The Pirate Bay: https://thepiratebay.org/</li>
-<li>Search for "Fight Night Round 4 PC" and find a torrent that has good ratings and comments.</li>
-<li>Click on the magnet link or download the torrent file and open it with your torrent client.</li>
-<li>Wait for the download to finish and open the folder where the game files are stored.</li>
-<li>Follow the instructions in the readme file or crack folder to install and run the game.</li>
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- <p>Note: Downloading games from torrent sites may be illegal in some countries and may expose you to viruses or malware. Use this option at your own risk.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In this article, we have discussed what Fight Night Round 4 is, why you might want to play it on PC, and how you can download it for PC using two options: an emulator or a torrent site. We have also provided some links and tips to help you with each option.</p>
- <h3>Call to action</h3>
- <p>If you are a fan of boxing games and want to play Fight Night Round 4 on your PC, we hope this article has been helpful for you. Now it's time for you to choose your option and start downloading the game. Don't forget to share this article with your friends who might also be interested in playing Fight Night Round 4 on PC. Have fun!</p>
- <table>
-<tr><th colspan="2">FAQs</th></tr>
-<tr><td><b>Q: Is Fight Night Round 4 compatible with Windows 10?</b></td><td><b>A: Yes, both options should work fine with Windows 10.</b></td></tr>
-<tr><td><b>Q: Can I play Fight Night Round 4 online with other players?</b></td><td><b>A: Yes, if you use an emulator, you can use its online features as long as you have a valid PSN account. If you use a torrent site, you may need to use a VPN or a LAN software to play online with other players.</b></td></tr>
-<tr><td><b>Q: What are some other boxing games that I can play on PC?</b></td><td><b>A: Some other boxing games that you can play on PC are Real Boxing, Creed: Rise to Glory, Punch Club, Boxing School, and Knockout League.</b></td></tr>
-<tr><td><b>Q: What are some other emulators that I can use to play PS3 games on PC?</b></td><td><b>A: Some other emulators that you can use to play PS3 games on PC are ESX PS3 Emulator (https://esxemulator.com/) and PS Now (https://www.playstation.com/en-us/ps-now/).</b></td></tr>
-<tr><td><b>Q: What are some other torrent sites that I can use to download games for PC?</b></td><td><b>A: Some other torrent sites that you can use to download games for PC are RARBG (https://rarbg.to/), Kickass Torrents (https://katcr.co/), LimeTorrents (https://www.limetorrents.info/), and Torrentz2 (https://torrentz2.eu/).</b></td></tr>
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spaces/1gistliPinn/ChatGPT4/Examples/Audio Amplifier Pro Serial LINK.md

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-<h1>Audio Amplifier Pro Serial: How to Boost and Normalize Your Audio & Video Files</h1>
-<p>If you are looking for a simple and effective tool to adjust the volume of your audio and video files, you may want to try Audio Amplifier Pro. This software allows you to increase or decrease the volume of any audio or video file, without affecting the quality or the format. You can also normalize all your files to the maximum or average volume level, to avoid clipping or distortion.</p>
-<p>In this article, we will show you how to use Audio Amplifier Pro Serial to enhance your audio and video experience. We will also explain how to crack and register the full version of the software, so you can enjoy all its features and benefits.</p>
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-<h2>What is Audio Amplifier Pro?</h2>
-<p>Audio Amplifier Pro is a software that works with all key audio and video formats, such as MP3, WAV, WMA, OGG, FLAC, AVI, MP4, WMV, MOV, MKV, etc. It supports batch processing, so you can adjust the volume of multiple files at once. It has a user-friendly interface that makes it easy to use for anyone.</p>
-<p>Audio Amplifier Pro has two main functions: boost and normalize. Boosting means increasing or decreasing the volume of a file by a certain percentage or decibel. Normalizing means setting all files to a certain volume level, either maximum or average. Both functions can help you improve the sound quality of your files, especially if they are too low or too high in volume.</p>
-<h2>How to Use Audio Amplifier Pro Serial?</h2>
-<p>To use Audio Amplifier Pro Serial, you need to download and install the software from its official website or from a trusted source. Then, you need to copy and paste the serial key into the registration window, to activate the full version of the software. The serial key is usually provided by the crack file or by the online generator.</p>
-<p>Once you have registered the software, you can start using it to adjust the volume of your audio and video files. Here are the steps to follow:</p>
-<ol>
-<li>Launch Audio Amplifier Pro and click on the "Add Files" button to browse and select the files you want to process.</li>
-<li>Choose whether you want to boost or normalize the volume of your files. If you choose boost, you can enter the percentage or decibel value you want to increase or decrease by. If you choose normalize, you can select either maximum or average as the target volume level.</li>
-<li>Click on the "Save Changes" button to apply the volume adjustment to your files. You can choose whether you want to overwrite the original files or save them as new files in a different folder.</li>
-<li>Enjoy your enhanced audio and video files!</li>
-</ol>
-<h2>Why Choose Audio Amplifier Pro Serial?</h2>
-<p>There are many reasons why you may want to choose Audio Amplifier Pro Serial as your preferred tool for volume adjustment. Here are some of them:</p>
-<p></p>
-<ul>
-<li>It is simple and easy to use. You don't need any special skills or experience to use it.</li>
-<li>It works with all key audio and video formats. You don't need to convert your files before or after processing them.</li>
-<li>It preserves the original quality and format of your files. You don't need to worry about losing any data or information.</li>
-<li>It supports batch processing. You can save time and effort by processing multiple files at once.</li>
-<li>It offers both boost and normalize functions. You can choose the best option for your needs and preferences.</li>
-<li>It is affordable and reliable. You can get the full version of the software for free by using a serial key.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Audio Amplifier Pro Serial is a great software that can help you boost and normalize the volume of your audio and video files. It is simple, easy, fast, and effective. It works with all key audio and video formats, without affecting their quality or format. It supports batch processing, so you can process multiple files at once. It offers both boost and normalize functions, so you can choose the best option for your needs and preferences.</p>
-<p>If you want to enhance your audio and video experience, you should try Audio Amplifier Pro Serial today. You can download it from its official website or from a trusted source. You can also crack and register it for free by using a serial key. You will be amazed by how much difference it can make in your sound quality.</p>
-<h2>How to Download Audio Amplifier Pro Serial?</h2>
-<p>If you want to download Audio Amplifier Pro Serial, you have two options: you can either download it from its official website or from a trusted source. The official website offers a free trial version of the software, which you can use for a limited time and with some restrictions. You can also buy the full version of the software from the official website, which costs $29.95.</p>
-<p>However, if you don't want to spend money on the software, you can also download it from a trusted source, such as 4DOWNLOAD, AbbasPC, or OpenSea. These sources provide you with the crack file or the serial key generator, which you can use to activate the full version of the software for free. You just need to follow the instructions provided by these sources to download and install the software.</p>
-<h2>What are the Benefits of Audio Amplifier Pro Serial?</h2>
-<p>Audio Amplifier Pro Serial has many benefits that make it worth downloading and using. Here are some of them:</p>
-<ul>
-<li>It can help you improve the sound quality of your audio and video files, especially if they are too low or too high in volume.</li>
-<li>It can help you normalize all your files to the same volume level, either maximum or average, to avoid clipping or distortion.</li>
-<li>It can help you save disk space and bandwidth by reducing the size of your audio and video files.</li>
-<li>It can help you edit and customize your audio and video files by trimming, cropping, merging, splitting, adding effects, etc.</li>
-<li>It can help you convert your audio and video files to different formats, such as MP3, WAV, WMA, OGG, FLAC, AVI, MP4, WMV, MOV, MKV, etc.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Audio Amplifier Pro Serial is a great software that can help you boost and normalize the volume of your audio and video files. It is simple, easy, fast, and effective. It works with all key audio and video formats, without affecting their quality or format. It supports batch processing, so you can process multiple files at once. It offers both boost and normalize functions, so you can choose the best option for your needs and preferences.</p>
-<p>If you want to enhance your audio and video experience, you should try Audio Amplifier Pro Serial today. You can download it from its official website or from a trusted source. You can also crack and register it for free by using a serial key. You will be amazed by how much difference it can make in your sound quality.</p>
-<h2>How to Troubleshoot Audio Amplifier Pro Serial?</h2>
-<p>Sometimes, you may encounter some problems or errors when using Audio Amplifier Pro Serial. For example, you may get a message saying that the serial key is invalid or expired, or that the software cannot load or process your files. In such cases, you need to troubleshoot the issue and find a solution.</p>
-<p>Here are some common troubleshooting tips for Audio Amplifier Pro Serial:</p>
-<ul>
-<li>Make sure you have downloaded and installed the latest version of the software from its official website or from a trusted source.</li>
-<li>Make sure you have entered the correct serial key into the registration window, without any typos or spaces.</li>
-<li>Make sure you have a stable internet connection and that your firewall or antivirus software is not blocking the software.</li>
-<li>Make sure your audio and video files are not corrupted or damaged, and that they are compatible with the software.</li>
-<li>Make sure you have enough disk space and memory to run the software and process your files.</li>
-<li>If none of the above tips work, you can contact the customer support team of the software or visit their online forum for more help.</li>
-</ul>
-<h2>What are the Alternatives to Audio Amplifier Pro Serial?</h2>
-<p>Audio Amplifier Pro Serial is not the only software that can help you boost and normalize the volume of your audio and video files. There are many other alternatives that offer similar or different features and benefits. Here are some of them:</p>
-<ul>
-<li>Sound Booster: This is a software that can increase the volume of any program or browser on your computer, up to 500%. It can also amplify the sound of your speakers or headphones.</li>
-<li>FxSound Enhancer: This is a software that can improve the sound quality of your audio and video files, by enhancing the bass, treble, clarity, fidelity, and surround sound. It can also optimize your sound for different devices and scenarios.</li>
-<li>MP3Gain: This is a software that can normalize the volume of your MP3 files, without affecting their quality or format. It can also analyze and adjust the loudness of your files according to different standards.</li>
-<li>GiliSoft Video Editor: This is a software that can edit and customize your video files, by trimming, cropping, merging, splitting, adding effects, subtitles, watermarks, etc. It can also adjust the volume of your video files.</li>
-<li>Audacity: This is a software that can record and edit your audio files, by cutting, copying, pasting, mixing, applying effects, filters, noise reduction, etc. It can also normalize the volume of your audio files.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>Audio Amplifier Pro Serial is a great software that can help you boost and normalize the volume of your audio and video files. It is simple, easy, fast, and effective. It works with all key audio and video formats, without affecting their quality or format. It supports batch processing, so you can process multiple files at once. It offers both boost and normalize functions, so you can choose the best option for your needs and preferences.</p>
-<p>If you want to enhance your audio and video experience, you should try Audio Amplifier Pro Serial today. You can download it from its official website or from a trusted source. You can also crack and register it for free by using a serial key. You will be amazed by how much difference it can make in your sound quality.</p>
-<h2>Conclusion</h2>
-<p>Audio Amplifier Pro Serial is a great software that can help you boost and normalize the volume of your audio and video files. It is simple, easy, fast, and effective. It works with all key audio and video formats, without affecting their quality or format. It supports batch processing, so you can process multiple files at once. It offers both boost and normalize functions, so you can choose the best option for your needs and preferences.</p>
-<p>If you want to enhance your audio and video experience, you should try Audio Amplifier Pro Serial today. You can download it from its official website or from a trusted source. You can also crack and register it for free by using a serial key. You will be amazed by how much difference it can make in your sound quality.</p> 3cee63e6c2<br />
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spaces/1phancelerku/anime-remove-background/Comment jouer Jeux Ludo Master APK sur votre smartphone ou tablette.md

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-<h1>Jeux Ludo Master APK: How to Play the Classic Board Game on Your Android Device</h1>
-<p>Do you love playing board games with your friends and family? Do you want to enjoy a fun and exciting game of ludo on your smartphone or tablet? If you answered yes, then you should try <strong>Jeux Ludo Master APK</strong>, a cross-platform multiplayer ludo game that lets you play with up to six players online or offline. In this article, we will show you how to download and install Ludo Master APK, how to play it with your loved ones, and some tips and tricks to win more games. Let's get started!</p>
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-<h2>Introduction</h2>
-<h3>What is Ludo Master?</h3>
-<p>Ludo Master is a ludo board game app developed by Hippo Lab. It is based on the classic Indian game of Pachisi, which originated in the 6th century AD. The game involves four players, each with four tokens of the same color, who compete to move their tokens from their starting corner to the center of the board. The movement of the tokens is determined by rolling a six-sided dice. The game is simple to learn but challenging to master, as it requires both strategy and luck.</p>
-<h3>Why play Ludo Master?</h3>
-<p>Ludo Master is one of the best ludo games available on Android devices. Here are some of the reasons why you should play it:</p>
-<ul>
-<li>It has stunning graphics and sound effects that make the game more immersive and realistic.</li>
-<li>It has various game modes, such as Classic, Quick, Master, and Magic, that offer different levels of difficulty and fun.</li>
-<li>It has online and offline modes, so you can play with your friends and family anytime, anywhere.</li>
-<li>It has chat and emoji features, so you can communicate and express yourself with your opponents.</li>
-<li>It has leaderboards and achievements, so you can track your progress and compete with other players around the world.</li>
-</ul>
-<h2>How to download and install Ludo Master APK</h2>
-<h3>Step 1: Go to the Google Play Store or APKCombo</h3>
-<p>To download and install Ludo Master APK, you have two options. You can either go to the Google Play Store or APKCombo, which are both reliable sources of Android apps. If you choose the Google Play Store, you will need a Google account to access it. If you choose APKCombo, you will need to enable unknown sources on your device settings to install apps from outside the Play Store.</p>
-<h3>Step 2: Search for Ludo Master and tap on the app</h3>
-<p>Once you are on the Google Play Store or APKCombo, search for "Ludo Master" in the search bar. You will see a list of results that match your query. Tap on the app that has the logo of a red dice with four colored tokens. This is the official app of Ludo Master by Hippo Lab.</p>
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-<h3>Step 3: Click on Install or Download <h3>Step 3: Click on Install or Download APK</h3>
-<p>If you are on the Google Play Store, you will see a green button that says "Install". Click on it and wait for the app to download and install on your device. If you are on APKCombo, you will see a blue button that says "Download APK". Click on it and save the file to your device. Then, locate the file and tap on it to install the app.</p>
-<h3>Step 4: Follow the instructions on your screen</h3>
-<p>After installing the app, you will see an icon of Ludo Master on your home screen or app drawer. Tap on it to launch the app. You will be asked to grant some permissions, such as access to your contacts, storage, and location. Allow them to enjoy the full features of the app. You will also be asked to sign in with your Facebook account or play as a guest. Choose the option that suits you best. You are now ready to play Ludo Master!</p>
-<h2>How to play Ludo Master with your friends and family</h2>
-<h3>Step 1: Launch the app and choose your game mode</h3>
-<p>When you open the app, you will see four game modes: Classic, Quick, Master, and Magic. Classic mode is the traditional ludo game with four players and normal rules. Quick mode is a faster version of ludo with two players and fewer tokens. Master mode is a more challenging version of ludo with six players and special rules. Magic mode is a fun version of ludo with four players and power-ups. Choose the game mode that you prefer and tap on it.</p>
-<h3>Step 2: Invite your friends or join a random room</h3>
-<p>After choosing your game mode, you will see two options: Play with Friends or Play Online. If you want to play with your friends or family, tap on Play with Friends. You will see a code that you can share with them via WhatsApp, Messenger, or other apps. They will need to enter the code to join your room. You can also create a private room with a password if you want more security. If you want to play with strangers, tap on Play Online. You will be matched with other players who are online and looking for a game.</p>
-<h3>Step 3: Roll the dice and move your tokens</h3>
-<p>Once you are in a room with other players, you will see the ludo board and your tokens on the screen. You will also see a dice on the bottom right corner. Tap on it to roll it and see how many steps you can move your tokens. You can only move your tokens out of their base if you roll a six. You can also move your tokens forward by the number of steps shown on the dice. If you land on a square that already has another token, you can capture it and send it back to its base. If you land on a star square, you can get a power-up that can help you in the game.</p>
-<h3>Step 4: Be the first to reach the center of the board</h3>
-<p>The goal of the game is to be the first player to move all four of your tokens from their base to the center of the board. To do this, you need to roll the exact number of steps required to reach the center. For example, if your token is three steps away from the center, you need to roll a three to move it there. If you roll more than three, you cannot move your token and have to wait for another turn. The first player who reaches the center with all four tokens wins the game.</p>
-<h2>Tips and tricks to win Ludo Master games</h2>
-<h3>Tip 1: Use strategy and luck to your advantage</h3>
-<p>Ludo Master is a game that combines both strategy and luck. You need to use both of them to win more games. For example, you need to decide which token to move based on the situation of the board and your opponents. You also need to take risks sometimes and hope for a good roll of the dice. You can also use some math skills to calculate the probability of rolling certain numbers.</p>
-<h3>Tip 2: Avoid getting captured by your opponents</h3>
-<p>One of the most frustrating things in Ludo Master is getting captured by your opponents and losing your progress. To avoid this, you need to be careful where you place your tokens and how you move them. Try not to land on squares that are close to your opponents' bases or paths. Also, try not to leave your tokens alone or exposed on the board. Instead, try to form pairs or groups with your own tokens or allies' tokens for protection.</p>
-<h3>Tip 3: Use power-ups and boosters [user <h3>Tip 3: Use power-ups and boosters to enhance your gameplay</h3>
-<p>Ludo Master is not just a plain ludo game. It also has some special features that can make the game more fun and exciting. One of them is the power-ups, which are items that you can get by landing on star squares. There are four types of power-ups: Shield, Swap, Double, and Bomb. Shield protects your token from being captured for one turn. Swap lets you switch places with another token on the board. Double lets you roll the dice twice in one turn. Bomb lets you explode a nearby token and send it back to its base. You can use these power-ups wisely to gain an edge over your opponents.</p>
-<p>Another feature is the boosters, which are items that you can buy with coins or gems. There are three types of boosters: Dice, Token, and Board. Dice boosters let you choose the number you want to roll on the dice. Token boosters let you move your token faster or skip some steps. Board boosters let you change the color or shape of the board. You can use these boosters sparingly to enhance your gameplay.</p>
-<h2>Conclusion</h2>
-<p>Ludo Master is a great app for anyone who loves playing ludo games. It is easy to download and install, and it offers various game modes and features that make the game more enjoyable and challenging. You can play Ludo Master with your friends and family online or offline, and chat and interact with them during the game. You can also learn some tips and tricks to win more games and improve your skills. Ludo Master is a game that can bring you hours of fun and entertainment. Download it now and start playing!</p>
-<h2>FAQs</h2>
-<h4>Q: Is Ludo Master free to play?</h4>
-<p>A: Yes, Ludo Master is free to play. However, it also has some in-app purchases that can enhance your gameplay, such as coins, gems, boosters, and VIP membership.</p>
-<h4>Q: How can I play Ludo Master on my PC?</h4>
-<p>A: You can play Ludo Master on your PC by using an Android emulator, such as BlueStacks or NoxPlayer. These are software that allow you to run Android apps on your PC.</p>
-<h4>Q: How can I contact the developer of Ludo Master?</h4>
-<p>A: You can contact the developer of Ludo Master by sending an email to [email protected] or by visiting their Facebook page. You can also leave a review or feedback on the Google Play Store or APKCombo.</p>
-<h4>Q: How can I update Ludo Master to the latest version?</h4>
-<p>A: You can update Ludo Master to the latest version by going to the Google Play Store or APKCombo and checking for updates. You can also enable automatic updates on your device settings to get the latest version automatically.</p>
-<h4>Q: How can I uninstall Ludo Master from my device?</h4>
-<p>A: You can uninstall Ludo Master from your device by going to your device settings and finding the app in the list of installed apps. Then, tap on it and choose Uninstall. You can also long-press on the app icon on your home screen or app drawer and drag it to the Uninstall option.</p> 401be4b1e0<br />
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spaces/1phancelerku/anime-remove-background/Download Summertime Saga for PC in ZIP Format - Free and Easy.md

