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spaces/17TheWord/RealESRGAN/tests/test_model.py

@@ -1,126 +0,0 @@
-import torch
-import yaml
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.data.paired_image_dataset import PairedImageDataset
-from basicsr.losses.losses import GANLoss, L1Loss, PerceptualLoss
-
-from realesrgan.archs.discriminator_arch import UNetDiscriminatorSN
-from realesrgan.models.realesrgan_model import RealESRGANModel
-from realesrgan.models.realesrnet_model import RealESRNetModel
-
-
-def test_realesrnet_model():
-    with open('tests/data/test_realesrnet_model.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    # build model
-    model = RealESRNetModel(opt)
-    # test attributes
-    assert model.__class__.__name__ == 'RealESRNetModel'
-    assert isinstance(model.net_g, RRDBNet)
-    assert isinstance(model.cri_pix, L1Loss)
-    assert isinstance(model.optimizers[0], torch.optim.Adam)
-
-    # prepare data
-    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
-    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
-    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
-    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
-    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
-    model.feed_data(data)
-    # check dequeue
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # change probability to test if-else
-    model.opt['gaussian_noise_prob'] = 0
-    model.opt['gray_noise_prob'] = 0
-    model.opt['second_blur_prob'] = 0
-    model.opt['gaussian_noise_prob2'] = 0
-    model.opt['gray_noise_prob2'] = 0
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # ----------------- test nondist_validation -------------------- #
-    # construct dataloader
-    dataset_opt = dict(
-        name='Demo',
-        dataroot_gt='tests/data/gt',
-        dataroot_lq='tests/data/lq',
-        io_backend=dict(type='disk'),
-        scale=4,
-        phase='val')
-    dataset = PairedImageDataset(dataset_opt)
-    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    assert model.is_train is True
-    model.nondist_validation(dataloader, 1, None, False)
-    assert model.is_train is True
-
-
-def test_realesrgan_model():
-    with open('tests/data/test_realesrgan_model.yml', mode='r') as f:
-        opt = yaml.load(f, Loader=yaml.FullLoader)
-
-    # build model
-    model = RealESRGANModel(opt)
-    # test attributes
-    assert model.__class__.__name__ == 'RealESRGANModel'
-    assert isinstance(model.net_g, RRDBNet)  # generator
-    assert isinstance(model.net_d, UNetDiscriminatorSN)  # discriminator
-    assert isinstance(model.cri_pix, L1Loss)
-    assert isinstance(model.cri_perceptual, PerceptualLoss)
-    assert isinstance(model.cri_gan, GANLoss)
-    assert isinstance(model.optimizers[0], torch.optim.Adam)
-    assert isinstance(model.optimizers[1], torch.optim.Adam)
-
-    # prepare data
-    gt = torch.rand((1, 3, 32, 32), dtype=torch.float32)
-    kernel1 = torch.rand((1, 5, 5), dtype=torch.float32)
-    kernel2 = torch.rand((1, 5, 5), dtype=torch.float32)
-    sinc_kernel = torch.rand((1, 5, 5), dtype=torch.float32)
-    data = dict(gt=gt, kernel1=kernel1, kernel2=kernel2, sinc_kernel=sinc_kernel)
-    model.feed_data(data)
-    # check dequeue
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # change probability to test if-else
-    model.opt['gaussian_noise_prob'] = 0
-    model.opt['gray_noise_prob'] = 0
-    model.opt['second_blur_prob'] = 0
-    model.opt['gaussian_noise_prob2'] = 0
-    model.opt['gray_noise_prob2'] = 0
-    model.feed_data(data)
-    # check data shape
-    assert model.lq.shape == (1, 3, 8, 8)
-    assert model.gt.shape == (1, 3, 32, 32)
-
-    # ----------------- test nondist_validation -------------------- #
-    # construct dataloader
-    dataset_opt = dict(
-        name='Demo',
-        dataroot_gt='tests/data/gt',
-        dataroot_lq='tests/data/lq',
-        io_backend=dict(type='disk'),
-        scale=4,
-        phase='val')
-    dataset = PairedImageDataset(dataset_opt)
-    dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=1, shuffle=False, num_workers=0)
-    assert model.is_train is True
-    model.nondist_validation(dataloader, 1, None, False)
-    assert model.is_train is True
-
-    # ----------------- test optimize_parameters -------------------- #
-    model.feed_data(data)
-    model.optimize_parameters(1)
-    assert model.output.shape == (1, 3, 32, 32)
-    assert isinstance(model.log_dict, dict)
-    # check returned keys
-    expected_keys = ['l_g_pix', 'l_g_percep', 'l_g_gan', 'l_d_real', 'out_d_real', 'l_d_fake', 'out_d_fake']
-    assert set(expected_keys).issubset(set(model.log_dict.keys()))

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Activation Code For Renee Undeleter Mega Download and Install the Software Now.md

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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Amityville La Maison Du Diable French Torrent 57 The Complete History And Timeline Of The Events.md

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-<br />
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-<h1>Authorization Code for Dreamweaver CS3 Crack</h1>
-<p>If you are a web designer or developer, you might have heard of Dreamweaver CS3, one of the most popular and powerful web development tools in the market. But what if you don't have the money to buy it or you want to use it for free? In this article, we will show you how to get an authorization code for Dreamweaver CS3 crack, which will allow you to use this software without paying anything.</p>
-<h2>What is Dreamweaver CS3 and why do you need it?</h2>
-<p>Dreamweaver CS3 is a software application that lets you create, edit, and manage websites and web pages. It was released in 2007 by Adobe Systems as part of the Creative Suite 3 package. It has many features and benefits that make it a great tool for web design and development, such as:</p>
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-<h3>Dreamweaver CS3 features and benefits</h3>
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-<li>It supports HTML, CSS, JavaScript, PHP, ASP.NET, XML, and other web technologies.</li>
-<li>It has a visual interface that lets you design your web pages using drag-and-drop elements, templates, layouts, and widgets.</li>
-<li>It has a code editor that lets you write and edit your web code with syntax highlighting, code completion, code hints, and error checking.</li>
-<li>It has a live view mode that lets you preview your web pages in real-time as you make changes to them.</li>
-<li>It has a split view mode that lets you see both the design and the code of your web pages at the same time.</li>
-<li>It has a built-in FTP client that lets you upload and download your web files to and from your web server.</li>
-<li>It has a site manager that lets you organize your web files and folders in a logical structure.</li>
-<li>It has a testing server that lets you test your web pages locally before publishing them online.</li>
-<li>It has a spry framework that lets you add dynamic and interactive features to your web pages using Ajax.</li>
-<li>It has a CSS advisor that lets you check and fix any CSS issues in your web pages.</li>
-<li>It has a browser compatibility check that lets you see how your web pages look and work in different browsers.</li>
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-<h3>Dreamweaver CS3 system requirements and compatibility</h3>
-<p>To use Dreamweaver CS3, you need to have a computer that meets the following minimum system requirements:</p>
-<table>
-<tr><td>Operating system</td><td>Windows XP SP2 or later, or Mac OS X 10.4.8 or later</td></tr>
-<tr><td>Processor</td><td>Intel Pentium 4 or later, or PowerPC G5 or later</td></tr>
-<tr><td>Memory</td><td>512 MB of RAM or more</td></tr>
-<tr><td>Disk space</td><td>1 GB of available hard disk space or more</td></tr>
-<tr><td>Display</td><td>1024 x 768 resolution or higher</td></tr>
-<tr><td>Internet connection</td><td>Required for activation and updates</td></tr>
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-<p>Dreamweaver CS3 is compatible with the following web browsers:</p>
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-<li>Internet Explorer 6 or later</li>
-<li>Mozilla Firefox 2 or later</li>
-<li>Safari 2 or later</li>
-<li>Opera 9 or later</li>
-<li>Netscape Navigator 9 or later</li>
-</ul>
- <h2>What is a crack and why do you need it?</h2>
-<p>A crack is a software program that modifies or bypasses the security features of another software program. In this case, we are talking about a crack for Dreamweaver CS3 that allows you to use it without entering a valid serial number or activation code. A serial number is a unique code that identifies your copy of Dreamweaver CS3 and proves that you have purchased it legally. An activation code is another code that verifies your serial number online and unlocks all the features of Dreamweaver CS3. Without these codes, you cannot use Dreamweaver CS3 fully or at all.</p>
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- <h3>The difference between a crack and a serial number</h3>
- <p>A crack is different from a serial number in several ways:</p>
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- <li>A crack does not require any internet connection to work. A serial number requires an internet connection to activate Dreamweaver CS3 online.</li>
- <li>A crack does not have any expiration date or limit on how many times you can use it. A serial number can expire after a certain period of time or after a certain number of activations.</li>
- <li>A crack does not have any risk of being blacklisted or blocked by Adobe Systems. A serial number can be blacklisted or blocked by Adobe Systems if they detect that it has been used illegally or shared with others.</li>
- <li>A crack does not have any guarantee of working properly or safely. A serial number has a guarantee of working properly and safely as long as it is genuine and legal.</li>
- </ul>
- <h3>The risks and benefits of using a crack</h3>
- <p>Using a crack for Dreamweaver CS3 has some risks and benefits that you should be aware of before deciding to use it:</p>
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- <li>The main benefit of using a crack is that you can use Dreamweaver CS3 for free without paying anything. You can save money and enjoy all the features of this software without any limitations.</li>
- <li>The main risk of using a crack is that you can violate the terms and conditions of Adobe Systems. You can face legal consequences such as fines or lawsuits if they catch you using their software illegally. You can also lose your right to use their software legally in the future.</li>
- <li>Another risk of using a crack is that you can expose your computer to viruses, malware, spyware, or other harmful programs. You can damage your computer system or compromise your personal data if you download or install a crack from an untrusted source. You can also infect other computers if you share or distribute a crack with others.</li>
- <li>Another risk of using a crack is that you can experience errors, bugs, crashes, or compatibility issues with Dreamweaver CS3. You can lose your work or waste your time if the crack does not work properly or causes problems with your software. You can also miss out on updates, patches, fixes, or improvements from Adobe Systems if the crack prevents them from reaching your software.</li>
- </ul>
- <h2>How to get an authorization code for Dreamweaver CS3 crack?</h2>
- <p>If you still want to use a crack for Dreamweaver CS3 despite the risks involved, there are two main methods that you can try:</p>
- <h3>Method 1: Using a keygen program</h3>
- <p>A keygen program is a software program that generates random codes such as serial numbers or activation codes for other software programs. In this case, we are talking about a keygen program that generates an authorization code for Dreamweaver CS3 crack. Here are the steps to follow:</p>
- <h4>Step 1: Download and install the keygen program</h4>
- <p>You need to find and download a keygen program that works for Dreamweaver CS3 from the internet. You need to be careful about where you download it from because some sources may contain viruses or malware. You also need to scan it with an antivirus program before installing it on your computer. You may need to disable your antivirus program temporarily while installing it because some antivirus programs may detect it as a threat.</p>
- <h4>Step 2: Run the keygen program and generate an authorization code</h4>
- <h4>Step 3: Enter the authorization code in Dreamweaver CS3 and activate it</h4>
- <p>You need to enter the authorization code that you generated from the keygen program in Dreamweaver CS3. You may need to open Dreamweaver CS3 and go to the Help menu and select Activate. You may need to enter the serial number that came with your copy of Dreamweaver CS3 or use another serial number that you found online. You may need to select the option to activate by phone and enter the authorization code that you got from the keygen program. You may need to click on Activate or Finish to complete the process.</p>
- <h3>Method 2: Using a patch file</h3>
- <p>A patch file is a software program that modifies or replaces some files or codes of another software program. In this case, we are talking about a patch file that modifies or replaces some files or codes of Dreamweaver CS3 to make it work without an authorization code. Here are the steps to follow:</p>
- <h4>Step 1: Download and install the patch file</h4>
- <p>You need to find and download a patch file that works for Dreamweaver CS3 from the internet. You need to be careful about where you download it from because some sources may contain viruses or malware. You also need to scan it with an antivirus program before installing it on your computer. You may need to disable your antivirus program temporarily while installing it because some antivirus programs may detect it as a threat.</p>
- <h4>Step 2: Run the patch file and apply it to Dreamweaver CS3</h4>
- <p>You need to run the patch file on your computer after installing it. You may need to follow some instructions or click on some buttons depending on the patch file. You may need to locate and select your Dreamweaver CS3 installation folder or file. You may need to backup your original files or codes before applying the patch file. You may need to wait for a few seconds or minutes until the patch file finishes modifying or replacing your files or codes.</p>
- <h4>Step 3: Enjoy using Dreamweaver CS3 without any limitations</h4>
- <p>You can now use Dreamweaver CS3 without entering any authorization code or activating it online. You can access all the features and functions of this software without any restrictions. You can create, edit, and manage your websites and web pages with ease and efficiency.</p>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to get an authorization code for Dreamweaver CS3 crack using two methods: using a keygen program and using a patch file. We have also explained what is Dreamweaver CS3, what is a crack, and what are the risks and benefits of using a crack. We hope that this article has been helpful and informative for you. However, we do not recommend or endorse using a crack for Dreamweaver CS3 or any other software because it is illegal, unethical, and unsafe. We suggest that you buy a legal copy of Dreamweaver CS3 from Adobe Systems or use another free or cheap alternative web development tool instead.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about getting an authorization code for Dreamweaver CS3 crack:</p>
- <ol>
-<li><b>Is using a crack for Dreamweaver CS3 illegal?</b></li>
-<p>Yes, using a crack for Dreamweaver CS3 is illegal because it violates the copyright and license agreement of Adobe Systems. You can face legal consequences such as fines or lawsuits if they catch you using their software illegally.</p>
-<li><b>Is using a crack for Dreamweaver CS3 safe?</b></li>
-<p>No, using a crack for Dreamweaver CS3 is not safe because it can expose your computer to viruses, malware, spyware, or other harmful programs. You can damage your computer system or compromise your personal data if you download or install a crack from an untrusted source. You can also infect other computers if you share or distribute a crack with others.</p>
-<li><b>Is using a crack for Dreamweaver CS3 reliable?</b></li>
-<p>No, using a crack for Dreamweaver CS3 is not reliable because it can cause errors, bugs, crashes, or compatibility issues with your software. You can lose your work or waste your time if the crack does not work properly or causes problems with your software. You can also miss out on updates, patches, fixes, or improvements from Adobe Systems if the crack prevents them from reaching your software.</p>
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-<p>You can find a crack for Dreamweaver CS3 on various websites, forums, blogs, torrents, or other online sources that offer free downloads of software cracks. However, you should be careful about where you download it from because some sources may contain viruses or malware. You should also scan it with an antivirus program before installing it on your computer.</p>
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- <p>Digi SM 100 software is a Windows-based application that allows you to manage your scale data and label design in real time. You can use it to create and edit PLUs, departments, ingredients, nutrition facts, barcodes, logos, and other label elements. You can also use it to monitor and control your Digi SM 100 scale printer from your computer.</p>
- <h3>Requirements and compatibility</h3>
- <p>To download and install Digi SM 100 software, you will need the following:</p>
- <ul>
-<li>A computer running Windows XP, Vista, 7, 8, or 10</li>
-<li>A USB cable or a wireless LAN module to connect your Digi SM 100 scale printer to your computer</li>
-<li>An internet connection to download the software from the Digi website</li>
-<li>A license key to activate the software (you can obtain it from your Digi dealer or distributor)</li>
-</ul>
- <p>Digi SM 100 software is compatible with the following Digi scale printer models:</p>
- <ul>
-<li>SM-100</li>
-<li>SM-110</li>
-<li>SM-300</li>
-<li>SM-500</li>
-<li>SM-5100</li>
-<li>SM-5500</li>
-<li>SM-5600</li>
-<li>SM-5700</li>
-<li>SM-5800</li>
-<li>SM-5900</li>
-</ul>
- <h3>Steps to download and install Digi SM 100 software</h3>
- <p>To download and install Digi SM 100 software, follow these steps:</p>
- <ol>
-<li>Go to the Digi website at <a href="">https://www.digisystem.com/</a></li>
-<li>Click on the "Products" tab and select "Scale Printer"</li>
-<li>Find your Digi SM 100 model and click on it</li>
-<li>Scroll down to the "Downloads" section and click on the "Software" link</li>
-<li>Select the language and version of the software you want to download and click on the "Download" button</li>
-<li>Save the file to your computer and unzip it if necessary</li>
-<li>Run the setup.exe file and follow the instructions on the screen to install the software</li>
-<li>Enter your license key when prompted and complete the installation process</li>
-<li>Restart your computer if required</li>
-</ol>
- <p>Congratulations! You have successfully downloaded and installed Digi SM 100 software on your computer.</p>
- <h2>How to use Digi SM 100 software</h2>
- <p>Digi SM 100 software is easy to use and has a user-friendly interface. You can use it to perform various tasks, such as connecting your scale printer, configuring your settings, designing and printing labels, and more. Here are some of the main functions of Digi SM 100 software:</p>
- <h3>How to connect Digi SM 100 scale printer to your computer</h3>
- <p>To connect your Digi SM 100 scale printer to your computer, you can use either a USB cable or a wireless LAN module. Here are the steps for each method:</p>
- <ul>
-<li>USB cable: Connect one end of the USB cable to your scale printer and the other end to your computer. Turn on your scale printer and wait for your computer to recognize it. You should see a message on your screen saying that a new device has been detected. If not, you may need to install a driver for your scale printer from the Digi website.</li>
-<li>Wireless LAN module: Install the wireless LAN module on your scale printer according to the instructions provided with it. Turn on your scale printer and make sure that it is connected to the same network as your computer. On your computer, open Digi SM 100 software and go to "File" > "Connect". Select "Wireless LAN" as the connection type and enter the IP address of your scale printer. Click on "OK" to establish the connection.</li>
-</ul>
- <p>You should see a green icon on the bottom right corner of Digi SM 100 software indicating that you are connected to your scale printer. If not, you may need to check your network settings or contact Digi support for assistance.</p> b2dd77e56b<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/EaseUS Data Recovery Full Crack for Windows 11 What You Need to Know Before You Download It.md

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-<p>EaseUS Data Recovery is a popular software that can help you recover deleted, formatted, or lost data from various devices, such as hard drives, memory cards, USB flash drives, or digital cameras. However, the official version of EaseUS Data Recovery is not free, and you need to pay a certain amount of money to use its full features. This might lead some people to look for EaseUS Data Recovery full crack for Windows 11, which is a pirated version of the software that claims to offer the same functionality without any cost. But is it safe and legal to download EaseUS Data Recovery full crack for Windows 11? In this article, we will explain why you should avoid downloading EaseUS Data Recovery full crack for Windows 11 and how to download EaseUS Data Recovery legally and safely for Windows 11.</p>
-<h2>Why You Should Avoid Downloading EaseUS Data Recovery Full Crack for Windows 11</h2>
-<p>Downloading EaseUS Data Recovery full crack for Windows 11 might seem like a tempting option if you want to save money or try out the software without any limitations. However, there are many risks and drawbacks associated with this practice, such as:</p>
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-<h2>How to Download EaseUS Data Recovery Legally and Safely for Windows 11</h2>
-<p>There are several ways to download EaseUS Data Recovery legally and safely for Windows 11, depending on your needs and preferences. Here are some of the options you can choose from:</p>
-<h3>Download EaseUS Data Recovery Free Trial</h3>
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-<li>Enter your email address and click the <b>Free Trial</b> button.</li>
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spaces/1gistliPinn/ChatGPT4/Examples/Counter Strike Global Offensive Patch Fr !!TOP!!.md

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spaces/1gistliPinn/ChatGPT4/Examples/Download !LINK! Visualsvn.server.enterprise.edition.license.key.Serials.rar 16.md

@@ -1,153 +0,0 @@
-
-<h1>Download VisualSVN Server Enterprise Edition License Key Serials RAR 16</h1>
-
-<p>If you are looking for a way to download VisualSVN Server Enterprise Edition license key serials RAR 16, you are in the right place. In this article, I will show you how to get the license key serials for VisualSVN Server Enterprise Edition, a powerful and easy-to-use Subversion server for Windows. You will also learn what VisualSVN Server Enterprise Edition is, what features it offers, and why you should use it.</p>
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-<h2>What is VisualSVN Server Enterprise Edition?</h2>
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-<p>VisualSVN Server Enterprise Edition is a professional grade Subversion server for Windows that allows you to setup and maintain an enterprise level Apache Subversion server on your Windows platform. It is certified for Windows Server and trusted by thousands of SMBs and Fortune 500 companies such as General Electric, Siemens, ThyssenKrupp and Sony.</p>
-
-<p>VisualSVN Server Enterprise Edition offers many features that make it the most favored way to setup and maintain a Subversion server on Windows. Some of these features are:</p>
-
-<ul>
-<li>Active Directory Single Sign-On: Allows users to access VisualSVN Server using their current Active Directory domain credentials. Secure Kerberos V5 or NTLM authentication protocols are used. Support for two-factor authentication and smart cards is available.</li>
-<li>Multisite Repository Replication: Provides high-performance replication between geographically distributed sites using VisualSVN Distributed File System (VDFS) technology. Distributed repositories are writable and functionally equivalent to regular Subversion repositories.</li>
-<li>Full-Text Search: Search through the contents and history of your repositories — in any folder, at any revision. The search engine offers high performance, continuous indexing of new revisions and has virtually no limits on the repository sizes.</li>
-<li>Backup and Restore: Backup your repositories with minimal downtime using the hotcopy feature. Restore your repositories from backup files with a few clicks.</li>
-<li>Web Interface: Manage your repositories, users, groups, permissions, hooks, and settings via a user-friendly web interface.</li>
-<li>Apache Subversion Command-Line Tools: Includes the latest versions of Apache Subversion command-line tools that allow you to perform various operations on your repositories.</li>
-</ul>
-
-<h2>Why Use VisualSVN Server Enterprise Edition?</h2>
-
-<p>VisualSVN Server Enterprise Edition is the best choice for setting up and maintaining a Subversion server on Windows for several reasons:</p>
-
-<ul>
-<li>It is easy to install, configure and maintain. You can setup a full-featured and ready to use Subversion server in just a few clicks. Upgrades to newer versions are simple too.</li>
-<li>It is free for commercial use under the Community license. The free Community license does not require any registration, allows an unlimited number of repositories and up to 15 users.</li>
-<li>It is secure and reliable. It uses secure authentication protocols, encryption, backup and restore features, and other mechanisms to protect your data and ensure its availability.</li>
-<li>It is scalable and flexible. It supports multisite repository replication, full-text search, web interface, command-line tools, and other features that allow you to manage your repositories efficiently and effectively.</li>
-</ul>
-
-<h2>How to Download VisualSVN Server Enterprise Edition License Key Serials RAR 16?</h2>
-
-<p>To download VisualSVN Server Enterprise Edition license key serials RAR 16, you need to follow these steps:</p>
-
-<ol>
-<li>Visit the Downloads page on the VisualSVN website.</li>
-<li>Choose the VisualSVN Server Enterprise Edition package that suits your Windows platform (64-bit or 32-bit).</li>
-<li>Click on the Download button and save the installation file on your computer.</li>
-<li>Run the installation file and follow the instructions to install VisualSVN Server Enterprise Edition on your computer.</li>
-<li>After the installation is complete, launch VisualSVN Server Manager from the Start menu or desktop shortcut.</li>
-<li>Select Help | Enter License Key from the menu bar.</li>
-<li>Enter your name, email address, company name, and license key serial number in the corresponding fields.</li>
-<li>Click on OK to activate your license key serial number.</li>
-</ol>
-
-<p>You can get the license key serial number for VisualSVN Server Enterprise Edition from various sources online. However, some of these sources may not be reliable or trustworthy. They may provide you with invalid or expired license key serial numbers that may not work or may cause problems with your VisualSVN Server Enterprise Edition installation. Therefore, it is recommended that you purchase a valid license key serial number from the official VisualSVN website or from an authorized reseller.</p>
-<p></p>
-
-<h2>Conclusion</h2>
-
-<p>In this article, we have learned how to download VisualSVN Server Enterprise Edition license key serials RAR 16 and how to activate them on your computer. We have also learned what VisualSVN Server Enterprise Edition is, what features it offers, and why you should use it. VisualSVN Server Enterprise Edition is a professional grade Subversion server for Windows that allows you to setup and maintain an enterprise level Apache Subversion server on your Windows platform. It is easy to install, configure and maintain, free for commercial use under the Community license, secure and reliable, scalable and flexible. If you have any questions or comments, feel free to leave them below. Thanks for reading!</p>
-<h2>How to Uninstall VisualSVN Server Enterprise Edition</h2>
-
-<p>If you want to uninstall VisualSVN Server Enterprise Edition from your computer, you need to follow these steps:</p>
-
-<ol>
-<li>Close VisualSVN Server Manager and any other applications that may be using VisualSVN Server.</li>
-<li>Go to the Control Panel and select Programs and Features.</li>
-<li>Find VisualSVN Server Enterprise Edition in the list of installed programs and click on Uninstall.</li>
-<li>Follow the instructions to complete the uninstallation process.</li>
-<li>Restart your computer if prompted.</li>
-</ol>
-
-<p>Note that uninstalling VisualSVN Server Enterprise Edition will not delete your repositories or your license key serial number. You can keep them for future use or delete them manually if you want.</p>
-
-<h2>How to Upgrade VisualSVN Server Enterprise Edition</h2>
-
-<p>If you want to upgrade VisualSVN Server Enterprise Edition to a newer version, you need to follow these steps:</p>
-
-<ol>
-<li>Visit the Downloads page on the VisualSVN website and download the latest version of VisualSVN Server Enterprise Edition that suits your Windows platform (64-bit or 32-bit).</li>
-<li>Run the installation file and follow the instructions to install the new version of VisualSVN Server Enterprise Edition on your computer.</li>
-<li>The installation process will automatically detect your existing installation of VisualSVN Server Enterprise Edition and upgrade it to the new version.</li>
-<li>You do not need to enter your license key serial number again as it will be preserved during the upgrade process.</li>
-<li>Restart your computer if prompted.</li>
-</ol>
-
-<p>Note that upgrading VisualSVN Server Enterprise Edition will not affect your repositories or your settings. They will be preserved during the upgrade process.</p>
-
-<h2>How to Troubleshoot VisualSVN Server Enterprise Edition</h2>
-
-<p>If you encounter any problems with VisualSVN Server Enterprise Edition, such as errors, crashes, or performance issues, you can try some of these troubleshooting tips:</p>
-
-<ul>
-<li>Check the VisualSVN Server log files for any error messages or warnings. You can find the log files in the %VISUALSVN_SERVER%\Logs folder.</li>
-<li>Check the Windows Event Viewer for any system or application errors or warnings related to VisualSVN Server.</li>
-<li>Check the Apache Subversion log files for any error messages or warnings related to Subversion operations. You can find the log files in the %VISUALSVN_SERVER%\Repositories folder.</li>
-<li>Check the VisualSVN Server documentation for any solutions or tips related to common problems or scenarios.</li>
-<li>Contact the VisualSVN support team via email or phone if you need further assistance or guidance. You can find their contact details on the VisualSVN website.</li>
-</ul>
-<h2>How to Use VisualSVN Server Enterprise Edition</h2>
-
-<p>After you have installed and activated VisualSVN Server Enterprise Edition on your computer, you can start using it to manage your Subversion repositories and users. You can use VisualSVN Server Manager, a user-friendly graphical interface that allows you to perform various tasks such as:</p>
-
-<ul>
-<li>Create, delete, rename, or relocate your repositories.</li>
-<li>Manage your users, groups, permissions, and access rules.</li>
-<li>Configure your repository hooks, settings, and properties.</li>
-<li>Monitor your repository activity, performance, and statistics.</li>
-<li>Backup and restore your repositories.</li>
-<li>Upgrade your VisualSVN Server Enterprise Edition to a newer version.</li>
-</ul>
-
-<p>You can also use VisualSVN Server Web Interface, a web-based interface that allows you to perform some of the tasks that are available in VisualSVN Server Manager, such as:</p>
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-<li>Browse your repositories and view their contents and history.</li>
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-<li>View or edit your repository properties.</li>
-</ul>
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-<h2>How to Integrate VisualSVN Server Enterprise Edition with Visual Studio</h2>
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-<p>If you are a software developer who uses Visual Studio as your IDE, you can integrate VisualSVN Server Enterprise Edition with Visual Studio using VisualSVN, a professional grade Subversion integration plug-in for Visual Studio. VisualSVN allows you to perform various Subversion operations from within Visual Studio, such as:</p>
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-<ul>
-<li>Add, delete, rename, move, or copy files or directories in your solution or project.</li>
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-<li>Resolve conflicts or lock files.</li>
-<li>Use TortoiseSVN dialogs for advanced Subversion operations.</li>
-</ul>
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-<p>To integrate VisualSVN Server Enterprise Edition with Visual Studio using VisualSVN, you need to follow these steps:</p>
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-<ol>
-<li>Visit the Downloads page on the VisualSVN website and download the latest version of VisualSVN that suits your Visual Studio version (2022, 2019, 2017).</li>
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-<li>The solution or project will be added to Subversion and you can start using VisualSVN features from the menu bar or the context menu.</li>
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-<p>If you want to download files from VisualSVN Server Enterprise Edition to your computer, you can use one of these methods:</p>
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-<li>Use a Subversion client such as TortoiseSVN or Apache Subversion command-line tools. You can install these tools from the Downloads page on the VisualSVN website. To download files using a Subversion client, you need to know the repository URL and the revision number of the files that you want to download. You can use the checkout or export commands to download files from a repository.</li>
-<li>Use VisualSVN Server Web Interface. You can access the web interface by entering the repository URL in your web browser. To download files using the web interface, you need to browse to the file that you want to download and click on the Download button. You can also download multiple files or directories by selecting them and clicking on the Download button.</li>
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-<p>In this article, we have learned how to download VisualSVN Server Enterprise Edition license key serials RAR 16 and how to use them to activate VisualSVN Server Enterprise Edition on our computer. We have also learned what VisualSVN Server Enterprise Edition is, what features it offers, and why we should use it. VisualSVN Server Enterprise Edition is a professional grade Subversion server for Windows that allows us to setup and maintain an enterprise level Apache Subversion server on our Windows platform. It is easy to install, configure and maintain, free for commercial use under the Community license, secure and reliable, scalable and flexible. We have also learned how to use VisualSVN Server Enterprise Edition to manage our Subversion repositories and users, how to integrate it with Visual Studio using VisualSVN, and how to download files from it using Subversion clients or web interface. If we have any questions or comments, we can leave them below or contact the VisualSVN support team. Thanks for reading!</p> 3cee63e6c2<br />
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spaces/1line/AutoGPT/autogpt/promptgenerator.py

@@ -1,138 +0,0 @@
-""" A module for generating custom prompt strings."""
-from __future__ import annotations
-
-import json
-from typing import Any
-
-
-class PromptGenerator:
-    """
-    A class for generating custom prompt strings based on constraints, commands,
-        resources, and performance evaluations.
-    """
-
-    def __init__(self) -> None:
-        """
-        Initialize the PromptGenerator object with empty lists of constraints,
-            commands, resources, and performance evaluations.
-        """
-        self.constraints = []
-        self.commands = []
-        self.resources = []
-        self.performance_evaluation = []
-        self.response_format = {
-            "thoughts": {
-                "text": "thought",
-                "reasoning": "reasoning",
-                "plan": "- short bulleted\n- list that conveys\n- long-term plan",
-                "criticism": "constructive self-criticism",
-                "speak": "thoughts summary to say to user",
-            },
-            "command": {"name": "command name", "args": {"arg name": "value"}},
-        }
-
-    def add_constraint(self, constraint: str) -> None:
-        """
-        Add a constraint to the constraints list.
-
-        Args:
-            constraint (str): The constraint to be added.
-        """
-        self.constraints.append(constraint)
-
-    def add_command(self, command_label: str, command_name: str, args=None) -> None:
-        """
-        Add a command to the commands list with a label, name, and optional arguments.
-
-        Args:
-            command_label (str): The label of the command.
-            command_name (str): The name of the command.
-            args (dict, optional): A dictionary containing argument names and their
-              values. Defaults to None.
-        """
-        if args is None:
-            args = {}
-
-        command_args = {arg_key: arg_value for arg_key, arg_value in args.items()}
-
-        command = {
-            "label": command_label,
-            "name": command_name,
-            "args": command_args,
-        }
-
-        self.commands.append(command)
-
-    def _generate_command_string(self, command: dict[str, Any]) -> str:
-        """
-        Generate a formatted string representation of a command.
-
-        Args:
-            command (dict): A dictionary containing command information.
-
-        Returns:
-            str: The formatted command string.
-        """
-        args_string = ", ".join(
-            f'"{key}": "{value}"' for key, value in command["args"].items()
-        )
-        return f'{command["label"]}: "{command["name"]}", args: {args_string}'
-
-    def add_resource(self, resource: str) -> None:
-        """
-        Add a resource to the resources list.
-
-        Args:
-            resource (str): The resource to be added.
-        """
-        self.resources.append(resource)
-
-    def add_performance_evaluation(self, evaluation: str) -> None:
-        """
-        Add a performance evaluation item to the performance_evaluation list.
-
-        Args:
-            evaluation (str): The evaluation item to be added.
-        """
-        self.performance_evaluation.append(evaluation)
-
-    def _generate_numbered_list(self, items: list[Any], item_type="list") -> str:
-        """
-        Generate a numbered list from given items based on the item_type.
-
-        Args:
-            items (list): A list of items to be numbered.
-            item_type (str, optional): The type of items in the list.
-                Defaults to 'list'.
-
-        Returns:
-            str: The formatted numbered list.
-        """
-        if item_type == "command":
-            return "\n".join(
-                f"{i+1}. {self._generate_command_string(item)}"
-                for i, item in enumerate(items)
-            )
-        else:
-            return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
-
-    def generate_prompt_string(self) -> str:
-        """
-        Generate a prompt string based on the constraints, commands, resources,
-            and performance evaluations.
-
-        Returns:
-            str: The generated prompt string.
-        """
-        formatted_response_format = json.dumps(self.response_format, indent=4)
-        return (
-            f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
-            "Commands:\n"
-            f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
-            f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
-            "Performance Evaluation:\n"
-            f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
-            "You should only respond in JSON format as described below \nResponse"
-            f" Format: \n{formatted_response_format} \nEnsure the response can be"
-            " parsed by Python json.loads"
-        )

spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Crazy Fox MP3 Songs Free Download Stream and Download Crazy Fox Music.md

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-<h1>Download Crazy Fox Songs: How to Enjoy the Music of South Sudan's Rising Star</h1>
-<p>If you are a fan of South Sudanese music, you have probably heard of Crazy Fox. He is one of the most talented and popular singers in the country, and his songs are catchy, upbeat, and inspiring. In this article, we will tell you everything you need to know about Crazy Fox, why you should download his songs, and how to do it easily and safely.</p>
- <h2>Who is Crazy Fox?</h2>
-<p>Crazy Fox is a young and talented singer from South Sudan, who has been making waves in the music industry since 2016. He is also known as John Ohide, and he was born in 1995 in Juba. He grew up in a musical family, and he started singing at a young age. He was influenced by both traditional and modern music, and he developed his own unique style of singing and rapping.</p>
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- <h3>A brief biography of the singer</h3>
-<p>Crazy Fox started his musical career in 2016, when he released his first single, "Ana Gaid", which means "I am staying" in Arabic. The song was a hit, and it expressed his love for his country and his determination to stay despite the civil war and the hardships. He followed up with more singles, such as "Juba Juice", "Nyan Ci Yer", and "Wan Ci Bi". He also collaborated with other artists, such as Silver X, Kawaja Revolution, and MT7. He has performed in several concerts and festivals, both in South Sudan and abroad. He is currently working on his first album, which is expected to be released soon.</p>
- <h3>His musical style and influences</h3>
-<p>Crazy Fox's musical style is a blend of hip hop, dancehall, reggae, afrobeat, and traditional South Sudanese music. He sings and raps in English, Arabic, Dinka, Nuer, Bari, and other local languages. He uses catchy hooks, witty lyrics, and energetic beats to create songs that appeal to a wide audience. He is influenced by both local and international artists, such as Emmanuel Kembe, Yaba Angelosi, Bob Marley, Tupac Shakur, Wizkid, and Davido.</p>
- <h3>His most popular songs and videos</h3>
-<p>Some of Crazy Fox's most popular songs are:</p>
-<ul>
-<li>"Ana Gaid": This is his debut single, which was released in 2016. It is a patriotic song that celebrates his love for South Sudan and his refusal to leave his homeland. The song has over 172K views on YouTube.</li>
-<li>"Juba Juice": This is a dancehall song that was released in 2017. It is a fun song that praises the beauty and diversity of Juba, the capital city of South Sudan. The song has over 68K views on YouTube.</li>
-<li>"Nyan Ci Yer": This is a hip hop song that was released in 2018. It is a motivational song that encourages young people to work hard and pursue their dreams. The song has over 61K views on YouTube.</li>
-<li>"Wan Ci Bi": This is a reggae song that was released in 2019. It is a love song that expresses his feelings for a special girl. The song has over 36K views on YouTube.</li>
-</ul>
-<p>You can watch his official music videos on his YouTube channel, or you can listen to his songs on SoundCloud or Audiomack. You can also find his songs on other platforms, such as Spotify, Apple Music, and Deezer.</p>
- <h2>Why download Crazy Fox songs?</h2>
-<p>Downloading Crazy Fox songs is a great way to enjoy his music anytime and anywhere. There are many benefits of downloading music, such as:</p>
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-<p>Downloading Crazy Fox songs is also a good idea because streaming music in South Sudan can be challenging. South Sudan has one of the lowest internet penetration rates in the world, with only 2.5% of the population having access to the internet. The internet speed is also slow and unreliable, averaging at 0.6 Mbps. This means that streaming music online can be frustrating and expensive, especially if you want to listen to high-quality audio. By downloading Crazy Fox songs, you can avoid these problems and enjoy his music smoothly and conveniently.</p>
- <p>Another reason why you should download Crazy Fox songs is because his music has a positive cultural and social impact. His music celebrates the diversity and richness of South Sudanese culture, and promotes peace and unity among the people. His music also inspires and empowers young people to overcome their challenges and achieve their goals. His music is a source of joy and hope for many South Sudanese, especially in these difficult times. By downloading Crazy Fox songs, you can show your appreciation and support for his music and his message.</p>
- <h2>How to download Crazy Fox songs?</h2>
-<p>Downloading Crazy Fox songs is easy and simple, if you know where to look and what to do. Here are some of the best websites and apps to download his music, and some tips on how to use them.</p>
- <h3>The best websites and apps to download his music</h3>
-<h4>YouTube</h4>
-<p>YouTube is one of the most popular and convenient platforms to download Crazy Fox songs. You can find all his official music videos on his YouTube channel, as well as some live performances and interviews. You can also find some fan-made videos and covers of his songs. To download Crazy Fox songs from YouTube, you will need a YouTube downloader app or website, such as VidMate or Y2Mate. These apps and websites allow you to download YouTube videos in different formats, such as MP4, MP3, or M4A. You can choose the format that suits your device and preference, and then save the file to your device or cloud storage. You can also adjust the quality and size of the file, depending on your internet speed and storage space.</p>
- <h4>SoundCloud</h4>
-<p>SoundCloud is another great platform to download Crazy Fox songs. You can find all his official audio tracks on his SoundCloud page, as well as some remixes and collaborations with other artists. You can also discover new songs and artists that are similar to Crazy Fox's style. To download Crazy Fox songs from SoundCloud, you will need a SoundCloud downloader app or website, such as KlickAud or ScloudDownloader. These apps and websites allow you to download SoundCloud tracks in MP3 format, which is compatible with most devices. You just need to copy the URL of the track you want to download, paste it into the app or website, and click on the download button. You can then save the file to your device or cloud storage.</p>
- <h4>Audiomack</h4>
-<p>Audiomack is another excellent platform to download Crazy Fox songs. You can find all his official audio tracks on his Audiomack page, as well as some exclusive releases and playlists. You can also follow him on Audiomack to get notified of his latest uploads and updates. To download Crazy Fox songs from Audiomack, you will need the Audiomack app, which is available for both Android and iOS devices. The app allows you to download Audiomack tracks in MP3 format, which is compatible with most devices. You just need to tap on the download icon next to the track you want to download, and choose the quality option that suits your device and preference. You can then save the file to your device or cloud storage.</p>
- <h3>The best devices and formats to play his music</h3>
-<h4>Smartphones and tablets</h4>
-<p>Smartphones and tablets are the most common devices that people use to play music nowadays. They are portable, convenient, and versatile. You can play Crazy Fox songs on your smartphone or tablet using any music player app, such as Google Play Music, Apple Music, or VLC. You can also use headphones, earphones, or Bluetooth speakers to enhance the sound quality and volume. The best format to play Crazy Fox songs on your smartphone or tablet is MP3, which is a compressed and universal format that can save storage space and bandwidth. You can also use other formats, such as M4A, AAC, or WMA, depending on your device and preference.</p>
- <h4>MP3 players and speakers</h4>
-<p>MP3 players and speakers are another option to play music on the go. They are small, lightweight, and easy to carry. You can play Crazy Fox songs on your MP3 player or speaker using a USB cable, a memory card, or a Bluetooth connection. You can also use headphones, earphones, or external speakers to enhance the sound quality and volume. The best format to play Crazy Fox songs on your MP3 player or speaker is MP3, which is a compressed and universal format that can save storage space and battery life. You can also use other formats, such as WAV, FLAC, or OGG, depending on your device and preference.</p>
- <h4>Computers and laptops</h4>
-<p>Computers and laptops are the most powerful and versatile devices to play music. They have large screens, high-quality speakers, and fast processors. You can play Crazy Fox songs on your computer or laptop using any music player software, such as Windows Media Player, iTunes, or Winamp. You can also use headphones, earphones, or external speakers to enhance the sound quality and volume. The best format to play Crazy Fox songs on your computer or laptop is WAV, which is an uncompressed and lossless format that can preserve the original sound quality and details. You can also use other formats, such as MP3, FLAC, or OGG, depending on your device and preference.</p>
- <h2>Conclusion</h2>
-<p>Crazy Fox is one of the most talented and popular singers in South Sudan. His music is a blend of hip hop, dancehall, reggae, afrobeat, and traditional South Sudanese music. His music celebrates the diversity and richness of South Sudanese culture, and promotes peace and unity among the people. His music also inspires and empowers young people to overcome their challenges and achieve their goals. His music is a source of joy and hope for many South Sudanese, especially in these difficult times.</p>
- <p>Downloading Crazy Fox songs is a great way to enjoy his music anytime and anywhere. There are many benefits of downloading music, such as listening offline, creating playlists, transferring to different devices, sharing with friends and family, and supporting the artist. Downloading Crazy Fox songs is also a good idea because streaming music in South Sudan can be challenging due to low internet penetration, slow internet speed, and high data charges. By downloading Crazy Fox songs, you can avoid these problems and enjoy his music smoothly and conveniently.</p>
- <p>Downloading Crazy Fox songs is easy and simple if you know where to look and what to do. Some of the best websites and apps to download his music are YouTube  , SoundCloud, Audiomack, Spotify, Apple Music, Deezer, VidMate, Y2Mate, KlickAud, ScloudDownloader, Google Play Music, Apple Music, VLC, Windows Media Player, iTunes, Winamp. Some of the best devices and formats to play his music are smartphones, tablets, MP3 players, speakers, computers, laptops, MP3, M4A, AAC, WMA, WAV, FLAC, OGG.</p>
- <p>We hope this article has helped you learn more about Crazy Fox and how to download his songs. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy listening!</p>
- <h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Crazy Fox and his music.</p>
-<ol>
-<li>Where can I find Crazy Fox's social media accounts?</li>
-<p>You can follow Crazy Fox on his Facebook page, his Instagram account, and his Twitter account. You can also subscribe to his YouTube channel to get notified of his latest videos.</p>
-<li>How can I contact Crazy Fox for bookings or collaborations?</li>
-<p>You can contact Crazy Fox through his email address, [email protected], or through his phone number, +211 922 222 222. You can also send him a message on his Facebook page or his Instagram account.</p>
-<li>How can I support Crazy Fox and his music?</li>
-<p>You can support Crazy Fox and his music by downloading his songs, sharing them with your friends and family, leaving positive feedback and reviews, attending his concerts and events, buying his merchandise, and donating to his causes. You can also follow him on his social media accounts and show him some love and appreciation.</p>
-<li>What are some of the awards and achievements that Crazy Fox has received?</li>
-<p>Crazy Fox has received several awards and recognitions for his music and his contribution to the South Sudanese music industry. Some of them are:</p>
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-<li>The Best Hip Hop Artist of the Year at the South Sudan Music Awards in 2017 and 2018.</li>
-<li>The Best Male Artist of the Year at the Eye Radio Music Awards in 2018.</li>
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-<li>The Best Video of the Year for "Nyan Ci Yer" at the Eye Radio Music Awards in 2019.</li>
-<li>The Most Influential Artist of the Year at the South Sudan Youth Awards in 2020.</li>
-</ul>
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-<p>Crazy Fox is not only a singer, but also a humanitarian and an activist. He is involved in several causes and projects that aim to improve the lives and conditions of the South Sudanese people. Some of them are:</p>
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-<li>He is a goodwill ambassador for UNICEF South Sudan, where he advocates for children's rights and education.</li>
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- <p>With Criminal Case mod apk happymod, you will have unlimited money, energy, and stars in your game account. You can use these resources to buy items, unlock new crime scenes, perform actions, use hints and boosters, customize your avatar and your pet, join a team or create your own, etc. You will not have to worry about running out of money, energy, or stars ever again.</p>
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- <h2>Conclusion</h2>
- <p>Criminal Case is a great game for anyone who loves detective games and hidden object games. It has an engaging storyline, realistic graphics, diverse characters, and exciting gameplay. However, if you want to play this game with more features and benefits, you should try Criminal Case mod apk happymod. This is a modded version of the game that gives you unlimited money, energy, and stars, as well as no ads and no waiting time. You can download and install this modded version of the game from Happymod, a platform that provides safe and reliable modded apk files for various games and apps. By playing Criminal Case mod apk happymod, you will have more fun and challenge in solving murder cases and catching killers.</p>
- <p>I hope this article has helped you understand what Criminal Case mod apk happymod is, how to download and install it, and what benefits you can get from playing it. If you have any questions or feedback, please feel free to leave them in the comment section below. Thank you for reading and happy gaming!</p>
- <p>Here are some FAQs that you might find useful:</p>
- <h4>Q: Is Criminal Case mod apk happymod safe to use?</h4>
-<p>A: Yes, Criminal Case mod apk happymod is safe to use as long as you download it from Happymod or from a trusted source. You can also scan the mod apk file with any antivirus software before installing it. However, you should be aware that using modded apk files may violate the terms and conditions of the original game and may result in your account being banned or suspended. Therefore, you should use Criminal Case mod apk happymod at your own risk and discretion.</p>
- <h4>Q: Do I need to root my device to use Criminal Case mod apk happymod?</h4>
-<p>A: No, you do not need to root your device to use Criminal Case mod apk happymod. You can install and play the modded version of the game without any root access or permission. However, some features of the modded version may require root access to work properly, such as unlimited money, energy, and stars. If you want to use these features, you may need to root your device first.</p>
- <h4>Q: Can I play Criminal Case mod apk happymod offline?</h4>
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-<p>A: You can update Criminal Case mod apk happymod by downloading and installing the latest version of the mod apk file from Happymod or from any trusted source. You can also check for updates in the Happymod app itself and tap on the update button if available. However, you should be careful when updating the mod apk file, as it may overwrite your previous game data and progress. Therefore, you should backup your game data before updating the mod apk file.</p> 197e85843d<br />
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Download City Bus Simulator 3D Offline Mod Apk and Enjoy Realistic Bus Driving.md

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- <h2>Introduction</h2>
-<p>Do you love driving games? Do you want to experience the thrill of driving a city bus in a realistic environment? If yes, then you should try City Bus Simulator 3D Offline Mod APK, a fun and addictive game that will test your skills and patience as a bus driver.</p>
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-<p>City Bus Simulator 3D Offline Mod APK is a modified version of the original game, City Bus Simulator 2020, developed by This Gen Gamez. It is an offline game that lets you drive various buses in different cities and scenarios. You can pick up passengers, follow the traffic rules, avoid accidents, and complete your routes on time.</p>
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-<p>City Bus Simulator 3D Offline Mod APK is a game that will keep you entertained for hours. You will enjoy the following benefits when you play this game:</p>
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-<li>You will have access to unlimited money and unlocked items that will enhance your gameplay and customization options.</li>
-</ul>
- <h2>Features of City Bus Simulator 3D Offline Mod APK</h2>
-<p>City Bus Simulator 3D Offline Mod APK has many features that make it one of the best driving games available. Here are some of the features that you will love:</p>
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-<p>The game has a variety of buses that you can drive, such as city buses, school buses, tourist buses, double-decker buses, and more. Each bus has its own characteristics, such as speed, handling, capacity, and fuel consumption. You can also customize your bus with different colors, stickers, wheels, and accessories. The game also has different routes that you can choose from, such as city routes, highway routes, mountain routes, desert routes, and more. Each route has its own challenges, such as traffic jams, roadblocks, sharp turns, steep slopes, and weather changes.</p>
- <h3>Realistic traffic and weather conditions</h3>
-<p>The game has realistic traffic and weather conditions that will affect your driving experience. You will encounter different vehicles on the road, such as cars, trucks, motorcycles, bicycles, and pedestrians. You will have to follow the traffic rules, such as stopping at red lights, giving way to other vehicles, and using indicators. You will also face different weather conditions, such as sunny days, rainy days, foggy days, snowy days, and stormy days. You will have to adjust your driving style according to the weather conditions.</p>
- <h3>Challenging missions and achievements</h3>
-<p>The <p>The game has challenging missions and achievements that will test your skills and patience as a bus driver. You will have to complete different tasks, such as picking up and dropping off passengers, following the schedule, avoiding collisions, and parking the bus. You will also have to earn stars, coins, and diamonds that you can use to buy new buses and items. You will also unlock new levels and routes as you progress in the game.</p>
- <h3>Unlimited money and unlocked items</h3>
-<p>The game has unlimited money and unlocked items that will make your gameplay more enjoyable and easier. You will not have to worry about running out of money or fuel, or having to watch ads to get more resources. You will also have access to all the buses and items that are normally locked or require real money to purchase. You can customize your bus and gameplay as you wish.</p>
- <h2>How to download and install City Bus Simulator 3D Offline Mod APK?</h2>
-<p>If you want to play City Bus Simulator 3D Offline Mod APK, you will have to download and install it on your device. Here are the steps that you need to follow:</p>
- <h3>Step 1: Download the APK file from a trusted source</h3>
-<p>You will have to download the APK file of City Bus Simulator 3D Offline Mod APK from a trusted source, such as [this link]. The file size is about 100 MB, so make sure you have enough space on your device. You can also scan the file with an antivirus program before opening it.</p>
- <h3>Step 2: Enable unknown sources on your device</h3>
-<p>You will have to enable unknown sources on your device to install the APK file. To do this, go to your device settings, then security, then unknown sources. Turn on the option that allows you to install apps from unknown sources. This will allow you to install the APK file without any problems.</p>
- <h3>Step 3: Install the APK file and launch the game</h3>
-<p>You will have to install the APK file by tapping on it and following the instructions on the screen. It may take a few minutes for the installation to complete. Once it is done, you can launch the game by tapping on its icon on your home screen or app drawer. You can now enjoy playing City Bus Simulator 3D Offline Mod APK.</p>
- <h2>Conclusion</h2>
-<p>City Bus Simulator 3D Offline Mod APK is a fun and realistic driving game that will give you hours of entertainment and excitement. You will be able to drive various buses in different cities and scenarios, with amazing 3D graphics and sound effects, realistic traffic and weather conditions, challenging missions and achievements, unlimited money and unlocked items, and more. You can download and install City Bus Simulator 3D Offline Mod APK easily by following the steps above. If you love driving games, you should definitely try City Bus Simulator 3D Offline Mod APK.</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>Is City Bus Simulator 3D Offline Mod APK safe to use?</b></li>
-<p>Yes, City Bus Simulator 3D Offline Mod APK is safe to use, as long as you download it from a trusted source, such as [this link]. You can also scan the file with an antivirus program before installing it.</p>
-<li><b>Is City Bus Simulator 3D Offline Mod APK free to play?</b></li>
-<p>Yes, City Bus Simulator 3D Offline Mod APK is free to play, and you do not need an internet connection to play it. You also do not need to spend any real money to buy any buses or items in the game.</p>
-<li><b>How can I update City Bus Simulator 3D Offline Mod APK?</b></li>
-<p>You can update City Bus Simulator 3D Offline Mod APK by downloading the latest version of the APK file from [this link] and installing it over the existing one. You do not need to uninstall the previous version before installing the new one.</p>
-<li><b>How can I contact the developer of City Bus Simulator 3D Offline Mod APK?</b></li>
-<p>You can contact the developer of City Bus Simulator 3D Offline Mod APK by visiting their website at [this link] or by sending them an email at [email protected].</p>
-<li><b>What are some other games like City Bus Simulator 3D Offline Mod APK?</b></li>
-<p>Some other games like City Bus Simulator 3D Offline Mod APK are Coach Bus Simulator, Heavy Bus Simulator, Public Transport Simulator, Euro Truck Driver, and Driving School Sim.</p>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Brawl Stars Hack Mod APK The Ultimate Cheat for Brawl Stars Fans.md