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-<h1>How to Download and Install Summertime Saga Zip File on Your PC</h1>
- <h2>Introduction</h2>
-- What is Summertime Saga and why is it popular? - What are the benefits of downloading the zip file version of the game? - What are the requirements and steps to download and install the game on your PC? <h2>What is Summertime Saga and Why is it Popular?</h2>
-- Summertime Saga is a graphical adventure game for adults that features a rich story, diverse characters, and multiple choices. - The game is set in a small town where the protagonist has to deal with various challenges, such as school, romance, mysteries, and secrets. - The game has over 65 characters to interact with, 30 locations to explore, and 20 mini-games to play. - The game is popular because of its humor, quality, and content. It has a loyal fan base and receives regular updates from the developers. <h2>What are the Benefits of Downloading the Zip File Version of the Game?</h2>
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-- The requirements to download and install the game on your PC are:   - A Windows PC with at least 2 GB of RAM and 2 GB of free disk space.   - A stable internet connection.   - A web browser that supports downloading files. - The steps to download and install the game on your PC are:   - Go to [Summertime Saga 0.20 - Download for PC Free - Malavida](^1^) and click on the green "Download" button.   - Choose a location to save the zip file on your PC and wait for the download to finish.   - Once the download is complete, locate the zip file on your PC and right-click on it. Select "Extract All" from the menu and choose a destination folder for the extracted files.   - Open the destination folder and double-click on the "SummertimeSaga.exe" file to launch the game.   - Enjoy playing Summertime Saga on your PC! <h2>Conclusion</h2>
-- Summertime Saga is a fun and entertaining graphical adventure game for adults that you can download and play on your PC. - By downloading the zip file version of the game, you can save space, time, and hassle. - To download and install the game on your PC, you just need to follow a few simple steps that we have outlined in this article. <h2>FAQs</h2>
-<h3>Q: Is Summertime Saga free to play?</h3>
-<h4>A: Yes, Summertime Saga is free to play. You can download it from its official website or from other sources. However, you can also support the developers by donating or becoming a patron.</h4>
- <h3>Q: Is Summertime Saga safe to download?</h3>
-<h4>A: Yes, Summertime Saga is safe to download. However, you should always download it from trusted sources and scan it with an antivirus program before installing it. Also, be careful of any pop-ups or ads that may appear while downloading or playing the game.</h4>
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-<h4>A: Summertime Saga gets updated regularly by its developers. The latest version of the game is 0.20, which was released in November 2022. You can check for updates on its official website or social media pages.</h4>
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-<h4>A: You can save your progress in Summertime Saga by using the in-game menu. You can access it by clicking on the phone icon at the top right corner of the screen. You can then choose to save or load your game from one of the available slots. You can also use the auto-save feature that saves your game automatically every time you change locations or complete an event.</h4>
- <h3>Q: How can I unlock more content and features in Summertime Saga?</h3>
-<h4>A: You can unlock more content and features in Summertime Saga by completing quests, exploring locations, interacting with characters, and making choices. You can also use cheats or mods to access hidden or extra content, but be aware that this may cause bugs or errors in the game.</h4>
- <h2></h2></p>
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spaces/1phancelerku/anime-remove-background/Enjoy Stick War Legacy with MOD APK Zip (All Modes All Weapons 999 Army).md

@@ -1,143 +0,0 @@
-<br />
-<h1>Stick War Legacy Mod APK Zip: A Guide for Gamers</h1>
-<p>If you are a fan of strategy games, you might have heard of <strong>Stick War Legacy</strong>, a popular and challenging game that lets you control an army of stickmen in a battle for supremacy. But did you know that there is a way to make the game even more fun and exciting? That's right, we are talking about <strong>Stick War Legacy Mod APK Zip</strong>, a modified version of the game that gives you unlimited resources and army to dominate your enemies. In this article, we will tell you everything you need to know about Stick War Legacy Mod APK Zip, including what it is, how to download and install it, what are its benefits and drawbacks, what are some alternatives, and what are some reviews and FAQs about it. So, without further ado, let's get started!</p>
-<h2>stick war legacy mod apk zip</h2><br /><p><b><b>DOWNLOAD</b> &#10037;&#10037;&#10037; <a href="https://jinyurl.com/2uNLea">https://jinyurl.com/2uNLea</a></b></p><br /><br />
- <h2>What is Stick War Legacy?</h2>
-<h3>A popular and challenging strategy game</h3>
-<p>Stick War Legacy is a game for iOS and Android devices that allows you to control an army of stickmen in a battle for supremacy. The game features a campaign mode with over 50 levels to play through, as well as a multiplayer mode where you can take on other players from around the world. The gameplay in Stick War Legacy is simple but addictive. You have to build your army, mine gold, learn the way of the sword, spear, archer, mage, and giant, destroy the enemy statue, and capture all territories. You can also customize your units with different weapons and skins, and use spells to boost your performance. The game has amazing graphics and sound effects that make the battles more realistic and immersive.</p>
- <h3>Features and game modes of Stick War Legacy</h3>
-<p>Stick War Legacy has many features that make it one of the best strategy games on mobile devices. Some of these features are:</p>
-<ul>
-<li><strong>Missions Mode:</strong> New levels released every Friday that challenge your skills and strategy.</li>
-<li><strong>Saga Style Map:</strong> A map with multiple rewards and secrets to discover.</li>
-<li><strong>Crowns:</strong> Unlock crowns for each difficulty level (Normal, Hard, Insane) and show off your achievements.</li>
-<li><strong>Skins:</strong> Skins are now available for all characters. Unlock powerful weapons and armor, each with their own unique perks.</li>
-<li><strong>Spells:</strong> Spells can help you turn the tide of battle. Use them wisely to gain an advantage over your enemies.</li>
-</ul>
-<p>Stick War Legacy also has four different game modes that offer different challenges and experiences. These are:</p>
-<ul>
-<li><strong>Campaign Mode:</strong> The standard story mode where you have to complete a series of missions to progress through the story.</li>
-<li><strong>Crown of Inamorta:</strong> A round-based tournament where you have to defeat other nations in order to win the crown.</li>
-<li><strong>Endless Deads:</strong> A zombie survival mode where you have to defend your base from endless waves of undead.</li>
-<li><strong>Weekly Missions:</strong> A condition-based mission mode where you have to complete specific objectives in order to win.</li>
-</ul>
- <h2>What is Stick War Legacy Mod APK Zip?</h2>
-<h3>A modified version of the game with unlimited resources and army</h <h3>A modified version of the game with unlimited resources and army</h3>
-<p>Stick War Legacy Mod APK Zip is a modified version of the game that gives you unlimited resources and army to dominate your enemies. By downloading and installing this mod apk file, you can enjoy the game with more freedom and fun. You can unlock all the weapons and skins, upgrade your units to the max level, and recruit as many soldiers as you want. You can also use unlimited gems to buy spells and items that can help you in the battle. With Stick War Legacy Mod APK Zip, you can experience the game in a whole new way.</p>
- <h3>Benefits and drawbacks of using Stick War Legacy Mod APK Zip</h3>
-<p>Using Stick War Legacy Mod APK Zip has its benefits and drawbacks. Here are some of them:</p>
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-<table>
-<tr>
-<th>Benefits</th>
-<th>Drawbacks</th>
-</tr>
-<tr>
-<td>- You can enjoy the game without any limitations or restrictions.</td>
-<td>- You might lose the challenge and thrill of the game.</td>
-</tr>
-<tr>
-<td>- You can customize your army with different weapons and skins.</td>
-<td>- You might encounter some bugs or glitches in the game.</td>
-</tr>
-<tr>
-<td>- You can use unlimited gems to buy spells and items.</td>
-<td>- You might get banned from the multiplayer mode or the game itself.</td>
-</tr>
-<tr>
-<td>- You can recruit as many soldiers as you want.</td>
-<td>- You might harm your device with viruses or malware.</td>
-</tr>
-</table>
- <h2>How to download and install Stick War Legacy Mod APK Zip?</h2>
-<h3>Steps to download and install the mod apk file</h3>
-<p>If you want to download and install Stick War Legacy Mod APK Zip, you need to follow these steps:</p>
-<ol>
-<li>First, you need to find a reliable source for downloading the mod apk file. You can use one of these links:  or . Make sure you have enough storage space on your device before downloading.</li>
-<li>Second, you need to enable the installation of unknown sources on your device. To do this, go to Settings > Security > Unknown Sources and toggle it on. This will allow you to install apps from sources other than the Google Play Store.</li>
-<li>Third, you need to locate the downloaded mod apk file on your device. You can use a file manager app to find it in your Downloads folder or wherever you saved it.</li>
-<li>Fourth, you need to tap on the mod apk file and follow the instructions on the screen to install it. It might take a few minutes for the installation to complete.</li>
-<li>Fifth, you need to launch the game and enjoy it with unlimited resources and army.</li>
-</ol>
- <h3>Tips to avoid viruses and malware</h3>
-<p>While downloading and installing Stick War Legacy Mod APK Zip can be fun and exciting, it can also be risky and dangerous. There are many sources that offer mod apk files that contain viruses or malware that can harm your device or steal your personal information. To avoid this, you need to follow these tips:</p>
-<ul>
-<li>- Always download mod apk files from trusted and reputable sources. Do not click on suspicious links or pop-ups that claim to offer mod apk files.</li>
-<li>- Always scan the mod apk file with an antivirus app before installing it. This will help you detect any malicious code or software in the file.</li>
-<li>- Always backup your data before installing any mod apk file. This will help you restore your data in case something goes wrong during or after the installation.</li>
-<li>- Always update your device's software and security patches regularly. This will help you protect your device from vulnerabilities and threats.</li>
-</ul>
- <h2>Alternatives to Stick War Legacy Mod APK Zip</h2> <h2>Alternatives to Stick War Legacy Mod APK Zip</h2>
-<h3>Other mod apk files for Stick War Legacy</h3>
-<p>If you are not satisfied with Stick War Legacy Mod APK Zip, or you want to try something different, you can also download other mod apk files for Stick War Legacy. These mod apk files offer different features and modifications that can enhance your gaming experience. Some of these mod apk files are:</p>
-<ul>
-<li><strong>Stick War: Legacy v2023.2.85 Mod APK Unlimited money:</strong> This mod apk file gives you unlimited money to buy anything you want in the game. You can also unlock all the weapons and skins, and upgrade your units to the max level. You can download this mod apk file from .</li>
-<li><strong>Stick War: Legacy v2023.2.85 Mod APK Unlimited gems:</strong> This mod apk file gives you unlimited gems to buy spells and items that can help you in the battle. You can also unlock all the weapons and skins, and upgrade your units to the max level. You can download this mod apk file from .</li>
-<li><strong>Stick War: Legacy v2023.2.85 Mod APK God mode:</strong> This mod apk file gives you god mode, which means you are invincible and cannot be killed by any enemy. You can also unlock all the weapons and skins, and upgrade your units to the max level. You can download this mod apk file from .</li>
-<li><strong>Stick War: Legacy v2023.2.85 Mod APK Mega mod:</strong> This mod apk file gives you a combination of all the features mentioned above, plus some extra ones such as unlimited mana, no ads, no cooldown, and more. You can download this mod apk file from .</li>
-</ul>
- <h3>Other strategy games similar to Stick War Legacy</h3>
-<p>If you are looking for other strategy games similar to Stick War Legacy, you can also check out these games that offer similar gameplay and features. These games are:</p>
-<ul>
-<li><strong>Stick Warriors:</strong> This is a game where you can control a team of stickmen warriors with different skills and abilities. You can fight against other stickmen teams in various modes and arenas. You can also customize your warriors with different weapons and outfits.</li>
-<li><strong>Kingdom Revenge -Ultimate Realtime Strategy Battle:</strong> This is a game where you can build your own kingdom and army, and fight against other players in real-time battles. You can also explore the map, collect resources, upgrade your buildings and units, and use spells and tactics to win.</li>
-<li><strong>Stickman Army: World War Legacy Fight:</strong> This is a game where you can lead a stickman army in a world war scenario. You can deploy your troops, use weapons and vehicles, and defend your base from enemy attacks.</li>
-<li><strong>Stickman: Legacy of War 3D:</strong> This is a game where you can create your own stickman hero and fight against evil forces in a 3D world. You can use various weapons and skills, upgrade your hero, and complete missions and quests.</li>
-<li><strong>Battle Simulator World War 2 - Stickman Warriors:</strong> This is a game where you can simulate historical battles of World War 2 with stickman soldiers. You can choose your side, select your units, and watch them fight on the battlefield.</li>
-</ul>
- <h2>Reviews and FAQs about Stick War Legacy Mod APK Zip</h2>
-<h3>What users say about the mod apk file</h3>
-<p>Stick War Legacy Mod APK Zip has received mixed reviews from users who have tried it. Some users praise the mod apk file for making the game more fun and easy, while others criticize it for ruining the game's balance and challenge. Here are some examples of user reviews:</p>
-<blockquote>"This is the best mod ever! I love having unlimited resources and army, it makes the game so much more enjoyable. I can try different strategies and weapons without worrying about running out of money or soldiers."</blockquote>
-<blockquote>"This is the worst mod ever! It takes away all the challenge and thrill of the game. It makes the game too easy and boring, there is no point in playing it anymore."</blockquote>
-<blockquote>"This is a good mod for casual players who just want to have fun and relax. But for hardcore players who want to test their skills and strategy, this is not a good mod. It depends on what you are looking for in the game."</blockquote>
- <h3>Frequently asked questions and answers about the mod apk file</h3>
-<p>If you have any questions or doubts about Stick War Legacy Mod APK Zip, you might find some answers here. We <p>If you have any questions or doubts about Stick War Legacy Mod APK Zip, you might find some answers here. We have compiled some of the most frequently asked questions and answers about the mod apk file. These are:</p>
-<ul>
-<li><strong>Q: Is Stick War Legacy Mod APK Zip safe to use?</strong></li>
-<li><strong>A: Stick War Legacy Mod APK Zip is not an official version of the game, and it is not endorsed or supported by the developers. Therefore, it is not guaranteed to be safe or secure to use. You should always download and install mod apk files at your own risk, and follow the tips we mentioned above to avoid viruses and malware.</strong></li>
-<li><strong>Q: Does Stick War Legacy Mod APK Zip work on all devices?</strong></li>
-<li><strong>A: Stick War Legacy Mod APK Zip is designed to work on most iOS and Android devices that support the original game. However, it might not work on some devices due to compatibility issues or technical problems. You should always check the requirements and specifications of the mod apk file before downloading and installing it.</strong></li>
-<li><strong>Q: Can I play multiplayer mode with Stick War Legacy Mod APK Zip?</strong></li>
-<li><strong>A: Stick War Legacy Mod APK Zip might allow you to play multiplayer mode with other players who have the same mod apk file. However, it might not allow you to play with players who have the original game or a different mod apk file. Moreover, you might get banned from the multiplayer mode or the game itself if you are detected using a mod apk file.</strong></li>
-<li><strong>Q: Can I update Stick War Legacy Mod APK Zip?</strong></li>
-<li><strong>A: Stick War Legacy Mod APK Zip might not be compatible with the latest updates and patches of the original game. Therefore, you might not be able to update the mod apk file or enjoy the new features and improvements of the original game. You should always check the availability and compatibility of the mod apk file before updating it.</strong></li>
-</ul>
- <h2>Conclusion</h2>
-<p>Stick War Legacy Mod APK Zip is a modified version of the game that gives you unlimited resources and army to dominate your enemies. It can make the game more fun and exciting, but it can also ruin the game's balance and challenge. It can also be risky and dangerous to use, as it might contain viruses or malware, or get you banned from the game. Therefore, you should always download and install mod apk files at your own risk, and follow the tips we mentioned above to avoid any problems. Alternatively, you can also try other mod apk files or other strategy games similar to Stick War Legacy. We hope this article has helped you learn more about Stick War Legacy Mod APK Zip, and we wish you a happy gaming experience!</p> 401be4b1e0<br />
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spaces/7hao/bingo/cloudflare/worker.js

@@ -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));
-  },
-};

spaces/7hao/bingo/src/pages/api/healthz.ts

@@ -1,7 +0,0 @@
-'use server'
-
-import { NextApiRequest, NextApiResponse } from 'next'
-
-export default async function handler(req: NextApiRequest, res: NextApiResponse) {
-  res.status(200).end('ok')
-}

spaces/AIGC-Audio/AudioGPT/text_to_speech/modules/vocoder/parallel_wavegan/losses/stft_loss.py

@@ -1,154 +0,0 @@
-# -*- coding: utf-8 -*-
-
-# Copyright 2019 Tomoki Hayashi
-#  MIT License (https://opensource.org/licenses/MIT)
-
-"""STFT-based Loss modules."""
-
-import torch
-import torch.nn.functional as F
-
-
-def stft(x, fft_size, hop_size, win_length, window):
-    """Perform STFT and convert to magnitude spectrogram.
-
-    Args:
-        x (Tensor): Input signal tensor (B, T).
-        fft_size (int): FFT size.
-        hop_size (int): Hop size.
-        win_length (int): Window length.
-        window (str): Window function type.
-
-    Returns:
-        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
-
-    """
-    x_stft = torch.stft(x, fft_size, hop_size, win_length, window)
-    real = x_stft[..., 0]
-    imag = x_stft[..., 1]
-
-    # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
-    return torch.sqrt(torch.clamp(real ** 2 + imag ** 2, min=1e-7)).transpose(2, 1)
-
-
-class SpectralConvergengeLoss(torch.nn.Module):
-    """Spectral convergence loss module."""
-
-    def __init__(self):
-        """Initilize spectral convergence loss module."""
-        super(SpectralConvergengeLoss, self).__init__()
-
-    def forward(self, x_mag, y_mag):
-        """Calculate forward propagation.
-
-        Args:
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
-
-        Returns:
-            Tensor: Spectral convergence loss value.
-
-        """
-        return torch.norm(y_mag - x_mag, p="fro") / torch.norm(y_mag, p="fro")
-
-
-class LogSTFTMagnitudeLoss(torch.nn.Module):
-    """Log STFT magnitude loss module."""
-
-    def __init__(self):
-        """Initilize los STFT magnitude loss module."""
-        super(LogSTFTMagnitudeLoss, self).__init__()
-
-    def forward(self, x_mag, y_mag):
-        """Calculate forward propagation.
-
-        Args:
-            x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).
-            y_mag (Tensor): Magnitude spectrogram of groundtruth signal (B, #frames, #freq_bins).
-
-        Returns:
-            Tensor: Log STFT magnitude loss value.
-
-        """
-        return F.l1_loss(torch.log(y_mag), torch.log(x_mag))
-
-
-class STFTLoss(torch.nn.Module):
-    """STFT loss module."""
-
-    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window"):
-        """Initialize STFT loss module."""
-        super(STFTLoss, self).__init__()
-        self.fft_size = fft_size
-        self.shift_size = shift_size
-        self.win_length = win_length
-        self.window = getattr(torch, window)(win_length)
-        self.spectral_convergenge_loss = SpectralConvergengeLoss()
-        self.log_stft_magnitude_loss = LogSTFTMagnitudeLoss()
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-
-        Returns:
-            Tensor: Spectral convergence loss value.
-            Tensor: Log STFT magnitude loss value.
-
-        """
-        self.window = self.window.to(x.device)
-        x_mag = stft(x, self.fft_size, self.shift_size, self.win_length, self.window)
-        y_mag = stft(y, self.fft_size, self.shift_size, self.win_length, self.window)
-        sc_loss = self.spectral_convergenge_loss(x_mag, y_mag)
-        mag_loss = self.log_stft_magnitude_loss(x_mag, y_mag)
-
-        return sc_loss, mag_loss
-
-
-class MultiResolutionSTFTLoss(torch.nn.Module):
-    """Multi resolution STFT loss module."""
-
-    def __init__(self,
-                 fft_sizes=[1024, 2048, 512],
-                 hop_sizes=[120, 240, 50],
-                 win_lengths=[600, 1200, 240],
-                 window="hann_window"):
-        """Initialize Multi resolution STFT loss module.
-
-        Args:
-            fft_sizes (list): List of FFT sizes.
-            hop_sizes (list): List of hop sizes.
-            win_lengths (list): List of window lengths.
-            window (str): Window function type.
-
-        """
-        super(MultiResolutionSTFTLoss, self).__init__()
-        assert len(fft_sizes) == len(hop_sizes) == len(win_lengths)
-        self.stft_losses = torch.nn.ModuleList()
-        for fs, ss, wl in zip(fft_sizes, hop_sizes, win_lengths):
-            self.stft_losses += [STFTLoss(fs, ss, wl, window)]
-
-    def forward(self, x, y):
-        """Calculate forward propagation.
-
-        Args:
-            x (Tensor): Predicted signal (B, T).
-            y (Tensor): Groundtruth signal (B, T).
-
-        Returns:
-            Tensor: Multi resolution spectral convergence loss value.
-            Tensor: Multi resolution log STFT magnitude loss value.
-
-        """
-        sc_loss = 0.0
-        mag_loss = 0.0
-        for f in self.stft_losses:
-            sc_l, mag_l = f(x, y)
-            sc_loss += sc_l
-            mag_loss += mag_l
-        sc_loss /= len(self.stft_losses)
-        mag_loss /= len(self.stft_losses)
-
-        return sc_loss, mag_loss

spaces/ALSv/FSW/app.py

@@ -1,72 +0,0 @@
-# -* coding:UTF-8 -*
-# !/usr/bin/env python
-import numpy as np
-import gradio as gr
-import roop.globals
-from roop.core import (
-    start,
-    decode_execution_providers,
-    suggest_max_memory,
-    suggest_execution_threads,
-)
-from roop.processors.frame.core import get_frame_processors_modules
-from roop.utilities import normalize_output_path
-import os
-from PIL import Image
-
-
-def swap_face(source_file, target_file,doFaceEnhancer):
-
-    source_path = "input.jpg"
-    target_path = "target.jpg"
-
-    source_image = Image.fromarray(source_file)
-    source_image.save(source_path)
-    target_image = Image.fromarray(target_file)
-    target_image.save(target_path)
-
-    print("source_path: ", source_path)
-    print("target_path: ", target_path)
-
-    roop.globals.source_path = source_path
-    roop.globals.target_path = target_path
-    output_path = "output.jpg"
-    roop.globals.output_path = normalize_output_path(
-        roop.globals.source_path, roop.globals.target_path, output_path
-    )
-    if doFaceEnhancer == True:
-        roop.globals.frame_processors = ["face_swapper","face_enhancer"]
-    else:
-        roop.globals.frame_processors = ["face_swapper"]
-    roop.globals.headless = True
-    roop.globals.keep_fps = True
-    roop.globals.keep_audio = True
-    roop.globals.keep_frames = False
-    roop.globals.many_faces = False
-    roop.globals.video_encoder = "libx264"
-    roop.globals.video_quality = 18
-    roop.globals.max_memory = suggest_max_memory()
-    roop.globals.execution_providers = decode_execution_providers(["cuda"])
-    roop.globals.execution_threads = suggest_execution_threads()
-
-    print(
-        "start process",
-        roop.globals.source_path,
-        roop.globals.target_path,
-        roop.globals.output_path,
-    )
-
-    for frame_processor in get_frame_processors_modules(
-        roop.globals.frame_processors
-    ):
-        if not frame_processor.pre_check():
-            return
-
-    start()
-    return output_path
-
-
-app = gr.Interface(
-    fn=swap_face, inputs=[gr.Image(), gr.Image(),gr.Checkbox(label="face_enhancer?", info="do face enhancer?")], outputs="image"
-)
-app.launch()

spaces/AUST001/True-GPT4/README.md

@@ -1,13 +0,0 @@
----
-title: True GPT4
-emoji: ⚡
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: cc-by-nc-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Abhilashvj/planogram-compliance/utils/loggers/__init__.py

@@ -1,578 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Logging utils
-"""
-
-import os
-import warnings
-from pathlib import Path
-
-import pkg_resources as pkg
-import torch
-from torch.utils.tensorboard import SummaryWriter
-
-from utils.general import LOGGER, colorstr, cv2
-from utils.loggers.clearml.clearml_utils import ClearmlLogger
-from utils.loggers.wandb.wandb_utils import WandbLogger
-from utils.plots import plot_images, plot_labels, plot_results
-from utils.torch_utils import de_parallel
-
-LOGGERS = (
-    "csv",
-    "tb",
-    "wandb",
-    "clearml",
-    "comet",
-)  # *.csv, TensorBoard, Weights & Biases, ClearML
-RANK = int(os.getenv("RANK", -1))
-
-try:
-    import wandb
-
-    assert hasattr(wandb, "__version__")  # verify package import not local dir
-    if pkg.parse_version(wandb.__version__) >= pkg.parse_version(
-        "0.12.2"
-    ) and RANK in {0, -1}:
-        try:
-            wandb_login_success = wandb.login(timeout=30)
-        except wandb.errors.UsageError:  # known non-TTY terminal issue
-            wandb_login_success = False
-        if not wandb_login_success:
-            wandb = None
-except (ImportError, AssertionError):
-    wandb = None
-
-try:
-    import clearml
-
-    assert hasattr(
-        clearml, "__version__"
-    )  # verify package import not local dir
-except (ImportError, AssertionError):
-    clearml = None
-
-try:
-    if RANK not in [0, -1]:
-        comet_ml = None
-    else:
-        import comet_ml
-
-        assert hasattr(
-            comet_ml, "__version__"
-        )  # verify package import not local dir
-        from utils.loggers.comet import CometLogger
-
-except (ModuleNotFoundError, ImportError, AssertionError):
-    comet_ml = None
-
-
-class Loggers:
-    # YOLOv5 Loggers class
-    def __init__(
-        self,
-        save_dir=None,
-        weights=None,
-        opt=None,
-        hyp=None,
-        logger=None,
-        include=LOGGERS,
-    ):
-        self.save_dir = save_dir
-        self.weights = weights
-        self.opt = opt
-        self.hyp = hyp
-        self.plots = not opt.noplots  # plot results
-        self.logger = logger  # for printing results to console
-        self.include = include
-        self.keys = [
-            "train/box_loss",
-            "train/obj_loss",
-            "train/cls_loss",  # train loss
-            "metrics/precision",
-            "metrics/recall",
-            "metrics/mAP_0.5",
-            "metrics/mAP_0.5:0.95",  # metrics
-            "val/box_loss",
-            "val/obj_loss",
-            "val/cls_loss",  # val loss
-            "x/lr0",
-            "x/lr1",
-            "x/lr2",
-        ]  # params
-        self.best_keys = [
-            "best/epoch",
-            "best/precision",
-            "best/recall",
-            "best/mAP_0.5",
-            "best/mAP_0.5:0.95",
-        ]
-        for k in LOGGERS:
-            setattr(self, k, None)  # init empty logger dictionary
-        self.csv = True  # always log to csv
-
-        # Messages
-        # if not wandb:
-        #     prefix = colorstr('Weights & Biases: ')
-        #     s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases"
-        #     self.logger.info(s)
-        if not clearml:
-            prefix = colorstr("ClearML: ")
-            s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML"
-            self.logger.info(s)
-        if not comet_ml:
-            prefix = colorstr("Comet: ")
-            s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet"
-            self.logger.info(s)
-        # TensorBoard
-        s = self.save_dir
-        if "tb" in self.include and not self.opt.evolve:
-            prefix = colorstr("TensorBoard: ")
-            self.logger.info(
-                f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/"
-            )
-            self.tb = SummaryWriter(str(s))
-
-        # W&B
-        if wandb and "wandb" in self.include:
-            wandb_artifact_resume = isinstance(
-                self.opt.resume, str
-            ) and self.opt.resume.startswith("wandb-artifact://")
-            run_id = (
-                torch.load(self.weights).get("wandb_id")
-                if self.opt.resume and not wandb_artifact_resume
-                else None
-            )
-            self.opt.hyp = self.hyp  # add hyperparameters
-            self.wandb = WandbLogger(self.opt, run_id)
-            # temp warn. because nested artifacts not supported after 0.12.10
-            # if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.11'):
-            #    s = "YOLOv5 temporarily requires wandb version 0.12.10 or below. Some features may not work as expected."
-            #    self.logger.warning(s)
-        else:
-            self.wandb = None
-
-        # ClearML
-        if clearml and "clearml" in self.include:
-            try:
-                self.clearml = ClearmlLogger(self.opt, self.hyp)
-            except Exception:
-                self.clearml = None
-                prefix = colorstr("ClearML: ")
-                LOGGER.warning(
-                    f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging."
-                    f" See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme"
-                )
-
-        else:
-            self.clearml = None
-
-        # Comet
-        if comet_ml and "comet" in self.include:
-            if isinstance(self.opt.resume, str) and self.opt.resume.startswith(
-                "comet://"
-            ):
-                run_id = self.opt.resume.split("/")[-1]
-                self.comet_logger = CometLogger(
-                    self.opt, self.hyp, run_id=run_id
-                )
-
-            else:
-                self.comet_logger = CometLogger(self.opt, self.hyp)
-
-        else:
-            self.comet_logger = None
-
-    @property
-    def remote_dataset(self):
-        # Get data_dict if custom dataset artifact link is provided
-        data_dict = None
-        if self.clearml:
-            data_dict = self.clearml.data_dict
-        if self.wandb:
-            data_dict = self.wandb.data_dict
-        if self.comet_logger:
-            data_dict = self.comet_logger.data_dict
-
-        return data_dict
-
-    def on_train_start(self):
-        if self.comet_logger:
-            self.comet_logger.on_train_start()
-
-    def on_pretrain_routine_start(self):
-        if self.comet_logger:
-            self.comet_logger.on_pretrain_routine_start()
-
-    def on_pretrain_routine_end(self, labels, names):
-        # Callback runs on pre-train routine end
-        if self.plots:
-            plot_labels(labels, names, self.save_dir)
-            paths = self.save_dir.glob("*labels*.jpg")  # training labels
-            if self.wandb:
-                self.wandb.log(
-                    {
-                        "Labels": [
-                            wandb.Image(str(x), caption=x.name) for x in paths
-                        ]
-                    }
-                )
-            # if self.clearml:
-            #    pass  # ClearML saves these images automatically using hooks
-            if self.comet_logger:
-                self.comet_logger.on_pretrain_routine_end(paths)
-
-    def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
-        log_dict = dict(zip(self.keys[0:3], vals))
-        # Callback runs on train batch end
-        # ni: number integrated batches (since train start)
-        if self.plots:
-            if ni < 3:
-                f = self.save_dir / f"train_batch{ni}.jpg"  # filename
-                plot_images(imgs, targets, paths, f)
-                if ni == 0 and self.tb and not self.opt.sync_bn:
-                    log_tensorboard_graph(
-                        self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)
-                    )
-            if ni == 10 and (self.wandb or self.clearml):
-                files = sorted(self.save_dir.glob("train*.jpg"))
-                if self.wandb:
-                    self.wandb.log(
-                        {
-                            "Mosaics": [
-                                wandb.Image(str(f), caption=f.name)
-                                for f in files
-                                if f.exists()
-                            ]
-                        }
-                    )
-                if self.clearml:
-                    self.clearml.log_debug_samples(files, title="Mosaics")
-
-        if self.comet_logger:
-            self.comet_logger.on_train_batch_end(log_dict, step=ni)
-
-    def on_train_epoch_end(self, epoch):
-        # Callback runs on train epoch end
-        if self.wandb:
-            self.wandb.current_epoch = epoch + 1
-
-        if self.comet_logger:
-            self.comet_logger.on_train_epoch_end(epoch)
-
-    def on_val_start(self):
-        if self.comet_logger:
-            self.comet_logger.on_val_start()
-
-    def on_val_image_end(self, pred, predn, path, names, im):
-        # Callback runs on val image end
-        if self.wandb:
-            self.wandb.val_one_image(pred, predn, path, names, im)
-        if self.clearml:
-            self.clearml.log_image_with_boxes(path, pred, names, im)
-
-    def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
-        if self.comet_logger:
-            self.comet_logger.on_val_batch_end(
-                batch_i, im, targets, paths, shapes, out
-            )
-
-    def on_val_end(
-        self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix
-    ):
-        # Callback runs on val end
-        if self.wandb or self.clearml:
-            files = sorted(self.save_dir.glob("val*.jpg"))
-            if self.wandb:
-                self.wandb.log(
-                    {
-                        "Validation": [
-                            wandb.Image(str(f), caption=f.name) for f in files
-                        ]
-                    }
-                )
-            if self.clearml:
-                self.clearml.log_debug_samples(files, title="Validation")
-
-        if self.comet_logger:
-            self.comet_logger.on_val_end(
-                nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix
-            )
-
-    def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
-        # Callback runs at the end of each fit (train+val) epoch
-        x = dict(zip(self.keys, vals))
-        if self.csv:
-            file = self.save_dir / "results.csv"
-            n = len(x) + 1  # number of cols
-            s = (
-                ""
-                if file.exists()
-                else (
-                    ("%20s," * n % tuple(["epoch"] + self.keys)).rstrip(",")
-                    + "\n"
-                )
-            )  # add header
-            with open(file, "a") as f:
-                f.write(
-                    s
-                    + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",")
-                    + "\n"
-                )
-
-        if self.tb:
-            for k, v in x.items():
-                self.tb.add_scalar(k, v, epoch)
-        elif self.clearml:  # log to ClearML if TensorBoard not used
-            for k, v in x.items():
-                title, series = k.split("/")
-                self.clearml.task.get_logger().report_scalar(
-                    title, series, v, epoch
-                )
-
-        if self.wandb:
-            if best_fitness == fi:
-                best_results = [epoch] + vals[3:7]
-                for i, name in enumerate(self.best_keys):
-                    self.wandb.wandb_run.summary[name] = best_results[
-                        i
-                    ]  # log best results in the summary
-            self.wandb.log(x)
-            self.wandb.end_epoch(best_result=best_fitness == fi)
-
-        if self.clearml:
-            self.clearml.current_epoch_logged_images = (
-                set()
-            )  # reset epoch image limit
-            self.clearml.current_epoch += 1
-
-        if self.comet_logger:
-            self.comet_logger.on_fit_epoch_end(x, epoch=epoch)
-
-    def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
-        # Callback runs on model save event
-        if (
-            (epoch + 1) % self.opt.save_period == 0
-            and not final_epoch
-            and self.opt.save_period != -1
-        ):
-            if self.wandb:
-                self.wandb.log_model(
-                    last.parent,
-                    self.opt,
-                    epoch,
-                    fi,
-                    best_model=best_fitness == fi,
-                )
-            if self.clearml:
-                self.clearml.task.update_output_model(
-                    model_path=str(last),
-                    model_name="Latest Model",
-                    auto_delete_file=False,
-                )
-
-        if self.comet_logger:
-            self.comet_logger.on_model_save(
-                last, epoch, final_epoch, best_fitness, fi
-            )
-
-    def on_train_end(self, last, best, epoch, results):
-        # Callback runs on training end, i.e. saving best model
-        if self.plots:
-            plot_results(
-                file=self.save_dir / "results.csv"
-            )  # save results.png
-        files = [
-            "results.png",
-            "confusion_matrix.png",
-            *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R")),
-        ]
-        files = [
-            (self.save_dir / f) for f in files if (self.save_dir / f).exists()
-        ]  # filter
-        self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
-
-        if (
-            self.tb and not self.clearml
-        ):  # These images are already captured by ClearML by now, we don't want doubles
-            for f in files:
-                self.tb.add_image(
-                    f.stem,
-                    cv2.imread(str(f))[..., ::-1],
-                    epoch,
-                    dataformats="HWC",
-                )
-
-        if self.wandb:
-            self.wandb.log(dict(zip(self.keys[3:10], results)))
-            self.wandb.log(
-                {
-                    "Results": [
-                        wandb.Image(str(f), caption=f.name) for f in files
-                    ]
-                }
-            )
-            # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
-            if not self.opt.evolve:
-                wandb.log_artifact(
-                    str(best if best.exists() else last),
-                    type="model",
-                    name=f"run_{self.wandb.wandb_run.id}_model",
-                    aliases=["latest", "best", "stripped"],
-                )
-            self.wandb.finish_run()
-
-        if self.clearml and not self.opt.evolve:
-            self.clearml.task.update_output_model(
-                model_path=str(best if best.exists() else last),
-                name="Best Model",
-                auto_delete_file=False,
-            )
-
-        if self.comet_logger:
-            final_results = dict(zip(self.keys[3:10], results))
-            self.comet_logger.on_train_end(
-                files, self.save_dir, last, best, epoch, final_results
-            )
-
-    def on_params_update(self, params: dict):
-        # Update hyperparams or configs of the experiment
-        if self.wandb:
-            self.wandb.wandb_run.config.update(params, allow_val_change=True)
-        if self.comet_logger:
-            self.comet_logger.on_params_update(params)
-
-
-class GenericLogger:
-    """
-    YOLOv5 General purpose logger for non-task specific logging
-    Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
-    Arguments
-        opt:             Run arguments
-        console_logger:  Console logger
-        include:         loggers to include
-    """
-
-    def __init__(self, opt, console_logger, include=("tb", "wandb")):
-        # init default loggers
-        self.save_dir = Path(opt.save_dir)
-        self.include = include
-        self.console_logger = console_logger
-        self.csv = self.save_dir / "results.csv"  # CSV logger
-        if "tb" in self.include:
-            prefix = colorstr("TensorBoard: ")
-            self.console_logger.info(
-                f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/"
-            )
-            self.tb = SummaryWriter(str(self.save_dir))
-
-        if wandb and "wandb" in self.include:
-            self.wandb = wandb.init(
-                project=web_project_name(str(opt.project)),
-                name=None if opt.name == "exp" else opt.name,
-                config=opt,
-            )
-        else:
-            self.wandb = None
-
-    def log_metrics(self, metrics, epoch):
-        # Log metrics dictionary to all loggers
-        if self.csv:
-            keys, vals = list(metrics.keys()), list(metrics.values())
-            n = len(metrics) + 1  # number of cols
-            s = (
-                ""
-                if self.csv.exists()
-                else (
-                    ("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n"
-                )
-            )  # header
-            with open(self.csv, "a") as f:
-                f.write(
-                    s
-                    + ("%23.5g," * n % tuple([epoch] + vals)).rstrip(",")
-                    + "\n"
-                )
-
-        if self.tb:
-            for k, v in metrics.items():
-                self.tb.add_scalar(k, v, epoch)
-
-        if self.wandb:
-            self.wandb.log(metrics, step=epoch)
-
-    def log_images(self, files, name="Images", epoch=0):
-        # Log images to all loggers
-        files = [
-            Path(f)
-            for f in (files if isinstance(files, (tuple, list)) else [files])
-        ]  # to Path
-        files = [f for f in files if f.exists()]  # filter by exists
-
-        if self.tb:
-            for f in files:
-                self.tb.add_image(
-                    f.stem,
-                    cv2.imread(str(f))[..., ::-1],
-                    epoch,
-                    dataformats="HWC",
-                )
-
-        if self.wandb:
-            self.wandb.log(
-                {name: [wandb.Image(str(f), caption=f.name) for f in files]},
-                step=epoch,
-            )
-
-    def log_graph(self, model, imgsz=(640, 640)):
-        # Log model graph to all loggers
-        if self.tb:
-            log_tensorboard_graph(self.tb, model, imgsz)
-
-    def log_model(self, model_path, epoch=0, metadata={}):
-        # Log model to all loggers
-        if self.wandb:
-            art = wandb.Artifact(
-                name=f"run_{wandb.run.id}_model",
-                type="model",
-                metadata=metadata,
-            )
-            art.add_file(str(model_path))
-            wandb.log_artifact(art)
-
-    def update_params(self, params):
-        # Update the paramters logged
-        if self.wandb:
-            wandb.run.config.update(params, allow_val_change=True)
-
-
-def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
-    # Log model graph to TensorBoard
-    try:
-        p = next(model.parameters())  # for device, type
-        imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz  # expand
-        im = (
-            torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p)
-        )  # input image (WARNING: must be zeros, not empty)
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")  # suppress jit trace warning
-            tb.add_graph(
-                torch.jit.trace(de_parallel(model), im, strict=False), []
-            )
-    except Exception as e:
-        LOGGER.warning(
-            f"WARNING ⚠️ TensorBoard graph visualization failure {e}"
-        )
-
-
-def web_project_name(project):
-    # Convert local project name to web project name
-    if not project.startswith("runs/train"):
-        return project
-    suffix = (
-        "-Classify"
-        if project.endswith("-cls")
-        else "-Segment"
-        if project.endswith("-seg")
-        else ""
-    )
-    return f"YOLOv5{suffix}"