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-<h1>Brawl Stars Hack Mod APK Free Download: Everything You Need to Know</h1>
-<p>If you are a fan of fast-paced multiplayer action games, you might have heard of Brawl Stars. This is a popular game developed by Supercell, the same company behind Clash of Clans and Clash Royale. In this game, you can choose from a variety of brawlers, each with their own unique skills and abilities, and compete with other players in different modes. You can also team up with your friends or join a club to chat and share tips.</p>
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-<p>But what if you want to get unlimited gems, coins, and other resources in the game without spending real money? Well, that's where Brawl Stars Hack Mod APK comes in. This is a modified version of the original game that gives you access to various cheats and hacks that can enhance your gaming experience. In this article, we will tell you everything you need to know about Brawl Stars Hack Mod APK, including its features, benefits, risks, and how to download and install it on your device.</p>
- <h2>What is Brawl Stars?</h2>
-<p>Brawl Stars is a 3v3 online multiplayer action game that was released in 2018 for Android and iOS devices. The game features a colorful and cartoonish graphics style that appeals to both kids and adults. The game has over 40 brawlers that you can unlock and upgrade as you progress in the game. Each brawler has a unique personality, appearance, and skill set that makes them suitable for different roles and strategies.</p>
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-<p>Some of the main features of Brawl Stars are:</p>
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-<p>To play Brawl Stars, you need to have a stable internet connection and a compatible device. You can download the game for free from the Google Play Store or the App Store. Once you have installed the game, you need to create an account or log in with your existing one. You can then choose your preferred game mode and start playing.</p>
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-<p>Brawl Stars Hack Mod APK is a modified version of the original Brawl Stars game that allows you to access various cheats and hacks that are not available in the official game. For example, you can get unlimited gems, coins, tickets, and other resources that you can use to unlock and upgrade your brawlers, buy skins, and participate in events. You can also unlock all the brawlers, gadgets, and star powers without spending any money or time. You can also use some features such as auto-aim, wallhack, speedhack, and more to gain an edge over your opponents.</p>
-<h3>Benefits of using Brawl Stars Hack Mod APK</h3>
-<p>Some of the benefits of using Brawl Stars Hack Mod APK are:</p>
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-<p>However, using Brawl Stars Hack Mod APK also comes with some risks that you should be aware of:</p>
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-<li>You may lose your account or progress. Supercell has a strict anti-cheat system that can detect and ban users who use hacks or mods. You may lose your account permanently or temporarily if you are caught. You may also lose your progress and achievements if you uninstall the mod or switch to the official game.</li>
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-<p>If you still want to try Brawl Stars Hack Mod APK despite the risks, you need to follow these steps to download and install it on your device:</p>
-<h3>Step 1: Enable unknown sources</h3>
-<p>Before you can install any APK file on your device, you need to enable unknown sources in your settings. This will allow you to install apps from sources other than the Google Play Store or the App Store. To do this, go to your settings > security > unknown sources and toggle it on.</p>
-<h3>Step 2: Download the APK file</h3>
-<p>Next, you need to download the APK file of Brawl Stars Hack Mod from a reliable and trustworthy source. You can search for it online or use this link: [Brawl Stars Hack Mod APK]. Make sure you download the latest version of the mod that is compatible with your device and the official game.</p>
-<h3>Step 3: Install the APK file</h3>
-<p>Once you have downloaded the APK file, you need to locate it in your file manager and tap on it to start the installation process. You may see a warning message that says "this type of file can harm your device". Ignore it and tap on "install anyway". Wait for a few seconds until the installation is complete.</p>
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-<p>Finally, you can launch the game from your app drawer or home screen and enjoy the hack mod features. You should see a menu icon on the top left corner of the screen that will give you access to various cheats and hacks. You can also see your unlimited resources on the top right corner of the screen.</p>
- <h2>Conclusion</h2>
-<p>Brawl Stars is a fun and addictive multiplayer action game that offers a lot of variety and excitement. However, if you want to get unlimited resources and access various cheats and hacks in the game, you can try Brawl Stars Hack Mod APK. This is a modified version of the original game that gives you various advantages over other players. However, you should also be aware of the risks involved in using Brawl Stars Hack Mod APK, such as legal issues, account bans, or game experience degradation. Therefore, you should use it at your own risk and discretion. We hope this article has given you some useful information about Brawl Stars Hack Mod APK and how to download and install it on your device. If you have any questions or feedback, feel free to leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Brawl Stars Hack Mod APK:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
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-<td>Is Brawl Stars Hack Mod APK safe to use?</td>
-<td>Brawl Stars Hack Mod APK is not safe to use as it may contain malware or viruses that can harm your device or data. It may also violate the terms of service and privacy policy of Supercell and expose you to legal issues. Moreover, it may be detected and banned by the anti-cheat system of Supercell and cause you to lose your account or progress.</td>
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-<td>Brawl Stars Hack Mod APK is free to download from various sources online. However, you should be careful and only download it from reliable and trustworthy sources. You should also avoid clicking on any suspicious links or ads that may redirect you to malicious sites or download unwanted files.</td>
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-<td>You can use Brawl Stars Hack Mod APK with your existing account, but it is not recommended. You may risk losing your account or progress if you are caught using hacks or mods by the anti-cheat system of Supercell. You may also face legal issues if you are found violating the terms of service and privacy policy of Supercell. Therefore, it is better to use a new or secondary account if you want to try Brawl Stars Hack Mod APK.</td>
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-<td>You can play Brawl Stars Hack Mod APK with other players who are also using the same mod. However, you may not be able to play with players who are using the official game or a different mod. You may also face unfair competition or imbalance in the game as some players may have more advantages than others.</td>
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spaces/1phancelerku/anime-remove-background/Craftsman Building Craft - Google Playde En ok ndirilen Oyun.md

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-<h1>Craftsman Yukle: How to Play Craftsman: Building Craft on PC</h1>
-<p>Do you love building and exploring in sandbox games? Do you want to unleash your creativity and imagination in a virtual world? If yes, then you should try Craftsman: Building Craft, a popular game that lets you design houses, castles, and other structures. You can play it alone or with your friends online. But did you know that you can also play it on your PC? In this article, we will show you how to download and install Craftsman: Building Craft on your PC using BlueStacks, an Android emulator that allows you to run mobile apps and games on your computer. But first, let's find out more about this game.</p>
-<h2>What is Craftsman: Building Craft?</h2>
-<p>Craftsman: Building Craft is a sandbox game that was developed by StarGame22 and released in 2020. It has over 50 million downloads on the Google Play Store and has a rating of 4.1 out of 5 stars. The game is inspired by Minecraft, but has its own features and style. Here are some of the main aspects of the game:</p>
-<h2>craftsman yukle</h2><br /><p><b><b>DOWNLOAD</b> &#9193; <a href="https://jinyurl.com/2uNMcY">https://jinyurl.com/2uNMcY</a></b></p><br /><br />
-<h3>A sandbox game with creative and survival modes</h3>
-<p>In Craftsman: Building Craft, you can choose between two modes: creative and survival. In creative mode, you have unlimited resources and can build anything you want without any restrictions. You can also fly around and explore different biomes, such as forests, deserts, mountains, and oceans. In survival mode, you have to gather resources, craft tools and weapons, fight enemies, and survive the night. You also have to manage your hunger and health bars.</p>
-<h3>A game with stunning graphics and realistic sound</h3>
-<p>One of the most impressive features of Craftsman: Building Craft is its graphics. The game has a pixelated style that gives it a retro feel, but also has realistic lighting, shadows, and textures. The game also has dynamic weather effects, such as rain, snow, fog, and thunderstorms. The sound effects are also very immersive, as you can hear the sounds of nature, animals, monsters, and explosions.</p>
-<h3>A game with multiplayer and offline options</h3>
-<p>Craftsman: Building Craft is not only a single-player game, but also a multiplayer one. You can join online servers and play with other players from around the world. You can chat with them, collaborate with them, or compete with them. You can also create your own server and invite your friends to join. If you prefer playing offline, you can also do that. You can play the game without an internet connection and enjoy it at your own pace.</p>
-<h2>Why play Craftsman: Building Craft on PC?</h2>
-<p>While Craftsman: Building Craft is a great game to play on your mobile device, it can also be more enjoyable to play it on your PC. Here are some of the reasons why:</p>
-<h3>Enjoy a bigger screen and better controls</h3>
-<p>Playing Craftsman: Building Craft on your PC means that you can see the game in full HD resolution on a larger screen. This will make the game more detailed and vivid, as well as easier to see and navigate. You can also use your keyboard and mouse to control the game instead of tapping on a small touchscreen. This will give you more accuracy and comfort when building, crafting, fighting, and exploring.</p>
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-<h3>Use BlueStacks <h3>Use BlueStacks to enhance your gaming experience</h3>
-<p>BlueStacks is a powerful and reliable Android emulator that allows you to run mobile apps and games on your PC. By using BlueStacks, you can enjoy Craftsman: Building Craft with many advantages, such as:</p>
-<ul>
-<li>Fast and smooth performance: BlueStacks uses advanced technology to optimize the game's speed and stability, so you can play without any lag or crashes.</li>
-<li>Customizable settings: BlueStacks lets you adjust the game's graphics, sound, and language according to your preferences. You can also change the key mapping and mouse sensitivity to suit your play style.</li>
-<li>Multiple instances: BlueStacks allows you to run multiple instances of the game at the same time, so you can switch between different accounts or modes easily.</li>
-<li>Screen recording and streaming: BlueStacks enables you to record your gameplay and save it as a video file, or stream it live to platforms like Twitch, YouTube, or Facebook.</li>
-</ul>
-<h3>Access the BlueStacks Macro Community for more fun</h3>
-<p>Another feature that makes BlueStacks stand out is the Macro Community, a place where you can find and share macros for various games. Macros are sequences of commands that automate certain actions in the game, such as building, crafting, or fighting. By using macros, you can save time and effort, as well as improve your skills and efficiency. You can create your own macros using the BlueStacks Macro Editor, or download macros from other users in the Macro Community. You can also rate and comment on the macros, or share your own with others.</p>
-<h2>How to download and install Craftsman: Building Craft on PC?</h2>
-<p>Now that you know why playing Craftsman: Building Craft on PC is a good idea, let's see how to do it. The process is very simple and only takes a few minutes. Here are the steps you need to follow:</p>
-<h3>Download and install BlueStacks on your PC</h3>
-<p>The first thing you need to do is download BlueStacks from its official website: <a href="">https://www.bluestacks.com/</a>. You can choose between the Windows or Mac version depending on your operating system. Once you have downloaded the installer file, double-click on it and follow the instructions to install BlueStacks on your PC. The installation may take some time depending on your internet speed and PC specifications.</p>
-<h3>Launch BlueStacks and search for Craftsman: Building Craft</h3>
-<p>After installing BlueStacks, launch it from your desktop or start menu. You will see the BlueStacks home screen with various icons and options. On the top right corner, you will see a search bar where you can type the name of the game you want to play. In this case, type "Craftsman: Building Craft" and hit enter. You will see a list of results with the game's icon and name.</p>
-<h3>Install the game from the Google Play Store or the APK file</h3>
-<p>To install Craftsman: Building Craft on your PC, you have two options: either from the Google Play Store or from an APK file. The Google Play Store is the official source of Android apps and games, where you can download them safely and securely. To access it, click on the game's icon from the search results and then click on the "Install" button. You may need to sign in with your Google account if you haven't done so before. The game will start downloading and installing automatically.</p>
-<p>The other option is to use an APK file, which is a compressed file that contains the game's data and code. You can download an APK file from various websites on the internet, but be careful as some of them may contain viruses or malware. To use an APK file, click on the "Install APK" button on the bottom right corner of the BlueStacks home screen. Then browse your PC folders and select the APK file you want to install. The game will start installing automatically.</p>
-<h3>Start playing and enjoy the game</h3>
-<p>Once you have installed Craftsman: Building Craft on your PC, you can start playing it right away. To launch it, click on its icon from the BlueStacks home screen or from your desktop shortcut. You will see the game's loading screen and then its main menu. From there, you can choose between creative or survival mode, join or create a server, or customize your settings. You can also access the BlueStacks features such as macros, screen recording, streaming, etc.</p>
-<h2>Conclusion</h2>
-<p>Craftsman: Building Craft is a fun and addictive sandbox game that lets you build and explore in a pixelated world. You can <p>Craftsman: Building Craft is a fun and addictive sandbox game that lets you build and explore in a pixelated world. You can play it on your mobile device, but you can also play it on your PC using BlueStacks, an Android emulator that offers many benefits and features. By playing Craftsman: Building Craft on PC, you can enjoy a bigger screen, better controls, faster performance, customizable settings, multiple instances, screen recording, streaming, and macros. To play Craftsman: Building Craft on PC, you just need to download and install BlueStacks, search for the game, and install it from the Google Play Store or an APK file. Then you can start playing and enjoy the game.</p>
-<h2>FAQs</h2>
-<p>Here are some of the frequently asked questions about Craftsman: Building Craft and BlueStacks:</p>
-<table>
-<tr>
-<th>Question</th>
-<th>Answer</th>
-</tr>
-<tr>
-<td>Is Craftsman: Building Craft free to play?</td>
-<td>Yes, Craftsman: Building Craft is free to download and play. However, it may contain ads and in-app purchases.</td>
-</tr>
-<tr>
-<td>Is Craftsman: Building Craft safe to play?</td>
-<td>Yes, Craftsman: Building Craft is safe to play as long as you download it from a trusted source, such as the Google Play Store or the official BlueStacks website.</td>
-</tr>
-<tr>
-<td>Is BlueStacks free to use?</td>
-<td>Yes, BlueStacks is free to download and use. However, it may offer some optional premium features and services that require a subscription or a payment.</td>
-</tr>
-<tr>
-<td>Is BlueStacks safe to use?</td>
-<td>Yes, BlueStacks is safe to use as long as you download it from its official website: <a href="">https://www.bluestacks.com/</a>. BlueStacks is also compliant with the Google Play Protect and other security standards.</td>
-</tr>
-<tr>
-<td>How can I contact the support team of Craftsman: Building Craft or BlueStacks?</td>
-<td>If you have any issues or questions about Craftsman: Building Craft, you can contact the developer of the game through their email address: [email protected]. If you have any issues or questions about BlueStacks, you can contact the support team of BlueStacks through their website: <a href="">https://support.bluestacks.com/</a>.</td>
-</tr>
-</table></p> 197e85843d<br />
-<br />
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spaces/1toTree/lora_test/ppdiffusers/pipelines/paint_by_example/image_encoder.py

@@ -1,75 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import paddle
-from paddle import nn
-
-from paddlenlp.transformers import (
-    CLIPPretrainedModel,
-    CLIPVisionConfig,
-    CLIPVisionModel,
-)
-
-from ...models.attention import BasicTransformerBlock
-from ...utils import logging
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class PaintByExampleImageEncoder(CLIPPretrainedModel):
-    config_class = CLIPVisionConfig
-
-    def __init__(self, config: CLIPVisionConfig):
-        super().__init__(config)
-        self.projection_dim = config.projection_dim
-
-        self.model = CLIPVisionModel(config)
-
-        self.mapper = PaintByExampleMapper(config)
-        self.final_layer_norm = nn.LayerNorm(config.hidden_size)
-        self.proj_out = nn.Linear(config.hidden_size, self.projection_dim)
-
-        # uncondition for scaling
-        self.uncond_vector = self.create_parameter(
-            [1, 1, self.projection_dim],
-            dtype=paddle.get_default_dtype(),
-            default_initializer=nn.initializer.Assign(paddle.rand((1, 1, self.projection_dim))),
-        )
-
-    def forward(self, pixel_values):
-        clip_output = self.model(pixel_values=pixel_values)
-        latent_states = clip_output.pooler_output
-        latent_states = self.mapper(latent_states[:, None])
-        latent_states = self.final_layer_norm(latent_states)
-        latent_states = self.proj_out(latent_states)
-        return latent_states
-
-
-class PaintByExampleMapper(nn.Layer):
-    def __init__(self, config):
-        super().__init__()
-        num_layers = (config.num_hidden_layers + 1) // 5
-        hid_size = config.hidden_size
-        num_heads = 1
-        self.blocks = nn.LayerList(
-            [
-                BasicTransformerBlock(hid_size, num_heads, hid_size, activation_fn="gelu", attention_bias=True)
-                for _ in range(num_layers)
-            ]
-        )
-
-    def forward(self, hidden_states):
-        for block in self.blocks:
-            hidden_states = block(hidden_states)
-
-        return hidden_states

spaces/2023Liu2023/bingo/src/components/external-link.tsx

@@ -1,30 +0,0 @@
-export function ExternalLink({
-  href,
-  children
-}: {
-  href: string
-  children: React.ReactNode
-}) {
-  return (
-    <a
-      href={href}
-      target="_blank"
-      rel="noreferrer"
-      className="inline-flex flex-1 justify-center gap-1 underline"
-    >
-      <span>{children}</span>
-      <svg
-        aria-hidden="true"
-        height="7"
-        viewBox="0 0 6 6"
-        width="7"
-        className="opacity-70"
-      >
-        <path
-          d="M1.25215 5.54731L0.622742 4.9179L3.78169 1.75597H1.3834L1.38936 0.890915H5.27615V4.78069H4.40513L4.41109 2.38538L1.25215 5.54731Z"
-          fill="currentColor"
-        ></path>
-      </svg>
-    </a>
-  )
-}

spaces/801artistry/RVC801/Fixes/local_fixes.py

@@ -1,136 +0,0 @@
-import os
-import sys
-import time
-import shutil
-import requests
-import zipfile
-
-def insert_new_line(file_name, line_to_find, text_to_insert):
-    lines = []
-    with open(file_name, 'r', encoding='utf-8') as read_obj:
-        lines = read_obj.readlines()
-    already_exists = False
-    with open(file_name + '.tmp', 'w', encoding='utf-8') as write_obj:
-        for i in range(len(lines)):
-            write_obj.write(lines[i])
-            if lines[i].strip() == line_to_find:
-                # If next line exists and starts with sys.path.append, skip
-                if i+1 < len(lines) and lines[i+1].strip().startswith("sys.path.append"):
-                    print('It was already fixed! Skip adding a line...')
-                    already_exists = True
-                    break
-                else:
-                    write_obj.write(text_to_insert + '\n')
-    # If no existing sys.path.append line was found, replace the original file
-    if not already_exists:
-        os.replace(file_name + '.tmp', file_name)
-        return True
-    else:
-        # If existing line was found, delete temporary file
-        os.remove(file_name + '.tmp')
-        return False
-
-def replace_in_file(file_name, old_text, new_text):
-    with open(file_name, 'r', encoding='utf-8') as file:
-        file_contents = file.read()
-
-    if old_text in file_contents:
-        file_contents = file_contents.replace(old_text, new_text)
-        with open(file_name, 'w', encoding='utf-8') as file:
-            file.write(file_contents)
-            return True
-
-    return False
-
-if __name__ == "__main__":
-    current_path = os.getcwd()
-    file_name = os.path.join(current_path, "infer", "modules", "train", "extract", "extract_f0_print.py")
-    line_to_find = 'import numpy as np, logging'
-    text_to_insert = "sys.path.append(r'" + current_path + "')"
-    
-
-    success_1 = insert_new_line(file_name, line_to_find, text_to_insert)
-    if success_1:
-        print('The first operation was successful!')
-    else:
-        print('He skipped the first operation because it was already fixed!')
-
-    file_name = 'infer-web.py'
-    old_text = 'with gr.Blocks(theme=gr.themes.Soft()) as app:'
-    new_text = 'with gr.Blocks() as app:'
-
-    success_2 = replace_in_file(file_name, old_text, new_text)
-    if success_2:
-        print('The second operation was successful!')
-    else:
-        print('The second operation was omitted because it was already fixed!')
-
-    print('Local corrections successful! You should now be able to infer and train locally in Applio RVC Fork.')
-    
-    time.sleep(5)
-
-def find_torchcrepe_directory(directory):
-    """
-    Recursively searches for the topmost folder named 'torchcrepe' within a directory.
-    Returns the path of the directory found or None if none is found.
-    """
-    for root, dirs, files in os.walk(directory):
-        if 'torchcrepe' in dirs:
-            return os.path.join(root, 'torchcrepe')
-    return None
-
-def download_and_extract_torchcrepe():
-    url = 'https://github.com/maxrmorrison/torchcrepe/archive/refs/heads/master.zip'
-    temp_dir = 'temp_torchcrepe'
-    destination_dir = os.getcwd()
-
-    try:
-        torchcrepe_dir_path = os.path.join(destination_dir, 'torchcrepe')
-
-        if os.path.exists(torchcrepe_dir_path):
-            print("Skipping the torchcrepe download. The folder already exists.")
-            return
-
-        # Download the file
-        print("Starting torchcrepe download...")
-        response = requests.get(url)
-
-        # Raise an error if the GET request was unsuccessful
-        response.raise_for_status()
-        print("Download completed.")
-
-        # Save the downloaded file
-        zip_file_path = os.path.join(temp_dir, 'master.zip')
-        os.makedirs(temp_dir, exist_ok=True)
-        with open(zip_file_path, 'wb') as file:
-            file.write(response.content)
-        print(f"Zip file saved to {zip_file_path}")
-
-        # Extract the zip file
-        print("Extracting content...")
-        with zipfile.ZipFile(zip_file_path, 'r') as zip_file:
-            zip_file.extractall(temp_dir)
-        print("Extraction completed.")
-
-        # Locate the torchcrepe folder and move it to the destination directory
-        torchcrepe_dir = find_torchcrepe_directory(temp_dir)
-        if torchcrepe_dir:
-            shutil.move(torchcrepe_dir, destination_dir)
-            print(f"Moved the torchcrepe directory to {destination_dir}!")
-        else:
-            print("The torchcrepe directory could not be located.")
-
-    except Exception as e:
-        print("Torchcrepe not successfully downloaded", e)
-
-    # Clean up temporary directory
-    if os.path.exists(temp_dir):
-        shutil.rmtree(temp_dir)
-
-# Run the function
-download_and_extract_torchcrepe()
-
-temp_dir = 'temp_torchcrepe'
-
-if os.path.exists(temp_dir):
-    shutil.rmtree(temp_dir)

spaces/AI-Dashboards/Streamlit-Plotly_Graph-Objects/app.py

@@ -1,29 +0,0 @@
-import streamlit as st
-import plotly.graph_objects as go
-
-import plotly.graph_objects as go
-
-def create_sunburst_plot(labels, parents, values, ids, text):
-    fig = go.Figure(go.Sunburst(
-        labels=labels,
-        parents=parents,
-        values=values,
-        ids=ids,
-        text=text,
-        hoverinfo="label+value",
-        branchvalues="total",
-    ))
-
-    fig.update_layout(margin=dict(t=0, l=0, r=0, b=0))
-    return fig
-
-# Replace these lists with your own data
-labels = ["Root", "Hip Surgery", "Knee Surgery", "CPT1", "CPT2", "CPT3", "CPT4"]
-parents = ["", "Root", "Root", "Hip Surgery", "Hip Surgery", "Knee Surgery", "Knee Surgery"]
-values = [None, 30, 40, 20, 10, 25, 15]
-ids = ["Root", "Hip Surgery", "Knee Surgery", "CPT1", "CPT2", "CPT3", "CPT4"]
-text = ["Root", "Hip Surgery", "Knee Surgery", "CPT1", "CPT2", "CPT3", "CPT4"]
-
-fig = create_sunburst_plot(labels, parents, values, ids, text)
-st.plotly_chart(fig)
-

spaces/AIGC-Audio/AudioGPT/NeuralSeq/configs/tts/libritts/pre_align.py

@@ -1,27 +0,0 @@
-import os
-
-from data_gen.tts.base_preprocess import BasePreprocessor
-import glob
-
-
-class LibrittsPreAlign(BasePreprocessor):
-    def meta_data(self):
-        wav_fns = sorted(glob.glob(f'{self.raw_data_dir}/*/*/*.wav'))
-        for wav_fn in wav_fns:
-            item_name = os.path.basename(wav_fn)[:-4]
-            txt_fn = f'{wav_fn[:-4]}.normalized.txt'
-            with open(txt_fn, 'r') as f:
-                txt = f.readlines()
-                f.close()
-            spk = item_name.split("_")[0]
-            # Example:
-            #
-            # 'item_name': '103_1241_000000_000001'
-            # 'wav_fn': 'LibriTTS/train-clean-100/103/1241/103_1241_000000_000001.wav'
-            # 'txt': 'matthew Cuthbert is surprised'
-            # 'spk_name': '103'
-            yield {'item_name': item_name, 'wav_fn': wav_fn, 'txt': txt[0], 'spk_name': spk}
-
-
-if __name__ == "__main__":
-    LibrittsPreAlign().process()

spaces/AIGText/GlyphControl/ldm/modules/image_degradation/__init__.py

@@ -1,2 +0,0 @@
-from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
-from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light

spaces/AIZ2H/08-Search-Streamlit-Session-State-QueryParameters/app.py

@@ -1,209 +0,0 @@
-import time
-import re
-import pandas as pd
-import numpy as np
-import torch
-import torch.nn.functional as F
-from transformers import AutoTokenizer, AutoModel
-from tokenizers import Tokenizer, AddedToken
-import streamlit as st
-from st_click_detector import click_detector
-
-# This lil dealio is my test of the new experiemntal primitives which promise to put cach in streamlit within striking distance of simulating cognitive episodic memory (personalized feelings about a moment through space time), and semantic memory (factual memories we are ready to share and communicate like your email address or physical address yo
-# Goal of this is to solve AI problem of two types of memory and their part in cognitive AGI along with the theory of model making as functional design of intelligence :
-# Type 1 Memory - Semantic Memory:
-# Semantic memory is conscious long-term memory for meaning, understanding, and conceptual facts about the world. Semantic memory is one of the two main varieties of explicit, conscious, long-term memory, which is memory that can be retrieved into conscious awareness after a long delay (from several seconds to years).
-# Type 2 Memory - Episodic Memory:
-# Episodic memory refers to the conscious recollection of a personal experience that contains information on what has happened and also where and when it happened. Recollection from episodic memory also implies a kind of first-person subjectivity that has been termed autonoetic consciousness.
-# Functional Design of Intelligence: The brain uses map like structures to build a models repeatedly as part of LTM and STM memory by creating hundreds of thousands of models of everything we know. This allows us to answer important questions about how we perceive the world, why we have a sense of self, and the origin of higher level thought processes.
-# Research Interests: AGI and ML Pipelines, Ambient IoT AI, Behavior Cognitive and Memory AI, Clinical Medical and Nursing AI, Genomics AI,  GAN Gaming GAIL  AR VR XR and Simulation AI, Graph Ontology KR KE AI, Languages and NLP AI, Quantum Compute GPU TPU NPU AI, Vision Image Document and Audio/Video AI
-# Layman terms for interest with keyword intersection for plot search.
-
-
-# callback to update query param on selectbox change
-def update_params():
-    try:
-        print("update1")
-        #st.experimental_set_query_params(option=st.session_state.query)
-    except ValueError:
-        pass
-        
-# RADIO BUTTON SET PERSIST
-# radio button persistance - plan is to hydrate when selected and change url along with textbox and search
-options = ["artificial intelligence", "robot", "VR", "medicine", "genomics", "cure", "heal", "brain", "support", "friendship", "memory", "aging", "pharma", "virus", "nurse", "doctor", "therapist", "nutrition", "technology", "computer", "software", "neuroscience", "birth", "death", "soul", "space", "sci-fi"] # these options come from my research interests blended with keywords across film genres
-
-query_params = st.experimental_get_query_params()
-ix = 0
-if query_params:
-    try:
-        q0 = query_params['query'][0]
-        ix = options.index(q0)
-    except ValueError:
-        pass
-selected_option = st.radio(
-    "Param", options, index=ix, key="query", on_change=update_params
-)
-st.write("<style>div.row-widget.stRadio > div{flex-direction:row;}</style>", unsafe_allow_html=True)
-
-
-st.experimental_set_query_params(option=selected_option)
-
-try:
-    st.session_state.query = query  # if set already above.  this prevents two interface elements setting it first time once
-except: # catch exception and set query param to predefined value
-    print("Error cant set after init")
-
-
-# Text Input, check the query params  set the text input to query value if in session
-# check if here for the first time then set the query
-if 'query' not in st.session_state:
-    #st.session_state['query'] = 'AI'
-    query = st.text_input("", value="artificial intelligence", key="query")
-    #st.session_state.query = 'AI' 
-    #st.write(st.session_state.query)
-else:
-    query = st.text_input("", value=st.session_state["query"], key="query")
-try:
-    query_params = st.experimental_get_query_params()
-    query_option = query_params['query'][0] #throws an exception when visiting http://host:port
-    option_selected = st.sidebar.selectbox('Pick option', options, index=options.index(query_option))
-except: # catch exception and set query param to predefined value
-    st.experimental_set_query_params(query="health") # set default
-    query_params = st.experimental_get_query_params()
-    query_option = query_params['query'][0]
-    query_option = "ai"
-    
-DEVICE = "cpu"
-MODEL_OPTIONS = ["msmarco-distilbert-base-tas-b", "all-mpnet-base-v2"]
-DESCRIPTION = """
-# Semantic search
-**Enter your query and hit enter**
-Built with 🤗 Hugging Face's [transformers](https://huggingface.co/transformers/) library, [SentenceBert](https://www.sbert.net/) models, [Streamlit](https://streamlit.io/) and 44k movie descriptions from the Kaggle [Movies Dataset](https://www.kaggle.com/rounakbanik/the-movies-dataset)
-"""
-
-# Session state - search parms
-if 'key' not in st.session_state:
-    st.session_state['key'] = 'value'
-if 'key' not in st.session_state:
-    st.session_state.key = 'value'
-st.write(st.session_state.key)
-st.write(st.session_state)
-
-#st.session_state
-for key in st.session_state.keys():
-    del st.session_state[key]
-#st.text_input("Your name", key="name")
-#st.session_state.name
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={
-        AutoModel: lambda _: None,
-        AutoTokenizer: lambda _: None,
-        dict: lambda _: None,
-    },
-)
-def load():
-    models, tokenizers, embeddings = [], [], []
-    for model_option in MODEL_OPTIONS:
-        tokenizers.append(
-            AutoTokenizer.from_pretrained(f"sentence-transformers/{model_option}")
-        )
-        models.append(
-            AutoModel.from_pretrained(f"sentence-transformers/{model_option}").to(
-                DEVICE
-            )
-        )
-    embeddings.append(np.load("embeddings.npy"))
-    embeddings.append(np.load("embeddings2.npy"))
-    df = pd.read_csv("movies.csv")
-    return tokenizers, models, embeddings, df
-
-tokenizers, models, embeddings, df = load()
-def pooling(model_output):
-    return model_output.last_hidden_state[:, 0]
-
-def compute_embeddings(texts):
-    encoded_input = tokenizers[0](
-        texts, padding=True, truncation=True, return_tensors="pt"
-    ).to(DEVICE)
-
-    with torch.no_grad():
-        model_output = models[0](**encoded_input, return_dict=True)
-
-    embeddings = pooling(model_output)
-    return embeddings.cpu().numpy()
-
-def pooling2(model_output, attention_mask):
-    token_embeddings = model_output[0]
-    input_mask_expanded = (
-        attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
-    )
-    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
-        input_mask_expanded.sum(1), min=1e-9
-    )
-
-def compute_embeddings2(list_of_strings):
-    encoded_input = tokenizers[1](
-        list_of_strings, padding=True, truncation=True, return_tensors="pt"
-    ).to(DEVICE)
-    with torch.no_grad():
-        model_output = models[1](**encoded_input)
-    sentence_embeddings = pooling2(model_output, encoded_input["attention_mask"])
-    return F.normalize(sentence_embeddings, p=2, dim=1).cpu().numpy()
-
-@st.cache(
-    show_spinner=False,
-    hash_funcs={Tokenizer: lambda _: None, AddedToken: lambda _: None},
-)
-def semantic_search(query, model_id):
-    start = time.time()
-    if len(query.strip()) == 0:
-        return ""
-    if "[Similar:" not in query:
-        if model_id == 0:
-            query_embedding = compute_embeddings([query])
-        else:
-            query_embedding = compute_embeddings2([query])
-    else:
-        match = re.match(r"\[Similar:(\d{1,5}).*", query)
-        if match:
-            idx = int(match.groups()[0])
-            query_embedding = embeddings[model_id][idx : idx + 1, :]
-            if query_embedding.shape[0] == 0:
-                return ""
-        else:
-            return ""
-    indices = np.argsort(embeddings[model_id] @ np.transpose(query_embedding)[:, 0])[
-        -1:-11:-1
-    ]
-    if len(indices) == 0:
-        return ""
-    result = "<ol>"
-    for i in indices:
-        result += f"<li style='padding-top: 10px'><b>{df.iloc[i].title}</b> ({df.iloc[i].release_date}). {df.iloc[i].overview} "
-        #result += f"<a id='{i}' href='#'>Similar movies</a></li>"
-        #result += f"<a id='{i}' href=https://www.imdb.com/find?q={df.iloc[i].title}&ref_=nv_sr_sm>IMDB</a></li>"
-    delay = "%.3f" % (time.time() - start)
-    return f"<p><i>Computation time: {delay} seconds</i></p>{result}</ol>"
-
-st.sidebar.markdown(DESCRIPTION)
-
-model_choice = st.sidebar.selectbox("Similarity model", options=MODEL_OPTIONS)
-model_id = 0 if model_choice == MODEL_OPTIONS[0] else 1
-
-clicked = click_detector(semantic_search(query, model_id))
-
-if clicked != "":
-    st.markdown(clicked)
-    change_query = False
-    if "last_clicked" not in st.session_state:
-        st.session_state["last_clicked"] = clicked
-        change_query = True
-    else:
-        if clicked != st.session_state["last_clicked"]:
-            st.session_state["last_clicked"] = clicked
-            change_query = True
-    if change_query:
-        st.session_state["query"] = f"[Similar:{clicked}] {df.iloc[int(clicked)].title}"
-        st.experimental_rerun()