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/board-plugin.js

@@ -1,40 +0,0 @@
-import ObjectFactory from './board/ObjectFactory.js';
-
-import BoardFactory from './board/board/Factory.js';
-import HexagonFactory from './board/grid/hexagon/Factory.js';
-import QuadFactory from './board/grid/quad/Factory.js';
-import ShapeFactory from './board/shape/Factory.js';
-
-import MoveToFactory from './board/moveto/Factory.js';
-import MatchFactory from './board/match/Factory.js';
-import PathFinderFactory from './board/pathfinder/Factory.js';
-import FieldOfViewFactory from './board/fieldofview/Factory.js';
-import MonopolyFactory from './board/monopoly/Factory.js';
-
-import MiniBoardFactory from './board/miniboard/Factory.js';
-
-import HexagonMap from './board/hexagonmap/index.js';
-
-import CreateTileTexture from './board/texture/CreateTileTexture.js';
-
-import CreateBoardFromTilemap from './board/tilemap/CreateBoardFromTilemap.js';
-
-class BoardPlugin extends Phaser.Plugins.ScenePlugin {
-    constructor(scene, pluginManager) {
-        super(scene, pluginManager);
-
-        this.add = new ObjectFactory(scene);
-
-        // Helper functions
-        this.hexagonMap = HexagonMap;
-        this.createTileTexture = CreateTileTexture;
-        this.createBoardFromTilemap = CreateBoardFromTilemap;
-    }
-
-    start() {
-        var eventEmitter = this.scene.sys.events;
-        eventEmitter.on('destroy', this.destroy, this);
-    }
-}
-
-export default BoardPlugin;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/plugins/swirlpipeline.d.ts

@@ -1,2 +0,0 @@
-import SwirlPostFxPipeline from './shaders/swirl/SwirlPostFxPipeline';
-export default SwirlPostFxPipeline;

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/vq.md

@@ -1,15 +0,0 @@
-# VQModel
-
-The VQ-VAE model was introduced in [Neural Discrete Representation Learning](https://huggingface.co/papers/1711.00937) by Aaron van den Oord, Oriol Vinyals and Koray Kavukcuoglu. The model is used in 🤗 Diffusers to decode latent representations into images. Unlike [`AutoencoderKL`], the [`VQModel`] works in a quantized latent space.
-
-The abstract from the paper is:
-
-*Learning useful representations without supervision remains a key challenge in machine learning. In this paper, we propose a simple yet powerful generative model that learns such discrete representations. Our model, the Vector Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways: the encoder network outputs discrete, rather than continuous, codes; and the prior is learnt rather than static. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). Using the VQ method allows the model to circumvent issues of "posterior collapse" -- where the latents are ignored when they are paired with a powerful autoregressive decoder -- typically observed in the VAE framework. Pairing these representations with an autoregressive prior, the model can generate high quality images, videos, and speech as well as doing high quality speaker conversion and unsupervised learning of phonemes, providing further evidence of the utility of the learnt representations.*
-
-## VQModel
-
-[[autodoc]] VQModel
-
-## VQEncoderOutput
-
-[[autodoc]] models.vq_model.VQEncoderOutput

spaces/Andy1621/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py

@@ -1,11 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
-model = dict(
-    backbone=dict(
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        norm_eval=False,
-        plugins=[
-            dict(
-                cfg=dict(type='ContextBlock', ratio=1. / 4),
-                stages=(False, True, True, True),
-                position='after_conv3')
-        ]))

spaces/Andy1621/uniformer_image_detection/mmdet/core/export/__init__.py

@@ -1,8 +0,0 @@
-from .pytorch2onnx import (build_model_from_cfg,
-                           generate_inputs_and_wrap_model,
-                           preprocess_example_input)
-
-__all__ = [
-    'build_model_from_cfg', 'generate_inputs_and_wrap_model',
-    'preprocess_example_input'
-]

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/start_macos.sh

@@ -1,67 +0,0 @@
-#!/bin/bash
-
-cd "$(dirname "${BASH_SOURCE[0]}")"
-
-if [[ "$(pwd)" =~ " " ]]; then echo This script relies on Miniconda which can not be silently installed under a path with spaces. && exit; fi
-
-# deactivate existing conda envs as needed to avoid conflicts
-{ conda deactivate && conda deactivate && conda deactivate; } 2> /dev/null
-
-# M Series or Intel
-OS_ARCH=$(uname -m)
-case "${OS_ARCH}" in
-    x86_64*)    OS_ARCH="x86_64";;
-    arm64*)     OS_ARCH="arm64";;
-    *)          echo "Unknown system architecture: $OS_ARCH! This script runs only on x86_64 or arm64" && exit
-esac
-
-# config
-INSTALL_DIR="$(pwd)/installer_files"
-CONDA_ROOT_PREFIX="$(pwd)/installer_files/conda"
-INSTALL_ENV_DIR="$(pwd)/installer_files/env"
-MINICONDA_DOWNLOAD_URL="https://repo.anaconda.com/miniconda/Miniconda3-py310_23.3.1-0-MacOSX-${OS_ARCH}.sh"
-conda_exists="F"
-
-# figure out whether git and conda needs to be installed
-if "$CONDA_ROOT_PREFIX/bin/conda" --version &>/dev/null; then conda_exists="T"; fi
-
-# (if necessary) install git and conda into a contained environment
-# download miniconda
-if [ "$conda_exists" == "F" ]; then
-    echo "Downloading Miniconda from $MINICONDA_DOWNLOAD_URL to $INSTALL_DIR/miniconda_installer.sh"
-
-    mkdir -p "$INSTALL_DIR"
-    curl -Lk "$MINICONDA_DOWNLOAD_URL" > "$INSTALL_DIR/miniconda_installer.sh"
-
-    chmod u+x "$INSTALL_DIR/miniconda_installer.sh"
-    bash "$INSTALL_DIR/miniconda_installer.sh" -b -p $CONDA_ROOT_PREFIX
-
-    # test the conda binary
-    echo "Miniconda version:"
-    "$CONDA_ROOT_PREFIX/bin/conda" --version
-fi
-
-# create the installer env
-if [ ! -e "$INSTALL_ENV_DIR" ]; then
-    "$CONDA_ROOT_PREFIX/bin/conda" create -y -k --prefix "$INSTALL_ENV_DIR" python=3.10
-fi
-
-# check if conda environment was actually created
-if [ ! -e "$INSTALL_ENV_DIR/bin/python" ]; then
-    echo "Conda environment is empty."
-    exit
-fi
-
-# environment isolation
-export PYTHONNOUSERSITE=1
-unset PYTHONPATH
-unset PYTHONHOME
-export CUDA_PATH="$INSTALL_ENV_DIR"
-export CUDA_HOME="$CUDA_PATH"
-
-# activate installer env
-source "$CONDA_ROOT_PREFIX/etc/profile.d/conda.sh" # otherwise conda complains about 'shell not initialized' (needed when running in a script)
-conda activate "$INSTALL_ENV_DIR"
-
-# setup installer env
-python one_click.py $@

spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/CLIP/clip/model.py

@@ -1,432 +0,0 @@
-from collections import OrderedDict
-from typing import Tuple, Union
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self, inplanes, planes, stride=1):
-        super().__init__()
-
-        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
-        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-
-        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
-        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = None
-        self.stride = stride
-
-        if stride > 1 or inplanes != planes * Bottleneck.expansion:
-            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
-            self.downsample = nn.Sequential(OrderedDict([
-                ("-1", nn.AvgPool2d(stride)),
-                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
-                ("1", nn.BatchNorm2d(planes * self.expansion))
-            ]))
-
-    def forward(self, x: torch.Tensor):
-        identity = x
-
-        out = self.relu(self.bn1(self.conv1(x)))
-        out = self.relu(self.bn2(self.conv2(out)))
-        out = self.avgpool(out)
-        out = self.bn3(self.conv3(out))
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out += identity
-        out = self.relu(out)
-        return out
-
-
-class AttentionPool2d(nn.Module):
-    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
-        super().__init__()
-        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
-        self.k_proj = nn.Linear(embed_dim, embed_dim)
-        self.q_proj = nn.Linear(embed_dim, embed_dim)
-        self.v_proj = nn.Linear(embed_dim, embed_dim)
-        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
-        self.num_heads = num_heads
-
-    def forward(self, x):
-        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1)  # NCHW -> (HW)NC
-        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
-        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
-        x, _ = F.multi_head_attention_forward(
-            query=x, key=x, value=x,
-            embed_dim_to_check=x.shape[-1],
-            num_heads=self.num_heads,
-            q_proj_weight=self.q_proj.weight,
-            k_proj_weight=self.k_proj.weight,
-            v_proj_weight=self.v_proj.weight,
-            in_proj_weight=None,
-            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
-            bias_k=None,
-            bias_v=None,
-            add_zero_attn=False,
-            dropout_p=0,
-            out_proj_weight=self.c_proj.weight,
-            out_proj_bias=self.c_proj.bias,
-            use_separate_proj_weight=True,
-            training=self.training,
-            need_weights=False
-        )
-
-        return x[0]
-
-
-class ModifiedResNet(nn.Module):
-    """
-    A ResNet class that is similar to torchvision's but contains the following changes:
-    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
-    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
-    - The final pooling layer is a QKV attention instead of an average pool
-    """
-
-    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
-        super().__init__()
-        self.output_dim = output_dim
-        self.input_resolution = input_resolution
-
-        # the 3-layer stem
-        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(width // 2)
-        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(width // 2)
-        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(width)
-        self.avgpool = nn.AvgPool2d(2)
-        self.relu = nn.ReLU(inplace=True)
-
-        # residual layers
-        self._inplanes = width  # this is a *mutable* variable used during construction
-        self.layer1 = self._make_layer(width, layers[0])
-        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
-        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
-        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
-
-        embed_dim = width * 32  # the ResNet feature dimension
-        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)
-
-    def _make_layer(self, planes, blocks, stride=1):
-        layers = [Bottleneck(self._inplanes, planes, stride)]
-
-        self._inplanes = planes * Bottleneck.expansion
-        for _ in range(1, blocks):
-            layers.append(Bottleneck(self._inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        def stem(x):
-            for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
-                x = self.relu(bn(conv(x)))
-            x = self.avgpool(x)
-            return x
-
-        x = x.type(self.conv1.weight.dtype)
-        x = stem(x)
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.attnpool(x)
-
-        return x
-
-
-class LayerNorm(nn.LayerNorm):
-    """Subclass torch's LayerNorm to handle fp16."""
-
-    def forward(self, x: torch.Tensor):
-        orig_type = x.dtype
-        ret = super().forward(x.type(torch.float32))
-        return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
-    def forward(self, x: torch.Tensor):
-        return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
-    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-
-        self.attn = nn.MultiheadAttention(d_model, n_head)
-        self.ln_1 = LayerNorm(d_model)
-        self.mlp = nn.Sequential(OrderedDict([
-            ("c_fc", nn.Linear(d_model, d_model * 4)),
-            ("gelu", QuickGELU()),
-            ("c_proj", nn.Linear(d_model * 4, d_model))
-        ]))
-        self.ln_2 = LayerNorm(d_model)
-        self.attn_mask = attn_mask
-
-    def attention(self, x: torch.Tensor):
-        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
-        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
-    def forward(self, x: torch.Tensor):
-        x = x + self.attention(self.ln_1(x))
-        x = x + self.mlp(self.ln_2(x))
-        return x
-
-
-class Transformer(nn.Module):
-    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
-        super().__init__()
-        self.width = width
-        self.layers = layers
-        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
-
-    def forward(self, x: torch.Tensor):
-        return self.resblocks(x)
-
-
-class VisionTransformer(nn.Module):
-    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.output_dim = output_dim
-        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
-        scale = width ** -0.5
-        self.class_embedding = nn.Parameter(scale * torch.randn(width))
-        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
-        self.ln_pre = LayerNorm(width)
-
-        self.transformer = Transformer(width, layers, heads)
-
-        self.ln_post = LayerNorm(width)
-        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
-
-    def forward(self, x: torch.Tensor):
-        x = self.conv1(x)  # shape = [*, width, grid, grid]
-        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
-        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
-        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
-        x = x + self.positional_embedding.to(x.dtype)
-        x = self.ln_pre(x)
-
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-
-        x = self.ln_post(x[:, 0, :])
-
-        if self.proj is not None:
-            x = x @ self.proj
-
-        return x
-
-
-class CLIP(nn.Module):
-    def __init__(self,
-                 embed_dim: int,
-                 # vision
-                 image_resolution: int,
-                 vision_layers: Union[Tuple[int, int, int, int], int],
-                 vision_width: int,
-                 vision_patch_size: int,
-                 # text
-                 context_length: int,
-                 vocab_size: int,
-                 transformer_width: int,
-                 transformer_heads: int,
-                 transformer_layers: int
-                 ):
-        super().__init__()
-
-        self.context_length = context_length
-
-        if isinstance(vision_layers, (tuple, list)):
-            vision_heads = vision_width * 32 // 64
-            self.visual = ModifiedResNet(
-                layers=vision_layers,
-                output_dim=embed_dim,
-                heads=vision_heads,
-                input_resolution=image_resolution,
-                width=vision_width
-            )
-        else:
-            vision_heads = vision_width // 64
-            self.visual = VisionTransformer(
-                input_resolution=image_resolution,
-                patch_size=vision_patch_size,
-                width=vision_width,
-                layers=vision_layers,
-                heads=vision_heads,
-                output_dim=embed_dim
-            )
-
-        self.transformer = Transformer(
-            width=transformer_width,
-            layers=transformer_layers,
-            heads=transformer_heads,
-            attn_mask=self.build_attention_mask()
-        )
-
-        self.vocab_size = vocab_size
-        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
-        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
-        self.ln_final = LayerNorm(transformer_width)
-
-        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
-        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
-
-        self.initialize_parameters()
-
-    def initialize_parameters(self):
-        nn.init.normal_(self.token_embedding.weight, std=0.02)
-        nn.init.normal_(self.positional_embedding, std=0.01)
-
-        if isinstance(self.visual, ModifiedResNet):
-            if self.visual.attnpool is not None:
-                std = self.visual.attnpool.c_proj.in_features ** -0.5
-                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
-                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)
-
-            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
-                for name, param in resnet_block.named_parameters():
-                    if name.endswith("bn3.weight"):
-                        nn.init.zeros_(param)
-
-        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
-        attn_std = self.transformer.width ** -0.5
-        fc_std = (2 * self.transformer.width) ** -0.5
-        for block in self.transformer.resblocks:
-            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
-            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
-            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
-            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
-
-        if self.text_projection is not None:
-            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
-
-    def build_attention_mask(self):
-        # lazily create causal attention mask, with full attention between the vision tokens
-        # pytorch uses additive attention mask; fill with -inf
-        mask = torch.empty(self.context_length, self.context_length)
-        mask.fill_(float("-inf"))
-        mask.triu_(1)  # zero out the lower diagonal
-        return mask
-
-    @property
-    def dtype(self):
-        return self.visual.conv1.weight.dtype
-
-    def encode_image(self, image):
-        return self.visual(image.type(self.dtype))
-
-    def encode_text(self, text):
-        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]
-
-        x = x + self.positional_embedding.type(self.dtype)
-        x = x.permute(1, 0, 2)  # NLD -> LND
-        x = self.transformer(x)
-        x = x.permute(1, 0, 2)  # LND -> NLD
-        x = self.ln_final(x).type(self.dtype)
-
-        # x.shape = [batch_size, n_ctx, transformer.width]
-        # take features from the eot embedding (eot_token is the highest number in each sequence)
-        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
-
-        return x
-
-    def forward(self, image, text):
-        image_features = self.encode_image(image)
-        text_features = self.encode_text(text)
-
-        # normalized features
-        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
-        text_features = text_features / text_features.norm(dim=-1, keepdim=True)
-
-        # cosine similarity as logits
-        logit_scale = self.logit_scale.exp()
-        logits_per_image = logit_scale * image_features @ text_features.t()
-        logits_per_text = logit_scale * text_features @ image_features.t()
-
-        # shape = [global_batch_size, global_batch_size]
-        return logits_per_image, logits_per_text
-
-
-def convert_weights(model: nn.Module):
-    """Convert applicable model parameters to fp16"""
-
-    def _convert_weights_to_fp16(l):
-        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
-            l.weight.data = l.weight.data.half()
-            if l.bias is not None:
-                l.bias.data = l.bias.data.half()
-
-        if isinstance(l, nn.MultiheadAttention):
-            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
-                tensor = getattr(l, attr)
-                if tensor is not None:
-                    tensor.data = tensor.data.half()
-
-        for name in ["text_projection", "proj"]:
-            if hasattr(l, name):
-                attr = getattr(l, name)
-                if attr is not None:
-                    attr.data = attr.data.half()
-
-    model.apply(_convert_weights_to_fp16)
-
-
-def build_model(state_dict: dict):
-    vit = "visual.proj" in state_dict
-
-    if vit:
-        vision_width = state_dict["visual.conv1.weight"].shape[0]
-        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
-        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
-        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
-        image_resolution = vision_patch_size * grid_size
-    else:
-        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
-        vision_layers = tuple(counts)
-        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
-        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
-        vision_patch_size = None
-        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
-        image_resolution = output_width * 32
-
-    embed_dim = state_dict["text_projection"].shape[1]
-    context_length = state_dict["positional_embedding"].shape[0]
-    vocab_size = state_dict["token_embedding.weight"].shape[0]
-    transformer_width = state_dict["ln_final.weight"].shape[0]
-    transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
-
-    model = CLIP(
-        embed_dim,
-        image_resolution, vision_layers, vision_width, vision_patch_size,
-        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
-    )
-
-    for key in ["input_resolution", "context_length", "vocab_size"]:
-        if key in state_dict:
-            del state_dict[key]
-
-    convert_weights(model)
-    model.load_state_dict(state_dict)
-    return model.eval()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/operations/build/wheel_editable.py