spaces/AchyuthGamer/Free-Accounts-Generator/index.html

@@ -1,58 +0,0 @@
-<!DOCTYPE HTML>
-<html>
-
-<head>
-    <title>Free Steam Account Generator</title>
-    <link rel="icon" type="image/png" href="https://huggingface.co/spaces/AchyuthGamer/Free-Accounts-Generator/resolve/main/img/steam-chrome-logo.png">
-
-    <!-- Add background image -->
-    <style>
-        body {
-            background-image: url('https://huggingface.co/spaces/AchyuthGamer/aivvm/resolve/main/steam-bg.jpg');
-            background-size: cover;
-            background-repeat: no-repeat;
-            background-attachment: fixed;
-        }
-    </style>
-
-    <!-- Corrected meta tag -->
-    <meta name="description" content="minecraft, alt generator, free minecraft account, minecraft alts free, minecraft accounts for free">
-
-    <!-- Rest of your code -->
-    <meta name="keywords" content="minecraft, alt generator, free minecraft account, minecraft alts free, minecraft accounts for free">
-    <meta http-equiv="cache-control" content="no-cache" />
-    <meta http-equiv="Pragma" content="no-cache" />
-    <meta http-equiv="Expires" content="-1" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <link rel="stylesheet" href="css/style.css" />
-    <link href="https://fonts.googleapis.com/css?family=Montserrat:400,700" rel='stylesheet' type='text/css'>
-    <script type='text/javascript' src='js/d173ouchebag.js'></script>
-    <script type="text/javascript">
-        document.oncontextmenu = new Function("return false;")
-        document.onselectstart = new Function("return false;")
-    </script>
-    <style>
-        header { margin-top: 40px; position: absolute; float: left; font-size: 24px; font-weight: bold; }
-        nav { margin-top: 40px; float: right; color: #FFF; fong: 1px; } nav ut-size: 16px; letter-spacil { list-style: none; margin: 0; margin: 0; } nav li { display: inline; float:left; } nav li a { text-decoration: none; margin: 0px 10px 0px 10px; color: #FFF; } nav li a:hover { color: #191919; transition: 0.3s; }
-    </style>
-</head>
-
-<body>
-    <header>
-        <span style="cursor: pointer;">Free Accounts Paradise</span>
-    </header>
-    <nav>
-        <ul>
-            <li><a href="fortnite/index.html">Fortnite</a></li>
-            <li><a href="minecraft/index.html">Minecraft</a></li>
-            <li><a href="https://discord.gg/gZwP9gRWZN">Discord</a></li>
-        </ul>
-    </nav>
-    <section>
-        <h1>Steam Account Generator</h1>
-<FORM NAME="WordForm">
-<INPUT TYPE=TEXT NAME="WordBox" id="wordbox"><BR>
-<INPUT TYPE=BUTTON VALUE="Generate" onClick="PickRandomWord(document.WordForm);" id="button">
-    </section>
-</body>
-</html>

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

@@ -1,7 +0,0 @@
-import AlphaMaskImage from './AlphaMaskImage';
-
-export default function (
-    x?: number, y?: number,
-    key?: string, frame?: string,
-    config?: AlphaMaskImage.IConfig
-): AlphaMaskImage;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/dynamictext/DynamicText.d.ts

@@ -1,2 +0,0 @@
-import DynamicText from '../../../plugins/dynamictext';
-export default DynamicText;

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

@@ -1,18 +0,0 @@
-import MergeStyle from './utils/MergeStyle.js';
-import FixWidthButtons from '../../fixwidthbuttons/FixWidthButtons.js';
-import CreateChild from './utils/CreateChild.js';
-import CreateChildren from './utils/CreateChildren.js';
-
-var CreateFixWidthButtons = function (scene, data, view, styles, customBuilders) {
-    data = MergeStyle(data, styles);
-
-    // Replace data by child game object
-    CreateChild(scene, data, 'background', view, styles, customBuilders);
-    CreateChildren(scene, data, 'buttons', view, styles, customBuilders);
-
-    var gameObject = new FixWidthButtons(scene, data);
-    scene.add.existing(gameObject);
-    return gameObject;
-};
-
-export default CreateFixWidthButtons;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/numberbar/NumberBar.js

@@ -1,232 +0,0 @@
-import Sizer from '../sizer/Sizer.js';
-import AddChildMask from '../../../plugins/gameobjects/container/containerlite/mask/AddChildMask.js';
-import Slider from '../slider/Slider.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class NumberBar extends Sizer {
-    constructor(scene, config) {
-        // Create sizer
-        super(scene, config);
-        this.type = 'rexNumberBar';
-
-        // Add elements
-        var background = GetValue(config, 'background', undefined);
-        var icon = GetValue(config, 'icon', undefined);
-        var iconMask = GetValue(config, 'iconMask', undefined);
-        var sliderConfig = GetValue(config, 'slider', undefined);
-        var text = GetValue(config, 'text', undefined);
-
-        // Space
-        var iconSpace = GetValue(config, 'space.icon', 0);
-        var sliderSpace = GetValue(config, 'space.slider', 0);
-
-        if (background) {
-            this.addBackground(background);
-        }
-
-        if (icon) {
-            var padding;
-            if (this.orientation === 0) {
-                if (sliderConfig || text) {
-                    padding = { right: iconSpace };
-                }
-            } else {
-                if (sliderConfig || text) {
-                    padding = { bottom: iconSpace };
-                }
-            }
-
-            this.add(icon,
-                {
-                    proportion: 0,
-                    align: 'center',
-                    padding: padding
-                }
-            )
-
-            if (iconMask) {
-                iconMask = AddChildMask.call(this, icon, icon, 1); // Circle mask
-            }
-        }
-
-        var slider;
-        if (sliderConfig) {
-            sliderConfig.orientation = this.orientation;
-            sliderConfig.eventEmitter = this;
-            sliderConfig.value = null;
-            if (!sliderConfig.hasOwnProperty('input')) {
-                sliderConfig.input = -1;
-            }
-            slider = new Slider(scene, sliderConfig);
-            scene.add.existing(slider);
-
-            var padding;
-            if (this.orientation === 0) {
-                if (text) {
-                    padding = { right: sliderSpace };
-                }
-            } else {
-                if (text) {
-                    padding = { bottom: sliderSpace };
-                }
-            }
-
-            var proportion;
-            if (this.orientation === 0) {
-                var sliderWidth = GetValue(sliderConfig, 'width', undefined);
-                proportion = (sliderWidth === undefined) ? 1 : 0;
-            } else {
-                var sliderHeight = GetValue(sliderConfig, 'height', undefined);
-                proportion = (sliderHeight === undefined) ? 1 : 0;
-            }
-
-            this.add(slider,
-                {
-                    proportion: proportion,
-                    align: 'center',
-                    padding: padding
-                }
-            )
-        }
-
-
-        if (text) {
-            this.add(text);
-        }
-
-        this.addChildrenMap('background', background);
-        this.addChildrenMap('icon', icon);
-        this.addChildrenMap('iconMask', iconMask);
-        this.addChildrenMap('slider', slider);
-        this.addChildrenMap('text', text);
-
-        var callback = GetValue(config, 'valuechangeCallback', null);
-        if (callback !== null) {
-            var scope = GetValue(config, 'valuechangeCallbackScope', undefined);
-            this.on('valuechange', callback, scope);
-        }
-        this.setEnable(GetValue(config, 'enable', undefined));
-        this.setValue(GetValue(config, 'value', 0));
-    }
-
-    get enable() {
-        if (this.childrenMap.slider) {
-            return this.childrenMap.slider.enable;
-        } else {
-            return false;
-        }
-    }
-
-    set enable(value) {
-        if (this.childrenMap.slider) {
-            this.childrenMap.slider.setEnable(value);
-        }
-    }
-
-    setEnable(enable) {
-        if (enable === undefined) {
-            enable = true;
-        }
-        this.enable = enable;
-        return this;
-    }
-
-    get value() {
-        if (this.childrenMap.slider) {
-            return this.childrenMap.slider.value;
-        } else {
-            return 0;
-        }
-    }
-
-    set value(value) {
-        if (!this.childrenMap.slider) {
-            return;
-        }
-        this.childrenMap.slider.value = value;
-    }
-
-    setValue(value, min, max) {
-        if (this.childrenMap.slider) {
-            this.childrenMap.slider.setValue(value, min, max);
-        }
-        return this;
-    }
-
-    addValue(inc, min, max) {
-        if (this.childrenMap.slider) {
-            this.childrenMap.slider.addValue(inc, min, max);
-        }
-        return this;
-    }
-
-    getValue(min, max) {
-        if (this.childrenMap.slider) {
-            return this.childrenMap.slider.getValue(min, max);
-        } else {
-            return 0;
-        }
-    }
-
-    easeValueTo(value, min, max) {
-        if (this.childrenMap.slider) {
-            this.childrenMap.slider.easeValueTo(value, min, max);
-        }
-        return this;
-    }
-
-    stopEaseValue() {
-        if (this.childrenMap.slider) {
-            this.childrenMap.slider.stopEaseValue();
-        }
-        return this;
-    }
-
-    setEaseValueDuration(duration) {
-        if (this.childrenMap.slider) {
-            this.childrenMap.slider.setEaseValueDuration(duration);
-        }
-        return this;
-    }
-
-    setEaseValueFunction(ease) {
-        if (this.childrenMap.slider) {
-            this.childrenMap.slider.setEaseValueFunction(ease);
-        }
-        return this;
-    }
-
-    get text() {
-        var textObject = this.childrenMap.text;
-        if (textObject === undefined) {
-            return '';
-        }
-        var value;
-        if (textObject.text) {
-            value = textObject.text;
-        } else {
-            value = textObject.getData('text');
-        }
-        return value;
-    }
-
-    set text(value) {
-        var textObject = this.childrenMap.text;
-        if (textObject === undefined) {
-            return;
-        }
-        if (textObject.setText) {
-            textObject.setText(value);
-        } else {
-            textObject.setData('text', value);
-        }
-    }
-
-    setText(value) {
-        this.text = value;
-        return this;
-    }
-}
-
-export default NumberBar;

spaces/AlexWang/lama/fetch_data/eval_sampler.py

@@ -1,21 +0,0 @@
-import os
-import random
-
-
-val_files_path = os.path.abspath('.') + '/places_standard_dataset/original/val/'
-val_files      = [val_files_path + image for image in os.listdir(val_files_path)]
-
-print(f'found {len(val_files)} images in {val_files_path}')
-
-random.shuffle(val_files)
-val_files_random = val_files[0:2000]
-
-list_of_random_val_files = os.path.abspath('.') \
-+ '/places_standard_dataset/original/eval_random_files.txt'
-
-print(f'copying 2000 random images to {list_of_random_val_files}')
-with open(list_of_random_val_files, 'w') as fw:
-    for filename in val_files_random:
-        fw.write(filename+'\n')
-print('...done')      
-

spaces/Alpaca233/SadTalker/src/face3d/extract_kp_videos_safe.py

@@ -1,151 +0,0 @@
-import os
-import cv2
-import time
-import glob
-import argparse
-import numpy as np
-from PIL import Image
-import torch
-from tqdm import tqdm
-from itertools import cycle
-from torch.multiprocessing import Pool, Process, set_start_method
-
-from facexlib.alignment import landmark_98_to_68
-from facexlib.detection import init_detection_model
-
-from facexlib.utils import load_file_from_url
-from src.face3d.util.my_awing_arch import FAN
-
-def init_alignment_model(model_name, half=False, device='cuda', model_rootpath=None):
-    if model_name == 'awing_fan':
-        model = FAN(num_modules=4, num_landmarks=98, device=device)
-        model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/alignment_WFLW_4HG.pth'
-    else:
-        raise NotImplementedError(f'{model_name} is not implemented.')
-
-    model_path = load_file_from_url(
-        url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath)
-    model.load_state_dict(torch.load(model_path, map_location=device)['state_dict'], strict=True)
-    model.eval()
-    model = model.to(device)
-    return model
-
-
-class KeypointExtractor():
-    def __init__(self, device='cuda'):
-
-        ### gfpgan/weights
-        try:
-            import webui  # in webui
-            root_path = 'extensions/SadTalker/gfpgan/weights' 
-
-        except:
-            root_path = 'gfpgan/weights'
-
-        self.detector = init_alignment_model('awing_fan',device=device, model_rootpath=root_path)   
-        self.det_net = init_detection_model('retinaface_resnet50', half=False,device=device, model_rootpath=root_path)
-
-    def extract_keypoint(self, images, name=None, info=True):
-        if isinstance(images, list):
-            keypoints = []
-            if info:
-                i_range = tqdm(images,desc='landmark Det:')
-            else:
-                i_range = images
-
-            for image in i_range:
-                current_kp = self.extract_keypoint(image)
-                # current_kp = self.detector.get_landmarks(np.array(image))
-                if np.mean(current_kp) == -1 and keypoints:
-                    keypoints.append(keypoints[-1])
-                else:
-                    keypoints.append(current_kp[None])
-
-            keypoints = np.concatenate(keypoints, 0)
-            np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-        else:
-            while True:
-                try:
-                    with torch.no_grad():
-                        # face detection -> face alignment.
-                        img = np.array(images)
-                        bboxes = self.det_net.detect_faces(images, 0.97)
-                        
-                        bboxes = bboxes[0]
-                        img = img[int(bboxes[1]):int(bboxes[3]), int(bboxes[0]):int(bboxes[2]), :]
-
-                        keypoints = landmark_98_to_68(self.detector.get_landmarks(img)) # [0]
-
-                        #### keypoints to the original location
-                        keypoints[:,0] += int(bboxes[0])
-                        keypoints[:,1] += int(bboxes[1])
-
-                        break
-                except RuntimeError as e:
-                    if str(e).startswith('CUDA'):
-                        print("Warning: out of memory, sleep for 1s")
-                        time.sleep(1)
-                    else:
-                        print(e)
-                        break    
-                except TypeError:
-                    print('No face detected in this image')
-                    shape = [68, 2]
-                    keypoints = -1. * np.ones(shape)                    
-                    break
-            if name is not None:
-                np.savetxt(os.path.splitext(name)[0]+'.txt', keypoints.reshape(-1))
-            return keypoints
-
-def read_video(filename):
-    frames = []
-    cap = cv2.VideoCapture(filename)
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if ret:
-            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-            frame = Image.fromarray(frame)
-            frames.append(frame)
-        else:
-            break
-    cap.release()
-    return frames
-
-def run(data):
-    filename, opt, device = data
-    os.environ['CUDA_VISIBLE_DEVICES'] = device
-    kp_extractor = KeypointExtractor()
-    images = read_video(filename)
-    name = filename.split('/')[-2:]
-    os.makedirs(os.path.join(opt.output_dir, name[-2]), exist_ok=True)
-    kp_extractor.extract_keypoint(
-        images, 
-        name=os.path.join(opt.output_dir, name[-2], name[-1])
-    )
-
-if __name__ == '__main__':
-    set_start_method('spawn')
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-    parser.add_argument('--output_dir', type=str, help='the folder of the output files')
-    parser.add_argument('--device_ids', type=str, default='0,1')
-    parser.add_argument('--workers', type=int, default=4)
-
-    opt = parser.parse_args()
-    filenames = list()
-    VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
-    VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
-    extensions = VIDEO_EXTENSIONS
-    
-    for ext in extensions:
-        os.listdir(f'{opt.input_dir}')
-        print(f'{opt.input_dir}/*.{ext}')
-        filenames = sorted(glob.glob(f'{opt.input_dir}/*.{ext}'))
-    print('Total number of videos:', len(filenames))
-    pool = Pool(opt.workers)
-    args_list = cycle([opt])
-    device_ids = opt.device_ids.split(",")
-    device_ids = cycle(device_ids)
-    for data in tqdm(pool.imap_unordered(run, zip(filenames, args_list, device_ids))):
-        None

spaces/Ammar-alhaj-ali/LayoutLMv3-Invoice/app.py

@@ -1,113 +0,0 @@
-import os
-os.system('pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu')
-os.system('pip install -q git+https://github.com/huggingface/transformers.git')
-os.system('pip install pytesseract')
-
-
-
-import gradio as gr
-import numpy as np
-from transformers import AutoModelForTokenClassification
-from datasets.features import ClassLabel
-from transformers import AutoProcessor
-from datasets import Features, Sequence, ClassLabel, Value, Array2D, Array3D
-import torch
-from datasets import load_metric
-from transformers import LayoutLMv3ForTokenClassification
-from transformers.data.data_collator import default_data_collator
-
-
-from transformers import AutoModelForTokenClassification
-from datasets import load_dataset
-from PIL import Image, ImageDraw, ImageFont
-
-
-processor = AutoProcessor.from_pretrained("Ammar-alhaj-ali/LayoutLMv3-Fine-Tuning-Invoice", apply_ocr=True)
-model = AutoModelForTokenClassification.from_pretrained("Ammar-alhaj-ali/LayoutLMv3-Fine-Tuning-Invoice")
-
-
-
-# define id2label, label2color
-labels = ['O', 'B-ABN', 'B-BILLER', 'B-BILLER_ADDRESS', 'B-BILLER_POST_CODE', 'B-DUE_DATE', 'B-GST', 'B-INVOICE_DATE', 'B-INVOICE_NUMBER', 'B-SUBTOTAL', 'B-TOTAL', 'I-BILLER_ADDRESS']
-id2label = {v: k for v, k in enumerate(labels)}
-label2color = {
-    "B-ABN": 'blue',
-    "B-BILLER": 'red',
-    "B-BILLER_ADDRESS": 'green',
-    "B-BILLER_POST_CODE": 'orange',
-    "B-DUE_DATE": "blue",
-    "B-GST": 'green',
-    "B-INVOICE_DATE": 'violet',
-    "B-INVOICE_NUMBER": 'orange',
-    "B-SUBTOTAL": 'green',
-    "B-TOTAL": 'red',
-    "I-BILLER_ADDRESS": 'blue',
-    "O": 'orange'
-  }  
-
-def unnormalize_box(bbox, width, height):
-     return [
-         width * (bbox[0] / 1000),
-         height * (bbox[1] / 1000),
-         width * (bbox[2] / 1000),
-         height * (bbox[3] / 1000),
-     ]
-
-
-def iob_to_label(label):
-    return label
-
-
-
-def process_image(image):
-
-    print(type(image))
-    width, height = image.size
-
-    # encode
-    encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
-    offset_mapping = encoding.pop('offset_mapping')
-
-    # forward pass
-    outputs = model(**encoding)
-
-    # get predictions
-    predictions = outputs.logits.argmax(-1).squeeze().tolist()
-    token_boxes = encoding.bbox.squeeze().tolist()
-
-    # only keep non-subword predictions
-    is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
-    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
-    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
-
-    # draw predictions over the image
-    draw = ImageDraw.Draw(image)
-    font = ImageFont.load_default()
-    for prediction, box in zip(true_predictions, true_boxes):
-        predicted_label = iob_to_label(prediction)
-        draw.rectangle(box, outline=label2color[predicted_label]) #label2color[predicted_label]
-        draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font) #label2color[predicted_label]
-    
-    return image
-
-
-title = "Extracting information from invoice Dataset using the LayoutLMv3 "
-description = "I Fine tuned LayoutLMv3 on Invoice Dataset [darentang/generated] "
-
-article="<b>References</b><br>[1] Y. Xu et al., “LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking.” 2022. <a href='https://arxiv.org/abs/2204.08387'>Paper Link</a><br>[2]" 
-
-examples =[['img1.png'],['img2.png'],['img3.png']]
-
-css = """.output_image, .input_image {height: 600px !important}"""
-
-iface = gr.Interface(fn=process_image, 
-                     inputs=gr.inputs.Image(type="pil"), 
-                     outputs=gr.outputs.Image(type="pil", label="annotated image"),
-                     title=title,
-                     description=description,
-                     article=article,
-                     examples=examples,
-                     css=css,
-                     analytics_enabled = True, enable_queue=True)
-
-iface.launch(inline=False, share=False, debug=False)

spaces/AnTo2209/3D_Zeroshot_Neural_Style_Transfer/src/utils/renderer.py

@@ -1,328 +0,0 @@
-import numpy as np
-import torch, os, imageio, sys
-from tqdm.auto import tqdm
-from src.decoder.tensoRF_decoder import raw2alpha, TensorVMSplit, AlphaGridMask
-from src.decoder.utils import *
-from src.dataset.ray_utils import ndc_rays_blender, denormalize_vgg, normalize_vgg, get_rays
-from src.utils.utils import rgb_lpips, rgb_ssim
-
-
-def OctreeRender_trilinear_fast(rays, tensorf, chunk=4096, N_samples=-1, ndc_ray=False, white_bg=True, is_train=False,
-                                render_feature=False, style_img=None, device='cuda'):
-    rgbs, alphas, depth_maps, weights, uncertainties = [], [], [], [], []
-    features, accs = [], []
-    s_mean_std_mat = None
-    if style_img is not None:
-        with torch.no_grad():
-            style_feature = tensorf.encoder(normalize_vgg(style_img))
-        s_mean_std_mat = tensorf.stylizer.get_style_mean_std_matrix(style_feature.relu3_1.flatten(2))
-
-    N_rays_all = rays.shape[0]
-    for chunk_idx in range(N_rays_all // chunk + int(N_rays_all % chunk > 0)):
-        rays_chunk = rays[chunk_idx * chunk:(chunk_idx + 1) * chunk].to(device)
-
-        if render_feature:
-            feature_map, acc_map = tensorf.render_feature_map(rays_chunk, s_mean_std_mat=s_mean_std_mat,
-                                                              is_train=is_train, ndc_ray=ndc_ray, N_samples=N_samples)
-            features.append(feature_map)
-            accs.append(acc_map)
-        else:
-            rgb_map, depth_map = tensorf(rays_chunk, is_train=is_train, white_bg=white_bg, ndc_ray=ndc_ray,
-                                         N_samples=N_samples)
-            rgbs.append(rgb_map)
-            depth_maps.append(depth_map)
-
-    if render_feature:
-        if style_img is not None:
-            return torch.cat(features), torch.cat(accs), style_feature
-        return torch.cat(features), torch.cat(accs)
-
-    return torch.cat(rgbs), None, torch.cat(depth_maps), None, None
-
-
-def OctreeRender_trilinear_fast_depth(rays, tensorf, chunk=4096, N_samples=-1, ndc_ray=False, is_train=False,
-                                      device='cuda'):
-    depth_maps = []
-    N_rays_all = rays.shape[0]
-    for chunk_idx in range(N_rays_all // chunk + int(N_rays_all % chunk > 0)):
-        rays_chunk = rays[chunk_idx * chunk:(chunk_idx + 1) * chunk].to(device)
-        depth_map = tensorf.render_depth_map(rays_chunk, is_train=is_train, ndc_ray=ndc_ray, N_samples=N_samples)
-        depth_maps.append(depth_map)
-
-    return torch.cat(depth_maps)
-
-
-@torch.no_grad()
-def evaluation_feature(test_dataset, tensorf, renderer, chunk_size=2048, savePath=None, N_vis=10, prtx='',
-                       N_samples=-1,
-                       white_bg=False, ndc_ray=False, compute_extra_metrics=False, style_img=None, device='cuda'):
-    '''
-    To see if the decoded feature map is similar to gt rgb map
-    '''
-    PSNRs, rgb_maps, vis_feature_maps = [], [], []
-    ssims, l_alex, l_vgg = [], [], []
-    os.makedirs(savePath, exist_ok=True)
-    os.makedirs(savePath + '/feature', exist_ok=True)
-    W, H = test_dataset.img_wh
-
-    try:
-        tqdm._instances.clear()
-    except Exception:
-        pass
-
-    near_far = test_dataset.near_far
-    img_eval_interval = 1 if N_vis < 0 else max(test_dataset.all_rays_stack.shape[0] // N_vis, 1)
-    idxs = list(range(0, test_dataset.all_rays_stack.shape[0], img_eval_interval))
-    for idx, samples in tqdm(enumerate(test_dataset.all_rays_stack[0::img_eval_interval]), file=sys.stdout):
-
-        rays = samples.view(-1, samples.shape[-1])
-
-        if style_img is None:
-            feature_map, _ = renderer(rays, tensorf, chunk=chunk_size, N_samples=N_samples, ndc_ray=ndc_ray,
-                                      white_bg=white_bg, render_feature=True, device=device)
-        else:
-            feature_map, _, _ = renderer(rays, tensorf, chunk=chunk_size, N_samples=N_samples, ndc_ray=ndc_ray,
-                                         white_bg=white_bg, render_feature=True, style_img=style_img, device=device)
-
-        feature_map = feature_map.reshape(H, W, 256)[None, ...].permute(0, 3, 1, 2)
-
-        recon_rgb = denormalize_vgg(tensorf.decoder(feature_map))
-        recon_rgb = recon_rgb.permute(0, 2, 3, 1).clamp(0, 1)
-
-        vis_feature_map = torch.sigmoid(feature_map[:, [1, 2, 3], :, :].permute(0, 2, 3, 1))
-
-        if test_dataset.white_bg:
-            mask = test_dataset.all_masks[idx:idx + 1].to(device)
-            recon_rgb = mask * recon_rgb + (1. - mask)
-            vis_feature_map = mask * vis_feature_map + (1. - mask)
-
-        recon_rgb = recon_rgb.reshape(H, W, 3).cpu()
-        vis_feature_map = vis_feature_map.squeeze().cpu()
-
-        if len(test_dataset.all_rgbs_stack):
-            gt_rgb = test_dataset.all_rgbs_stack[idxs[idx]].view(H, W, 3)
-            loss = torch.mean((recon_rgb - gt_rgb) ** 2)
-            PSNRs.append(-10.0 * np.log(loss.item()) / np.log(10.0))
-
-            if compute_extra_metrics:
-                ssim = rgb_ssim(recon_rgb, gt_rgb, 1)
-                l_a = rgb_lpips(gt_rgb.numpy(), recon_rgb.numpy(), 'alex', tensorf.device)
-                l_v = rgb_lpips(gt_rgb.numpy(), recon_rgb.numpy(), 'vgg', tensorf.device)
-                ssims.append(ssim)
-                l_alex.append(l_a)
-                l_vgg.append(l_v)
-
-        recon_rgb = (recon_rgb.numpy() * 255).astype('uint8')
-        vis_feature_map = (vis_feature_map.numpy() * 255).astype('uint8')
-        gt_rgb = (gt_rgb.numpy() * 255).astype('uint8')
-
-        if savePath is not None:
-            # rgb_map = np.concatenate((recon_rgb, gt_rgb), axis=1)
-            rgb_maps.append(recon_rgb)
-            vis_feature_maps.append(vis_feature_map)
-            imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', recon_rgb)
-            imageio.imwrite(f'{savePath}/feature/feature_{idx:03d}.png', vis_feature_map)
-
-    imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=8)
-    imageio.mimwrite(f'{savePath}/feature/feature_video.mp4', np.stack(vis_feature_maps), fps=30, quality=8)
-
-    if PSNRs:
-        psnr = np.mean(np.asarray(PSNRs))
-        if compute_extra_metrics:
-            ssim = np.mean(np.asarray(ssims))
-            l_a = np.mean(np.asarray(l_alex))
-            l_v = np.mean(np.asarray(l_vgg))
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr, ssim, l_a, l_v]))
-        else:
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr]))
-
-    return {"video_path": f'{savePath}/{prtx}video.mp4',
-            "PSNR": PSNRs}
-
-
-@torch.no_grad()
-def evaluation_feature_path(test_dataset, tensorf, c2ws, renderer, chunk_size=2048, savePath=None, N_vis=5, prtx='',
-                            N_samples=-1,
-                            white_bg=False, ndc_ray=False, compute_extra_metrics=False, style_img=None, device='cuda'):
-    PSNRs, rgb_maps, vis_feature_maps, depth_maps = [], [], [], []
-    ssims, l_alex, l_vgg = [], [], []
-    os.makedirs(savePath, exist_ok=True)
-    os.makedirs(savePath + '/feature', exist_ok=True)
-    W, H = test_dataset.img_wh
-
-    try:
-        tqdm._instances.clear()
-    except Exception:
-        pass
-
-    near_far = test_dataset.near_far
-    for idx, c2w in tqdm(enumerate(c2ws)):
-
-        c2w = torch.FloatTensor(c2w)
-        rays_o, rays_d = get_rays(test_dataset.directions, c2w)  # both (h*w, 3)
-        if ndc_ray:
-            rays_o, rays_d = ndc_rays_blender(H, W, test_dataset.focal[0], 1.0, rays_o, rays_d)
-        rays = torch.cat([rays_o, rays_d], 1).reshape(H, W, 6).permute(2, 0, 1)  # (6,H,W)
-        rays = rays.permute(1, 2, 0).reshape(-1, 6)  # (H * W, 6)
-
-        if style_img is None:
-            feature_map, _ = renderer(rays, tensorf, chunk=chunk_size, N_samples=N_samples, ndc_ray=ndc_ray,
-                                      white_bg=white_bg, render_feature=True, device=device)
-        else:
-            feature_map, _, _ = renderer(rays, tensorf, chunk=chunk_size, N_samples=N_samples, ndc_ray=ndc_ray,
-                                         white_bg=white_bg, render_feature=True, style_img=style_img, device=device)
-
-        feature_map = feature_map.reshape(H, W, 256)[None, ...].permute(0, 3, 1, 2)
-
-        recon_rgb = denormalize_vgg(tensorf.decoder(feature_map))
-        recon_rgb = recon_rgb.permute(0, 2, 3, 1).clamp(0, 1)
-        recon_rgb = recon_rgb.reshape(H, W, 3).cpu()
-        recon_rgb = (recon_rgb.numpy() * 255).astype('uint8')
-        rgb_maps.append(recon_rgb)
-
-        vis_feature_map = torch.sigmoid(feature_map[:, [1, 2, 3], :, :].permute(0, 2, 3, 1))
-        vis_feature_map = vis_feature_map.squeeze().cpu()
-        vis_feature_map = (vis_feature_map.numpy() * 255).astype('uint8')
-        vis_feature_maps.append(vis_feature_map)
-
-        if savePath is not None:
-            imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', recon_rgb)
-            imageio.imwrite(f'{savePath}/feature/feature_{idx:03d}.png', vis_feature_map)
-
-    imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=8)
-    imageio.mimwrite(f'{savePath}/feature/feature_video.mp4', np.stack(vis_feature_maps), fps=30, quality=8)
-
-    if PSNRs:
-        psnr = np.mean(np.asarray(PSNRs))
-        if compute_extra_metrics:
-            ssim = np.mean(np.asarray(ssims))
-            l_a = np.mean(np.asarray(l_alex))
-            l_v = np.mean(np.asarray(l_vgg))
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr, ssim, l_a, l_v]))
-        else:
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr]))
-
-    return PSNRs
-
-
-@torch.no_grad()
-def evaluation(test_dataset, tensorf, args, renderer, savePath=None, N_vis=5, prtx='', N_samples=-1,
-               white_bg=False, ndc_ray=False, compute_extra_metrics=True, device='cuda'):
-    PSNRs, rgb_maps, depth_maps = [], [], []
-    ssims, l_alex, l_vgg = [], [], []
-    os.makedirs(savePath, exist_ok=True)
-    os.makedirs(savePath + "/rgbd", exist_ok=True)
-
-    try:
-        tqdm._instances.clear()
-    except Exception:
-        pass
-
-    near_far = test_dataset.near_far
-    img_eval_interval = 1 if N_vis < 0 else max(test_dataset.all_rays_stack.shape[0] // N_vis, 1)
-    idxs = list(range(0, test_dataset.all_rays_stack.shape[0], img_eval_interval))
-    for idx, samples in tqdm(enumerate(test_dataset.all_rays_stack[0::img_eval_interval]), file=sys.stdout):
-
-        W, H = test_dataset.img_wh
-        rays = samples.view(-1, samples.shape[-1])
-
-        rgb_map, _, depth_map, _, _ = renderer(rays, tensorf, chunk=4096, N_samples=N_samples,
-                                               ndc_ray=ndc_ray, white_bg=white_bg, device=device)
-        rgb_map = rgb_map.clamp(0.0, 1.0)
-
-        rgb_map, depth_map = rgb_map.reshape(H, W, 3).cpu(), depth_map.reshape(H, W).cpu()
-
-        depth_map, _ = visualize_depth_numpy(depth_map.numpy(), near_far)
-        if len(test_dataset.all_rgbs_stack):
-            gt_rgb = test_dataset.all_rgbs_stack[idxs[idx]].view(H, W, 3)
-            loss = torch.mean((rgb_map - gt_rgb) ** 2)
-            PSNRs.append(-10.0 * np.log(loss.item()) / np.log(10.0))
-
-            if compute_extra_metrics:
-                ssim = rgb_ssim(rgb_map, gt_rgb, 1)
-                l_a = rgb_lpips(gt_rgb.numpy(), rgb_map.numpy(), 'alex', tensorf.device)
-                l_v = rgb_lpips(gt_rgb.numpy(), rgb_map.numpy(), 'vgg', tensorf.device)
-                ssims.append(ssim)
-                l_alex.append(l_a)
-                l_vgg.append(l_v)
-
-        rgb_map = (rgb_map.numpy() * 255).astype('uint8')
-        # rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
-        rgb_maps.append(rgb_map)
-        depth_maps.append(depth_map)
-        if savePath is not None:
-            imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
-            # rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
-            imageio.imwrite(f'{savePath}/rgbd/{prtx}{idx:03d}.png', depth_map)
-
-    imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=10)
-    imageio.mimwrite(f'{savePath}/{prtx}depthvideo.mp4', np.stack(depth_maps), fps=30, quality=10)
-
-    if PSNRs:
-        psnr = np.mean(np.asarray(PSNRs))
-        if compute_extra_metrics:
-            ssim = np.mean(np.asarray(ssims))
-            l_a = np.mean(np.asarray(l_alex))
-            l_v = np.mean(np.asarray(l_vgg))
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr, ssim, l_a, l_v]))
-        else:
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr]))
-
-    return PSNRs
-
-
-@torch.no_grad()
-def evaluation_path(test_dataset, tensorf, c2ws, renderer, savePath=None, N_vis=5, prtx='', N_samples=-1,
-                    white_bg=False, ndc_ray=False, compute_extra_metrics=True, device='cuda'):
-    PSNRs, rgb_maps, depth_maps = [], [], []
-    ssims, l_alex, l_vgg = [], [], []
-    os.makedirs(savePath, exist_ok=True)
-    os.makedirs(savePath + "/rgbd", exist_ok=True)
-
-    try:
-        tqdm._instances.clear()
-    except Exception:
-        pass
-
-    near_far = test_dataset.near_far
-    for idx, c2w in tqdm(enumerate(c2ws)):
-
-        W, H = test_dataset.img_wh
-
-        c2w = torch.FloatTensor(c2w)
-        rays_o, rays_d = get_rays(test_dataset.directions, c2w)  # both (h*w, 3)
-        if ndc_ray:
-            rays_o, rays_d = ndc_rays_blender(H, W, test_dataset.focal[0], 1.0, rays_o, rays_d)
-        rays = torch.cat([rays_o, rays_d], 1)  # (h*w, 6)
-
-        rgb_map, _, depth_map, _, _ = renderer(rays, tensorf, chunk=8192, N_samples=N_samples,
-                                               ndc_ray=ndc_ray, white_bg=white_bg, device=device)
-        rgb_map = rgb_map.clamp(0.0, 1.0)
-
-        rgb_map, depth_map = rgb_map.reshape(H, W, 3).cpu(), depth_map.reshape(H, W).cpu()
-
-        depth_map, _ = visualize_depth_numpy(depth_map.numpy(), near_far)
-
-        rgb_map = (rgb_map.numpy() * 255).astype('uint8')
-        # rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
-        rgb_maps.append(rgb_map)
-        depth_maps.append(depth_map)
-        if savePath is not None:
-            imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
-            rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
-            imageio.imwrite(f'{savePath}/rgbd/{prtx}{idx:03d}.png', rgb_map)
-
-    imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=8)
-    imageio.mimwrite(f'{savePath}/{prtx}depthvideo.mp4', np.stack(depth_maps), fps=30, quality=8)
-
-    if PSNRs:
-        psnr = np.mean(np.asarray(PSNRs))
-        if compute_extra_metrics:
-            ssim = np.mean(np.asarray(ssims))
-            l_a = np.mean(np.asarray(l_alex))
-            l_v = np.mean(np.asarray(l_vgg))
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr, ssim, l_a, l_v]))
-        else:
-            np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr]))
-
-    return PSNRs

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/scripts/convert_shap_e_to_diffusers.py