@@ -1,46 +0,0 @@
-import logging
-import os
-from typing import Optional
-
-from pip._vendor.pyproject_hooks import BuildBackendHookCaller, HookMissing
-
-from pip._internal.utils.subprocess import runner_with_spinner_message
-
-logger = logging.getLogger(__name__)
-
-
-def build_wheel_editable(
-    name: str,
-    backend: BuildBackendHookCaller,
-    metadata_directory: str,
-    tempd: str,
-) -> Optional[str]:
-    """Build one InstallRequirement using the PEP 660 build process.
-
-    Returns path to wheel if successfully built. Otherwise, returns None.
-    """
-    assert metadata_directory is not None
-    try:
-        logger.debug("Destination directory: %s", tempd)
-
-        runner = runner_with_spinner_message(
-            f"Building editable for {name} (pyproject.toml)"
-        )
-        with backend.subprocess_runner(runner):
-            try:
-                wheel_name = backend.build_editable(
-                    tempd,
-                    metadata_directory=metadata_directory,
-                )
-            except HookMissing as e:
-                logger.error(
-                    "Cannot build editable %s because the build "
-                    "backend does not have the %s hook",
-                    name,
-                    e,
-                )
-                return None
-    except Exception:
-        logger.error("Failed building editable for %s", name)
-        return None
-    return os.path.join(tempd, wheel_name)

spaces/Awiny/Image2Paragraph/models/grit_src/grit/predictor.py

@@ -1,66 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Jialian Wu from https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/visualizer.py
-import torch
-
-from detectron2.engine.defaults import DefaultPredictor
-from detectron2.utils.visualizer import ColorMode, Visualizer
-
-
-class Visualizer_GRiT(Visualizer):
-    def __init__(self, image, instance_mode=None):
-        super().__init__(image, instance_mode=instance_mode)
-
-    def draw_instance_predictions(self, predictions):
-        boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None
-        scores = predictions.scores if predictions.has("scores") else None
-        classes = predictions.pred_classes.tolist() if predictions.has("pred_classes") else None
-        object_description = predictions.pred_object_descriptions.data
-        # uncomment to output scores in visualized images
-        # object_description = [c + '|' + str(round(s.item(), 1)) for c, s in zip(object_description, scores)]
-
-        if self._instance_mode == ColorMode.SEGMENTATION and self.metadata.get("thing_colors"):
-            colors = [
-                self._jitter([x / 255 for x in self.metadata.thing_colors[c]]) for c in classes
-            ]
-            alpha = 0.8
-        else:
-            colors = None
-            alpha = 0.5
-
-        if self._instance_mode == ColorMode.IMAGE_BW:
-            self.output.reset_image(
-                self._create_grayscale_image(
-                    (predictions.pred_masks.any(dim=0) > 0).numpy()
-                    if predictions.has("pred_masks")
-                    else None
-                )
-            )
-            alpha = 0.3
-
-        self.overlay_instances(
-            masks=None,
-            boxes=boxes,
-            labels=object_description,
-            keypoints=None,
-            assigned_colors=colors,
-            alpha=alpha,
-        )
-        return self.output
-
-
-class VisualizationDemo(object):
-    def __init__(self, cfg, instance_mode=ColorMode.IMAGE):
-        self.cpu_device = torch.device("cpu")
-        self.instance_mode = instance_mode
-
-        self.predictor = DefaultPredictor(cfg)
-
-    def run_on_image(self, image):
-        predictions = self.predictor(image)
-        # Convert image from OpenCV BGR format to Matplotlib RGB format.
-        image = image[:, :, ::-1]
-        visualizer = Visualizer_GRiT(image, instance_mode=self.instance_mode)
-        instances = predictions["instances"].to(self.cpu_device)
-        vis_output = visualizer.draw_instance_predictions(predictions=instances)
-
-        return predictions, vis_output

spaces/Benson/text-generation/Examples/Cookie Ejecutar Reino Pc Descargar Ldplayer.md

@@ -1,58 +0,0 @@
-
-<h1>Cómo jugar Cookie Run: Reino en el PC con LDPlayer</h1>
-<p>Cookie Run: Kingdom es un popular juego móvil que combina acción-RPG, construcción de bases y elementos gacha. Cuenta con un amplio elenco de cookies únicas, un reino personalizable y varios modos de juego y eventos. Si usted es un fan de este juego, es posible que se pregunte cómo jugar en su PC para una mejor experiencia de juego. En este artículo, te mostraremos cómo jugar a Cookie Run: Kingdom en PC con LDPlayer, un emulador de Android gratuito y rápido que proporciona potentes funciones orientadas a los jugadores. </p>
-<h2>cookie ejecutar reino pc descargar ldplayer</h2><br /><p><b><b>Download Zip</b> &#10003;&#10003;&#10003; <a href="https://bltlly.com/2v6MNG">https://bltlly.com/2v6MNG</a></b></p><br /><br />
-<h2>¿Qué es Cookie Run: Reino? </h2>
-<h3>Un divertido y colorido juego de acción-RPG con cookies</h3>
-<p>Cookie Run: Kingdom es el octavo juego de la serie Cookie Run, desarrollado por Devsisters. Es una desviación de la jugabilidad del corredor del original, ya que cuenta con estrategia de batalla en tiempo real y construcción de ciudades. Puedes crear tu propio escuadrón de galletas, cada uno con sus propias habilidades y disfraces, y dirigirlos en batallas contra los enemigos de la Oscuridad. También puedes explorar otros reinos antiguos, desentrañar los misterios de los héroes antiguos y participar en varios festivales. </p>
-<h3>Un juego creativo y personalizable con cookies</h3>
-<p>Cookie Run: Kingdom también te permite construir tu propio reino con cookies. Puedes decorar y diseñar tu reino a tu gusto, utilizando diferentes decoraciones y edificios. También puede crear artículos, producir materiales y mejorar sus instalaciones. Tu reino será el hogar de tu escuadrón de galletas, donde pueden descansar, interactuar y divertirse. También puedes visitar los reinos de otros jugadores y ver cómo han construido su paraíso de galletas. </p>
-<h3>Un juego emocionante y desafiante con varios modos y eventos</h3>
-
-<h2>¿Qué es LDPlayer? </h2>
-<h3>Un emulador de Android gratuito y rápido para PC</h3>
-<p>LDPlayer es un emulador de Android que te permite ejecutar juegos móviles en tu PC con ratón y teclado. Proporciona el rendimiento más rápido para juegos Android, soporta varios sistemas Windows y las aplicaciones y juegos más populares. Es gratis de descargar y usar, y no contiene ningún malware o spyware. </p>
-<h3>Un emulador potente y orientado a los jugadores con características y funciones</h3>
-<p>LDPlayer no es solo un simple emulador, sino también uno orientado a los jugadores. Proporciona características y funciones potentes que mejorarán su experiencia de juego en PC. Algunas de estas características son:</p>
-<p></p>
-<ul>
-<li>Macros de teclado y asignación personalizada para juegos: Puede establecer macros y crear una asignación personalizada para los juegos que juega, lo que le dará un mejor control y rendimiento. </li>
-<li>Sincronizador para un mejor control de múltiples posiciones: Puede ejecutar varias instancias de LDPlayer en su PC, lo que le permitirá jugar varios juegos o cuentas al mismo tiempo. También puede usar el sincronizador para controlar todas las instancias con un teclado. </li>
-<li>Video Recorder: Puedes grabar <p>Video Recorder: Puedes grabar tu juego y compartirlo con tus amigos o redes sociales. También puede editar sus vídeos con filtros, pegatinas y música. </li>
-<li>Game Booster: Puede optimizar el rendimiento de su PC y acelerar sus juegos con un solo clic. También puede personalizar la configuración de CPU, RAM, resolución y más. </li>
-<li>Game Center: Puedes acceder a una enorme biblioteca de juegos de varios géneros y categorías. También puedes descubrir juegos nuevos y populares, y descargarlos con facilidad. </li>
-</ul>
-<h3>Un emulador compatible y estable con soporte para los últimos juegos</h3>
-
-<h2>Cómo descargar e instalar Cookie Run: Reino en el PC con LDPlayer</h2>
-<h3>Paso 1: Descargue e instale LDPlayer en su escritorio</h3>
-<p>El primer paso para jugar Cookie Run: Kingdom en PC con LDPlayer es descargar e instalar LDPlayer en su escritorio. Puede descargar LDPlayer desde su sitio web oficial o desde este enlace: <a href="">https://www.ldplayer.net/</a>. El proceso de instalación es simple y rápido, y solo tienes que seguir las instrucciones en la pantalla. </p>
-<h3>Paso 2: Abra LDPlayer y busque Cookie Run: Kingdom from LD Store</h3>
-<p>El siguiente paso es abrir LDPlayer y buscar Cookie Run: Kingdom desde LD Store. LD Store es la tienda de aplicaciones integrada de LDPlayer, donde puede encontrar y descargar varias aplicaciones y juegos. Puede acceder a LD Store desde la pantalla de inicio de LDPlayer o desde la barra de herramientas del lado derecho. También puede utilizar la barra de búsqueda para escribir Ejecutar cookies: Reino, o navegar por las categorías para encontrarlo. </p>
-<h3>Paso 3: Instalar Cookie Run: Reino en su emulador de LDPlayer Android</h3>
-<p>El tercer paso es instalar Cookie Run: Kingdom en su emulador LDPlayer Android. Una vez que encuentre Cookie Run: Kingdom de LD Store, solo tiene que hacer clic en el botón de instalación y esperar unos minutos para que se complete la instalación. También puede comprobar el progreso de la instalación desde la barra de herramientas en el lado derecho. </p>
-<h3>Paso 4: Abrir el juego y disfrutar de jugar Cookie Run: Reino en el PC con LDPlayer</h3>
-<p>El paso final es abrir el juego y disfrutar jugando Cookie Run: Kingdom en PC con LDPlayer. Puede encontrar el icono del juego en la pantalla de inicio de LDPlayer, o desde la barra de herramientas en el lado derecho. También puede crear un acceso directo para el juego en su escritorio para facilitar el acceso. Una vez que abras el juego, puedes iniciar sesión con tu cuenta, o crear una nueva si no tienes una. También puedes ajustar la configuración del juego, como gráficos, sonido, idioma, etc. </p>
-<h2>Conclusión</h2>
-
-<h2>Preguntas frecuentes</h2>
-<ul>
-<li><b>Q: Es Cookie Run: Reino libre para jugar? </b></li>
-<li>A: Sí, Cookie Run: Kingdom es gratis para jugar, pero también ofrece compras en la aplicación para algunos elementos y características. </li>
-<li><b>Q: ¿Es seguro usar LDPlayer? </b></li>
-<li>A: Sí, LDPlayer es seguro de usar, ya que no contiene ningún malware o spyware. También respeta su privacidad y no recopila datos personales. </li>
-<li><b>Q: ¿Puedo jugar Cookie Run: Reino en el PC sin un emulador? </b></li>
-<li>A: No, no puedes jugar Cookie Run: Kingdom en PC sin un emulador, ya que es un juego móvil que solo está disponible para dispositivos Android e iOS. </li>
-<li><b>Q: ¿Puedo jugar Cookie Run: Kingdom con mis amigos? </b></li>
-<li>A: Sí, tú A: Sí, puedes jugar Cookie Run: Kingdom con tus amigos, ya que tiene un aspecto social y cooperativo. Puedes unirte a Cookie Alliance, donde puedes chatear, intercambiar y colaborar con otros jugadores. También puedes invitar a tus amigos a visitar tu reino, o visitar el de ellos. También puedes retar a tus amigos en la Arena del Reino, donde puedes competir en batallas PvP en tiempo real. </li>
-<li><b>Q: ¿Cómo puedo actualizar Cookie Run: Kingdom en PC con LDPlayer? </b></li>
-<li>A: Puede actualizar Cookie Run: Kingdom en PC con LDPlayer siguiendo estos pasos:</li>
-<ol>
-<li>Abra LDPlayer y vaya a LD Store.</li>
-<li>Búsqueda de Cookie Run: Reino y haga clic en el icono del juego. </li>
-<li> Si hay una actualización disponible, verá un botón de actualización. Haga clic en él y espere a que la actualización termine. </li>
-<li>Una vez que se hace la actualización, puede abrir el juego y disfrutar de la última versión. </li>
-</ol>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/tenacity/wait.py