@@ -1,1080 +0,0 @@
-import argparse
-import tempfile
-
-import torch
-from accelerate import load_checkpoint_and_dispatch
-
-from diffusers.models.prior_transformer import PriorTransformer
-from diffusers.pipelines.shap_e import ShapERenderer
-
-
-"""
-Example - From the diffusers root directory:
-
-Download weights:
-```sh
-$ wget  "https://openaipublic.azureedge.net/main/shap-e/text_cond.pt"
-```
-
-Convert the model:
-```sh
-$ python scripts/convert_shap_e_to_diffusers.py \
-      --prior_checkpoint_path  /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/text_cond.pt \
-      --prior_image_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/image_cond.pt \
-      --transmitter_checkpoint_path /home/yiyi_huggingface_co/shap-e/shap_e_model_cache/transmitter.pt\
-      --dump_path /home/yiyi_huggingface_co/model_repo/shap-e-img2img/shap_e_renderer\
-      --debug renderer
-```
-"""
-
-
-# prior
-
-PRIOR_ORIGINAL_PREFIX = "wrapped"
-
-PRIOR_CONFIG = {
-    "num_attention_heads": 16,
-    "attention_head_dim": 1024 // 16,
-    "num_layers": 24,
-    "embedding_dim": 1024,
-    "num_embeddings": 1024,
-    "additional_embeddings": 0,
-    "time_embed_act_fn": "gelu",
-    "norm_in_type": "layer",
-    "encoder_hid_proj_type": None,
-    "added_emb_type": None,
-    "time_embed_dim": 1024 * 4,
-    "embedding_proj_dim": 768,
-    "clip_embed_dim": 1024 * 2,
-}
-
-
-def prior_model_from_original_config():
-    model = PriorTransformer(**PRIOR_CONFIG)
-
-    return model
-
-
-def prior_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    # <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1
-    diffusers_checkpoint.update(
-        {
-            "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.weight"],
-            "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_fc.bias"],
-        }
-    )
-
-    # <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2
-    diffusers_checkpoint.update(
-        {
-            "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.weight"],
-            "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.time_embed.c_proj.bias"],
-        }
-    )
-
-    # <original>.input_proj -> <diffusers>.proj_in
-    diffusers_checkpoint.update(
-        {
-            "proj_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.weight"],
-            "proj_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.input_proj.bias"],
-        }
-    )
-
-    # <original>.clip_emb -> <diffusers>.embedding_proj
-    diffusers_checkpoint.update(
-        {
-            "embedding_proj.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.weight"],
-            "embedding_proj.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.clip_embed.bias"],
-        }
-    )
-
-    # <original>.pos_emb -> <diffusers>.positional_embedding
-    diffusers_checkpoint.update({"positional_embedding": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.pos_emb"][None, :]})
-
-    # <original>.ln_pre -> <diffusers>.norm_in
-    diffusers_checkpoint.update(
-        {
-            "norm_in.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.weight"],
-            "norm_in.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_pre.bias"],
-        }
-    )
-
-    # <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x>
-    for idx in range(len(model.transformer_blocks)):
-        diffusers_transformer_prefix = f"transformer_blocks.{idx}"
-        original_transformer_prefix = f"{PRIOR_ORIGINAL_PREFIX}.backbone.resblocks.{idx}"
-
-        # <original>.attn -> <diffusers>.attn1
-        diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1"
-        original_attention_prefix = f"{original_transformer_prefix}.attn"
-        diffusers_checkpoint.update(
-            prior_attention_to_diffusers(
-                checkpoint,
-                diffusers_attention_prefix=diffusers_attention_prefix,
-                original_attention_prefix=original_attention_prefix,
-                attention_head_dim=model.attention_head_dim,
-            )
-        )
-
-        # <original>.mlp -> <diffusers>.ff
-        diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff"
-        original_ff_prefix = f"{original_transformer_prefix}.mlp"
-        diffusers_checkpoint.update(
-            prior_ff_to_diffusers(
-                checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix
-            )
-        )
-
-        # <original>.ln_1 -> <diffusers>.norm1
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[
-                    f"{original_transformer_prefix}.ln_1.weight"
-                ],
-                f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"],
-            }
-        )
-
-        # <original>.ln_2 -> <diffusers>.norm3
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[
-                    f"{original_transformer_prefix}.ln_2.weight"
-                ],
-                f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"],
-            }
-        )
-
-    # <original>.ln_post -> <diffusers>.norm_out
-    diffusers_checkpoint.update(
-        {
-            "norm_out.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.weight"],
-            "norm_out.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.ln_post.bias"],
-        }
-    )
-
-    # <original>.output_proj -> <diffusers>.proj_to_clip_embeddings
-    diffusers_checkpoint.update(
-        {
-            "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.weight"],
-            "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_ORIGINAL_PREFIX}.output_proj.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-def prior_attention_to_diffusers(
-    checkpoint, *, diffusers_attention_prefix, original_attention_prefix, attention_head_dim
-):
-    diffusers_checkpoint = {}
-
-    # <original>.c_qkv -> <diffusers>.{to_q, to_k, to_v}
-    [q_weight, k_weight, v_weight], [q_bias, k_bias, v_bias] = split_attentions(
-        weight=checkpoint[f"{original_attention_prefix}.c_qkv.weight"],
-        bias=checkpoint[f"{original_attention_prefix}.c_qkv.bias"],
-        split=3,
-        chunk_size=attention_head_dim,
-    )
-
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.to_q.weight": q_weight,
-            f"{diffusers_attention_prefix}.to_q.bias": q_bias,
-            f"{diffusers_attention_prefix}.to_k.weight": k_weight,
-            f"{diffusers_attention_prefix}.to_k.bias": k_bias,
-            f"{diffusers_attention_prefix}.to_v.weight": v_weight,
-            f"{diffusers_attention_prefix}.to_v.bias": v_bias,
-        }
-    )
-
-    # <original>.c_proj -> <diffusers>.to_out.0
-    diffusers_checkpoint.update(
-        {
-            f"{diffusers_attention_prefix}.to_out.0.weight": checkpoint[f"{original_attention_prefix}.c_proj.weight"],
-            f"{diffusers_attention_prefix}.to_out.0.bias": checkpoint[f"{original_attention_prefix}.c_proj.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-def prior_ff_to_diffusers(checkpoint, *, diffusers_ff_prefix, original_ff_prefix):
-    diffusers_checkpoint = {
-        # <original>.c_fc -> <diffusers>.net.0.proj
-        f"{diffusers_ff_prefix}.net.{0}.proj.weight": checkpoint[f"{original_ff_prefix}.c_fc.weight"],
-        f"{diffusers_ff_prefix}.net.{0}.proj.bias": checkpoint[f"{original_ff_prefix}.c_fc.bias"],
-        # <original>.c_proj -> <diffusers>.net.2
-        f"{diffusers_ff_prefix}.net.{2}.weight": checkpoint[f"{original_ff_prefix}.c_proj.weight"],
-        f"{diffusers_ff_prefix}.net.{2}.bias": checkpoint[f"{original_ff_prefix}.c_proj.bias"],
-    }
-
-    return diffusers_checkpoint
-
-
-# done prior
-
-
-# prior_image (only slightly different from prior)
-
-
-PRIOR_IMAGE_ORIGINAL_PREFIX = "wrapped"
-
-# Uses default arguments
-PRIOR_IMAGE_CONFIG = {
-    "num_attention_heads": 8,
-    "attention_head_dim": 1024 // 8,
-    "num_layers": 24,
-    "embedding_dim": 1024,
-    "num_embeddings": 1024,
-    "additional_embeddings": 0,
-    "time_embed_act_fn": "gelu",
-    "norm_in_type": "layer",
-    "embedding_proj_norm_type": "layer",
-    "encoder_hid_proj_type": None,
-    "added_emb_type": None,
-    "time_embed_dim": 1024 * 4,
-    "embedding_proj_dim": 1024,
-    "clip_embed_dim": 1024 * 2,
-}
-
-
-def prior_image_model_from_original_config():
-    model = PriorTransformer(**PRIOR_IMAGE_CONFIG)
-
-    return model
-
-
-def prior_image_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-
-    # <original>.time_embed.c_fc -> <diffusers>.time_embedding.linear_1
-    diffusers_checkpoint.update(
-        {
-            "time_embedding.linear_1.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.weight"],
-            "time_embedding.linear_1.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_fc.bias"],
-        }
-    )
-
-    # <original>.time_embed.c_proj -> <diffusers>.time_embedding.linear_2
-    diffusers_checkpoint.update(
-        {
-            "time_embedding.linear_2.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.weight"],
-            "time_embedding.linear_2.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.time_embed.c_proj.bias"],
-        }
-    )
-
-    # <original>.input_proj -> <diffusers>.proj_in
-    diffusers_checkpoint.update(
-        {
-            "proj_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.weight"],
-            "proj_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.input_proj.bias"],
-        }
-    )
-
-    # <original>.clip_embed.0 -> <diffusers>.embedding_proj_norm
-    diffusers_checkpoint.update(
-        {
-            "embedding_proj_norm.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.weight"],
-            "embedding_proj_norm.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.0.bias"],
-        }
-    )
-
-    # <original>..clip_embed.1 -> <diffusers>.embedding_proj
-    diffusers_checkpoint.update(
-        {
-            "embedding_proj.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.weight"],
-            "embedding_proj.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.clip_embed.1.bias"],
-        }
-    )
-
-    # <original>.pos_emb -> <diffusers>.positional_embedding
-    diffusers_checkpoint.update(
-        {"positional_embedding": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.pos_emb"][None, :]}
-    )
-
-    # <original>.ln_pre -> <diffusers>.norm_in
-    diffusers_checkpoint.update(
-        {
-            "norm_in.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.weight"],
-            "norm_in.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_pre.bias"],
-        }
-    )
-
-    # <original>.backbone.resblocks.<x> -> <diffusers>.transformer_blocks.<x>
-    for idx in range(len(model.transformer_blocks)):
-        diffusers_transformer_prefix = f"transformer_blocks.{idx}"
-        original_transformer_prefix = f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.backbone.resblocks.{idx}"
-
-        # <original>.attn -> <diffusers>.attn1
-        diffusers_attention_prefix = f"{diffusers_transformer_prefix}.attn1"
-        original_attention_prefix = f"{original_transformer_prefix}.attn"
-        diffusers_checkpoint.update(
-            prior_attention_to_diffusers(
-                checkpoint,
-                diffusers_attention_prefix=diffusers_attention_prefix,
-                original_attention_prefix=original_attention_prefix,
-                attention_head_dim=model.attention_head_dim,
-            )
-        )
-
-        # <original>.mlp -> <diffusers>.ff
-        diffusers_ff_prefix = f"{diffusers_transformer_prefix}.ff"
-        original_ff_prefix = f"{original_transformer_prefix}.mlp"
-        diffusers_checkpoint.update(
-            prior_ff_to_diffusers(
-                checkpoint, diffusers_ff_prefix=diffusers_ff_prefix, original_ff_prefix=original_ff_prefix
-            )
-        )
-
-        # <original>.ln_1 -> <diffusers>.norm1
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_transformer_prefix}.norm1.weight": checkpoint[
-                    f"{original_transformer_prefix}.ln_1.weight"
-                ],
-                f"{diffusers_transformer_prefix}.norm1.bias": checkpoint[f"{original_transformer_prefix}.ln_1.bias"],
-            }
-        )
-
-        # <original>.ln_2 -> <diffusers>.norm3
-        diffusers_checkpoint.update(
-            {
-                f"{diffusers_transformer_prefix}.norm3.weight": checkpoint[
-                    f"{original_transformer_prefix}.ln_2.weight"
-                ],
-                f"{diffusers_transformer_prefix}.norm3.bias": checkpoint[f"{original_transformer_prefix}.ln_2.bias"],
-            }
-        )
-
-    # <original>.ln_post -> <diffusers>.norm_out
-    diffusers_checkpoint.update(
-        {
-            "norm_out.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.weight"],
-            "norm_out.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.ln_post.bias"],
-        }
-    )
-
-    # <original>.output_proj -> <diffusers>.proj_to_clip_embeddings
-    diffusers_checkpoint.update(
-        {
-            "proj_to_clip_embeddings.weight": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.weight"],
-            "proj_to_clip_embeddings.bias": checkpoint[f"{PRIOR_IMAGE_ORIGINAL_PREFIX}.output_proj.bias"],
-        }
-    )
-
-    return diffusers_checkpoint
-
-
-# done prior_image
-
-
-# renderer
-
-## create the lookup table for marching cubes method used in MeshDecoder
-
-MC_TABLE = [
-    [],
-    [[0, 1, 0, 2, 0, 4]],
-    [[1, 0, 1, 5, 1, 3]],
-    [[0, 4, 1, 5, 0, 2], [1, 5, 1, 3, 0, 2]],
-    [[2, 0, 2, 3, 2, 6]],
-    [[0, 1, 2, 3, 0, 4], [2, 3, 2, 6, 0, 4]],
-    [[1, 0, 1, 5, 1, 3], [2, 6, 0, 2, 3, 2]],
-    [[3, 2, 2, 6, 3, 1], [3, 1, 2, 6, 1, 5], [1, 5, 2, 6, 0, 4]],
-    [[3, 1, 3, 7, 3, 2]],
-    [[0, 2, 0, 4, 0, 1], [3, 7, 2, 3, 1, 3]],
-    [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0]],
-    [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5]],
-    [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6]],
-    [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6]],
-    [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7]],
-    [[0, 4, 1, 5, 3, 7], [0, 4, 3, 7, 2, 6]],
-    [[4, 0, 4, 6, 4, 5]],
-    [[0, 2, 4, 6, 0, 1], [4, 6, 4, 5, 0, 1]],
-    [[1, 5, 1, 3, 1, 0], [4, 6, 5, 4, 0, 4]],
-    [[5, 1, 1, 3, 5, 4], [5, 4, 1, 3, 4, 6], [4, 6, 1, 3, 0, 2]],
-    [[2, 0, 2, 3, 2, 6], [4, 5, 0, 4, 6, 4]],
-    [[6, 4, 4, 5, 6, 2], [6, 2, 4, 5, 2, 3], [2, 3, 4, 5, 0, 1]],
-    [[2, 6, 2, 0, 3, 2], [1, 0, 1, 5, 3, 1], [6, 4, 5, 4, 0, 4]],
-    [[1, 3, 5, 4, 1, 5], [1, 3, 4, 6, 5, 4], [1, 3, 3, 2, 4, 6], [3, 2, 2, 6, 4, 6]],
-    [[3, 1, 3, 7, 3, 2], [6, 4, 5, 4, 0, 4]],
-    [[4, 5, 0, 1, 4, 6], [0, 1, 0, 2, 4, 6], [7, 3, 2, 3, 1, 3]],
-    [[3, 2, 1, 0, 3, 7], [1, 0, 1, 5, 3, 7], [6, 4, 5, 4, 0, 4]],
-    [[3, 7, 3, 2, 1, 5], [3, 2, 6, 4, 1, 5], [1, 5, 6, 4, 5, 4], [3, 2, 2, 0, 6, 4]],
-    [[3, 7, 2, 6, 3, 1], [2, 6, 2, 0, 3, 1], [5, 4, 0, 4, 6, 4]],
-    [[1, 0, 1, 3, 5, 4], [1, 3, 2, 6, 5, 4], [1, 3, 3, 7, 2, 6], [5, 4, 2, 6, 4, 6]],
-    [[0, 1, 1, 5, 0, 2], [0, 2, 1, 5, 2, 6], [2, 6, 1, 5, 3, 7], [4, 5, 0, 4, 4, 6]],
-    [[6, 2, 4, 6, 4, 5], [4, 5, 5, 1, 6, 2], [6, 2, 5, 1, 7, 3]],
-    [[5, 1, 5, 4, 5, 7]],
-    [[0, 1, 0, 2, 0, 4], [5, 7, 1, 5, 4, 5]],
-    [[1, 0, 5, 4, 1, 3], [5, 4, 5, 7, 1, 3]],
-    [[4, 5, 5, 7, 4, 0], [4, 0, 5, 7, 0, 2], [0, 2, 5, 7, 1, 3]],
-    [[2, 0, 2, 3, 2, 6], [7, 5, 1, 5, 4, 5]],
-    [[2, 6, 0, 4, 2, 3], [0, 4, 0, 1, 2, 3], [7, 5, 1, 5, 4, 5]],
-    [[5, 7, 1, 3, 5, 4], [1, 3, 1, 0, 5, 4], [6, 2, 0, 2, 3, 2]],
-    [[3, 1, 3, 2, 7, 5], [3, 2, 0, 4, 7, 5], [3, 2, 2, 6, 0, 4], [7, 5, 0, 4, 5, 4]],
-    [[3, 7, 3, 2, 3, 1], [5, 4, 7, 5, 1, 5]],
-    [[0, 4, 0, 1, 2, 0], [3, 1, 3, 7, 2, 3], [4, 5, 7, 5, 1, 5]],
-    [[7, 3, 3, 2, 7, 5], [7, 5, 3, 2, 5, 4], [5, 4, 3, 2, 1, 0]],
-    [[0, 4, 2, 3, 0, 2], [0, 4, 3, 7, 2, 3], [0, 4, 4, 5, 3, 7], [4, 5, 5, 7, 3, 7]],
-    [[2, 0, 3, 1, 2, 6], [3, 1, 3, 7, 2, 6], [4, 5, 7, 5, 1, 5]],
-    [[1, 3, 3, 7, 1, 0], [1, 0, 3, 7, 0, 4], [0, 4, 3, 7, 2, 6], [5, 7, 1, 5, 5, 4]],
-    [[2, 6, 2, 0, 3, 7], [2, 0, 4, 5, 3, 7], [3, 7, 4, 5, 7, 5], [2, 0, 0, 1, 4, 5]],
-    [[4, 0, 5, 4, 5, 7], [5, 7, 7, 3, 4, 0], [4, 0, 7, 3, 6, 2]],
-    [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0]],
-    [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6]],
-    [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7]],
-    [[0, 2, 4, 6, 5, 7], [0, 2, 5, 7, 1, 3]],
-    [[5, 1, 4, 0, 5, 7], [4, 0, 4, 6, 5, 7], [3, 2, 6, 2, 0, 2]],
-    [[2, 3, 2, 6, 0, 1], [2, 6, 7, 5, 0, 1], [0, 1, 7, 5, 1, 5], [2, 6, 6, 4, 7, 5]],
-    [[0, 4, 4, 6, 0, 1], [0, 1, 4, 6, 1, 3], [1, 3, 4, 6, 5, 7], [2, 6, 0, 2, 2, 3]],
-    [[3, 1, 2, 3, 2, 6], [2, 6, 6, 4, 3, 1], [3, 1, 6, 4, 7, 5]],
-    [[4, 6, 5, 7, 4, 0], [5, 7, 5, 1, 4, 0], [2, 3, 1, 3, 7, 3]],
-    [[1, 0, 0, 2, 1, 5], [1, 5, 0, 2, 5, 7], [5, 7, 0, 2, 4, 6], [3, 2, 1, 3, 3, 7]],
-    [[0, 1, 0, 4, 2, 3], [0, 4, 5, 7, 2, 3], [0, 4, 4, 6, 5, 7], [2, 3, 5, 7, 3, 7]],
-    [[7, 5, 3, 7, 3, 2], [3, 2, 2, 0, 7, 5], [7, 5, 2, 0, 6, 4]],
-    [[0, 4, 4, 6, 5, 7], [0, 4, 5, 7, 1, 5], [0, 2, 1, 3, 3, 7], [3, 7, 2, 6, 0, 2]],
-    [
-        [3, 1, 7, 3, 6, 2],
-        [6, 2, 0, 1, 3, 1],
-        [6, 4, 0, 1, 6, 2],
-        [6, 4, 5, 1, 0, 1],
-        [6, 4, 7, 5, 5, 1],
-    ],
-    [
-        [4, 0, 6, 4, 7, 5],
-        [7, 5, 1, 0, 4, 0],
-        [7, 3, 1, 0, 7, 5],
-        [7, 3, 2, 0, 1, 0],
-        [7, 3, 6, 2, 2, 0],
-    ],
-    [[7, 3, 6, 2, 6, 4], [7, 5, 7, 3, 6, 4]],
-    [[6, 2, 6, 7, 6, 4]],
-    [[0, 4, 0, 1, 0, 2], [6, 7, 4, 6, 2, 6]],
-    [[1, 0, 1, 5, 1, 3], [7, 6, 4, 6, 2, 6]],
-    [[1, 3, 0, 2, 1, 5], [0, 2, 0, 4, 1, 5], [7, 6, 4, 6, 2, 6]],
-    [[2, 3, 6, 7, 2, 0], [6, 7, 6, 4, 2, 0]],
-    [[4, 0, 0, 1, 4, 6], [4, 6, 0, 1, 6, 7], [6, 7, 0, 1, 2, 3]],
-    [[6, 4, 2, 0, 6, 7], [2, 0, 2, 3, 6, 7], [5, 1, 3, 1, 0, 1]],
-    [[1, 5, 1, 3, 0, 4], [1, 3, 7, 6, 0, 4], [0, 4, 7, 6, 4, 6], [1, 3, 3, 2, 7, 6]],
-    [[3, 2, 3, 1, 3, 7], [6, 4, 2, 6, 7, 6]],
-    [[3, 7, 3, 2, 1, 3], [0, 2, 0, 4, 1, 0], [7, 6, 4, 6, 2, 6]],
-    [[1, 5, 3, 7, 1, 0], [3, 7, 3, 2, 1, 0], [4, 6, 2, 6, 7, 6]],
-    [[2, 0, 0, 4, 2, 3], [2, 3, 0, 4, 3, 7], [3, 7, 0, 4, 1, 5], [6, 4, 2, 6, 6, 7]],
-    [[7, 6, 6, 4, 7, 3], [7, 3, 6, 4, 3, 1], [3, 1, 6, 4, 2, 0]],
-    [[0, 1, 4, 6, 0, 4], [0, 1, 6, 7, 4, 6], [0, 1, 1, 3, 6, 7], [1, 3, 3, 7, 6, 7]],
-    [[0, 2, 0, 1, 4, 6], [0, 1, 3, 7, 4, 6], [0, 1, 1, 5, 3, 7], [4, 6, 3, 7, 6, 7]],
-    [[7, 3, 6, 7, 6, 4], [6, 4, 4, 0, 7, 3], [7, 3, 4, 0, 5, 1]],
-    [[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5]],
-    [[2, 6, 6, 7, 2, 0], [2, 0, 6, 7, 0, 1], [0, 1, 6, 7, 4, 5]],
-    [[6, 7, 4, 5, 6, 2], [4, 5, 4, 0, 6, 2], [3, 1, 0, 1, 5, 1]],
-    [[2, 0, 2, 6, 3, 1], [2, 6, 4, 5, 3, 1], [2, 6, 6, 7, 4, 5], [3, 1, 4, 5, 1, 5]],
-    [[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7]],
-    [[0, 1, 2, 3, 6, 7], [0, 1, 6, 7, 4, 5]],
-    [[0, 2, 2, 3, 0, 4], [0, 4, 2, 3, 4, 5], [4, 5, 2, 3, 6, 7], [1, 3, 0, 1, 1, 5]],
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-    [[4, 0, 6, 2, 4, 5], [6, 2, 6, 7, 4, 5], [1, 3, 7, 3, 2, 3]],
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-        [5, 4, 0, 2, 6, 2],
-        [5, 1, 0, 2, 5, 4],
-        [5, 1, 3, 2, 0, 2],
-        [5, 1, 7, 3, 3, 2],
-    ],
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-    [[1, 0, 3, 1, 3, 7], [3, 7, 7, 6, 1, 0], [1, 0, 7, 6, 5, 4]],
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-        [7, 6, 2, 0, 7, 3],
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-        [7, 6, 5, 4, 4, 0],
-    ],
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-        [3, 2, 7, 6, 6, 4],
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-        [0, 4, 3, 7, 2, 3],
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-        [4, 5, 5, 7, 3, 7],
-        [6, 7, 4, 6, 2, 6],
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-        [5, 7, 1, 5, 4, 5],
-    ],
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-        [0, 2, 3, 7, 6, 7],
-        [0, 1, 3, 7, 0, 2],
-        [0, 1, 5, 7, 3, 7],
-        [0, 1, 4, 5, 5, 7],
-    ],
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-        [7, 5, 3, 1, 1, 0],
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-        [2, 6, 6, 7, 5, 7],
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-        [0, 4, 6, 7, 5, 7],
-        [0, 4, 2, 6, 6, 7],
-    ],
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-        [0, 2, 3, 7, 6, 7],
-        [0, 2, 1, 3, 3, 7],
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-        [2, 0, 6, 4, 4, 5],
-    ],
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-        [4, 5, 6, 7, 7, 3],
-    ],
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-        [6, 2, 4, 0, 0, 1],
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-        [1, 5, 4, 6, 7, 6],
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-        [1, 0, 2, 6, 4, 6],
-        [1, 0, 3, 2, 2, 6],
-    ],
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-        [1, 0, 7, 6, 3, 7],
-        [1, 0, 0, 4, 7, 6],
-        [0, 4, 4, 6, 7, 6],
-        [2, 6, 0, 2, 3, 2],
-    ],
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-        [6, 4, 5, 1, 0, 1],
-        [6, 7, 5, 1, 6, 4],
-        [6, 7, 3, 1, 5, 1],
-        [6, 7, 2, 3, 3, 1],
-    ],
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-        [1, 3, 7, 6, 2, 6],
-        [1, 5, 7, 6, 1, 3],
-        [1, 5, 4, 6, 7, 6],
-        [1, 5, 0, 4, 4, 6],
-    ],
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-    [[0, 1, 2, 6, 0, 2], [0, 1, 6, 7, 2, 6], [0, 1, 4, 6, 6, 7], [0, 4, 4, 6, 0, 1]],
-    [[6, 7, 6, 2, 6, 4]],
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-    [[6, 2, 7, 3, 6, 4], [7, 3, 7, 5, 6, 4], [0, 1, 5, 1, 3, 1]],
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-    [[3, 2, 3, 7, 1, 0], [3, 7, 6, 4, 1, 0], [3, 7, 7, 5, 6, 4], [1, 0, 6, 4, 0, 4]],
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-        [4, 6, 2, 3, 7, 3],
-        [4, 0, 2, 3, 4, 6],
-        [4, 0, 1, 3, 2, 3],
-        [4, 0, 5, 1, 1, 3],
-    ],
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-    [[2, 3, 3, 1, 2, 6], [2, 6, 3, 1, 6, 4], [6, 4, 3, 1, 7, 5], [0, 1, 2, 0, 0, 4]],
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-        [5, 1, 3, 2, 0, 2],
-        [5, 7, 3, 2, 5, 1],
-        [5, 7, 6, 2, 3, 2],
-        [5, 7, 4, 6, 6, 2],
-    ],
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-    [[0, 2, 1, 0, 1, 5], [1, 5, 5, 7, 0, 2], [0, 2, 5, 7, 4, 6]],
-    [[5, 7, 4, 6, 4, 0], [5, 1, 5, 7, 4, 0]],
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-    [[0, 1, 0, 2, 4, 5], [0, 2, 3, 7, 4, 5], [4, 5, 3, 7, 5, 7], [0, 2, 2, 6, 3, 7]],
-    [[5, 4, 4, 0, 5, 7], [5, 7, 4, 0, 7, 3], [7, 3, 4, 0, 6, 2], [1, 0, 5, 1, 1, 3]],
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-        [2, 0, 4, 5, 1, 5],
-        [2, 6, 4, 5, 2, 0],
-        [2, 6, 7, 5, 4, 5],
-        [2, 6, 3, 7, 7, 5],
-    ],
-    [[2, 3, 0, 4, 2, 0], [2, 3, 4, 5, 0, 4], [2, 3, 3, 7, 4, 5], [3, 7, 7, 5, 4, 5]],
-    [[3, 2, 7, 3, 7, 5], [7, 5, 5, 4, 3, 2], [3, 2, 5, 4, 1, 0]],
-    [
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-        [2, 3, 4, 5, 0, 4],
-        [2, 3, 3, 7, 4, 5],
-        [3, 7, 7, 5, 4, 5],
-        [1, 5, 3, 1, 0, 1],
-    ],
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-        [5, 7, 6, 2, 3, 2],
-        [5, 4, 6, 2, 5, 7],
-        [5, 4, 0, 2, 6, 2],
-        [5, 4, 1, 0, 0, 2],
-    ],
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-        [2, 6, 7, 5, 4, 5],
-        [2, 3, 7, 5, 2, 6],
-        [2, 3, 1, 5, 7, 5],
-        [2, 3, 0, 1, 1, 5],
-    ],
-    [[2, 3, 2, 0, 2, 6], [1, 5, 7, 5, 4, 5]],
-    [[5, 7, 4, 5, 4, 0], [4, 0, 0, 2, 5, 7], [5, 7, 0, 2, 1, 3]],
-    [[5, 4, 1, 0, 1, 3], [5, 7, 5, 4, 1, 3]],
-    [[0, 2, 4, 5, 0, 4], [0, 2, 5, 7, 4, 5], [0, 2, 1, 5, 5, 7], [0, 1, 1, 5, 0, 2]],
-    [[5, 4, 5, 1, 5, 7]],
-    [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3]],
-    [[4, 6, 6, 2, 4, 5], [4, 5, 6, 2, 5, 1], [5, 1, 6, 2, 7, 3], [0, 2, 4, 0, 0, 1]],
-    [[3, 7, 3, 1, 2, 6], [3, 1, 5, 4, 2, 6], [3, 1, 1, 0, 5, 4], [2, 6, 5, 4, 6, 4]],
-    [
-        [6, 4, 2, 6, 3, 7],
-        [3, 7, 5, 4, 6, 4],
-        [3, 1, 5, 4, 3, 7],
-        [3, 1, 0, 4, 5, 4],
-        [3, 1, 2, 0, 0, 4],
-    ],
-    [[2, 0, 2, 3, 6, 4], [2, 3, 1, 5, 6, 4], [6, 4, 1, 5, 4, 5], [2, 3, 3, 7, 1, 5]],
-    [
-        [0, 4, 1, 0, 3, 2],
-        [3, 2, 6, 4, 0, 4],
-        [3, 7, 6, 4, 3, 2],
-        [3, 7, 5, 4, 6, 4],
-        [3, 7, 1, 5, 5, 4],
-    ],
-    [
-        [1, 3, 0, 1, 4, 5],
-        [4, 5, 7, 3, 1, 3],
-        [4, 6, 7, 3, 4, 5],
-        [4, 6, 2, 3, 7, 3],
-        [4, 6, 0, 2, 2, 3],
-    ],
-    [[3, 7, 3, 1, 3, 2], [5, 4, 6, 4, 0, 4]],
-    [[3, 1, 2, 6, 3, 2], [3, 1, 6, 4, 2, 6], [3, 1, 1, 5, 6, 4], [1, 5, 5, 4, 6, 4]],
-    [
-        [3, 1, 2, 6, 3, 2],
-        [3, 1, 6, 4, 2, 6],
-        [3, 1, 1, 5, 6, 4],
-        [1, 5, 5, 4, 6, 4],
-        [0, 4, 1, 0, 2, 0],
-    ],
-    [[4, 5, 6, 4, 6, 2], [6, 2, 2, 3, 4, 5], [4, 5, 2, 3, 0, 1]],
-    [[2, 3, 6, 4, 2, 6], [2, 3, 4, 5, 6, 4], [2, 3, 0, 4, 4, 5], [2, 0, 0, 4, 2, 3]],
-    [[1, 3, 5, 1, 5, 4], [5, 4, 4, 6, 1, 3], [1, 3, 4, 6, 0, 2]],
-    [[1, 3, 0, 4, 1, 0], [1, 3, 4, 6, 0, 4], [1, 3, 5, 4, 4, 6], [1, 5, 5, 4, 1, 3]],
-    [[4, 6, 0, 2, 0, 1], [4, 5, 4, 6, 0, 1]],
-    [[4, 6, 4, 0, 4, 5]],
-    [[4, 0, 6, 2, 7, 3], [4, 0, 7, 3, 5, 1]],
-    [[1, 5, 0, 1, 0, 2], [0, 2, 2, 6, 1, 5], [1, 5, 2, 6, 3, 7]],
-    [[3, 7, 1, 3, 1, 0], [1, 0, 0, 4, 3, 7], [3, 7, 0, 4, 2, 6]],
-    [[3, 1, 2, 0, 2, 6], [3, 7, 3, 1, 2, 6]],
-    [[0, 4, 2, 0, 2, 3], [2, 3, 3, 7, 0, 4], [0, 4, 3, 7, 1, 5]],
-    [[3, 7, 1, 5, 1, 0], [3, 2, 3, 7, 1, 0]],
-    [[0, 4, 1, 3, 0, 1], [0, 4, 3, 7, 1, 3], [0, 4, 2, 3, 3, 7], [0, 2, 2, 3, 0, 4]],
-    [[3, 7, 3, 1, 3, 2]],
-    [[2, 6, 3, 2, 3, 1], [3, 1, 1, 5, 2, 6], [2, 6, 1, 5, 0, 4]],
-    [[1, 5, 3, 2, 1, 3], [1, 5, 2, 6, 3, 2], [1, 5, 0, 2, 2, 6], [1, 0, 0, 2, 1, 5]],
-    [[2, 3, 0, 1, 0, 4], [2, 6, 2, 3, 0, 4]],
-    [[2, 3, 2, 0, 2, 6]],
-    [[1, 5, 0, 4, 0, 2], [1, 3, 1, 5, 0, 2]],
-    [[1, 5, 1, 0, 1, 3]],
-    [[0, 2, 0, 1, 0, 4]],
-    [],
-]
-
-
-def create_mc_lookup_table():
-    cases = torch.zeros(256, 5, 3, dtype=torch.long)
-    masks = torch.zeros(256, 5, dtype=torch.bool)
-
-    edge_to_index = {
-        (0, 1): 0,
-        (2, 3): 1,
-        (4, 5): 2,
-        (6, 7): 3,
-        (0, 2): 4,
-        (1, 3): 5,
-        (4, 6): 6,
-        (5, 7): 7,
-        (0, 4): 8,
-        (1, 5): 9,
-        (2, 6): 10,
-        (3, 7): 11,
-    }
-
-    for i, case in enumerate(MC_TABLE):
-        for j, tri in enumerate(case):
-            for k, (c1, c2) in enumerate(zip(tri[::2], tri[1::2])):
-                cases[i, j, k] = edge_to_index[(c1, c2) if c1 < c2 else (c2, c1)]
-            masks[i, j] = True
-    return cases, masks
-
-
-RENDERER_CONFIG = {}
-
-
-def renderer_model_from_original_config():
-    model = ShapERenderer(**RENDERER_CONFIG)
-
-    return model
-
-
-RENDERER_MLP_ORIGINAL_PREFIX = "renderer.nerstf"
-
-RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX = "encoder.params_proj"
-
-
-def renderer_model_original_checkpoint_to_diffusers_checkpoint(model, checkpoint):
-    diffusers_checkpoint = {}
-    diffusers_checkpoint.update(
-        {f"mlp.{k}": checkpoint[f"{RENDERER_MLP_ORIGINAL_PREFIX}.{k}"] for k in model.mlp.state_dict().keys()}
-    )
-
-    diffusers_checkpoint.update(
-        {
-            f"params_proj.{k}": checkpoint[f"{RENDERER_PARAMS_PROJ_ORIGINAL_PREFIX}.{k}"]
-            for k in model.params_proj.state_dict().keys()
-        }
-    )
-
-    diffusers_checkpoint.update({"void.background": model.state_dict()["void.background"]})
-
-    cases, masks = create_mc_lookup_table()
-
-    diffusers_checkpoint.update({"mesh_decoder.cases": cases})
-    diffusers_checkpoint.update({"mesh_decoder.masks": masks})
-
-    return diffusers_checkpoint
-
-
-# done renderer
-
-
-# TODO maybe document and/or can do more efficiently (build indices in for loop and extract once for each split?)
-def split_attentions(*, weight, bias, split, chunk_size):
-    weights = [None] * split
-    biases = [None] * split
-
-    weights_biases_idx = 0
-
-    for starting_row_index in range(0, weight.shape[0], chunk_size):
-        row_indices = torch.arange(starting_row_index, starting_row_index + chunk_size)
-
-        weight_rows = weight[row_indices, :]
-        bias_rows = bias[row_indices]
-
-        if weights[weights_biases_idx] is None:
-            assert weights[weights_biases_idx] is None
-            weights[weights_biases_idx] = weight_rows
-            biases[weights_biases_idx] = bias_rows
-        else:
-            assert weights[weights_biases_idx] is not None
-            weights[weights_biases_idx] = torch.concat([weights[weights_biases_idx], weight_rows])
-            biases[weights_biases_idx] = torch.concat([biases[weights_biases_idx], bias_rows])
-
-        weights_biases_idx = (weights_biases_idx + 1) % split
-
-    return weights, biases
-
-
-# done unet utils
-
-
-# Driver functions
-
-
-def prior(*, args, checkpoint_map_location):
-    print("loading prior")
-
-    prior_checkpoint = torch.load(args.prior_checkpoint_path, map_location=checkpoint_map_location)
-
-    prior_model = prior_model_from_original_config()
-
-    prior_diffusers_checkpoint = prior_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint)
-
-    del prior_checkpoint
-
-    load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model)
-
-    print("done loading prior")
-
-    return prior_model
-
-
-def prior_image(*, args, checkpoint_map_location):
-    print("loading prior_image")
-
-    print(f"load checkpoint from {args.prior_image_checkpoint_path}")
-    prior_checkpoint = torch.load(args.prior_image_checkpoint_path, map_location=checkpoint_map_location)
-
-    prior_model = prior_image_model_from_original_config()
-
-    prior_diffusers_checkpoint = prior_image_original_checkpoint_to_diffusers_checkpoint(prior_model, prior_checkpoint)
-
-    del prior_checkpoint
-
-    load_prior_checkpoint_to_model(prior_diffusers_checkpoint, prior_model)
-
-    print("done loading prior_image")
-
-    return prior_model
-
-
-def renderer(*, args, checkpoint_map_location):
-    print(" loading renderer")
-
-    renderer_checkpoint = torch.load(args.transmitter_checkpoint_path, map_location=checkpoint_map_location)
-
-    renderer_model = renderer_model_from_original_config()
-
-    renderer_diffusers_checkpoint = renderer_model_original_checkpoint_to_diffusers_checkpoint(
-        renderer_model, renderer_checkpoint
-    )
-
-    del renderer_checkpoint
-
-    load_checkpoint_to_model(renderer_diffusers_checkpoint, renderer_model, strict=True)
-
-    print("done loading renderer")
-
-    return renderer_model
-
-
-# prior model will expect clip_mean and clip_std, whic are missing from the state_dict
-PRIOR_EXPECTED_MISSING_KEYS = ["clip_mean", "clip_std"]
-
-
-def load_prior_checkpoint_to_model(checkpoint, model):
-    with tempfile.NamedTemporaryFile() as file:
-        torch.save(checkpoint, file.name)
-        del checkpoint
-        missing_keys, unexpected_keys = model.load_state_dict(torch.load(file.name), strict=False)
-        missing_keys = list(set(missing_keys) - set(PRIOR_EXPECTED_MISSING_KEYS))
-
-        if len(unexpected_keys) > 0:
-            raise ValueError(f"Unexpected keys when loading prior model: {unexpected_keys}")
-        if len(missing_keys) > 0:
-            raise ValueError(f"Missing keys when loading prior model: {missing_keys}")
-
-
-def load_checkpoint_to_model(checkpoint, model, strict=False):
-    with tempfile.NamedTemporaryFile() as file:
-        torch.save(checkpoint, file.name)
-        del checkpoint
-        if strict:
-            model.load_state_dict(torch.load(file.name), strict=True)
-        else:
-            load_checkpoint_and_dispatch(model, file.name, device_map="auto")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
-
-    parser.add_argument(
-        "--prior_checkpoint_path",
-        default=None,
-        type=str,
-        required=False,
-        help="Path to the prior checkpoint to convert.",
-    )
-
-    parser.add_argument(
-        "--prior_image_checkpoint_path",
-        default=None,
-        type=str,
-        required=False,
-        help="Path to the prior_image checkpoint to convert.",
-    )
-
-    parser.add_argument(
-        "--transmitter_checkpoint_path",
-        default=None,
-        type=str,
-        required=False,
-        help="Path to the transmitter checkpoint to convert.",
-    )
-
-    parser.add_argument(
-        "--checkpoint_load_device",
-        default="cpu",
-        type=str,
-        required=False,
-        help="The device passed to `map_location` when loading checkpoints.",
-    )
-
-    parser.add_argument(
-        "--debug",
-        default=None,
-        type=str,
-        required=False,
-        help="Only run a specific stage of the convert script. Used for debugging",
-    )
-
-    args = parser.parse_args()
-
-    print(f"loading checkpoints to {args.checkpoint_load_device}")
-
-    checkpoint_map_location = torch.device(args.checkpoint_load_device)
-
-    if args.debug is not None:
-        print(f"debug: only executing {args.debug}")
-
-    if args.debug is None:
-        print("YiYi TO-DO")
-    elif args.debug == "prior":
-        prior_model = prior(args=args, checkpoint_map_location=checkpoint_map_location)
-        prior_model.save_pretrained(args.dump_path)
-    elif args.debug == "prior_image":
-        prior_model = prior_image(args=args, checkpoint_map_location=checkpoint_map_location)
-        prior_model.save_pretrained(args.dump_path)
-    elif args.debug == "renderer":
-        renderer_model = renderer(args=args, checkpoint_map_location=checkpoint_map_location)
-        renderer_model.save_pretrained(args.dump_path)
-    else:
-        raise ValueError(f"unknown debug value : {args.debug}")

spaces/Andy1621/uniformer_image_detection/configs/nas_fpn/README.md

@@ -1,26 +0,0 @@
-# NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
-
-## Introduction
-
-[ALGORITHM]
-
-```latex
-@inproceedings{ghiasi2019fpn,
-  title={Nas-fpn: Learning scalable feature pyramid architecture for object detection},
-  author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V},
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  pages={7036--7045},
-  year={2019}
-}
-```
-
-## Results and Models
-
-We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper.
-
-| Backbone    | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
-|:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
-| R-50-FPN    | 50e     | 12.9     | 22.9           | 37.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) |
-| R-50-NASFPN | 50e     | 13.2     | 23.0           | 40.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) |
-
-**Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower.

spaces/Andy1621/uniformer_image_segmentation/configs/deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './deeplabv3plus_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/runner/epoch_based_runner.py