@@ -1,228 +0,0 @@
-# Copyright 2016–2021 Julien Danjou
-# Copyright 2016 Joshua Harlow
-# Copyright 2013-2014 Ray Holder
-#
-# 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 abc
-import random
-import typing
-
-from pip._vendor.tenacity import _utils
-
-if typing.TYPE_CHECKING:
-    from pip._vendor.tenacity import RetryCallState
-
-
-class wait_base(abc.ABC):
-    """Abstract base class for wait strategies."""
-
-    @abc.abstractmethod
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        pass
-
-    def __add__(self, other: "wait_base") -> "wait_combine":
-        return wait_combine(self, other)
-
-    def __radd__(self, other: "wait_base") -> typing.Union["wait_combine", "wait_base"]:
-        # make it possible to use multiple waits with the built-in sum function
-        if other == 0:  # type: ignore[comparison-overlap]
-            return self
-        return self.__add__(other)
-
-
-WaitBaseT = typing.Union[wait_base, typing.Callable[["RetryCallState"], typing.Union[float, int]]]
-
-
-class wait_fixed(wait_base):
-    """Wait strategy that waits a fixed amount of time between each retry."""
-
-    def __init__(self, wait: _utils.time_unit_type) -> None:
-        self.wait_fixed = _utils.to_seconds(wait)
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        return self.wait_fixed
-
-
-class wait_none(wait_fixed):
-    """Wait strategy that doesn't wait at all before retrying."""
-
-    def __init__(self) -> None:
-        super().__init__(0)
-
-
-class wait_random(wait_base):
-    """Wait strategy that waits a random amount of time between min/max."""
-
-    def __init__(self, min: _utils.time_unit_type = 0, max: _utils.time_unit_type = 1) -> None:  # noqa
-        self.wait_random_min = _utils.to_seconds(min)
-        self.wait_random_max = _utils.to_seconds(max)
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        return self.wait_random_min + (random.random() * (self.wait_random_max - self.wait_random_min))
-
-
-class wait_combine(wait_base):
-    """Combine several waiting strategies."""
-
-    def __init__(self, *strategies: wait_base) -> None:
-        self.wait_funcs = strategies
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        return sum(x(retry_state=retry_state) for x in self.wait_funcs)
-
-
-class wait_chain(wait_base):
-    """Chain two or more waiting strategies.
-
-    If all strategies are exhausted, the very last strategy is used
-    thereafter.
-
-    For example::
-
-        @retry(wait=wait_chain(*[wait_fixed(1) for i in range(3)] +
-                               [wait_fixed(2) for j in range(5)] +
-                               [wait_fixed(5) for k in range(4)))
-        def wait_chained():
-            print("Wait 1s for 3 attempts, 2s for 5 attempts and 5s
-                   thereafter.")
-    """
-
-    def __init__(self, *strategies: wait_base) -> None:
-        self.strategies = strategies
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        wait_func_no = min(max(retry_state.attempt_number, 1), len(self.strategies))
-        wait_func = self.strategies[wait_func_no - 1]
-        return wait_func(retry_state=retry_state)
-
-
-class wait_incrementing(wait_base):
-    """Wait an incremental amount of time after each attempt.
-
-    Starting at a starting value and incrementing by a value for each attempt
-    (and restricting the upper limit to some maximum value).
-    """
-
-    def __init__(
-        self,
-        start: _utils.time_unit_type = 0,
-        increment: _utils.time_unit_type = 100,
-        max: _utils.time_unit_type = _utils.MAX_WAIT,  # noqa
-    ) -> None:
-        self.start = _utils.to_seconds(start)
-        self.increment = _utils.to_seconds(increment)
-        self.max = _utils.to_seconds(max)
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        result = self.start + (self.increment * (retry_state.attempt_number - 1))
-        return max(0, min(result, self.max))
-
-
-class wait_exponential(wait_base):
-    """Wait strategy that applies exponential backoff.
-
-    It allows for a customized multiplier and an ability to restrict the
-    upper and lower limits to some maximum and minimum value.
-
-    The intervals are fixed (i.e. there is no jitter), so this strategy is
-    suitable for balancing retries against latency when a required resource is
-    unavailable for an unknown duration, but *not* suitable for resolving
-    contention between multiple processes for a shared resource. Use
-    wait_random_exponential for the latter case.
-    """
-
-    def __init__(
-        self,
-        multiplier: typing.Union[int, float] = 1,
-        max: _utils.time_unit_type = _utils.MAX_WAIT,  # noqa
-        exp_base: typing.Union[int, float] = 2,
-        min: _utils.time_unit_type = 0,  # noqa
-    ) -> None:
-        self.multiplier = multiplier
-        self.min = _utils.to_seconds(min)
-        self.max = _utils.to_seconds(max)
-        self.exp_base = exp_base
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        try:
-            exp = self.exp_base ** (retry_state.attempt_number - 1)
-            result = self.multiplier * exp
-        except OverflowError:
-            return self.max
-        return max(max(0, self.min), min(result, self.max))
-
-
-class wait_random_exponential(wait_exponential):
-    """Random wait with exponentially widening window.
-
-    An exponential backoff strategy used to mediate contention between multiple
-    uncoordinated processes for a shared resource in distributed systems. This
-    is the sense in which "exponential backoff" is meant in e.g. Ethernet
-    networking, and corresponds to the "Full Jitter" algorithm described in
-    this blog post:
-
-    https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/
-
-    Each retry occurs at a random time in a geometrically expanding interval.
-    It allows for a custom multiplier and an ability to restrict the upper
-    limit of the random interval to some maximum value.
-
-    Example::
-
-        wait_random_exponential(multiplier=0.5,  # initial window 0.5s
-                                max=60)          # max 60s timeout
-
-    When waiting for an unavailable resource to become available again, as
-    opposed to trying to resolve contention for a shared resource, the
-    wait_exponential strategy (which uses a fixed interval) may be preferable.
-
-    """
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        high = super().__call__(retry_state=retry_state)
-        return random.uniform(0, high)
-
-
-class wait_exponential_jitter(wait_base):
-    """Wait strategy that applies exponential backoff and jitter.
-
-    It allows for a customized initial wait, maximum wait and jitter.
-
-    This implements the strategy described here:
-    https://cloud.google.com/storage/docs/retry-strategy
-
-    The wait time is min(initial * 2**n + random.uniform(0, jitter), maximum)
-    where n is the retry count.
-    """
-
-    def __init__(
-        self,
-        initial: float = 1,
-        max: float = _utils.MAX_WAIT,  # noqa
-        exp_base: float = 2,
-        jitter: float = 1,
-    ) -> None:
-        self.initial = initial
-        self.max = max
-        self.exp_base = exp_base
-        self.jitter = jitter
-
-    def __call__(self, retry_state: "RetryCallState") -> float:
-        jitter = random.uniform(0, self.jitter)
-        try:
-            exp = self.exp_base ** (retry_state.attempt_number - 1)
-            result = self.initial * exp + jitter
-        except OverflowError:
-            result = self.max
-        return max(0, min(result, self.max))

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/mfb/net.py

@@ -1,62 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Pengbing Gao https://github.com/nbgao
-# --------------------------------------------------------
-
-from openvqa.models.mfb.mfb import CoAtt
-from openvqa.models.mfb.adapter import Adapter
-import torch
-import torch.nn as nn
-
-
-# -------------------------------------------------------
-# ---- Main MFB/MFH model with Co-Attention Learning ----
-# -------------------------------------------------------
-
-
-class Net(nn.Module):
-    def __init__(self, __C, pretrained_emb, token_size, answer_size):
-        super(Net, self).__init__()
-        self.__C = __C
-        self.adapter = Adapter(__C)
-
-        self.embedding = nn.Embedding(
-            num_embeddings=token_size,
-            embedding_dim=__C.WORD_EMBED_SIZE
-        )
-
-        # Loading the GloVe embedding weights
-        if __C.USE_GLOVE:
-            self.embedding.weight.data.copy_(torch.from_numpy(pretrained_emb))
-
-        self.lstm = nn.LSTM(
-            input_size=__C.WORD_EMBED_SIZE,
-            hidden_size=__C.LSTM_OUT_SIZE,
-            num_layers=1,
-            batch_first=True
-        )
-        self.dropout = nn.Dropout(__C.DROPOUT_R)
-        self.dropout_lstm = nn.Dropout(__C.DROPOUT_R)
-        self.backbone = CoAtt(__C)
-
-        if __C.HIGH_ORDER:      # MFH
-            self.proj = nn.Linear(2*__C.MFB_O, answer_size)
-        else:                   # MFB
-            self.proj = nn.Linear(__C.MFB_O, answer_size)
-
-    def forward(self, frcn_feat, grid_feat, bbox_feat, ques_ix):
-
-        img_feat, _ = self.adapter(frcn_feat, grid_feat, bbox_feat)  # (N, C, FRCN_FEAT_SIZE)
-
-        # Pre-process Language Feature
-        ques_feat = self.embedding(ques_ix)     # (N, T, WORD_EMBED_SIZE)
-        ques_feat = self.dropout(ques_feat)
-        ques_feat, _ = self.lstm(ques_feat)     # (N, T, LSTM_OUT_SIZE)
-        ques_feat = self.dropout_lstm(ques_feat)
-
-        z = self.backbone(img_feat, ques_feat)  # MFH:(N, 2*O) / MFB:(N, O)
-        proj_feat = self.proj(z)                # (N, answer_size)
-
-        return proj_feat
-

spaces/Cong723/gpt-academic-public/request_llm/README.md

@@ -1,54 +0,0 @@
-# 如何使用其他大语言模型
-
-## ChatGLM
-
-- 安装依赖 `pip install -r request_llm/requirements_chatglm.txt`
-- 修改配置，在config.py中将LLM_MODEL的值改为"chatglm"
-
-``` sh
-LLM_MODEL = "chatglm"
-```
-- 运行！
-``` sh
-`python main.py`
-``` 
-
-
----
-## Text-Generation-UI (TGUI，调试中，暂不可用)
-
-### 1. 部署TGUI
-``` sh
-# 1 下载模型
-git clone https://github.com/oobabooga/text-generation-webui.git
-# 2 这个仓库的最新代码有问题，回滚到几周之前
-git reset --hard fcda3f87767e642d1c0411776e549e1d3894843d
-# 3 切换路径
-cd text-generation-webui
-# 4 安装text-generation的额外依赖
-pip install accelerate bitsandbytes flexgen gradio llamacpp markdown numpy peft requests rwkv safetensors sentencepiece tqdm datasets git+https://github.com/huggingface/transformers
-# 5 下载模型
-python download-model.py facebook/galactica-1.3b
-# 其他可选如 facebook/opt-1.3b
-#           facebook/galactica-1.3b
-#           facebook/galactica-6.7b
-#           facebook/galactica-120b
-#           facebook/pygmalion-1.3b 等
-# 详情见 https://github.com/oobabooga/text-generation-webui
-
-# 6 启动text-generation
-python server.py --cpu --listen --listen-port 7865 --model facebook_galactica-1.3b
-```
-
-### 2. 修改config.py
-
-``` sh
-# LLM_MODEL格式:   tgui:[模型]@[ws地址]:[ws端口] ,   端口要和上面给定的端口一致
-LLM_MODEL = "tgui:galactica-1.3b@localhost:7860"
-```
-
-### 3. 运行！
-``` sh
-cd chatgpt-academic
-python main.py
-```

spaces/DHEIVER/CoronaryAngioSegment/app.py

@@ -1,142 +0,0 @@
-import os
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-
-import gradio as gr
-import torch
-import cv2
-import numpy as np
-from preprocess import unsharp_masking
-import glob
-import time
-from sklearn.cluster import KMeans  # Import K-means clustering
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_paths = {
-    'SE-RegUNet 4GF': './model/SERegUNet4GF.pt',
-    'SE-RegUNet 16GF': './model/SERegUNet16GF.pt',
-    'AngioNet': './model/AngioNet.pt',
-    'EffUNet++ B5': './model/EffUNetppb5.pt',
-    'Reg-SA-UNet++': './model/RegSAUnetpp.pt',
-    'UNet3+': './model/UNet3plus.pt',
-}
-
-print("torch: ", torch.__version__)
-
-def filesort(img, model):
-    ori = img.copy()
-    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    h, w = img.shape
-    img_out = preprocessing(img, model)
-    return img_out, h, w, img, ori
-
-def preprocessing(img, model='SE-RegUNet 4GF'):
-    img = cv2.resize(img, (512, 512))
-    img = unsharp_masking(img).astype(np.uint8)
-    if model == 'AngioNet' or model == 'UNet3+':
-        img = np.float32((img - img.min()) / (img.max() - img.min() + 1e-6))
-        img_out = np.expand_dims(img, axis=0)
-    elif model == 'SE-RegUNet 4GF':
-        clahe1 = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        clahe2 = cv2.createCLAHE(clipLimit=8.0, tileGridSize=(8, 8))
-        image1 = clahe1.apply(img)
-        image2 = clahe2.apply(img)
-        img = np.float32((img - img.min()) / (img.max() - img.min() + 1e-6))
-        image1 = np.float32((image1 - image1.min()) / (image1.max() - image1.min() + 1e-6))
-        image2 = np.float32((image2 - image2.min()) / (image2.max() - image2.min() + 1e-6))
-        img_out = np.stack((img, image1, image2), axis=0)
-    else:
-        clahe1 = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        image1 = clahe1.apply(img)
-        image1 = np.float32((image1 - image1.min()) / (image1.max() - image1.min() + 1e-6))
-        img_out = np.stack((image1,) * 3, axis=0)
-    return img_out
-
-def process_input_image(img, model):
-    pipe = torch.jit.load(model_paths[model])
-    pipe = pipe.to(device).eval()
-    start = time.time()
-    img, h, w, ori_gray, ori = filesort(img, model)
-    img = torch.FloatTensor(img).unsqueeze(0).to(device)
-    with torch.no_grad():
-        if model == 'AngioNet':
-            img = torch.cat([img, img], dim=0)
-        logit = np.round(torch.softmax(pipe.forward(img), dim=1).detach().cpu().numpy()[0, 0]).astype(np.uint8)
-    spent = time.time() - start
-    spent = f"{spent:.3f} second(s)"
-
-    if h != 512 or w != 512:
-        logit = cv2.resize(logit, (h, w))
-
-    logit = logit.astype(bool)
-    img_out = ori.copy()
-
-    # Change the color of the segmented mask to red
-    img_out[logit, :] = [255, 0, 0]  # Red color for the mask
-
-    # Add a white border to the mask
-    contours, _ = cv2.findContours(logit.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    cv2.drawContours(img_out, contours, -1, [255, 255, 255], 2)  # White color for the border
-
-    # Perform K-means clustering on the segmented mask
-    masked_image = ori_gray.copy()
-    masked_image[~logit] = 0  # Set non-segmented regions to 0
-    flattened_masked_image = masked_image.reshape((-1, 1))
-
-    # You can adjust the number of clusters (n_clusters) based on your requirements
-    n_clusters = 2
-    kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(flattened_masked_image)
-    cluster_labels = kmeans.labels_
-
-    # Determine the potential for anomalies based on the cluster centroids
-    cluster_centers = kmeans.cluster_centers_
-    anomaly_potential = np.abs(cluster_centers[0] - cluster_centers[1])
-
-    # Define a higher threshold for classifying anomalies
-    anomaly_threshold = 50  # Adjust this threshold as needed for higher rigor
-
-    # Check if anomaly potential is above the threshold
-    is_anomaly = np.sum(anomaly_potential) > anomaly_threshold
-
-    # Provide a detailed message for cardiologists only when there's high confidence
-    if is_anomaly:
-        anomaly_label = "Potential Anomaly Detected: Consult a Cardiologist for Further Assessment and Diagnosis."
-    else:
-        anomaly_label = "No Potential Anomaly Detected. Continue Routine Cardiac Assessment."
-
-    return spent, img_out, anomaly_label
-
-with gr.Column():
-    time_spent = gr.Label(label="Time Spent (Preprocessing + Inference)")
-    img_output = gr.Image(label="Output Mask")
-    anomaly_label = gr.Label(label="Anomaly Status")
-
-my_app = gr.Blocks()
-with my_app:
-    gr.Markdown("Coronary Angiogram Segmentation with Gradio.")
-    with gr.Tabs():
-        with gr.TabItem("Select your image"):
-            with gr.Row():
-                with gr.Column():
-                    img_source = gr.Image(label="Please select angiogram.", value='./example/angio.png', shape=(512, 512))
-                    model_choice = gr.Dropdown(['SE-RegUNet 4GF', 'SE-RegUNet 16GF', 'AngioNet', 'EffUNet++ B5', 
-                                                'Reg-SA-UNet++', 'UNet3+'], label='Model', info='Which model to infer?')
-                    source_image_loader = gr.Button("Vessel Segment")
-                with gr.Column():
-                    time_spent = gr.Label(label="Time Spent (Preprocessing + Inference)")
-                    img_output = gr.Image(label="Output Mask")
-                    anomaly_label = gr.Label(label="Anomaly Status")
-
-        source_image_loader.click(
-            process_input_image,
-            [
-                img_source,
-                model_choice
-            ],
-            [
-                time_spent,
-                img_output,
-                anomaly_label  # Display the anomaly status label
-            ]
-        )
-
-my_app.launch(debug=True)

spaces/Dagfinn1962/stablediffusion-articlera/appworks.py

@@ -1,80 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-
-models = [
-    {"name": "Stable Diffusion 1.4","url": "CompVis/stable-diffusion-v1-4"},
-    {"name": "Stable Diffusion 1.5","url": "runwayml/stable-diffusion-v1-5"},
-    ]
-
-current_model = models[0]
-
-text_gen = gr.Interface.load("spaces/daspartho/prompt-extend") 
-
-models2 = []
-for model in models:
-    model_url = f"models/{model['url']}"
-    loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
-    models2.append(loaded_model)
-
-
-def text_it(inputs, text_gen=text_gen):
-    return text_gen(inputs)
-
-
-def set_model(current_model_index):
-    global current_model
-    current_model = models[current_model_index]
-    return gr.update(value=f"{current_model['name']}")
-
-
-def send_it(inputs, model_choice):
-    proc = models2[model_choice]
-    return proc(inputs)
-
-
-with gr.Blocks() as myface:
-    gr.HTML("""
-    <head> <style> with global {width: 500px; position; absolute; background-color: #000000; height: 100%; margin-left:2px; margin-right: 2px; font-weight:800; font-size: 24px; margin-right: 10px; padding: 10px;} </style> </head>"""
-   
-    )
-    with gr.Row():
-        input_text = gr.Textbox(label=" ",placeholder="PROMPT HERE ",lines=4)
-        # Model selection dropdown
-        model_name1 = gr.Dropdown(
-        label=" ",
-        choices=[m["name"] for m in models],
-        type="index",
-        value=current_model["name"],
-        interactive=True,
-    
-        
-        )
-    with gr.Row():
-        see_prompts = gr.Button("Generate Prompts")                                                      
-        run = gr.Button("Generate Images", varant="primery")
-        
-    with gr.Row():
-        output1 = gr.Image(label="")
-        output2 = gr.Image(label="")
-        output3 = gr.Image(label="")
-    with gr.Row():          
-        magic1 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic2 = gr.Textbox(label="Generated Prompt", lines=2)
-        magic3 = gr.Textbox(label="Generated Prompt", lines=2)
-   
-    model_name1.change(set_model, inputs=model_name1, outputs=[output1, output2, output3,])
-
-    run.click(send_it, inputs=[magic1, model_name1], outputs=[output1])
-    run.click(send_it, inputs=[magic2, model_name1], outputs=[output2])
-    run.click(send_it, inputs=[magic3, model_name1], outputs=[output3])
-   
-
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic1])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic2])
-    see_prompts.click(text_it, inputs=[input_text], outputs=[magic3])
-   
-
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=False, max_threads=400)

spaces/Deepaksiwania12/Face-Landmark-Detection/README.md

@@ -1,12 +0,0 @@
----
-title: Face Landmark Detection
-emoji: 👁
-colorFrom: gray
-colorTo: purple
-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

spaces/DhilshaM/MyGenAI/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

spaces/ECCV2022/bytetrack/deploy/TensorRT/cpp/src/STrack.cpp

@@ -1,192 +0,0 @@
-#include "STrack.h"
-
-STrack::STrack(vector<float> tlwh_, float score)
-{
-	_tlwh.resize(4);
-	_tlwh.assign(tlwh_.begin(), tlwh_.end());
-
-	is_activated = false;
-	track_id = 0;
-	state = TrackState::New;
-	
-	tlwh.resize(4);
-	tlbr.resize(4);
-
-	static_tlwh();
-	static_tlbr();
-	frame_id = 0;
-	tracklet_len = 0;
-	this->score = score;
-	start_frame = 0;
-}
-
-STrack::~STrack()
-{
-}
-
-void STrack::activate(byte_kalman::KalmanFilter &kalman_filter, int frame_id)
-{
-	this->kalman_filter = kalman_filter;
-	this->track_id = this->next_id();
-
-	vector<float> _tlwh_tmp(4);
-	_tlwh_tmp[0] = this->_tlwh[0];
-	_tlwh_tmp[1] = this->_tlwh[1];
-	_tlwh_tmp[2] = this->_tlwh[2];
-	_tlwh_tmp[3] = this->_tlwh[3];
-	vector<float> xyah = tlwh_to_xyah(_tlwh_tmp);
-	DETECTBOX xyah_box;
-	xyah_box[0] = xyah[0];
-	xyah_box[1] = xyah[1];
-	xyah_box[2] = xyah[2];
-	xyah_box[3] = xyah[3];
-	auto mc = this->kalman_filter.initiate(xyah_box);
-	this->mean = mc.first;
-	this->covariance = mc.second;
-
-	static_tlwh();
-	static_tlbr();
-
-	this->tracklet_len = 0;
-	this->state = TrackState::Tracked;
-	if (frame_id == 1)
-	{
-		this->is_activated = true;
-	}
-	//this->is_activated = true;
-	this->frame_id = frame_id;
-	this->start_frame = frame_id;
-}
-
-void STrack::re_activate(STrack &new_track, int frame_id, bool new_id)
-{
-	vector<float> xyah = tlwh_to_xyah(new_track.tlwh);
-	DETECTBOX xyah_box;
-	xyah_box[0] = xyah[0];
-	xyah_box[1] = xyah[1];
-	xyah_box[2] = xyah[2];
-	xyah_box[3] = xyah[3];
-	auto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);
-	this->mean = mc.first;
-	this->covariance = mc.second;
-
-	static_tlwh();
-	static_tlbr();
-
-	this->tracklet_len = 0;
-	this->state = TrackState::Tracked;
-	this->is_activated = true;
-	this->frame_id = frame_id;
-	this->score = new_track.score;
-	if (new_id)
-		this->track_id = next_id();
-}
-
-void STrack::update(STrack &new_track, int frame_id)
-{
-	this->frame_id = frame_id;
-	this->tracklet_len++;
-
-	vector<float> xyah = tlwh_to_xyah(new_track.tlwh);
-	DETECTBOX xyah_box;
-	xyah_box[0] = xyah[0];
-	xyah_box[1] = xyah[1];
-	xyah_box[2] = xyah[2];
-	xyah_box[3] = xyah[3];
-
-	auto mc = this->kalman_filter.update(this->mean, this->covariance, xyah_box);
-	this->mean = mc.first;
-	this->covariance = mc.second;
-
-	static_tlwh();
-	static_tlbr();
-
-	this->state = TrackState::Tracked;
-	this->is_activated = true;
-
-	this->score = new_track.score;
-}
-
-void STrack::static_tlwh()
-{
-	if (this->state == TrackState::New)
-	{
-		tlwh[0] = _tlwh[0];
-		tlwh[1] = _tlwh[1];
-		tlwh[2] = _tlwh[2];
-		tlwh[3] = _tlwh[3];
-		return;
-	}
-
-	tlwh[0] = mean[0];
-	tlwh[1] = mean[1];
-	tlwh[2] = mean[2];
-	tlwh[3] = mean[3];
-
-	tlwh[2] *= tlwh[3];
-	tlwh[0] -= tlwh[2] / 2;
-	tlwh[1] -= tlwh[3] / 2;
-}
-
-void STrack::static_tlbr()
-{
-	tlbr.clear();
-	tlbr.assign(tlwh.begin(), tlwh.end());
-	tlbr[2] += tlbr[0];
-	tlbr[3] += tlbr[1];
-}
-
-vector<float> STrack::tlwh_to_xyah(vector<float> tlwh_tmp)
-{
-	vector<float> tlwh_output = tlwh_tmp;
-	tlwh_output[0] += tlwh_output[2] / 2;
-	tlwh_output[1] += tlwh_output[3] / 2;
-	tlwh_output[2] /= tlwh_output[3];
-	return tlwh_output;
-}
-
-vector<float> STrack::to_xyah()
-{
-	return tlwh_to_xyah(tlwh);
-}
-
-vector<float> STrack::tlbr_to_tlwh(vector<float> &tlbr)
-{
-	tlbr[2] -= tlbr[0];
-	tlbr[3] -= tlbr[1];
-	return tlbr;
-}
-
-void STrack::mark_lost()
-{
-	state = TrackState::Lost;
-}
-
-void STrack::mark_removed()
-{
-	state = TrackState::Removed;
-}
-
-int STrack::next_id()
-{
-	static int _count = 0;
-	_count++;
-	return _count;
-}
-
-int STrack::end_frame()
-{
-	return this->frame_id;
-}
-
-void STrack::multi_predict(vector<STrack*> &stracks, byte_kalman::KalmanFilter &kalman_filter)
-{
-	for (int i = 0; i < stracks.size(); i++)
-	{
-		if (stracks[i]->state != TrackState::Tracked)
-		{
-			stracks[i]->mean[7] = 0;
-		}
-		kalman_filter.predict(stracks[i]->mean, stracks[i]->covariance);
-	}
-}

spaces/Eddycrack864/Applio-Inference/demucs/separate.py

@@ -1,185 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import sys
-from pathlib import Path
-import subprocess
-
-import julius
-import torch as th
-import torchaudio as ta
-
-from .audio import AudioFile, convert_audio_channels
-from .pretrained import is_pretrained, load_pretrained
-from .utils import apply_model, load_model
-
-
-def load_track(track, device, audio_channels, samplerate):
-    errors = {}
-    wav = None
-
-    try:
-        wav = AudioFile(track).read(
-            streams=0,
-            samplerate=samplerate,
-            channels=audio_channels).to(device)
-    except FileNotFoundError:
-        errors['ffmpeg'] = 'Ffmpeg is not installed.'
-    except subprocess.CalledProcessError:
-        errors['ffmpeg'] = 'FFmpeg could not read the file.'
-
-    if wav is None:
-        try:
-            wav, sr = ta.load(str(track))
-        except RuntimeError as err:
-            errors['torchaudio'] = err.args[0]
-        else:
-            wav = convert_audio_channels(wav, audio_channels)
-            wav = wav.to(device)
-            wav = julius.resample_frac(wav, sr, samplerate)
-
-    if wav is None:
-        print(f"Could not load file {track}. "
-              "Maybe it is not a supported file format? ")
-        for backend, error in errors.items():
-            print(f"When trying to load using {backend}, got the following error: {error}")
-        sys.exit(1)
-    return wav
-
-
-def encode_mp3(wav, path, bitrate=320, samplerate=44100, channels=2, verbose=False):
-    try:
-        import lameenc
-    except ImportError:
-        print("Failed to call lame encoder. Maybe it is not installed? "
-              "On windows, run `python.exe -m pip install -U lameenc`, "
-              "on OSX/Linux, run `python3 -m pip install -U lameenc`, "
-              "then try again.", file=sys.stderr)
-        sys.exit(1)
-    encoder = lameenc.Encoder()
-    encoder.set_bit_rate(bitrate)
-    encoder.set_in_sample_rate(samplerate)
-    encoder.set_channels(channels)
-    encoder.set_quality(2)  # 2-highest, 7-fastest
-    if not verbose:
-        encoder.silence()
-    wav = wav.transpose(0, 1).numpy()
-    mp3_data = encoder.encode(wav.tobytes())
-    mp3_data += encoder.flush()
-    with open(path, "wb") as f:
-        f.write(mp3_data)
-
-
-def main():
-    parser = argparse.ArgumentParser("demucs.separate",
-                                     description="Separate the sources for the given tracks")
-    parser.add_argument("tracks", nargs='+', type=Path, default=[], help='Path to tracks')
-    parser.add_argument("-n",
-                        "--name",
-                        default="demucs_quantized",
-                        help="Model name. See README.md for the list of pretrained models. "
-                             "Default is demucs_quantized.")
-    parser.add_argument("-v", "--verbose", action="store_true")
-    parser.add_argument("-o",
-                        "--out",
-                        type=Path,
-                        default=Path("separated"),
-                        help="Folder where to put extracted tracks. A subfolder "
-                        "with the model name will be created.")
-    parser.add_argument("--models",
-                        type=Path,
-                        default=Path("models"),
-                        help="Path to trained models. "
-                        "Also used to store downloaded pretrained models")
-    parser.add_argument("-d",
-                        "--device",
-                        default="cuda" if th.cuda.is_available() else "cpu",
-                        help="Device to use, default is cuda if available else cpu")
-    parser.add_argument("--shifts",
-                        default=0,
-                        type=int,
-                        help="Number of random shifts for equivariant stabilization."
-                        "Increase separation time but improves quality for Demucs. 10 was used "
-                        "in the original paper.")
-    parser.add_argument("--overlap",
-                        default=0.25,
-                        type=float,
-                        help="Overlap between the splits.")
-    parser.add_argument("--no-split",
-                        action="store_false",
-                        dest="split",
-                        default=True,
-                        help="Doesn't split audio in chunks. This can use large amounts of memory.")
-    parser.add_argument("--float32",
-                        action="store_true",
-                        help="Convert the output wavefile to use pcm f32 format instead of s16. "
-                        "This should not make a difference if you just plan on listening to the "
-                        "audio but might be needed to compute exactly metrics like SDR etc.")
-    parser.add_argument("--int16",
-                        action="store_false",
-                        dest="float32",
-                        help="Opposite of --float32, here for compatibility.")
-    parser.add_argument("--mp3", action="store_true",
-                        help="Convert the output wavs to mp3.")
-    parser.add_argument("--mp3-bitrate",
-                        default=320,
-                        type=int,
-                        help="Bitrate of converted mp3.")
-
-    args = parser.parse_args()
-    name = args.name + ".th"
-    model_path = args.models / name
-    if model_path.is_file():
-        model = load_model(model_path)
-    else:
-        if is_pretrained(args.name):
-            model = load_pretrained(args.name)
-        else:
-            print(f"No pre-trained model {args.name}", file=sys.stderr)
-            sys.exit(1)
-    model.to(args.device)
-
-    out = args.out / args.name
-    out.mkdir(parents=True, exist_ok=True)
-    print(f"Separated tracks will be stored in {out.resolve()}")
-    for track in args.tracks:
-        if not track.exists():
-            print(
-                f"File {track} does not exist. If the path contains spaces, "
-                "please try again after surrounding the entire path with quotes \"\".",
-                file=sys.stderr)
-            continue
-        print(f"Separating track {track}")
-        wav = load_track(track, args.device, model.audio_channels, model.samplerate)
-
-        ref = wav.mean(0)
-        wav = (wav - ref.mean()) / ref.std()
-        sources = apply_model(model, wav, shifts=args.shifts, split=args.split,
-                              overlap=args.overlap, progress=True)
-        sources = sources * ref.std() + ref.mean()
-
-        track_folder = out / track.name.rsplit(".", 1)[0]
-        track_folder.mkdir(exist_ok=True)
-        for source, name in zip(sources, model.sources):
-            source = source / max(1.01 * source.abs().max(), 1)
-            if args.mp3 or not args.float32:
-                source = (source * 2**15).clamp_(-2**15, 2**15 - 1).short()
-            source = source.cpu()
-            stem = str(track_folder / name)
-            if args.mp3:
-                encode_mp3(source, stem + ".mp3",
-                           bitrate=args.mp3_bitrate,
-                           samplerate=model.samplerate,
-                           channels=model.audio_channels,
-                           verbose=args.verbose)
-            else:
-                wavname = str(track_folder / f"{name}.wav")
-                ta.save(wavname, source, sample_rate=model.samplerate)
-
-
-if __name__ == "__main__":
-    main()

spaces/Eddycrack864/Applio-Inference/julius/__init__.py

@@ -1,41 +0,0 @@
-# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details.
-# Author: adefossez, 2020
-
-# flake8: noqa
-"""
-.. image:: ../logo.png
-
-Julius contains different Digital Signal Processing algorithms implemented
-with PyTorch, so that they are differentiable and available on CUDA.
-Note that all the modules implemented here can be used with TorchScript.
-
-For now, I have implemented:
-
-- `julius.resample`: fast sinc resampling.
-- `julius.fftconv`: FFT based convolutions.
-- `julius.lowpass`: FIR low pass filter banks.
-- `julius.filters`: FIR high pass and band pass filters.
-- `julius.bands`: Decomposition of a waveform signal over mel-scale frequency bands.
-
-Along that, you might found useful utilities in:
-
-- `julius.core`: DSP related functions.
-- `julius.utils`: Generic utilities.
-
-
-Please checkout [the Github repository](https://github.com/adefossez/julius) for other informations.
-For a verification of the speed and correctness of Julius, check the benchmark module `bench`.
-
-
-This package is named in this honor of
-[Julius O. Smith](https://ccrma.stanford.edu/~jos/),
-whose books and website were a gold mine of information for me to learn about DSP. Go checkout his website if you want
-to learn more about DSP.
-"""
-
-from .bands import SplitBands, split_bands
-from .fftconv import fft_conv1d, FFTConv1d
-from .filters import bandpass_filter, BandPassFilter
-from .filters import highpass_filter, highpass_filters, HighPassFilter, HighPassFilters
-from .lowpass import lowpass_filter, lowpass_filters, LowPassFilters, LowPassFilter
-from .resample import resample_frac, ResampleFrac

spaces/EuroPython2022/mmocr-demo/configs/_base_/schedules/schedule_adam_step_600e.py

@@ -1,8 +0,0 @@
-# optimizer
-optimizer = dict(type='Adam', lr=1e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[200, 400])
-# running settings
-runner = dict(type='EpochBasedRunner', max_epochs=600)
-checkpoint_config = dict(interval=100)

spaces/Felladrin/MiniSearch/src/types.d.ts

@@ -1,24 +0,0 @@
-/// <reference types="vite/client" />
-
-declare module "loadbar" {
-  export default class Loadbar {
-    constructor(
-      options?: {
-        height?: string;
-        backgroundColor?: string;
-        easeFunction?: function;
-        zIndex?: number;
-        startPoint?: number;
-        pausePoint?: number;
-      },
-      el?: HTMLElement,
-    );
-    growTo(num: number): void;
-    start(): void;
-    loading(): void;
-    pause(): this;
-    stop(): void;
-    destroy(): void;
-    done(): void;
-  }
-}

spaces/GAIR/Factool/factool/utils/utils_json.py

@@ -1,12 +0,0 @@
-import json
-import numpy as np
-
-class CustomJSONEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.int64):
-            return int(obj)
-        elif isinstance(obj, tuple):
-            return list(obj)
-        elif isinstance(obj, np.ndarray):
-            return obj.tolist()
-        return super(CustomJSONEncoder, self).default(obj)

spaces/GT4SD/protein_properties/model_cards/description.md

@@ -1,7 +0,0 @@
-
-
-<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
-
-### Protein property prediction
-
-This is the GT4SD web-app for prediction of various protein (or peptide) properties. For **examples** and **documentation** of the supported properties, please see below. Please note that this API does not expose **all** properties that are supported in GT4SD (a list of the non-supported ones can be found at the bottom).

spaces/GastonMazzei/escher-inpaint-project/model-card.md

@@ -1,50 +0,0 @@
-# Overview
-
-This card describes the diffusion model GLIDE (filtered) and noised CLIP model described in the paper [GLIDE: Towards
-Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/abs/2112.10741)
-
-# Datasets
-
-GLIDE (filtered) was trained on a filtered version of a dataset comprised of several hundred million text-image pairs
-collected from the internet. We constructed a set of filters intended to remove all images of people, violent objects, and some
-and hate symbols (see Appendix F of the paper for details). The size of the dataset after filtering was approximately
-67M text-image pairs.
-
-Our noised CLIP model which was trained on the dataset described above, augmented with a filtered version of the dataset used
-to train the [original CLIP models](https://github.com/openai/clip). The total size of this augmented dataset is approximately 137M pairs.
-
-# Performance
-
-Qualitatively, we find that the generated images from GLIDE (filtered) often look semi-realistic, but the small size of the model hinders
-its ability to bind attributes to objects and perform compositional tasks. Because the dataset used to train GLIDE
-(filtered) has been preprocessed to remove images of people, this also limits its world knowledge, especially in regard
-to concepts that involve people.
-Finally, due to the dataset used to train GLIDE (filtered), the model has reduced capabilities to compose multiple objects in complex ways compared to models of a similar size trained on our internal dataset.
-
-We do not directly measure quantitative metrics for GLIDE (filtered). In particular, most of the evaluations we report for our other models are biased against GLIDE (filtered), since they use prompts that often require generations of people. Evaluating people-free models remains an open area of research.
-
-# Intended Use
-
-We release these models to help advance research in generative modeling. Due to the limitations and biases of GLIDE (filtered), we do not currently recommend it for commercial use.
-
-Functionally, these models are intended to be able to perform the following tasks for research purposes:
- * Generate images from natural language prompts
- * Iteratively edit and refine images using inpainting
-
-These models are explicitly not intended to generate images of people or other subjects we filtered for (see Appendix F of the paper for details).
-
-# Limitations
-
-Despite the dataset filtering applied before training, GLIDE (filtered) continues to exhibit biases that extend beyond those found in images of people.
-We explore some of these biases in our paper. For example:
-
-  * It produces different outputs when asked to generate toys for boys and toys for girls.
-  * It gravitates toward generating images of churches when asked to generate "a religious place",
-    and this bias is amplified by classifier-free guidance.
-  * It may have a greater propensity for generating hate symbols other than swastikas and confederate flags. Our filter
-    for hate symbols focused specifically on these two cases, as we found few relevant images of hate symbols in our
-    dataset. However, we also found that the model has diminished capabilities across a wider set of symbols.
-
-GLIDE (filtered) can fail to produce realistic outputs for complex prompts or for prompts that involve concepts that are
-not well-represented in its training data. While the data for the model was filtered to remove certain types of images,
-the data still exhibits biases toward Western-centric concepts.

spaces/Giuliano/Conversational-Datasets/README.md

@@ -1,45 +0,0 @@
----
-title: Conversational Datasets
-emoji: 🏃
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.