@@ -1,187 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import platform
-import shutil
-import time
-import warnings
-
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-from .base_runner import BaseRunner
-from .builder import RUNNERS
-from .checkpoint import save_checkpoint
-from .utils import get_host_info
-
-
-@RUNNERS.register_module()
-class EpochBasedRunner(BaseRunner):
-    """Epoch-based Runner.
-
-    This runner train models epoch by epoch.
-    """
-
-    def run_iter(self, data_batch, train_mode, **kwargs):
-        if self.batch_processor is not None:
-            outputs = self.batch_processor(
-                self.model, data_batch, train_mode=train_mode, **kwargs)
-        elif train_mode:
-            outputs = self.model.train_step(data_batch, self.optimizer,
-                                            **kwargs)
-        else:
-            outputs = self.model.val_step(data_batch, self.optimizer, **kwargs)
-        if not isinstance(outputs, dict):
-            raise TypeError('"batch_processor()" or "model.train_step()"'
-                            'and "model.val_step()" must return a dict')
-        if 'log_vars' in outputs:
-            self.log_buffer.update(outputs['log_vars'], outputs['num_samples'])
-        self.outputs = outputs
-
-    def train(self, data_loader, **kwargs):
-        self.model.train()
-        self.mode = 'train'
-        self.data_loader = data_loader
-        self._max_iters = self._max_epochs * len(self.data_loader)
-        self.call_hook('before_train_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_train_iter')
-            self.run_iter(data_batch, train_mode=True, **kwargs)
-            self.call_hook('after_train_iter')
-            self._iter += 1
-
-        self.call_hook('after_train_epoch')
-        self._epoch += 1
-
-    @torch.no_grad()
-    def val(self, data_loader, **kwargs):
-        self.model.eval()
-        self.mode = 'val'
-        self.data_loader = data_loader
-        self.call_hook('before_val_epoch')
-        time.sleep(2)  # Prevent possible deadlock during epoch transition
-        for i, data_batch in enumerate(self.data_loader):
-            self._inner_iter = i
-            self.call_hook('before_val_iter')
-            self.run_iter(data_batch, train_mode=False)
-            self.call_hook('after_val_iter')
-
-        self.call_hook('after_val_epoch')
-
-    def run(self, data_loaders, workflow, max_epochs=None, **kwargs):
-        """Start running.
-
-        Args:
-            data_loaders (list[:obj:`DataLoader`]): Dataloaders for training
-                and validation.
-            workflow (list[tuple]): A list of (phase, epochs) to specify the
-                running order and epochs. E.g, [('train', 2), ('val', 1)] means
-                running 2 epochs for training and 1 epoch for validation,
-                iteratively.
-        """
-        assert isinstance(data_loaders, list)
-        assert mmcv.is_list_of(workflow, tuple)
-        assert len(data_loaders) == len(workflow)
-        if max_epochs is not None:
-            warnings.warn(
-                'setting max_epochs in run is deprecated, '
-                'please set max_epochs in runner_config', DeprecationWarning)
-            self._max_epochs = max_epochs
-
-        assert self._max_epochs is not None, (
-            'max_epochs must be specified during instantiation')
-
-        for i, flow in enumerate(workflow):
-            mode, epochs = flow
-            if mode == 'train':
-                self._max_iters = self._max_epochs * len(data_loaders[i])
-                break
-
-        work_dir = self.work_dir if self.work_dir is not None else 'NONE'
-        self.logger.info('Start running, host: %s, work_dir: %s',
-                         get_host_info(), work_dir)
-        self.logger.info('Hooks will be executed in the following order:\n%s',
-                         self.get_hook_info())
-        self.logger.info('workflow: %s, max: %d epochs', workflow,
-                         self._max_epochs)
-        self.call_hook('before_run')
-
-        while self.epoch < self._max_epochs:
-            for i, flow in enumerate(workflow):
-                mode, epochs = flow
-                if isinstance(mode, str):  # self.train()
-                    if not hasattr(self, mode):
-                        raise ValueError(
-                            f'runner has no method named "{mode}" to run an '
-                            'epoch')
-                    epoch_runner = getattr(self, mode)
-                else:
-                    raise TypeError(
-                        'mode in workflow must be a str, but got {}'.format(
-                            type(mode)))
-
-                for _ in range(epochs):
-                    if mode == 'train' and self.epoch >= self._max_epochs:
-                        break
-                    epoch_runner(data_loaders[i], **kwargs)
-
-        time.sleep(1)  # wait for some hooks like loggers to finish
-        self.call_hook('after_run')
-
-    def save_checkpoint(self,
-                        out_dir,
-                        filename_tmpl='epoch_{}.pth',
-                        save_optimizer=True,
-                        meta=None,
-                        create_symlink=True):
-        """Save the checkpoint.
-
-        Args:
-            out_dir (str): The directory that checkpoints are saved.
-            filename_tmpl (str, optional): The checkpoint filename template,
-                which contains a placeholder for the epoch number.
-                Defaults to 'epoch_{}.pth'.
-            save_optimizer (bool, optional): Whether to save the optimizer to
-                the checkpoint. Defaults to True.
-            meta (dict, optional): The meta information to be saved in the
-                checkpoint. Defaults to None.
-            create_symlink (bool, optional): Whether to create a symlink
-                "latest.pth" to point to the latest checkpoint.
-                Defaults to True.
-        """
-        if meta is None:
-            meta = {}
-        elif not isinstance(meta, dict):
-            raise TypeError(
-                f'meta should be a dict or None, but got {type(meta)}')
-        if self.meta is not None:
-            meta.update(self.meta)
-            # Note: meta.update(self.meta) should be done before
-            # meta.update(epoch=self.epoch + 1, iter=self.iter) otherwise
-            # there will be problems with resumed checkpoints.
-            # More details in https://github.com/open-mmlab/mmcv/pull/1108
-        meta.update(epoch=self.epoch + 1, iter=self.iter)
-
-        filename = filename_tmpl.format(self.epoch + 1)
-        filepath = osp.join(out_dir, filename)
-        optimizer = self.optimizer if save_optimizer else None
-        save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta)
-        # in some environments, `os.symlink` is not supported, you may need to
-        # set `create_symlink` to False
-        if create_symlink:
-            dst_file = osp.join(out_dir, 'latest.pth')
-            if platform.system() != 'Windows':
-                mmcv.symlink(filename, dst_file)
-            else:
-                shutil.copy(filepath, dst_file)
-
-
-@RUNNERS.register_module()
-class Runner(EpochBasedRunner):
-    """Deprecated name of EpochBasedRunner."""
-
-    def __init__(self, *args, **kwargs):
-        warnings.warn(
-            'Runner was deprecated, please use EpochBasedRunner instead')
-        super().__init__(*args, **kwargs)

spaces/Aqdas/YouTube_Video_OpenAI_whisper/README.md

@@ -1,13 +0,0 @@
----
-title: YouTube Video OpenAI Whisper
-emoji: 📚
-colorFrom: pink
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.28.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ArchitSharma/Digital-Photo-Color-Restoration/src/deoldify/device_id.py

@@ -1,12 +0,0 @@
-from enum import IntEnum
-
-class DeviceId(IntEnum):
-    GPU0 = 0,
-    GPU1 = 1,
-    GPU2 = 2,
-    GPU3 = 3,
-    GPU4 = 4,
-    GPU5 = 5,
-    GPU6 = 6,
-    GPU7 = 7,
-    CPU = 99

spaces/Armandoliv/whisper-biomedical-ner/README.md

@@ -1,12 +0,0 @@
----
-title: Whisper Biomedical Ner
-emoji: 🔥
-colorFrom: indigo
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/ArnePan/German-LLM-leaderboard/constants.py

@@ -1,25 +0,0 @@
-ALL_COLUMNS = ["Model", "Type", "Source-type", "Size", "Gnad10", "MMLU-de", "Hellaswag-de","arc_challenge-de","Belebele","XLNI","Amazon reviews"]
-
-DATA_TYPES = {
-    "Model" : "str", 
-    "Type" : "str",
-    "Size" : "str",
-    "Source-type" : "str",
-    "Gnad10" : "number", 
-    "MMLU-de" : "number",
-    "Hellaswag-de" : "number",
-    "arc_challenge-de" : "number",
-    "Belebele" : "number",
-    "XLNI" : "number",
-    "Amazon reviews" : "number"
-}
-
-DATASETS =["Gnad10", "MMLU-de", "Hellaswag-de","arc_challenge-de","Belebele","XLNI","Amazon reviews"]
-
-DEFAULT_CHECK = ["Type","Size","Gnad10", "MMLU-de", "Hellaswag-de"]
-
-MODEL_SIZES = ["Unknown", "<1.5B", "~3B","~7B","~13B","~35B","+60B"]
-
-MODEL_TYPES = ["pre-trained", "instruction-tuned", "RL-tuned"]
-
-SOURCE_TYPES = ["Open-source", "Closed-source"]

spaces/Arnx/MusicGenXvAKN/tests/data/test_audio.py

@@ -1,239 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from itertools import product
-import random
-
-import numpy as np
-import torch
-import torchaudio
-
-from audiocraft.data.audio import audio_info, audio_read, audio_write, _av_read
-
-from ..common_utils import TempDirMixin, get_white_noise, save_wav
-
-
-class TestInfo(TempDirMixin):
-
-    def test_info_mp3(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            wav = get_white_noise(ch, int(sample_rate * duration))
-            path = self.get_temp_path('sample_wav.mp3')
-            save_wav(path, wav, sample_rate)
-            info = audio_info(path)
-            assert info.sample_rate == sample_rate
-            assert info.channels == ch
-            # we cannot trust torchaudio for num_frames, so we don't check
-
-    def _test_info_format(self, ext: str):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'sample_wav{ext}')
-            save_wav(path, wav, sample_rate)
-            info = audio_info(path)
-            assert info.sample_rate == sample_rate
-            assert info.channels == ch
-            assert np.isclose(info.duration, duration, atol=1e-5)
-
-    def test_info_wav(self):
-        self._test_info_format('.wav')
-
-    def test_info_flac(self):
-        self._test_info_format('.flac')
-
-    def test_info_ogg(self):
-        self._test_info_format('.ogg')
-
-    def test_info_m4a(self):
-        # TODO: generate m4a file programmatically
-        # self._test_info_format('.m4a')
-        pass
-
-
-class TestRead(TempDirMixin):
-
-    def test_read_full_wav(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            read_wav, read_sr = audio_read(path)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == wav.shape[1]
-            assert torch.allclose(read_wav, wav, rtol=1e-03, atol=1e-04)
-
-    def test_read_partial_wav(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        read_duration = torch.rand(1).item()
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            read_frames = int(sample_rate * read_duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            read_wav, read_sr = audio_read(path, 0, read_duration)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == read_frames
-            assert torch.allclose(read_wav[..., 0:read_frames], wav[..., 0:read_frames], rtol=1e-03, atol=1e-04)
-
-    def test_read_seek_time_wav(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        read_duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = torch.rand(1).item()
-            read_wav, read_sr = audio_read(path, seek_time, read_duration)
-            seek_frames = int(sample_rate * seek_time)
-            expected_frames = n_frames - seek_frames
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == expected_frames
-            assert torch.allclose(read_wav, wav[..., seek_frames:], rtol=1e-03, atol=1e-04)
-
-    def test_read_seek_time_wav_padded(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        read_duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            read_frames = int(sample_rate * read_duration)
-            wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99)
-            path = self.get_temp_path('sample_wav.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = torch.rand(1).item()
-            seek_frames = int(sample_rate * seek_time)
-            expected_frames = n_frames - seek_frames
-            read_wav, read_sr = audio_read(path, seek_time, read_duration, pad=True)
-            expected_pad_wav = torch.zeros(wav.shape[0], read_frames - expected_frames)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[1] == read_frames
-            assert torch.allclose(read_wav[..., :expected_frames], wav[..., seek_frames:], rtol=1e-03, atol=1e-04)
-            assert torch.allclose(read_wav[..., expected_frames:], expected_pad_wav)
-
-
-class TestAvRead(TempDirMixin):
-
-    def test_avread_seek_base(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 2.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'reference_a_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            for _ in range(100):
-                # seek will always load a full duration segment in the file
-                seek_time = random.uniform(0.0, 1.0)
-                seek_duration = random.uniform(0.001, 1.0)
-                read_wav, read_sr = _av_read(path, seek_time, seek_duration)
-                assert read_sr == sample_rate
-                assert read_wav.shape[0] == wav.shape[0]
-                assert read_wav.shape[-1] == int(seek_duration * sample_rate)
-
-    def test_avread_seek_partial(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'reference_b_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            for _ in range(100):
-                # seek will always load a partial segment
-                seek_time = random.uniform(0.5, 1.)
-                seek_duration = 1.
-                expected_num_frames = n_frames - int(seek_time * sample_rate)
-                read_wav, read_sr = _av_read(path, seek_time, seek_duration)
-                assert read_sr == sample_rate
-                assert read_wav.shape[0] == wav.shape[0]
-                assert read_wav.shape[-1] == expected_num_frames
-
-    def test_avread_seek_outofbound(self):
-        sample_rates = [8000, 16_000]
-        channels = [1, 2]
-        duration = 1.
-        for sample_rate, ch in product(sample_rates, channels):
-            n_frames = int(sample_rate * duration)
-            wav = get_white_noise(ch, n_frames)
-            path = self.get_temp_path(f'reference_c_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = 1.5
-            read_wav, read_sr = _av_read(path, seek_time, 1.)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[-1] == 0
-
-    def test_avread_seek_edge(self):
-        sample_rates = [8000, 16_000]
-        # some of these values will have
-        # int(((frames - 1) / sample_rate) * sample_rate) != (frames - 1)
-        n_frames = [1000, 1001, 1002]
-        channels = [1, 2]
-        for sample_rate, ch, frames in product(sample_rates, channels, n_frames):
-            duration = frames / sample_rate
-            wav = get_white_noise(ch, frames)
-            path = self.get_temp_path(f'reference_d_{sample_rate}_{ch}.wav')
-            save_wav(path, wav, sample_rate)
-            seek_time = (frames - 1) / sample_rate
-            seek_frames = int(seek_time * sample_rate)
-            read_wav, read_sr = _av_read(path, seek_time, duration)
-            assert read_sr == sample_rate
-            assert read_wav.shape[0] == wav.shape[0]
-            assert read_wav.shape[-1] == (frames - seek_frames)
-
-
-class TestAudioWrite(TempDirMixin):
-
-    def test_audio_write_wav(self):
-        torch.manual_seed(1234)
-        sample_rates = [8000, 16_000]
-        n_frames = [1000, 1001, 1002]
-        channels = [1, 2]
-        strategies = ["peak", "clip", "rms"]
-        formats = ["wav", "mp3"]
-        for sample_rate, ch, frames in product(sample_rates, channels, n_frames):
-            for format_, strategy in product(formats, strategies):
-                wav = get_white_noise(ch, frames)
-                path = self.get_temp_path(f'pred_{sample_rate}_{ch}')
-                audio_write(path, wav, sample_rate, format_, strategy=strategy)
-                read_wav, read_sr = torchaudio.load(f'{path}.{format_}')
-                if format_ == "wav":
-                    assert read_wav.shape == wav.shape
-
-                if format_ == "wav" and strategy in ["peak", "rms"]:
-                    rescaled_read_wav = read_wav / read_wav.abs().max() * wav.abs().max()
-                    # for a Gaussian, the typical max scale will be less than ~5x the std.
-                    # The error when writing to disk will ~ 1/2**15, and when rescaling, 5x that.
-                    # For RMS target, rescaling leaves more headroom by default, leading
-                    # to a 20x rescaling typically
-                    atol = (5 if strategy == "peak" else 20) / 2**15
-                    delta = (rescaled_read_wav - wav).abs().max()
-                    assert torch.allclose(wav, rescaled_read_wav, rtol=0, atol=atol), (delta, atol)
-            formats = ["wav"]  # faster unit tests

spaces/ArtGAN/Diffusion-API/diffusion_webui/diffusion_models/controlnet_pipeline.py

@@ -1,262 +0,0 @@
-import gradio as gr
-import torch
-import cv2
-from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
-from PIL import Image
-
-from diffusion_webui.diffusion_models.base_controlnet_pipeline import (
-    ControlnetPipeline,
-)
-from diffusion_webui.utils.model_list import (
-    controlnet_model_list,
-    stable_model_list,
-)
-from diffusion_webui.utils.preprocces_utils import PREPROCCES_DICT
-from diffusion_webui.utils.scheduler_list import (
-    SCHEDULER_MAPPING,
-    get_scheduler,
-)
-
-
-stable_model_list = [
-    "runwayml/stable-diffusion-v1-5",
-    "dreamlike-art/dreamlike-diffusion-1.0",
-    "kadirnar/maturemalemix_v0",
-    "kadirnar/DreamShaper_v6"
-]
-
-stable_inpiant_model_list = [
-    "stabilityai/stable-diffusion-2-inpainting",
-    "runwayml/stable-diffusion-inpainting",
-    "saik0s/realistic_vision_inpainting",
-]
-
-controlnet_model_list = [
-    "lllyasviel/control_v11p_sd15_canny",
-    "lllyasviel/control_v11f1p_sd15_depth",
-    "lllyasviel/control_v11p_sd15_openpose",
-    "lllyasviel/control_v11p_sd15_scribble",
-    "lllyasviel/control_v11p_sd15_mlsd",
-    "lllyasviel/control_v11e_sd15_shuffle",
-    "lllyasviel/control_v11e_sd15_ip2p",
-    "lllyasviel/control_v11p_sd15_lineart",
-    "lllyasviel/control_v11p_sd15s2_lineart_anime",
-    "lllyasviel/control_v11p_sd15_softedge",
-]
-
-class StableDiffusionControlNetGenerator(ControlnetPipeline):
-    def __init__(self):
-        self.pipe = None
-        
-    def load_model(self, stable_model_path, controlnet_model_path, scheduler):
-        if self.pipe is None or self.pipe.model_name != stable_model_path or self.pipe.scheduler_name != scheduler:
-            controlnet = ControlNetModel.from_pretrained(
-                controlnet_model_path, torch_dtype=torch.float16
-            )
-            self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
-                pretrained_model_name_or_path=stable_model_path,
-                controlnet=controlnet,
-                safety_checker=None,
-                torch_dtype=torch.float16,
-            )
-            self.pipe.model_name = stable_model_path
-            self.pipe.scheduler_name = scheduler
-            
-            self.pipe = get_scheduler(pipe=self.pipe, scheduler=scheduler)
-            self.pipe.scheduler_name = scheduler
-            self.pipe.to("cuda")
-            self.pipe.enable_xformers_memory_efficient_attention()
-    
-        return self.pipe
-
-
-    def controlnet_preprocces(
-        self,
-        read_image: str,
-        preprocces_type: str,
-    ):
-        processed_image = PREPROCCES_DICT[preprocces_type](read_image)
-        return processed_image
-
-    def generate_image(
-        self,
-        image_path: str,
-        stable_model_path: str,
-        controlnet_model_path: str,
-        height: int,
-        width: int,
-        guess_mode: bool,
-        controlnet_conditioning_scale: int,
-        prompt: str,
-        negative_prompt: str,
-        num_images_per_prompt: int,
-        guidance_scale: int,
-        num_inference_step: int,
-        scheduler: str,
-        seed_generator: int,
-        preprocces_type: str,
-    ):
-        pipe = self.load_model(
-            stable_model_path=stable_model_path,
-            controlnet_model_path=controlnet_model_path,
-            scheduler=scheduler,
-        )
-        if preprocces_type== "ScribbleXDOG":
-            read_image = cv2.imread(image_path)
-            controlnet_image = self.controlnet_preprocces(read_image=read_image, preprocces_type=preprocces_type)[0]
-            controlnet_image = Image.fromarray(controlnet_image)
-            
-        elif preprocces_type== "None":
-            controlnet_image = self.controlnet_preprocces(read_image=image_path, preprocces_type=preprocces_type)
-        else:
-            read_image = Image.open(image_path)
-            controlnet_image = self.controlnet_preprocces(read_image=read_image, preprocces_type=preprocces_type)
-
-        if seed_generator == 0:
-            random_seed = torch.randint(0, 1000000, (1,))
-            generator = torch.manual_seed(random_seed)
-        else:
-            generator = torch.manual_seed(seed_generator)
-        
-
-        output = pipe(
-            prompt=prompt,
-            height=height,
-            width=width,
-            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-            guess_mode=guess_mode,
-            image=controlnet_image,
-            negative_prompt=negative_prompt,
-            num_images_per_prompt=num_images_per_prompt,
-            num_inference_steps=num_inference_step,
-            guidance_scale=guidance_scale,
-            generator=generator,
-        ).images
-
-        return output
-
-    def app():
-        with gr.Blocks():
-            with gr.Row():
-                with gr.Column():
-                    controlnet_image_path = gr.Image(
-                        type="filepath", label="Image"
-                    ).style(height=260)
-                    controlnet_prompt = gr.Textbox(
-                        lines=1, placeholder="Prompt", show_label=False
-                    )
-                    controlnet_negative_prompt = gr.Textbox(
-                        lines=1, placeholder="Negative Prompt", show_label=False
-                    )
-
-                    with gr.Row():
-                        with gr.Column():
-                            controlnet_stable_model_path = gr.Dropdown(
-                                choices=stable_model_list,
-                                value=stable_model_list[0],
-                                label="Stable Model Path",
-                            )
-                            controlnet_preprocces_type = gr.Dropdown(
-                                choices=list(PREPROCCES_DICT.keys()),
-                                value=list(PREPROCCES_DICT.keys())[0],
-                                label="Preprocess Type",
-                            )
-                            controlnet_conditioning_scale = gr.Slider(
-                                minimum=0.0,
-                                maximum=1.0,
-                                step=0.1,
-                                value=1.0,
-                                label="ControlNet Conditioning Scale",
-                            )
-                            controlnet_guidance_scale = gr.Slider(
-                                minimum=0.1,
-                                maximum=15,
-                                step=0.1,
-                                value=7.5,
-                                label="Guidance Scale",
-                            )
-                            controlnet_height = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Height",
-                            )
-                            controlnet_width = gr.Slider(
-                                minimum=128,
-                                maximum=1280,
-                                step=32,
-                                value=512,
-                                label="Width",
-                            )
-
-                        with gr.Row():
-                            with gr.Column():
-                                controlnet_model_path = gr.Dropdown(
-                                    choices=controlnet_model_list,
-                                    value=controlnet_model_list[0],
-                                    label="ControlNet Model Path",
-                                )
-                                controlnet_scheduler = gr.Dropdown(
-                                    choices=list(SCHEDULER_MAPPING.keys()),
-                                    value=list(SCHEDULER_MAPPING.keys())[0],
-                                    label="Scheduler",
-                                )
-                                controlnet_num_inference_step = gr.Slider(
-                                    minimum=1,
-                                    maximum=150,
-                                    step=1,
-                                    value=30,
-                                    label="Num Inference Step",
-                                )
-
-                                controlnet_num_images_per_prompt = gr.Slider(
-                                    minimum=1,
-                                    maximum=4,
-                                    step=1,
-                                    value=1,
-                                    label="Number Of Images",
-                                )
-                                controlnet_seed_generator = gr.Slider(
-                                    minimum=0,
-                                    maximum=1000000,
-                                    step=1,
-                                    value=0,
-                                    label="Seed(0 for random)",
-                                )
-                                controlnet_guess_mode = gr.Checkbox(
-                                    label="Guess Mode"
-                                )
-
-                    # Button to generate the image
-                    predict_button = gr.Button(value="Generate Image")
-
-                with gr.Column():
-                    # Gallery to display the generated images
-                    output_image = gr.Gallery(
-                        label="Generated images",
-                        show_label=False,
-                        elem_id="gallery",
-                    ).style(grid=(1, 2))
-
-        predict_button.click(
-            fn=StableDiffusionControlNetGenerator().generate_image,
-            inputs=[
-                controlnet_image_path,
-                controlnet_stable_model_path,
-                controlnet_model_path,
-                controlnet_height,
-                controlnet_width,
-                controlnet_guess_mode,
-                controlnet_conditioning_scale,
-                controlnet_prompt,
-                controlnet_negative_prompt,
-                controlnet_num_images_per_prompt,
-                controlnet_guidance_scale,
-                controlnet_num_inference_step,
-                controlnet_scheduler,
-                controlnet_seed_generator,
-                controlnet_preprocces_type,
-            ],
-            outputs=[output_image],
-        )

spaces/AvinashRamesh23/AIEditor/README.md

@@ -1,12 +0,0 @@
----
-title: AIEditor
-emoji: 📉
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/BatuhanYilmaz/Whisper-Auto-Subtitled-Video-Generator/utils.py

@@ -1,96 +0,0 @@
-import textwrap
-import zlib
-from typing import Iterator, TextIO
-
-
-def exact_div(x, y):
-    assert x % y == 0
-    return x // y
-
-
-def str2bool(string):
-    str2val = {"True": True, "False": False}
-    if string in str2val:
-        return str2val[string]
-    else:
-        raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}")
-
-
-def optional_int(string):
-    return None if string == "None" else int(string)
-
-
-def optional_float(string):
-    return None if string == "None" else float(string)
-
-
-def compression_ratio(text) -> float:
-    return len(text) / len(zlib.compress(text.encode("utf-8")))
-
-
-def format_timestamp(seconds: float, always_include_hours: bool = False, fractionalSeperator: str = '.'):
-    assert seconds >= 0, "non-negative timestamp expected"
-    milliseconds = round(seconds * 1000.0)
-
-    hours = milliseconds // 3_600_000
-    milliseconds -= hours * 3_600_000
-
-    minutes = milliseconds // 60_000
-    milliseconds -= minutes * 60_000
-
-    seconds = milliseconds // 1_000
-    milliseconds -= seconds * 1_000
-
-    hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
-    return f"{hours_marker}{minutes:02d}:{seconds:02d}{fractionalSeperator}{milliseconds:03d}"
-
-
-def write_txt(transcript: Iterator[dict], file: TextIO):
-    for segment in transcript:
-        print(segment['text'].strip(), file=file, flush=True)
-
-
-def write_vtt(transcript: Iterator[dict], file: TextIO, maxLineWidth=None):
-    print("WEBVTT\n", file=file)
-    for segment in transcript:
-        text = processText(segment['text'], maxLineWidth).replace('-->', '->')
-
-        print(
-            f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n"
-            f"{text}\n",
-            file=file,
-            flush=True,
-        )
-
-
-def write_srt(transcript: Iterator[dict], file: TextIO, maxLineWidth=None):
-    """
-    Write a transcript to a file in SRT format.
-    Example usage:
-        from pathlib import Path
-        from whisper.utils import write_srt
-        result = transcribe(model, audio_path, temperature=temperature, **args)
-        # save SRT
-        audio_basename = Path(audio_path).stem
-        with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt:
-            write_srt(result["segments"], file=srt)
-    """
-    for i, segment in enumerate(transcript, start=1):
-        text = processText(segment['text'].strip(), maxLineWidth).replace('-->', '->')
-
-        # write srt lines
-        print(
-            f"{i}\n"
-            f"{format_timestamp(segment['start'], always_include_hours=True, fractionalSeperator=',')} --> "
-            f"{format_timestamp(segment['end'], always_include_hours=True, fractionalSeperator=',')}\n"
-            f"{text}\n",
-            file=file,
-            flush=True,
-        )
-
-def processText(text: str, maxLineWidth=None):
-    if (maxLineWidth is None or maxLineWidth < 0):
-        return text
-
-    lines = textwrap.wrap(text, width=maxLineWidth, tabsize=4)
-    return '\n'.join(lines)

spaces/Benson/text-generation/Examples/Ciudad Smash Hack Apk.md

@@ -1,67 +0,0 @@
-
-<h1>Ciudad Smash Hack APK: Cómo descargar e instalar</h1>
-<p>¿Te encanta destruir cosas con diferentes armas? ¿Quieres dar rienda suelta a tu Godzilla interior y destruir una ciudad con una bomba nuclear, misiles, agujero negro, rayo láser o rayo? Si es así, entonces puede que te interese City Smash, un juego de física que te permite hacer todo eso y más. Pero lo que si quieres tener más diversión y desbloquear todas las armas y características sin gastar dinero? Bueno, ahí es donde Ciudad Smash Hack APK entra en juego. En este artículo, le diremos qué es City Smash, ¿por qué debe utilizar City Smash Hack APK, y cómo descargar e instalar en su dispositivo Android. </p>
-<h2>ciudad smash hack apk</h2><br /><p><b><b>Download</b> >>> <a href="https://bltlly.com/2v6LXH">https://bltlly.com/2v6LXH</a></b></p><br /><br />
- <h2>¿Qué es City Smash? </h2>
-<h3>Un juego de juegos de física</h3>
-<p>City Smash es un juego desarrollado por Paradyme Games que te permite destruir una ciudad con varias armas y ver los efectos de destrucción realistas. El juego utiliza un motor de física que simula cómo los edificios se rompen y colapsan cuando son golpeados por diferentes fuerzas. Puede elegir entre diferentes ciudades y escenarios, como Nueva York, Tokio, París, Londres, Moscú y más. También puedes personalizar el clima, la hora del día y el ángulo de la cámara para crear tus propias escenas. </p>
-<h3>Características del juego</h3>
-<p>Algunas de las características de City Smash son:</p>
-<ul>
-<li>Puedes usar más de 50 armas y herramientas para destruir la ciudad, como bombas nucleares, misiles, agujeros negros, láseres, rayos, meteoros, zombis, dinosaurios, ovnis y más. </li>
-<li>Puedes desbloquear nuevas armas y herramientas ganando monedas o viendo anuncios. </li>
-<li> Puedes guardar y compartir tus escenas de destrucción con otros jugadores. </li>
-<li>Puedes ver las repeticiones de tus acciones y ralentizar o acelerar el tiempo. </li>
-<li> Puedes disfrutar de los gráficos realistas y efectos de sonido del juego. </li>
-</ul>
- <h2>¿Por qué utilizar Ciudad Smash Hack APK? </h2>
-<h3>Beneficios de usar el hack</h3>
-
-<ul>
-<li>Puedes obtener monedas ilimitadas para desbloquear todas las armas y herramientas sin gastar dinero real. </li>
-<li>Puedes eliminar todos los anuncios del juego y disfrutar de una experiencia de juego más fluida. </li>
-<li>No puedes obtener tiempo de reutilización de habilidades para usar cualquier arma o herramienta tantas veces como quieras. </li>
-</ul>
-<h3>Riesgos de usar el hack</h3>
-<p>Sin embargo, el uso de Ciudad Smash Hack APK también viene con algunos riesgos que usted debe ser consciente de antes de descargarlo. Algunos de los riesgos de usar Ciudad Smash Hack APK son:</p>
-<p></p>
-<ul>
-<li>Es posible que te prohíban participar en el juego o que pierdas tu progreso si los desarrolladores detectan que estás usando una versión hackeada. </li>
-<li>Usted podría exponer su dispositivo a malware o virus que podrían dañar sus datos o privacidad si descarga el hack de una fuente no confiable. </li>
-<li>Es posible que se pierda algunas actualizaciones o características que solo están disponibles en la versión oficial del juego. </li>
-</ul>
- <h2>Cómo descargar e instalar Ciudad Smash Hack APK? </h2>
-<h3>Pasos para descargar el hack</h3>
-<p>Si usted ha decidido utilizar Ciudad Smash Hack APK, aquí están los pasos para descargarlo:</p>
-<ol>
-<li>Ir a un sitio web confiable que ofrece Ciudad Smash Hack APK para su descarga gratuita. Por ejemplo, se puede utilizar [Wendgames]( 1 ) o [MODYOLO]( 2 ). </li>
-<li>Haga clic en el botón de descarga y espere a que el archivo se descargue en su dispositivo. </li>
-<li>Busque el archivo en el administrador de archivos de su dispositivo y toque en él para abrirlo. </li>
-</ol>
- <h3>Pasos para instalar el hack</h3>
-<p>Antes de instalar Ciudad Smash Hack APK, asegúrese de que ha habilitado la instalación de aplicaciones de fuentes desconocidas en la configuración de su dispositivo. Si no sabes cómo hacerlo, puedes seguir estos pasos:</p>
-<ol>
-<li>Ir a la configuración de su dispositivo y toque en la seguridad o la privacidad. </li>
-<li> Encontrar la opción que dice "Fuentes desconocidas" o "Instalar aplicaciones desconocidas" y alternar en. </li>
-<li>Confirme su elección tocando en OK o Permitir.</li>
-</ol>
-
-<ol>
-<li>Toque en el archivo que ha descargado y seleccione Instalar.</li>
-<li>Espera a que termine el proceso de instalación y toca Abrir para iniciar el juego. </li>
-<li>Disfruta destrozando la ciudad con monedas ilimitadas y sin anuncios. </li>
-</ol>
- <h2>Conclusión</h2>
-<p>City Smash es un juego divertido y adictivo que te permite destruir una ciudad con varias armas y herramientas. Sin embargo, si desea tener más diversión y desbloquear todas las características sin gastar dinero, puede utilizar Ciudad Smash Hack APK. Esta es una versión modificada del juego que te da monedas ilimitadas, sin anuncios y sin tiempo de reutilización de habilidades. Sin embargo, también debe tener cuidado con los riesgos de usar el hack, como ser prohibido, infectado, o perder las actualizaciones. Para descargar e instalar Ciudad Smash Hack APK, puede seguir los pasos que hemos proporcionado en este artículo. Esperamos que este artículo sea útil e informativo para usted. ¡Feliz éxito! </p>
- <h2>Preguntas frecuentes</h2>
-<h3> ¿Cuál es la última versión de Ciudad Smash Hack APK? </h3>
-<p>La última versión de la ciudad Smash Hack APK es 1.26.4, que fue actualizado el 15 de junio de 2023. Se puede descargar desde [Wendgames] o [MODYOLO]. </p>
-<h3> ¿Es Ciudad Smash Hack APK seguro de usar? </h3>
-<p>Ciudad Smash Hack APK es seguro de usar, siempre y cuando se descarga desde una fuente de confianza y escanear con un antivirus antes de instalarlo. Sin embargo, también debes ser consciente de los riesgos de usar una versión hackeada del juego, como ser prohibido, infectado o perderte actualizaciones. </p>
-<h3> ¿Cómo puedo actualizar Ciudad Smash Hack APK? </h3>
-<p>Para actualizar Ciudad Smash Hack APK, es necesario desinstalar la versión anterior y descargar e instalar la nueva versión de un sitio web confiable. Puede consultar las actualizaciones regularmente visitando [Wendgames] o [MODYOLO]. </p>
-<h3>¿Puedo jugar Ciudad Smash Hack APK en línea con otros jugadores? </h3>
-<p>No, Ciudad Smash Hack APK no es un juego en línea y no es compatible con el modo multijugador. Solo se puede jugar fuera de línea y en solitario. </p>
-<h3> ¿Puedo utilizar Ciudad Smash Hack APK en dispositivos iOS? </h3> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/help.py

@@ -1,131 +0,0 @@
-"""Module containing bug report helper(s)."""
-
-import json
-import platform
-import ssl
-import sys
-
-from pip._vendor import idna
-from pip._vendor import urllib3
-
-from . import __version__ as requests_version
-
-charset_normalizer = None
-
-try:
-    from pip._vendor import chardet
-except ImportError:
-    chardet = None
-
-try:
-    from pip._vendor.urllib3.contrib import pyopenssl
-except ImportError:
-    pyopenssl = None
-    OpenSSL = None
-    cryptography = None
-else:
-    import cryptography
-    import OpenSSL
-
-
-def _implementation():
-    """Return a dict with the Python implementation and version.
-
-    Provide both the name and the version of the Python implementation
-    currently running. For example, on CPython 3.10.3 it will return
-    {'name': 'CPython', 'version': '3.10.3'}.
-
-    This function works best on CPython and PyPy: in particular, it probably
-    doesn't work for Jython or IronPython. Future investigation should be done
-    to work out the correct shape of the code for those platforms.
-    """
-    implementation = platform.python_implementation()
-
-    if implementation == "CPython":
-        implementation_version = platform.python_version()
-    elif implementation == "PyPy":
-        implementation_version = "{}.{}.{}".format(
-            sys.pypy_version_info.major,
-            sys.pypy_version_info.minor,
-            sys.pypy_version_info.micro,
-        )
-        if sys.pypy_version_info.releaselevel != "final":
-            implementation_version = "".join(
-                [implementation_version, sys.pypy_version_info.releaselevel]
-            )
-    elif implementation == "Jython":
-        implementation_version = platform.python_version()  # Complete Guess
-    elif implementation == "IronPython":
-        implementation_version = platform.python_version()  # Complete Guess
-    else:
-        implementation_version = "Unknown"
-
-    return {"name": implementation, "version": implementation_version}
-
-
-def info():
-    """Generate information for a bug report."""
-    try:
-        platform_info = {
-            "system": platform.system(),
-            "release": platform.release(),
-        }
-    except OSError:
-        platform_info = {
-            "system": "Unknown",
-            "release": "Unknown",
-        }
-
-    implementation_info = _implementation()
-    urllib3_info = {"version": urllib3.__version__}
-    charset_normalizer_info = {"version": None}
-    chardet_info = {"version": None}
-    if charset_normalizer:
-        charset_normalizer_info = {"version": charset_normalizer.__version__}
-    if chardet:
-        chardet_info = {"version": chardet.__version__}
-
-    pyopenssl_info = {
-        "version": None,
-        "openssl_version": "",
-    }
-    if OpenSSL:
-        pyopenssl_info = {
-            "version": OpenSSL.__version__,
-            "openssl_version": f"{OpenSSL.SSL.OPENSSL_VERSION_NUMBER:x}",
-        }
-    cryptography_info = {
-        "version": getattr(cryptography, "__version__", ""),
-    }
-    idna_info = {
-        "version": getattr(idna, "__version__", ""),
-    }
-
-    system_ssl = ssl.OPENSSL_VERSION_NUMBER
-    system_ssl_info = {"version": f"{system_ssl:x}" if system_ssl is not None else ""}
-
-    return {
-        "platform": platform_info,
-        "implementation": implementation_info,
-        "system_ssl": system_ssl_info,
-        "using_pyopenssl": pyopenssl is not None,
-        "using_charset_normalizer": chardet is None,
-        "pyOpenSSL": pyopenssl_info,
-        "urllib3": urllib3_info,
-        "chardet": chardet_info,
-        "charset_normalizer": charset_normalizer_info,
-        "cryptography": cryptography_info,
-        "idna": idna_info,
-        "requests": {
-            "version": requests_version,
-        },
-    }
-
-
-def main():
-    """Pretty-print the bug information as JSON."""
-    print(json.dumps(info(), sort_keys=True, indent=2))
-
-
-if __name__ == "__main__":
-    main()

spaces/Big-Web/MMSD/env/Lib/site-packages/urllib3/__init__.py

@@ -1,102 +0,0 @@
-"""
-Python HTTP library with thread-safe connection pooling, file post support, user friendly, and more
-"""
-from __future__ import absolute_import
-
-# Set default logging handler to avoid "No handler found" warnings.
-import logging
-import warnings
-from logging import NullHandler
-
-from . import exceptions
-from ._version import __version__
-from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, connection_from_url
-from .filepost import encode_multipart_formdata
-from .poolmanager import PoolManager, ProxyManager, proxy_from_url
-from .response import HTTPResponse
-from .util.request import make_headers
-from .util.retry import Retry
-from .util.timeout import Timeout
-from .util.url import get_host
-
-# === NOTE TO REPACKAGERS AND VENDORS ===
-# Please delete this block, this logic is only
-# for urllib3 being distributed via PyPI.
-# See: https://github.com/urllib3/urllib3/issues/2680
-try:
-    import urllib3_secure_extra  # type: ignore # noqa: F401
-except ImportError:
-    pass
-else:
-    warnings.warn(
-        "'urllib3[secure]' extra is deprecated and will be removed "
-        "in a future release of urllib3 2.x. Read more in this issue: "
-        "https://github.com/urllib3/urllib3/issues/2680",
-        category=DeprecationWarning,
-        stacklevel=2,
-    )
-
-__author__ = "Andrey Petrov ([email protected])"
-__license__ = "MIT"
-__version__ = __version__
-
-__all__ = (
-    "HTTPConnectionPool",
-    "HTTPSConnectionPool",
-    "PoolManager",
-    "ProxyManager",
-    "HTTPResponse",
-    "Retry",
-    "Timeout",
-    "add_stderr_logger",
-    "connection_from_url",
-    "disable_warnings",
-    "encode_multipart_formdata",
-    "get_host",
-    "make_headers",
-    "proxy_from_url",
-)
-
-logging.getLogger(__name__).addHandler(NullHandler())
-
-
-def add_stderr_logger(level=logging.DEBUG):
-    """
-    Helper for quickly adding a StreamHandler to the logger. Useful for
-    debugging.
-
-    Returns the handler after adding it.
-    """
-    # This method needs to be in this __init__.py to get the __name__ correct
-    # even if urllib3 is vendored within another package.
-    logger = logging.getLogger(__name__)
-    handler = logging.StreamHandler()
-    handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)s %(message)s"))
-    logger.addHandler(handler)
-    logger.setLevel(level)
-    logger.debug("Added a stderr logging handler to logger: %s", __name__)
-    return handler
-
-
-# ... Clean up.
-del NullHandler
-
-
-# All warning filters *must* be appended unless you're really certain that they
-# shouldn't be: otherwise, it's very hard for users to use most Python
-# mechanisms to silence them.
-# SecurityWarning's always go off by default.
-warnings.simplefilter("always", exceptions.SecurityWarning, append=True)
-# SubjectAltNameWarning's should go off once per host
-warnings.simplefilter("default", exceptions.SubjectAltNameWarning, append=True)
-# InsecurePlatformWarning's don't vary between requests, so we keep it default.
-warnings.simplefilter("default", exceptions.InsecurePlatformWarning, append=True)
-# SNIMissingWarnings should go off only once.
-warnings.simplefilter("default", exceptions.SNIMissingWarning, append=True)
-
-
-def disable_warnings(category=exceptions.HTTPWarning):
-    """
-    Helper for quickly disabling all urllib3 warnings.
-    """
-    warnings.simplefilter("ignore", category)

spaces/Billyosoro/ESRGAN/realesrgan/models/realesrnet_model.py

@@ -1,188 +0,0 @@
-import numpy as np
-import random
-import torch
-from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt
-from basicsr.data.transforms import paired_random_crop
-from basicsr.models.sr_model import SRModel
-from basicsr.utils import DiffJPEG, USMSharp
-from basicsr.utils.img_process_util import filter2D
-from basicsr.utils.registry import MODEL_REGISTRY
-from torch.nn import functional as F
-
-
-@MODEL_REGISTRY.register()
-class RealESRNetModel(SRModel):
-    """RealESRNet Model for Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data.
-
-    It is trained without GAN losses.
-    It mainly performs:
-    1. randomly synthesize LQ images in GPU tensors
-    2. optimize the networks with GAN training.
-    """
-
-    def __init__(self, opt):
-        super(RealESRNetModel, self).__init__(opt)
-        self.jpeger = DiffJPEG(differentiable=False).cuda()  # simulate JPEG compression artifacts
-        self.usm_sharpener = USMSharp().cuda()  # do usm sharpening
-        self.queue_size = opt.get('queue_size', 180)
-
-    @torch.no_grad()
-    def _dequeue_and_enqueue(self):
-        """It is the training pair pool for increasing the diversity in a batch.
-
-        Batch processing limits the diversity of synthetic degradations in a batch. For example, samples in a
-        batch could not have different resize scaling factors. Therefore, we employ this training pair pool
-        to increase the degradation diversity in a batch.
-        """
-        # initialize
-        b, c, h, w = self.lq.size()
-        if not hasattr(self, 'queue_lr'):
-            assert self.queue_size % b == 0, f'queue size {self.queue_size} should be divisible by batch size {b}'
-            self.queue_lr = torch.zeros(self.queue_size, c, h, w).cuda()
-            _, c, h, w = self.gt.size()
-            self.queue_gt = torch.zeros(self.queue_size, c, h, w).cuda()
-            self.queue_ptr = 0
-        if self.queue_ptr == self.queue_size:  # the pool is full
-            # do dequeue and enqueue
-            # shuffle
-            idx = torch.randperm(self.queue_size)
-            self.queue_lr = self.queue_lr[idx]
-            self.queue_gt = self.queue_gt[idx]
-            # get first b samples
-            lq_dequeue = self.queue_lr[0:b, :, :, :].clone()
-            gt_dequeue = self.queue_gt[0:b, :, :, :].clone()
-            # update the queue
-            self.queue_lr[0:b, :, :, :] = self.lq.clone()
-            self.queue_gt[0:b, :, :, :] = self.gt.clone()
-
-            self.lq = lq_dequeue
-            self.gt = gt_dequeue
-        else:
-            # only do enqueue
-            self.queue_lr[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.lq.clone()
-            self.queue_gt[self.queue_ptr:self.queue_ptr + b, :, :, :] = self.gt.clone()
-            self.queue_ptr = self.queue_ptr + b
-
-    @torch.no_grad()
-    def feed_data(self, data):
-        """Accept data from dataloader, and then add two-order degradations to obtain LQ images.
-        """
-        if self.is_train and self.opt.get('high_order_degradation', True):
-            # training data synthesis
-            self.gt = data['gt'].to(self.device)
-            # USM sharpen the GT images
-            if self.opt['gt_usm'] is True:
-                self.gt = self.usm_sharpener(self.gt)
-
-            self.kernel1 = data['kernel1'].to(self.device)
-            self.kernel2 = data['kernel2'].to(self.device)
-            self.sinc_kernel = data['sinc_kernel'].to(self.device)
-
-            ori_h, ori_w = self.gt.size()[2:4]
-
-            # ----------------------- The first degradation process ----------------------- #
-            # blur
-            out = filter2D(self.gt, self.kernel1)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(out, scale_factor=scale, mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob']
-            if np.random.uniform() < self.opt['gaussian_noise_prob']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-            # JPEG compression
-            jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range'])
-            out = torch.clamp(out, 0, 1)  # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts
-            out = self.jpeger(out, quality=jpeg_p)
-
-            # ----------------------- The second degradation process ----------------------- #
-            # blur
-            if np.random.uniform() < self.opt['second_blur_prob']:
-                out = filter2D(out, self.kernel2)
-            # random resize
-            updown_type = random.choices(['up', 'down', 'keep'], self.opt['resize_prob2'])[0]
-            if updown_type == 'up':
-                scale = np.random.uniform(1, self.opt['resize_range2'][1])
-            elif updown_type == 'down':
-                scale = np.random.uniform(self.opt['resize_range2'][0], 1)
-            else:
-                scale = 1
-            mode = random.choice(['area', 'bilinear', 'bicubic'])
-            out = F.interpolate(
-                out, size=(int(ori_h / self.opt['scale'] * scale), int(ori_w / self.opt['scale'] * scale)), mode=mode)
-            # add noise
-            gray_noise_prob = self.opt['gray_noise_prob2']
-            if np.random.uniform() < self.opt['gaussian_noise_prob2']:
-                out = random_add_gaussian_noise_pt(
-                    out, sigma_range=self.opt['noise_range2'], clip=True, rounds=False, gray_prob=gray_noise_prob)
-            else:
-                out = random_add_poisson_noise_pt(
-                    out,
-                    scale_range=self.opt['poisson_scale_range2'],
-                    gray_prob=gray_noise_prob,
-                    clip=True,
-                    rounds=False)
-
-            # JPEG compression + the final sinc filter
-            # We also need to resize images to desired sizes. We group [resize back + sinc filter] together
-            # as one operation.
-            # We consider two orders:
-            #   1. [resize back + sinc filter] + JPEG compression
-            #   2. JPEG compression + [resize back + sinc filter]
-            # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines.
-            if np.random.uniform() < 0.5:
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-            else:
-                # JPEG compression
-                jpeg_p = out.new_zeros(out.size(0)).uniform_(*self.opt['jpeg_range2'])
-                out = torch.clamp(out, 0, 1)
-                out = self.jpeger(out, quality=jpeg_p)
-                # resize back + the final sinc filter
-                mode = random.choice(['area', 'bilinear', 'bicubic'])
-                out = F.interpolate(out, size=(ori_h // self.opt['scale'], ori_w // self.opt['scale']), mode=mode)
-                out = filter2D(out, self.sinc_kernel)
-
-            # clamp and round
-            self.lq = torch.clamp((out * 255.0).round(), 0, 255) / 255.
-
-            # random crop
-            gt_size = self.opt['gt_size']
-            self.gt, self.lq = paired_random_crop(self.gt, self.lq, gt_size, self.opt['scale'])
-
-            # training pair pool
-            self._dequeue_and_enqueue()
-            self.lq = self.lq.contiguous()  # for the warning: grad and param do not obey the gradient layout contract
-        else:
-            # for paired training or validation
-            self.lq = data['lq'].to(self.device)
-            if 'gt' in data:
-                self.gt = data['gt'].to(self.device)
-                self.gt_usm = self.usm_sharpener(self.gt)
-
-    def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
-        # do not use the synthetic process during validation
-        self.is_train = False
-        super(RealESRNetModel, self).nondist_validation(dataloader, current_iter, tb_logger, save_img)
-        self.is_train = True

spaces/CVPR/LIVE/thrust/thrust/pair.h

@@ -1,283 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-/*! \file pair.h
- *  \brief A type encapsulating a heterogeneous pair of elements
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-#include <utility>
-
-namespace thrust
-{
-
-/*! \addtogroup utility
- *  \{
- */
-
-/*! \addtogroup pair
- *  \{
- */
-
-/*! \p pair is a generic data structure encapsulating a heterogeneous
- *  pair of values.
- *
- *  \tparam T1 The type of \p pair's first object type.  There are no
- *          requirements on the type of \p T1. <tt>T1</tt>'s type is
- *          provided by <tt>pair::first_type</tt>.
- *
- *  \tparam T2 The type of \p pair's second object type.  There are no
- *          requirements on the type of \p T2. <tt>T2</tt>'s type is
- *          provided by <tt>pair::second_type</tt>.
- */
-template <typename T1, typename T2>
-  struct pair
-{
-  /*! \p first_type is the type of \p pair's first object type.
-   */
-  typedef T1 first_type;
-
-  /*! \p second_type is the type of \p pair's second object type.
-   */
-  typedef T2 second_type;
-
-  /*! The \p pair's first object.
-   */
-  first_type first;
-
-  /*! The \p pair's second object.
-   */
-  second_type second;
-
-  /*! \p pair's default constructor constructs \p first
-   *  and \p second using \c first_type & \c second_type's
-   *  default constructors, respectively.
-   */
-  __host__ __device__ pair(void);
-
-  /*! This constructor accepts two objects to copy into this \p pair.
-   *
-   *  \param x The object to copy into \p first.
-   *  \param y The object to copy into \p second.
-   */
-  inline __host__ __device__
-  pair(const T1 &x, const T2 &y);
-
-  /*! This copy constructor copies from a \p pair whose types are
-   *  convertible to this \p pair's \c first_type and \c second_type,
-   *  respectively.
-   *
-   *  \param p The \p pair to copy from.
-   *
-   *  \tparam U1 is convertible to \c first_type.
-   *  \tparam U2 is convertible to \c second_type.
-   */
-  template <typename U1, typename U2>
-  inline __host__ __device__
-  pair(const pair<U1,U2> &p);
-
-  /*! This copy constructor copies from a <tt>std::pair</tt> whose types are
-   *  convertible to this \p pair's \c first_type and \c second_type,
-   *  respectively.
-   *
-   *  \param p The <tt>std::pair</tt> to copy from.
-   *
-   *  \tparam U1 is convertible to \c first_type.
-   *  \tparam U2 is convertible to \c second_type.
-   */
-  template <typename U1, typename U2>
-  inline __host__ __device__
-  pair(const std::pair<U1,U2> &p);
-
-  /*! \p swap swaps the elements of two <tt>pair</tt>s.
-   *  
-   *  \param p The other <tt>pair</tt> with which to swap.
-   */
-  inline __host__ __device__
-  void swap(pair &p);
-}; // end pair
-
-
-/*! This operator tests two \p pairs for equality.
- *
- *  \param x The first \p pair to compare.
- *  \param y The second \p pair to compare.
- *  \return \c true if and only if <tt>x.first == y.first && x.second == y.second</tt>.
- *  
- *  \tparam T1 is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam T2 is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    bool operator==(const pair<T1,T2> &x, const pair<T1,T2> &y);
-
-
-/*! This operator tests two pairs for ascending ordering.
- *
- *  \param x The first \p pair to compare.
- *  \param y The second \p pair to compare.
- *  \return \c true if and only if <tt>x.first < y.first || (!(y.first < x.first) && x.second < y.second)</tt>.
- *
- *  \tparam T1 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *  \tparam T2 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    bool operator<(const pair<T1,T2> &x, const pair<T1,T2> &y);
-
-
-/*! This operator tests two pairs for inequality.
- *
- *  \param x The first \p pair to compare.
- *  \param y The second \p pair to compare.
- *  \return \c true if and only if <tt>!(x == y)</tt>.
- *
- *  \tparam T1 is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- *  \tparam T2 is a model of <a href="http://www.sgi.com/tech/stl/EqualityComparable.html">Equality Comparable</a>.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    bool operator!=(const pair<T1,T2> &x, const pair<T1,T2> &y);
-
-
-/*! This operator tests two pairs for descending ordering.
- *
- *  \param x The first \p pair to compare.
- *  \param y The second \p pair to compare.
- *  \return \c true if and only if <tt>y < x</tt>.
- *
- *  \tparam T1 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *  \tparam T2 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    bool operator>(const pair<T1,T2> &x, const pair<T1,T2> &y);
-
-
-/*! This operator tests two pairs for ascending ordering or equivalence.
- *
- *  \param x The first \p pair to compare.
- *  \param y The second \p pair to compare.
- *  \return \c true if and only if <tt>!(y < x)</tt>.
- *
- *  \tparam T1 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *  \tparam T2 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    bool operator<=(const pair<T1,T2> &x, const pair<T1,T2> &y);
-
-
-/*! This operator tests two pairs for descending ordering or equivalence.
- *
- *  \param x The first \p pair to compare.
- *  \param y The second \p pair to compare.
- *  \return \c true if and only if <tt>!(x < y)</tt>.
- *
- *  \tparam T1 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- *  \tparam T2 is a model of <a href="http://www.sgi.com/tech/stl/LessThanComparable.html">LessThan Comparable</a>.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    bool operator>=(const pair<T1,T2> &x, const pair<T1,T2> &y);
-
-
-/*! \p swap swaps the contents of two <tt>pair</tt>s.
- *
- *  \param x The first \p pair to swap.
- *  \param y The second \p pair to swap.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    void swap(pair<T1,T2> &x, pair<T1,T2> &y);
-
-
-/*! This convenience function creates a \p pair from two objects.
- *
- *  \param x The first object to copy from.
- *  \param y The second object to copy from.
- *  \return A newly-constructed \p pair copied from \p a and \p b.
- *
- *  \tparam T1 There are no requirements on the type of \p T1.
- *  \tparam T2 There are no requirements on the type of \p T2.
- */
-template <typename T1, typename T2>
-  inline __host__ __device__
-    pair<T1,T2> make_pair(T1 x, T2 y);
-
-
-/*! This convenience metafunction is included for compatibility with
- *  \p tuple. It returns either the type of a \p pair's
- *  \c first_type or \c second_type in its nested type, \c type.
- *
- *  \tparam N This parameter selects the member of interest.
- *  \tparam T A \c pair type of interest.
- */
-template<int N, typename T> struct tuple_element;
-
-
-/*! This convenience metafunction is included for compatibility with
- *  \p tuple. It returns \c 2, the number of elements of a \p pair,
- *  in its nested data member, \c value.
- *
- *  \tparam Pair A \c pair type of interest.
- */
-template<typename Pair> struct tuple_size;
-
-
-/*! This convenience function returns a reference to either the first or
- *  second member of a \p pair.
- *
- *  \param p The \p pair of interest.
- *  \return \c p.first or \c p.second, depending on the template
- *          parameter.
- *
- *  \tparam N This parameter selects the member of interest.
- */
-// XXX comment out these prototypes as a WAR to a problem on MSVC 2005
-//template<unsigned int N, typename T1, typename T2>
-//  inline __host__ __device__
-//    typename tuple_element<N, pair<T1,T2> >::type &
-//      get(pair<T1,T2> &p);
-
-
-/*! This convenience function returns a const reference to either the
- *  first or second member of a \p pair.
- *
- *  \param p The \p pair of interest.
- *  \return \c p.first or \c p.second, depending on the template
- *          parameter.
- *
- *  \tparam i This parameter selects the member of interest.
- */
-// XXX comment out these prototypes as a WAR to a problem on MSVC 2005
-//template<int N, typename T1, typename T2>
-//  inline __host__ __device__
-//    const typename tuple_element<N, pair<T1,T2> >::type &
-//      get(const pair<T1,T2> &p);
-
-/*! \} // pair
- */
-
-/*! \} // utility
- */
-
-} // end thrust
-
-#include <thrust/detail/pair.inl>
-

spaces/CVPR/LIVE/thrust/thrust/system/omp/detail/mismatch.h

@@ -1,23 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system inherits mismatch
-#include <thrust/system/cpp/detail/mismatch.h>
-