spaces/GoAPI/Midjourney-zoom-video-generator-GoAPI/zoom_video_composer.py

@@ -1,367 +0,0 @@
-#!/usr/bin/env python3
-
-# zoom_video_composer.py v0.3.2
-# https://github.com/mwydmuch/ZoomVideoComposer
-
-# Copyright (c) 2023 Marek Wydmuch and the respective contributors
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-import concurrent
-import os
-import shutil
-from concurrent.futures import ThreadPoolExecutor
-from hashlib import md5
-from multiprocessing import cpu_count
-import click
-from tqdm import tqdm
-
-from helpers import *
-
-VERSION = "0.3.2"
-
-@click.command()
-@click.argument(
-    "image_paths",
-    nargs=-1,
-    type=click.Path(exists=True),
-    required=True,
-)
-@click.option(
-    "-a",
-    "--audio_path",
-    type=click.Path(exists=True, dir_okay=False),
-    default=None,
-    help="Audio file path that will be added to the video.",
-)
-@click.option(
-    "-z",
-    "--zoom",
-    type=float,
-    default=2.0,
-    help="Zoom factor/ratio between images.",
-    show_default=True,
-)
-@click.option(
-    "-d",
-    "--duration",
-    type=float,
-    default=10.0,
-    help="Duration of the video in seconds.",
-    show_default=True,
-)
-@click.option(
-    "-e",
-    "--easing",
-    type=click.Choice(list(EASING_FUNCTIONS.keys())),
-    default=DEFAULT_EASING_KEY,
-    help="Easing function.",
-    show_default=True,
-)
-@click.option(
-    "--easing-power",
-    type=float,
-    default=DEFAULT_EASING_POWER,
-    help="Power argument of easeInPow, easeOutPow and easeInOutPow easing functions.",
-    show_default=True,
-)
-@click.option(
-    "--ease-duration",
-    type=float,
-    default=DEFAULT_EASE_DURATION,
-    help="Duration of easing in linearWithInOutEase as a fraction of video duration.",
-    show_default=True,
-)
-@click.option(
-    "-r",
-    "--direction",
-    type=click.Choice(["in", "out", "inout", "outin"]),
-    default="out",
-    help="Zoom direction. Inout and outin combine both directions.",
-    show_default=True,
-)
-@click.option(
-    "-f",
-    "--fps",
-    type=int,
-    default=30,
-    help="Frames per second of the output video.",
-    show_default=True,
-)
-@click.option(
-    "-w",
-    "--width",
-    type=float,
-    default=1,
-    help="Width of the output video. Values > 1 are interpreted as specific sizes in pixels. Values <= 1 are "
-    "interpreted as a fraction of the width of the first image.",
-    show_default=True,
-)
-@click.option(
-    "-h",
-    "--height",
-    type=float,
-    default=1,
-    help="Height of the output video. Values > 1 are interpreted as specific sizes in pixels. Values <= 1 are "
-    "interpreted as a fraction of the height of the first image.",
-    show_default=True,
-)
-@click.option(
-    "-s",
-    "--resampling",
-    type=click.Choice(list(RESAMPLING_FUNCTIONS_PIL.keys())),
-    default=DEFAULT_RESAMPLING_KEY,
-    help="Resampling technique to use when resizing images.",
-    show_default=True,
-)
-@click.option(
-    "-m",
-    "--margin",
-    type=float,
-    default=0.05,
-    help="Size of the margin to cut from the edges of each image for better blending with the next/previous image. "
-    "Values > 1 are interpreted as specific sizes in pixels. Values <= 1 are interpreted as a fraction of the "
-    "smaller size of the first image.",
-    show_default=True,
-)
-@click.option(
-    "-o",
-    "--output",
-    type=click.Path(),
-    default="output.mp4",
-    help="Output video file.",
-    show_default=True,
-)
-@click.option(
-    "-t",
-    "--threads",
-    type=int,
-    default=-1,
-    help="Number of threads to use to generate frames. Use values <= 0 for number of available threads on your "
-    "machine minus the provided absolute value.",
-    show_default=True,
-)
-@click.option(
-    "--tmp-dir",
-    type=click.Path(),
-    default="tmp",
-    help="Temporary directory to store frames.",
-    show_default=True,
-)
-@click.option(
-    "--keep-frames",
-    is_flag=True,
-    default=False,
-    help="Keep frames in the temporary directory. Otherwise, it will be deleted after the video is generated.",
-    show_default=True,
-)
-@click.option(
-    "--skip-video-generation",
-    is_flag=True,
-    default=False,
-    help="Skip video generation. Useful if you only want to generate the frames. This option will keep the temporary "
-    "directory similar to --keep-frames flag.",
-    show_default=True,
-)
-@click.option(
-    "--reverse-images",
-    is_flag=True,
-    default=False,
-    help="Reverse the order of the images.",
-    show_default=True,
-)
-@click.option(
-    "--image-engine",
-    type=click.Choice(list(IMAGE_CLASSES.keys())),
-    default=DEFAULT_IMAGE_ENGINE,
-    help="Image engine to use for image processing.",
-    show_default=True,
-)
-@click.option(
-    "--resume",
-    is_flag=True,
-    default=False,
-    help="Resume generation of the video.",
-    show_default=True,
-)
-def zoom_video_composer_cli(
-    image_paths,
-    audio_path=None,
-    zoom=2.0,
-    duration=10.0,
-    easing=DEFAULT_EASING_KEY,
-    easing_power=DEFAULT_EASING_POWER,
-    ease_duration=DEFAULT_EASE_DURATION,
-    direction="out",
-    fps=30,
-    reverse_images=False,
-    width=1,
-    height=1,
-    resampling=DEFAULT_RESAMPLING_KEY,
-    margin=0.05,
-    output="output.mp4",
-    threads=-1,
-    tmp_dir="tmp",
-    keep_frames=False,
-    skip_video_generation=False,
-    image_engine=DEFAULT_IMAGE_ENGINE,
-    resume=False,
-):
-    """Compose a zoom video from multiple provided images."""
-    zoom_video_composer(
-        image_paths,
-        audio_path,
-        zoom,
-        duration,
-        easing,
-        easing_power,
-        ease_duration,
-        direction,
-        fps,
-        reverse_images,
-        width,
-        height,
-        resampling,
-        margin,
-        output,
-        threads,
-        tmp_dir,
-        keep_frames,
-        skip_video_generation,
-        image_engine,
-        resume,
-    )
-
-
-def zoom_video_composer(
-    image_paths,
-    audio_path=None,
-    zoom=2.0,
-    duration=10.0,
-    easing=DEFAULT_EASING_KEY,
-    easing_power=DEFAULT_EASING_POWER,
-    ease_duration=DEFAULT_EASE_DURATION,
-    direction="out",
-    fps=30,
-    reverse_images=False,
-    width=1,
-    height=1,
-    resampling=DEFAULT_RESAMPLING_KEY,
-    margin=0.05,
-    output="output.mp4",
-    threads=-1,
-    tmp_dir="tmp",
-    keep_frames=False,
-    skip_video_generation=False,
-    image_engine=DEFAULT_IMAGE_ENGINE,
-    resume=False,
-    logger=click.echo,
-):
-    """Compose a zoom video from multiple provided images."""
-    video_params = f'zoom={zoom}, fps={fps}, dur={duration}, easing={easing}, easing_power={easing_power}, ease_duration={ease_duration}, direction={direction}, resampling={resampling}, margin={margin}, width={width}, height={height}'
-    logger(f"Starting zoom video composition with parameters:\n{video_params}")
-
-    # Read images
-    image_paths = get_image_paths(image_paths)
-    logger(f"Reading {len(image_paths)} image files ...")
-    images = read_images(image_paths, logger, image_engine)
-
-    # Setup some additional variables
-    easing_func = get_easing_function(easing, easing_power, ease_duration)
-    resampling_func = get_resampling_function(resampling, image_engine)
-
-    num_images = len(images) - 1
-    num_frames = int(duration * fps)
-    num_frames_half = int(num_frames / 2)
-    video_params_to_hash = video_params + "".join(image_paths)
-    tmp_dir_hash = os.path.join(
-        tmp_dir, md5(video_params_to_hash.encode("utf-8")).hexdigest()
-    )
-
-    # Calculate sizes based on arguments
-    width, height, margin = get_sizes(images[0], width, height, margin)
-
-    # Create tmp dir
-    if not os.path.exists(tmp_dir_hash):
-        logger(f"Creating temporary directory for frames: {tmp_dir_hash} ...")
-        os.makedirs(tmp_dir_hash, exist_ok=True)
-
-    # Reverse images
-    images = images_reverse(images, direction, reverse_images)
-
-    # Blend images (take care of margins)
-    logger(f"Blending {len(images)} images ...")
-    images = blend_images(images, margin, zoom, resampling_func)
-
-    # Create frames
-    n_jobs = threads if threads > 0 else cpu_count() - threads
-    logger(f"Creating frames in {n_jobs} threads ...")
-
-    start_frame = 0
-    if resume:
-        while os.path.exists(os.path.join(tmp_dir_hash, f"{start_frame:06d}.png")):
-            start_frame += 1
-
-    with ThreadPoolExecutor(max_workers=n_jobs) as executor:
-        futures = [
-            executor.submit(
-                process_frame,
-                i,
-                images,
-                direction,
-                easing_func,
-                num_frames,
-                num_frames_half,
-                num_images,
-                zoom,
-                width,
-                height,
-                resampling_func,
-                tmp_dir_hash,
-            )
-            for i in range(start_frame, num_frames)
-        ]
-        try:
-            completed = concurrent.futures.as_completed(futures)
-            for _ in tqdm(range(num_frames - start_frame), desc="Generating the frames"):
-                completed.__next__()
-        except KeyboardInterrupt:
-            executor.shutdown(wait=False, cancel_futures=True)
-            raise
-    
-    # Images are no longer needed
-    del images
-
-    # Create video clip using images in tmp dir and audio if provided
-    logger(f"Writting video in {n_jobs} threads to: {output} ...")
-    create_video_clip(output, fps, num_frames, tmp_dir_hash, audio_path, n_jobs)
-
-    # Remove tmp dir
-    if not keep_frames and not skip_video_generation:
-        logger(f"Removing temporary directory: {tmp_dir_hash} ...")
-        shutil.rmtree(tmp_dir_hash, ignore_errors=False, onerror=None)
-        if not os.listdir(tmp_dir):
-            os.rmdir(tmp_dir)
-
-    logger("Done!")
-    return output
-
-
-if __name__ == "__main__":
-    zoom_video_composer_cli()

spaces/Gradio-Blocks/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_512x512_80k_ade20k.py

@@ -1,7 +0,0 @@
-_base_ = [
-    '../_base_/models/psanet_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(mask_size=(66, 66), num_classes=150),
-    auxiliary_head=dict(num_classes=150))

spaces/Gradio-Blocks/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_769x769_80k_cityscapes.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/psanet_r50-d8.py',
-    '../_base_/datasets/cityscapes_769x769.py', '../_base_/default_runtime.py',
-    '../_base_/schedules/schedule_80k.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)))

spaces/Gradio-Blocks/uniformer_image_segmentation/configs/sem_fpn/fpn_r50_512x512_160k_ade20k.py

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/fpn_r50.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-model = dict(decode_head=dict(num_classes=150))

spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/datasets/cityscapes.py

@@ -1,217 +0,0 @@
-import os.path as osp
-import tempfile
-
-import mmcv
-import numpy as np
-from mmcv.utils import print_log
-from PIL import Image
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class CityscapesDataset(CustomDataset):
-    """Cityscapes dataset.
-
-    The ``img_suffix`` is fixed to '_leftImg8bit.png' and ``seg_map_suffix`` is
-    fixed to '_gtFine_labelTrainIds.png' for Cityscapes dataset.
-    """
-
-    CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole',
-               'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky',
-               'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
-               'bicycle')
-
-    PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 70], [102, 102, 156],
-               [190, 153, 153], [153, 153, 153], [250, 170, 30], [220, 220, 0],
-               [107, 142, 35], [152, 251, 152], [70, 130, 180], [220, 20, 60],
-               [255, 0, 0], [0, 0, 142], [0, 0, 70], [0, 60, 100],
-               [0, 80, 100], [0, 0, 230], [119, 11, 32]]
-
-    def __init__(self, **kwargs):
-        super(CityscapesDataset, self).__init__(
-            img_suffix='_leftImg8bit.png',
-            seg_map_suffix='_gtFine_labelTrainIds.png',
-            **kwargs)
-
-    @staticmethod
-    def _convert_to_label_id(result):
-        """Convert trainId to id for cityscapes."""
-        if isinstance(result, str):
-            result = np.load(result)
-        import cityscapesscripts.helpers.labels as CSLabels
-        result_copy = result.copy()
-        for trainId, label in CSLabels.trainId2label.items():
-            result_copy[result == trainId] = label.id
-
-        return result_copy
-
-    def results2img(self, results, imgfile_prefix, to_label_id):
-        """Write the segmentation results to images.
-
-        Args:
-            results (list[list | tuple | ndarray]): Testing results of the
-                dataset.
-            imgfile_prefix (str): The filename prefix of the png files.
-                If the prefix is "somepath/xxx",
-                the png files will be named "somepath/xxx.png".
-            to_label_id (bool): whether convert output to label_id for
-                submission
-
-        Returns:
-            list[str: str]: result txt files which contains corresponding
-            semantic segmentation images.
-        """
-        mmcv.mkdir_or_exist(imgfile_prefix)
-        result_files = []
-        prog_bar = mmcv.ProgressBar(len(self))
-        for idx in range(len(self)):
-            result = results[idx]
-            if to_label_id:
-                result = self._convert_to_label_id(result)
-            filename = self.img_infos[idx]['filename']
-            basename = osp.splitext(osp.basename(filename))[0]
-
-            png_filename = osp.join(imgfile_prefix, f'{basename}.png')
-
-            output = Image.fromarray(result.astype(np.uint8)).convert('P')
-            import cityscapesscripts.helpers.labels as CSLabels
-            palette = np.zeros((len(CSLabels.id2label), 3), dtype=np.uint8)
-            for label_id, label in CSLabels.id2label.items():
-                palette[label_id] = label.color
-
-            output.putpalette(palette)
-            output.save(png_filename)
-            result_files.append(png_filename)
-            prog_bar.update()
-
-        return result_files
-
-    def format_results(self, results, imgfile_prefix=None, to_label_id=True):
-        """Format the results into dir (standard format for Cityscapes
-        evaluation).
-
-        Args:
-            results (list): Testing results of the dataset.
-            imgfile_prefix (str | None): The prefix of images files. It
-                includes the file path and the prefix of filename, e.g.,
-                "a/b/prefix". If not specified, a temp file will be created.
-                Default: None.
-            to_label_id (bool): whether convert output to label_id for
-                submission. Default: False
-
-        Returns:
-            tuple: (result_files, tmp_dir), result_files is a list containing
-                the image paths, tmp_dir is the temporal directory created
-                for saving json/png files when img_prefix is not specified.
-        """
-
-        assert isinstance(results, list), 'results must be a list'
-        assert len(results) == len(self), (
-            'The length of results is not equal to the dataset len: '
-            f'{len(results)} != {len(self)}')
-
-        if imgfile_prefix is None:
-            tmp_dir = tempfile.TemporaryDirectory()
-            imgfile_prefix = tmp_dir.name
-        else:
-            tmp_dir = None
-        result_files = self.results2img(results, imgfile_prefix, to_label_id)
-
-        return result_files, tmp_dir
-
-    def evaluate(self,
-                 results,
-                 metric='mIoU',
-                 logger=None,
-                 imgfile_prefix=None,
-                 efficient_test=False):
-        """Evaluation in Cityscapes/default protocol.
-
-        Args:
-            results (list): Testing results of the dataset.
-            metric (str | list[str]): Metrics to be evaluated.
-            logger (logging.Logger | None | str): Logger used for printing
-                related information during evaluation. Default: None.
-            imgfile_prefix (str | None): The prefix of output image file,
-                for cityscapes evaluation only. It includes the file path and
-                the prefix of filename, e.g., "a/b/prefix".
-                If results are evaluated with cityscapes protocol, it would be
-                the prefix of output png files. The output files would be
-                png images under folder "a/b/prefix/xxx.png", where "xxx" is
-                the image name of cityscapes. If not specified, a temp file
-                will be created for evaluation.
-                Default: None.
-
-        Returns:
-            dict[str, float]: Cityscapes/default metrics.
-        """
-
-        eval_results = dict()
-        metrics = metric.copy() if isinstance(metric, list) else [metric]
-        if 'cityscapes' in metrics:
-            eval_results.update(
-                self._evaluate_cityscapes(results, logger, imgfile_prefix))
-            metrics.remove('cityscapes')
-        if len(metrics) > 0:
-            eval_results.update(
-                super(CityscapesDataset,
-                      self).evaluate(results, metrics, logger, efficient_test))
-
-        return eval_results
-
-    def _evaluate_cityscapes(self, results, logger, imgfile_prefix):
-        """Evaluation in Cityscapes protocol.
-
-        Args:
-            results (list): Testing results of the dataset.
-            logger (logging.Logger | str | None): Logger used for printing
-                related information during evaluation. Default: None.
-            imgfile_prefix (str | None): The prefix of output image file
-
-        Returns:
-            dict[str: float]: Cityscapes evaluation results.
-        """
-        try:
-            import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as CSEval  # noqa
-        except ImportError:
-            raise ImportError('Please run "pip install cityscapesscripts" to '
-                              'install cityscapesscripts first.')
-        msg = 'Evaluating in Cityscapes style'
-        if logger is None:
-            msg = '\n' + msg
-        print_log(msg, logger=logger)
-
-        result_files, tmp_dir = self.format_results(results, imgfile_prefix)
-
-        if tmp_dir is None:
-            result_dir = imgfile_prefix
-        else:
-            result_dir = tmp_dir.name
-
-        eval_results = dict()
-        print_log(f'Evaluating results under {result_dir} ...', logger=logger)
-
-        CSEval.args.evalInstLevelScore = True
-        CSEval.args.predictionPath = osp.abspath(result_dir)
-        CSEval.args.evalPixelAccuracy = True
-        CSEval.args.JSONOutput = False
-
-        seg_map_list = []
-        pred_list = []
-
-        # when evaluating with official cityscapesscripts,
-        # **_gtFine_labelIds.png is used
-        for seg_map in mmcv.scandir(
-                self.ann_dir, 'gtFine_labelIds.png', recursive=True):
-            seg_map_list.append(osp.join(self.ann_dir, seg_map))
-            pred_list.append(CSEval.getPrediction(CSEval.args, seg_map))
-
-        eval_results.update(
-            CSEval.evaluateImgLists(pred_list, seg_map_list, CSEval.args))
-
-        if tmp_dir is not None:
-            tmp_dir.cleanup()
-
-        return eval_results

spaces/HadiTajari/Penguins_pred_App/README.md

@@ -1,12 +0,0 @@
----
-title: Penguins Pred App 🐧
-emoji: 🦀
-colorFrom: indigo
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.19.0
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference