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- spaces/1368565466ki/ZSTRD/README.md +0 -11
- spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Global Mapper 20.1 Full Crack and Unlock All Its Features (But at What Cost?).md +0 -35
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spaces/1368565466ki/ZSTRD/README.md
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---
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license: apache-2.0
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title: ' vits-uma-genshin-honkai'
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sdk: gradio
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sdk_version: 3.7
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emoji: 🐨
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colorTo: yellow
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pinned: false
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app_file: app.py
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duplicated_from: 1368565466ki/ZSTR
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---
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Global Mapper 20.1 Full Crack and Unlock All Its Features (But at What Cost?).md
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<p>If you are an electronic professional, a service center, a repair shop, or a developer, you might be interested in Eca Vrt 2014. This software is a database and index for all kinds of semiconductors, such as diodes, transistors, thyristors, and integrated circuits. It allows you to search, compare, and select the best components for your projects. In this guide, we will show you what Eca Vrt 2014 is, how to download it for free, and how to use it effectively.</p>
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<p>The easiest way to download Eca Vrt 2014 is to visit the official website of ECA Electronic at <a href="https://www.eca.de/en/">https://www.eca.de/en/</a>. There you can find information about the software and its features. You can also order the software in their online shop at <a href="http://www.shop.eca.de">http://www.shop.eca.de</a>. The software costs €45.00 (about $50) and comes with a serial number that you need to activate it. You can pay with PayPal or other methods. After ordering, you will receive an email with a link to download the software.</p>
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<p>Eca Vrt 2014 has a user-friendly interface that allows you to access its main functions and options easily. Here are some of them:</p>
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```html SMD code (e.g., 1A), or text (e.g., LED). You can also use advanced search options to filter the results by parametric values, such as voltage, current, power, frequency, etc.</li>
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<li>The Compare function: This function allows you to compare up to four components side by side and see their data sheets and specifications. You can also use this function to find equivalent or substitute components for your projects.</li>
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<li>The Park function: This function allows you to save each type in a special table with your comments. You can use this function to create your own lists of components that you frequently use or need for your projects.</li>
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<li>The Online function: This function allows you to access additional online databases that are not included in the offline software. These databases are about audio ICs, STK/STR circuits, SMD/marking codes, and semiconductor package forms. You can also use this function to access the free search service if you can't find what you are looking for.</li>
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<li>The Help function: This function provides you with information and guidance on how to use the software and its features. You can also use this function to contact the support team of ECA Electronic if you have any questions or problems.</li>
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| 109 |
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<li>How to find a suitable diode for a rectifier circuit: If you want to build a rectifier circuit that converts AC voltage to DC voltage, you need a diode that can handle the input voltage and current. To find such a diode, you can use the Search function of Eca Vrt 2014 and enter the type "diode" and the parametric values of your input voltage and current. For example, if your input voltage is 12V AC and your current is 1A, you can enter "diode" in the type field and "12" in the VRRM field and "1" in the IF(AV) field. Then you will see a list of diodes that meet these criteria. You can compare them using the Compare function and select the one that suits your needs.</li>
|
| 110 |
-
<li>How to find an equivalent transistor for a switch circuit: If you want to replace a transistor in a switch circuit that controls a LED, you need a transistor that has similar characteristics as the original one. To find such a transistor, you can use the Compare function of Eca Vrt 2014 and enter the type of the original transistor in one of the fields. For example, if your original transistor is BC547, you can enter "BC547" in one of the fields. Then you will see its data sheet and specifications. You can then enter other types of transistors in the other fields and see their data sheets and specifications as well. You can look for transistors that have similar values of hFE (current gain), VCEO (collector-emitter voltage), IC (collector current), etc. You can also use the Search function to find transistors that have these values within a certain range.</li>
|
| 111 |
-
<li>How to find an integrated circuit for an audio amplifier: If you want to build an audio amplifier that amplifies an input signal from a microphone or a guitar, you need an integrated circuit that can do this job. To find such an integrated circuit, you can use the Online function of Eca Vrt 2014 and access the online database about audio ICs. There you can find information about various audio ICs, such as their functions, features, applications, pinouts, diagrams, etc. You can also use the Search function to find audio ICs by type (e.g., amplifier), device (e.g., LM386), or text (e.g., guitar). You can then see their data sheets and specifications and select the one that suits your needs.</li>
|
| 112 |
-
</ul>
|
| 113 |
-
<h3>The troubleshooting and support for Eca Vrt 2014 users</h3>
|
| 114 |
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<p>Eca Vrt 2014 is a reliable software that works smoothly on most Windows systems. However, if you encounter any issues or difficulties while using it, you can try some troubleshooting steps or contact the support team of ECA Electronic for help. Here are some suggestions:</p>
|
| 115 |
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<ul>
|
| 116 |
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<li>If you have problems with activating or registering the software, make sure you have entered the correct serial number that was sent to you by email after ordering. If you have lost or forgotten your serial number, contact ECA Electronic at <a href="mailto:[email protected]">[email protected]</a> with your order details and request a new serial number.</li>
|
| 117 |
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<li>If you have problems with accessing or updating the online databases, make sure you have an active internet connection and that your firewall or antivirus software is not blocking the software from connecting to the internet. If you have problems with logging in to your online account, make sure you have entered the correct username and password that were sent to you by email after ordering. If you have forgotten your username or password, contact ECA Electronic at <a href="mailto:[email protected]">[email protected]</a> with your order details and request a new username or password.</li>
|
| 118 |
-
<li>If you have problems with finding or selecting components, make sure you have entered the correct type, numeric part of the type, device, SMD code, or text in the Search function. If you still can't find what you are looking for, try using different search criteria or parameters. If you still can't find what you are looking for, use the Online function and access the free search service at <a href="https://www.ecadata.de/en/search-service/">https://www.ecadata.de/en/search-service/</a>. There you can submit your request and get an answer from ECA Electronic within 24 hours.</li>
|
| 119 |
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```html manuals, videos, etc. You can also contact ECA Electronic at <a href="mailto:[email protected]">[email protected]</a> with your questions or problems and get a reply within 24 hours.</li>
|
| 120 |
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</ul>
|
| 121 |
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<h2>Conclusion</h2>
|
| 122 |
-
<p>Eca Vrt 2014 is a powerful and useful software for electronic professionals who work with semiconductors. It provides a comprehensive and updated database of diodes, transistors, thyristors, and integrated circuits. It also provides various features and options that allow you to search, compare, select, save, and access the best components for your projects. It also provides examples and tutorials of how to use different components for different purposes. It also provides troubleshooting and support for its users. If you want to download Eca Vrt 2014 for free, you can visit the official website or online shop of ECA Electronic or try some alternative sources and links. However, be careful when downloading from untrusted sources and follow some precautions and tips to ensure that you download and install it safely. We hope this guide has helped you understand what Eca Vrt 2014 is, how to download it for free, and how to use it effectively.</p>
|
| 123 |
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<h2>FAQs</h2>
|
| 124 |
-
<h3>What are the system requirements for Eca Vrt 2014?</h3>
|
| 125 |
-
<p>The system requirements for Eca Vrt 2014 are:</p>
|
| 126 |
-
<ul>
|
| 127 |
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<li>Min. Pentium III system</li>
|
| 128 |
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<li>Windows XP/VISTA/Windows 7/Windows 8</li>
|
| 129 |
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<li>A DVD drive assembly</li>
|
| 130 |
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</ul>
|
| 131 |
-
<h3>How can I update Eca Vrt 2014?</h3>
|
| 132 |
-
<p>You can update Eca Vrt 2014 by visiting <a href="https://www.ecadata.de/en/support/">https://www.ecadata.de/en/support/</a> and downloading the latest version of the software. You can also check for updates within the software by using the Online function and clicking on the Update button.</p>
|
| 133 |
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<h3>How can I get more information about Eca Vrt 2014?</h3>
|
| 134 |
-
<p>You can get more information about Eca Vrt 2014 by visiting <a href="https://www.ecadata.de/en/">https://www.ecadata.de/en/</a> and reading the information and news provided there. You can also visit <a href="https://www.eca.de/en/">https://www.eca.de/en/</a> and reading the information and news provided there. You can also contact ECA Electronic at <a href="mailto:[email protected]">[email protected]</a> with your inquiries and requests.</p>
|
| 135 |
-
<h3>How can I share my feedback or suggestions about Eca Vrt 2014?</h3>
|
| 136 |
-
<p>You can share your feedback or suggestions about Eca Vrt 2014 by contacting ECA Electronic at <a href="mailto:[email protected]">[email protected]</a> with your comments and opinions. You can also visit <a href="https://www.ecadata.de/en/support/">https://www.ecadata.de/en/support/</a> and filling out the feedback form provided there.</p>
|
| 137 |
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<h3>How can I learn more about semiconductors and electronics?</h3>
|
| 138 |
-
<p>You can learn more about semiconductors and electronics by visiting <a href="https://www.ecadata.de/en/education/">https://www.ecadata.de/en/education/</a> and accessing the educational resources provided there. You can also visit <a href="https://www.ecadata.de/en/blog/">https://www.ecadata.de/en/blog/</a> and reading the articles and posts provided there.</p>
|
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</p> 0a6ba089eb<br />
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spaces/1gistliPinn/ChatGPT4/Examples/CRACK Eviews 5.1 Keygenerator.md
DELETED
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<br />
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| 2 |
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<h1>CRACK Eviews 5.1 Keygenerator: A Complete Guide</h1>
|
| 3 |
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| 4 |
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<p>If you are looking for a powerful and user-friendly software that can help you perform econometric analysis, forecasting, and simulation, you might want to try Eviews 5.1. This software is developed by Quantitative Micro Software and has many features and benefits that make it one of the best tools for data analysis and modeling. However, if you want to use the full version of Eviews 5.1, you will need to purchase a license key that can cost you some money. Fortunately, there is a way to get CRACK Eviews 5.1 keygenerator and enjoy all its functions without paying anything.</p>
|
| 5 |
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| 6 |
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<h2>What is CRACK Eviews 5.1 keygenerator?</h2>
|
| 7 |
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|
| 8 |
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<p>CRACK Eviews 5.1 keygenerator is a software that can generate a valid license key for Eviews 5.1 and activate the software for free.</p>
|
| 9 |
-
<h2>CRACK Eviews 5.1 keygenerator</h2><br /><p><b><b>DOWNLOAD</b> ⚹⚹⚹ <a href="https://imgfil.com/2uxY6e">https://imgfil.com/2uxY6e</a></b></p><br /><br />
|
| 10 |
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|
| 11 |
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<p>A license key is a code that verifies that you have purchased the software legally and allows you to use the software without any limitations.</p>
|
| 12 |
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|
| 13 |
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<p>A keygenerator is a software that creates a license key by using an algorithm that mimics the original software file.</p>
|
| 14 |
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|
| 15 |
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<p>By using CRACK Eviews 5.1 keygenerator, you can bypass the security checks and use Eviews 5.1 full version for free.</p>
|
| 16 |
-
|
| 17 |
-
<h2>How to get CRACK Eviews 5.1 keygenerator?</h2>
|
| 18 |
-
|
| 19 |
-
<p>If you want to use the full version of Eviews 5.1 without paying for a license key, you will need to get CRACK Eviews 5.1 keygenerator from the internet and use it to activate the software for free.</p>
|
| 20 |
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|
| 21 |
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<p>To get CRACK Eviews 5.1 keygenerator, you will need to follow these steps:</p>
|
| 22 |
-
|
| 23 |
-
<ol>
|
| 24 |
-
<li>Download Eviews 5.1 from the official website or from any trusted online source.</li>
|
| 25 |
-
<li>Install Eviews 5.1 on your computer by following the instructions.</li>
|
| 26 |
-
<li>Download CRACK Eviews 5.1 keygenerator from any reliable website or link that offers it.</li>
|
| 27 |
-
<li>Run CRACK Eviews 5.1 keygenerator as administrator and click on the generate button.</li>
|
| 28 |
-
<li>Copy the license key that appears on the screen and paste it into the activation window of Eviews 5.1.</li>
|
| 29 |
-
<li>Click on the activate button and wait for the confirmation message.</li>
|
| 30 |
-
<li>Restart your computer and enjoy using Eviews 5.1 full version for free.</li>
|
| 31 |
-
</ol>
|
| 32 |
-
|
| 33 |
-
<h2>What are the advantages and disadvantages of using CRACK Eviews 5.1 keygenerator?</h2>
|
| 34 |
-
|
| 35 |
-
<p>Using CRACK Eviews 5.1 keygenerator has some advantages and disadvantages that you should be aware of before deciding to use it.</p>
|
| 36 |
-
|
| 37 |
-
<h3>The advantages of using CRACK Eviews 5.1 keygenerator are:</h3>
|
| 38 |
-
|
| 39 |
-
<ul>
|
| 40 |
-
<li>You can save money by not buying a license key for the software.</li>
|
| 41 |
-
<li>You can enjoy all the features and functions of Eviews 5</p>
|
| 42 |
-
<p></p>
|
| 43 |
-
<h3>The disadvantages of using CRACK Eviews 5.1 keygenerator are:</h3>
|
| 44 |
-
|
| 45 |
-
<ul>
|
| 46 |
-
<li>You may violate the intellectual property rights of Quantitative Micro Software and face legal consequences.</li>
|
| 47 |
-
<li>You may expose your computer to viruses, malware, or spyware that may harm your system or steal your data.</li>
|
| 48 |
-
<li>You may not get any technical support or updates from Quantitative Micro Software for the software.</li>
|
| 49 |
-
<li>You may experience some errors, bugs, or crashes while using CRACK Eviews 5.1 keygenerator.</li>
|
| 50 |
-
</ul>
|
| 51 |
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|
| 52 |
-
<h2>How to use CRACK Eviews 5.1 keygenerator?</h2>
|
| 53 |
-
|
| 54 |
-
<p>After you have downloaded and installed CRACK Eviews 5.1 keygenerator, you can start using it to perform econometric analysis, forecasting, and simulation with Eviews 5.1.</p>
|
| 55 |
-
|
| 56 |
-
<p>To use CRACK Eviews 5.1 keygenerator, you will need to follow these steps:</p>
|
| 57 |
-
|
| 58 |
-
<ol>
|
| 59 |
-
<li>Launch Eviews 5.1 from your desktop or start menu.</li>
|
| 60 |
-
<li>Create a new workfile or open an existing one.</li>
|
| 61 |
-
<li>Import or enter your data into the workfile.</li>
|
| 62 |
-
<li>Use the menus, toolbars, or commands to perform various operations on your data, such as descriptive statistics, regression, hypothesis testing, etc.</li>
|
| 63 |
-
<li>Use the graphs, tables, or reports to display and analyze your results.</li>
|
| 64 |
-
<li>Save your workfile and export your results as needed.</li>
|
| 65 |
-
</ol>
|
| 66 |
-
|
| 67 |
-
<h2>Tips and tricks for using CRACK Eviews 5.1 keygenerator</h2>
|
| 68 |
-
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| 69 |
-
<p>To make the most out of CRACK Eviews 5.1 keygenerator, you can use some tips and tricks that can enhance your experience and performance.</p>
|
| 70 |
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| 71 |
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<p>Some of the tips and tricks for using CRACK Eviews 5.1 keygenerator are:</p>
|
| 72 |
-
|
| 73 |
-
<ul>
|
| 74 |
-
<li>You can customize the appearance and settings of Eviews 5.1 by clicking on the options icon and selecting general options.</li>
|
| 75 |
-
<li>You can view the help files or tutorials of Eviews 5.1 by clicking on the help icon and selecting help topics or tutorials.</li>
|
| 76 |
-
<li>You can use keyboard shortcuts to perform common tasks on Eviews 5.1, such as F2 to edit an object, F9 to run a command, F12 to exit, etc.</li>
|
| 77 |
-
<li>You can use the command window or the command capture window to enter commands directly or capture commands from menus or toolbars.</li>
|
| 78 |
-
<li>You can use the quick menu or the object menu to access various functions and options for an object by right-clicking on it.</li>
|
| 79 |
-
</ul>
|
| 80 |
-
|
| 81 |
-
<h2>What are the alternatives to CRACK Eviews 5.1 keygenerator?</h2>
|
| 82 |
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| 83 |
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<p>If you are not satisfied with CRACK Eviews 5.1 keygenerator or you want to try other options, you can look for some alternatives that can offer similar or better features and performance.</p>
|
| 84 |
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| 85 |
-
<p>Some of the alternatives to CRACK Eviews 5.1 keygenerator are:</p>
|
| 86 |
-
|
| 87 |
-
<ul>
|
| 88 |
-
<li>Stata: This is a software that can help you perform data analysis, data management, and graphics with ease and efficiency. You can also use Stata for econometrics, statistics, biostatistics, epidemiology, etc.</li>
|
| 89 |
-
<li>R: This is a software that can help you perform statistical computing and graphics with a powerful programming language and environment. You can also use R for data manipulation, visualization, modeling, machine learning, etc.</li>
|
| 90 |
-
<li>SPSS: This is a software that can help you perform statistical analysis and data mining with a user-friendly interface and a comprehensive set of tools. You can also use SPSS for predictive analytics, decision making, market research, etc.</li>
|
| 91 |
-
<li>SAS: This is a software that can help you perform data analysis and business intelligence with a flexible and scalable platform and a variety of solutions. You can also use SAS for analytics, data management, reporting, etc.</li>
|
| 92 |
-
<li>Eviews 10: This is the latest version of Eviews that offers more features and functions than Eviews 5
|
| 93 |
-
<h2>Conclusion</h2>
|
| 94 |
-
|
| 95 |
-
<p>In conclusion, CRACK Eviews 5.1 keygenerator is a software that can help you activate Eviews 5.1 for free and use it to perform econometric analysis, forecasting, and simulation.</p>
|
| 96 |
-
|
| 97 |
-
<p>However, if you want to use the full version of the software, you will need to buy a license key that can cost you some money.</p>
|
| 98 |
-
|
| 99 |
-
<p>If you want to save money and use Eviews 5.1 without paying anything, you can try to get CRACK Eviews 5.1 keygenerator from the internet and use it to generate a valid license key for the software.</p>
|
| 100 |
-
|
| 101 |
-
<p>However, you should also be aware of the risks and drawbacks of using CRACK Eviews 5.1 keygenerator, such as violating the law, exposing your computer to threats, or experiencing some problems with the software.</p>
|
| 102 |
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| 103 |
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<p>Therefore, you should weigh the pros and cons of using CRACK Eviews 5.1 keygenerator and decide whether it is worth it or not.</p>
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| 104 |
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| 105 |
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<p>If you are not satisfied with CRACK Eviews 5.1 keygenerator or you want to try other options, you can look for some alternatives that can offer similar or better features and performance.</p>
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| 106 |
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| 107 |
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<p>We hope this article has helped you understand more about CRACK Eviews 5.1 keygenerator and how to use it safely and effectively.</p>
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| 108 |
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| 109 |
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<p>Thank you for reading and have a nice day!</p>
|
| 110 |
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<h2>Conclusion</h2>
|
| 111 |
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| 112 |
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<p>In conclusion, CRACK Eviews 5.1 keygenerator is a software that can help you activate Eviews 5.1 for free and use it to perform econometric analysis, forecasting, and simulation.</p>
|
| 113 |
-
|
| 114 |
-
<p>However, if you want to use the full version of the software, you will need to buy a license key that can cost you some money.</p>
|
| 115 |
-
|
| 116 |
-
<p>If you want to save money and use Eviews 5.1 without paying anything, you can try to get CRACK Eviews 5.1 keygenerator from the internet and use it to generate a valid license key for the software.</p>
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| 117 |
-
|
| 118 |
-
<p>However, you should also be aware of the risks and drawbacks of using CRACK Eviews 5.1 keygenerator, such as violating the law, exposing your computer to threats, or experiencing some problems with the software.</p>
|
| 119 |
-
|
| 120 |
-
<p>Therefore, you should weigh the pros and cons of using CRACK Eviews 5.1 keygenerator and decide whether it is worth it or not.</p>
|
| 121 |
-
|
| 122 |
-
<p>If you are not satisfied with CRACK Eviews 5.1 keygenerator or you want to try other options, you can look for some alternatives that can offer similar or better features and performance.</p>
|
| 123 |
-
|
| 124 |
-
<p>We hope this article has helped you understand more about CRACK Eviews 5.1 keygenerator and how to use it safely and effectively.</p>
|
| 125 |
-
|
| 126 |
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<p>Thank you for reading and have a nice day!</p> 3cee63e6c2<br />
|
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spaces/1gistliPinn/ChatGPT4/Examples/Crack Xf Adesk2012x64 Exe.md
DELETED
|
@@ -1,82 +0,0 @@
|
|
| 1 |
-
<h2>crack xf adesk2012x64 exe</h2><br /><p><b><b>Download</b> ✶✶✶ <a href="https://imgfil.com/2uxYac">https://imgfil.com/2uxYac</a></b></p><br /><br />
|
| 2 |
-
<br />
|
| 3 |
-
%s"
|
| 4 |
-
|
| 5 |
-
#:../ubuntutweak/launchers/py.py:1059
|
| 6 |
-
|
| 7 |
-
#, python-format
|
| 8 |
-
|
| 9 |
-
msgid "Use _%s"
|
| 10 |
-
|
| 11 |
-
msgstr "Utilizar _%s"
|
| 12 |
-
|
| 13 |
-
#:../ubuntutweak/tweaks/window.py:69
|
| 14 |
-
|
| 15 |
-
msgid "Show _toolbar by default"
|
| 16 |
-
|
| 17 |
-
msgstr "Mostrar barra d'eines de principal"
|
| 18 |
-
|
| 19 |
-
#:../ubuntutweak/tweaks/window.py:70
|
| 20 |
-
|
| 21 |
-
msgid "Show _desktop toolbar by default"
|
| 22 |
-
|
| 23 |
-
msgstr "Mostrar barra d'eines d'escritoriu"
|
| 24 |
-
|
| 25 |
-
#:../ubuntutweak/tweaks/window.py:71
|
| 26 |
-
|
| 27 |
-
msgid "Show _pager by default"
|
| 28 |
-
|
| 29 |
-
msgstr "Mostrar os projectes "
|
| 30 |
-
|
| 31 |
-
#:../ubuntutweak/tweaks/window.py:72
|
| 32 |
-
|
| 33 |
-
msgid "Show empty _trash by default"
|
| 34 |
-
|
| 35 |
-
msgstr "Mostrar la cenrro buida d'especificos"
|
| 36 |
-
|
| 37 |
-
#:../ubuntutweak/tweaks/window.py:73
|
| 38 |
-
|
| 39 |
-
msgid "Use _classic graphics"
|
| 40 |
-
|
| 41 |
-
msgstr "Usar _sistemas de gràfics clásicos"
|
| 42 |
-
|
| 43 |
-
#:../ubuntutweak/tweaks/window.py:126
|
| 44 |
-
|
| 45 |
-
msgid "No Border"
|
| 46 |
-
|
| 47 |
-
msgstr "Sen bord"
|
| 48 |
-
|
| 49 |
-
#:../ubuntutweak/tweaks/window.py:127
|
| 50 |
-
|
| 51 |
-
msgid "No Titlebar"
|
| 52 |
-
|
| 53 |
-
msgstr "Sen barra de títol"
|
| 54 |
-
|
| 55 |
-
#:../ubuntutweak/tweaks/window.py:128
|
| 56 |
-
|
| 57 |
-
msgid "No Decorations"
|
| 58 |
-
|
| 59 |
-
msgstr "Sen aspatre"
|
| 60 |
-
|
| 61 |
-
#:../ubuntutweak/tweaks/window.py:129
|
| 62 |
-
|
| 63 |
-
msgid "No Frame"
|
| 64 |
-
|
| 65 |
-
msgstr "Sen quadre"
|
| 66 |
-
|
| 67 |
-
#:../ubuntutweak/tweaks/theme.py:69
|
| 68 |
-
|
| 69 |
-
msgid "Theme"
|
| 70 |
-
|
| 71 |
-
msgstr "Tema"
|
| 72 |
-
|
| 73 |
-
#:../ubuntutweak/tweaks/theme.py:70
|
| 74 |
-
|
| 75 |
-
msgid "Light Background"
|
| 76 |
-
|
| 77 |
-
msgstr "Fondo clau"
|
| 78 |
-
|
| 79 |
-
# 4fefd39f24<br />
|
| 80 |
-
<br />
|
| 81 |
-
<br />
|
| 82 |
-
<p></p>
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spaces/1gistliPinn/ChatGPT4/Examples/Dust to Dust Full Movie Online Free A Critically Acclaimed Film by Shawn Snyder.md
DELETED
|
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|
|
| 1 |
-
<br />
|
| 2 |
-
<p>Friday, Aug. 2, noon -- UW students with valid UW student IDs are eligible for free movie tickets on select Fridays this summer. Students may pick up tickets from the Wyoming Union information desk Aug. 2, Aug. 9 and Aug. 16 starting at noon on each date. Tickets are given on a first-come, first-served basis.</p>
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| 3 |
-
<p>Buy one mophie powerstation go rugged AC, get one free. Customer MUST add both products to cart for promotion rule to activate. Limit 4 free products per customer. Final sales prices for all products will be reflected in cart. Promotional offer is valid from 9 JAN 2023 12AM MT through 11 JAN 2023 11:59PM MT. Offer is valid only for online purchases and at participating ZAGG retail locations. This offer cannot be combined with any other offers, discounts, or promotions. Offer is not transferrable or valid for resale. Discount applies to merchandise only and is not valid on gift cards, shipping & handling charges, or tax.</p>
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| 4 |
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<h2>Dust to Dust full movie online free</h2><br /><p><b><b>Download File</b> ☑ <a href="https://imgfil.com/2uxYoz">https://imgfil.com/2uxYoz</a></b></p><br /><br /> aaccfb2cb3<br />
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| 6 |
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spaces/1gistliPinn/ChatGPT4/Examples/FORMS2XMLUTILITYDOWNLOAD !LINK!.md
DELETED
|
@@ -1,5 +0,0 @@
|
|
| 1 |
-
<br />
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| 2 |
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<p>https://coub.com/stories/3666042-forms2xmlutilitydownload-daralnoel Aanzox,FORMS2XMLUTILITYDOWNLOAD, 310 office freerunner 5.4.0.1.29. office freerunner,office freerunner,FORMS2XMLUTILITYDOWNLOAD, office freerunner 2.6.7,office freerunner 4.6.7,office freerunner 5.4.0.1,office freerunner 5.4.0.2,office freerunner 5.4.0.3,office freerunner 5.4.0.4,office freerunner 5.4.0.5,office freerunner 5.4.1,office freerunner 5.4.2,office freerunner 5.4.3,office freerunner 5.4.4,office freerunner 5.4.5,office freerunner 5.4.6,office freerunner 5.4.7,office freerunner 5.4.8,office freerunner 5.5.0,office freerunner 5.5.1,office freerunner 5.5.2,office freerunner 5.5.3,office freerunner 5.5.4,office freerunner 5.5.5,office freerunner 5.5.6,office freerunner 5.5.7,office freerunner 5.5.8,office freerunner 5.6.0,office freerunner 5.6.1,office freerunner 5.6.2,office freerunner 5.6.3,office freerunner 5.6.4,office freerunner 5.6.5,office freerunner 5.6.6,office freerunner 5.6.7,office freerunner 5.6.8,office freerunner 5.6.9,office freerunner 5.6.10,office freerunner 5.6.11,office freerunner 5.6.12,office freerunner 5.6.13,office freerunner 5.6.14,office freerunner 5.6.15,office freerunner 5.6.16,office freerunner 5.6.17,office freerunner 5.6.18,office freerunner 5.6.19,office freerunner 5.6.20,office freerunner 5.6.21,office freerunner 5.6.22,office freerunner 5.6.23,office freerunner 5.6.24,office freerunner 5.6.25,office freerunner 5.6.26,office freerunner 5.6.27,office freerunner 5.6.28,office freerunner 5.6.29,office freerunner 5.6.30,office freerunner 5.6.31,office freerunner 5.6.32,office freerunner 5.6.33,office freerunner 5.6.34,office freerunner 5.6.35,office freerunner 5.6.</p>
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<h2>FORMS2XMLUTILITYDOWNLOAD</h2><br /><p><b><b>DOWNLOAD</b> ››››› <a href="https://imgfil.com/2uy21z">https://imgfil.com/2uy21z</a></b></p><br /><br /> 899543212b<br />
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<br />
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| 5 |
-
<br />
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|
spaces/1line/AutoGPT/autogpt/__main__.py
DELETED
|
@@ -1,5 +0,0 @@
|
|
| 1 |
-
"""Auto-GPT: A GPT powered AI Assistant"""
|
| 2 |
-
import autogpt.cli
|
| 3 |
-
|
| 4 |
-
if __name__ == "__main__":
|
| 5 |
-
autogpt.cli.main()
|
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/APK Home - Android Cihaznz iin Kaliteli Uygulama ve Oyunlar.md
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
|
| 2 |
-
<h1>What is APK Endir and Why You Need It</h1>
|
| 3 |
-
<p>If you are an Android user, you probably know that Google Play Store is not the only source of apps and games for your device. There are many alternative app stores that offer a wider range of content, some of which may not be available or compatible with your region or device. One of these alternative app stores is <strong>APK Endir</strong>, which is a Turkish term that means "download APK".</p>
|
| 4 |
-
<p>APK Endir is a free app that allows you to download apps and games in APK format from Uptodown, one of the best APK download sites on the web. Uptodown has a huge catalog of thousands of Android apps and games, all tested and verified by its editorial team. You can also download older versions of your favorite apps and games, as well as XAPK files that contain additional OBB data.</p>
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| 5 |
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<h2>apk endir</h2><br /><p><b><b>DOWNLOAD</b> — <a href="https://urlin.us/2uSXKF">https://urlin.us/2uSXKF</a></b></p><br /><br />
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| 6 |
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<p>By using APK Endir, you can enjoy many benefits, such as:</p>
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| 7 |
-
<ul>
|
| 8 |
-
<li>No regional or country-specific restrictions. You can access any app or game you want, regardless of where you live.</li>
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| 9 |
-
<li>No registration or subscription required. You don't need to create an account or sign in with your Google Play credentials.</li>
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| 10 |
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<li>No ads or in-app purchases. You can download apps and games without any annoying ads or hidden costs.</li>
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<li>Fast and secure downloads. You can download apps and games at high speed and with complete security.</li>
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| 12 |
-
<li>Easy backup and update. You can backup your apps and games on your device or SD card, and update them automatically or manually.</li>
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| 13 |
-
</ul>
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| 14 |
-
<p>Now that you know what APK Endir is and why you need it, let's see how you can download and install it on your Android device.</p>
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| 15 |
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I hope this helps you write a good article on "apk endir". If you have any questions or feedback, please let me know.</p> 197e85843d<br />
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| 16 |
-
<br />
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| 17 |
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Cara Download Video TikTok Kualitas HD Langsung dari Aplikasi TikTok.md
DELETED
|
@@ -1,114 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>How to Download TikTok Videos in HD Quality</h1>
|
| 3 |
-
<p>TikTok is one of the most popular social networks for short videos, where you can watch, create, and share fun and engaging content with millions of users around the world. But what if you want to download your favorite TikTok videos and watch them offline, or share them with others on different platforms? And what if you want to download them in high-definition (HD) quality, so you can enjoy them on larger screens and appreciate their details?</p>
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| 4 |
-
<p>In this article, we will show you how to download TikTok videos in HD quality online or on your mobile devices, using reliable and easy-to-use tools. We will also explain what HD quality means and why it matters for video downloading. By the end of this article, you will be able to download any TikTok video you like in HD quality, without any hassle or compromise.</p>
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<h2>download tiktok kualitas hd</h2><br /><p><b><b>Download File</b> ⚙ <a href="https://urlin.us/2uT2TQ">https://urlin.us/2uT2TQ</a></b></p><br /><br />
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| 6 |
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<h2>What is TikTok and Why You Might Want to Download Its Videos</h2>
|
| 7 |
-
<h3>TikTok is a popular social network for short videos</h3>
|
| 8 |
-
<p>TikTok is an app that allows users to create and share short videos, usually with music, filters, stickers, and other effects. Users can also watch and discover millions of personalized videos from other users, based on their interests, preferences, and location. TikTok has over one billion active users worldwide, making it one of the most popular social networks today.</p>
|
| 9 |
-
<h3>You can download TikTok videos for various purposes</h3>
|
| 10 |
-
<p>There are many reasons why you might want to download TikTok videos, such as:</p>
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| 11 |
-
<ul>
|
| 12 |
-
<li>You want to watch them offline, without internet connection or buffering issues.</li>
|
| 13 |
-
<li>You want to save them on your device or cloud storage, so you can access them anytime and anywhere.</li>
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| 14 |
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<li>You want to share them with your friends or family on other platforms, such as WhatsApp, Instagram, Facebook, YouTube, etc.</li>
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| 15 |
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<li>You want to use them for your own projects, such as remixes, mashups, compilations, presentations, etc.</li>
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| 16 |
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<li>You want to backup or archive them for future reference or nostalgia.</li>
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| 17 |
-
</ul>
|
| 18 |
-
<h2>What is HD Quality and Why It Matters for Video Download ing</h2>
|
| 19 |
-
<h3>HD quality refers to the resolution and clarity of a video</h3>
|
| 20 |
-
<p>HD quality is a term that describes the resolution and clarity of a video, which are determined by the number of pixels (tiny dots) that make up the image. The more pixels a video has, the higher its resolution and clarity, and the better it looks on larger screens. HD quality is usually measured in pixels per inch (ppi) or pixels per centimeter (ppcm), which indicate how many pixels are displayed in a given area.</p>
|
| 21 |
-
<p>There are different levels of HD quality, such as:</p>
|
| 22 |
-
<ul>
|
| 23 |
-
<li>720p HD: This is the lowest level of HD quality, with a resolution of 1280 x 720 pixels, or about 0.9 megapixels. It is also known as HD Ready or Standard HD.</li>
|
| 24 |
-
<li>1080p HD: This is the most common level of HD quality, with a resolution of 1920 x 1080 pixels, or about 2.1 megapixels. It is also known as Full HD or True HD.</li>
|
| 25 |
-
<li>4K HD: This is the highest level of HD quality, with a resolution of 3840 x 2160 pixels, or about 8.3 megapixels. It is also known as Ultra HD or UHD.</li>
|
| 26 |
-
</ul>
|
| 27 |
-
<h3>HD quality enhances the viewing experience and preserves the original details</h3>
|
| 28 |
-
<p>HD quality has many benefits for video downloading, such as:</p>
|
| 29 |
-
<ul>
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| 30 |
-
<li>It enhances the viewing experience, by making the video sharper, clearer, and more realistic. You can see more details, colors, and textures, and enjoy a more immersive and lifelike experience.</li>
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| 31 |
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<li>It preserves the original details, by maintaining the same resolution and clarity as the source video. You can avoid pixelation, blurriness, or distortion, and appreciate the video as it was intended by the creator.</li>
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<li>It allows you to watch the video on larger screens, such as TVs, monitors, or projectors, without losing quality or clarity. You can enjoy the video on any device or platform, without compromising its appearance or performance.</li>
|
| 33 |
-
</ul>
|
| 34 |
-
<h2>How to Download TikTok Videos in HD Quality Online</h2>
|
| 35 |
-
<h3>Use a reliable TikTok downloader website that supports HD quality</h3>
|
| 36 |
-
<p>If you want to download TikTok videos in HD quality online, you need to use a reliable TikTok downloader website that supports HD quality. There are many websites that claim to offer this service, but not all of them are trustworthy or effective. Some of them may have hidden fees, malware, ads, or limitations that can affect your downloading experience.</p>
|
| 37 |
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<p>One of the best TikTok downloader websites that supports HD quality is [TikTok Downloader]. This website is free, fast, safe, and easy to use. It allows you to download any TikTok video in HD quality online, without any watermark, registration, or installation. It also supports downloading TikTok videos with sound, captions, hashtags, and other metadata.</p>
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<p>Cara download video tiktok kualitas hd tanpa watermark<br />
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Download video tiktok kualitas hd dengan aplikasi Documents by Readdle<br />
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Download video tiktok kualitas hd dengan curious.astro.cornell.edu<br />
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Cara download video tiktok kualitas hd di Windows 10<br />
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<h3>Follow these simple steps to download TikTok videos in HD quality online</h3>
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<p>To download TikTok videos in HD quality online using [TikTok Downloader], you just need to follow these simple steps:</p>
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<ol>
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<li>Copy the link of the TikTok video you want to download. You can do this by opening the TikTok app or website, finding the video you want to download, tapping on the share button (the arrow icon), and selecting copy link.</li>
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<li>Paste the link into the TikTok downloader website. You can do this by opening [TikTok Downloader] on your browser, pasting the link into the search box (the white bar), and clicking on the search button (the magnifying glass icon).</li>
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<li>Choose the HD quality option and click download. You can do this by scrolling down to the download options section (the blue box), selecting the HD quality option (the one with 1080p or higher), and clicking on the download button (the green button).</li>
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<li>Save the downloaded video to your device or cloud storage. You can do this by right-clicking on the downloaded video (the one that opens in a new tab), choosing save video as (or similar option), and selecting your desired location and name for the video.</li>
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</ol>
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<h2>How to Download TikTok Videos in HD Quality on Mobile Devices</h2>
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<h3>Use a dedicated TikTok downloader app that supports HD quality</h3>
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<p>If you want to download TikTok videos in HD quality on your mobile devices, such as smartphones or tablets, you need to use a dedicated TikTok downloader app that supports HD quality. There are many apps that claim to offer this service, but not all of them are trustworthy or effective. Some of them may have hidden fees, malware, ads, or limitations that can affect your downloading experience.</p>
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<p>One of the best TikTok downloader apps that supports HD quality is [TikTok Video Downloader]. This app is free, fast, safe, and easy to use. It allows you to download any TikTok video in HD quality on your mobile devices, without any watermark, registration, or installation. It also supports downloading TikTok videos with sound, captions, hashtags, and other metadata.</p>
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<h3>Follow these simple steps to download TikTok videos in HD quality on mobile devices</h3>
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<p>To download TikTok videos in HD quality on mobile devices using [TikTok Video Downloader], you just need to follow these simple steps:</p>
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<ol>
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<li>Install the TikTok downloader app from the app store or website. You can do this by opening the app store or website on your device, searching for [TikTok Video Downloader], and tapping on the install button.</li>
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<li>Open the TikTok app and find the video you want to download. You can do this by opening the TikTok app on your device, finding the video you want to download, and tapping on it.</li>
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<li>Tap on the share button and select the TikTok downloader app. You can do this by tapping on the share button (the arrow icon) at the bottom right corner of the video, and selecting [TikTok Video Downloader] from the list of options.</li>
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<li>Choose the HD quality option and tap download. You can do this by tapping on the HD quality option (the one with 1080p or higher) at the top of the screen, and tapping on the download button (the green button) at the bottom of the screen.</li>
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<li>Save the downloaded video to your device or cloud storage. You can do this by tapping on the downloaded video (the one that appears in a new window), choosing save video (or similar option), and selecting your desired location and name for the video.</li>
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</ol>
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<h2>Conclusion</h2>
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<p>In conclusion, downloading TikTok videos in HD quality is a great way to enjoy them offline, save them for later, share them with others, or use them for your own projects. You can download TikTok videos in HD quality online or on your mobile devices, using reliable and easy-to-use tools such as [TikTok Downloader] and [TikTok Video Downloader]. These tools allow you to download any TikTok video in HD quality, without any watermark, registration, or installation. They also support downloading TikTok videos with sound, captions, hashtags, and other metadata.</p>
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<p>If you want to learn more about downloading TikTok videos in HD quality, or if you have any questions or feedback, feel free to contact us or leave a comment below. We would love to hear from you and help you with your downloading needs. Happy downloading!</p>
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<h2>FAQs</h2>
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<h3>Q1. Is it legal to download TikTok videos?</h3>
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<p>A1. It depends on the content and purpose of the video. Generally speaking, it is legal to download TikTok videos for personal use only, as long as you respect the intellectual property rights of the original creators and do not infringe on their privacy or reputation. However, it is illegal to download TikTok videos for commercial use or distribution without permission from the original creators or owners. You should always check the terms and conditions of TikTok and the specific video before downloading it.</p>
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<h3>Q2. How can I download TikTok videos without watermark?</h3>
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<p>A2. You can download TikTok videos without watermark by using tools that remove or hide the watermark automatically during the downloading process. For example, [TikTok Downloader] and [TikTok Video Downloader] are tools that allow you to download TikTok videos without watermark online or on your mobile devices.</p>
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<h3>Q3. How can I download TikTok videos with sound?</h3>
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<p>A3. You can download TikTok videos with sound by using tools that preserve or extract the sound from the video during the downloading process. For example, [TikTok Downloader] and [TikTok Video Downloader] are tools that allow you to download TikTok videos with sound online or on your mobile devices.</p>
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<h3>Q4. How can I download TikTok videos in bulk?</h3>
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<p>A4. You can download TikTok videos in bulk by using tools that allow you to download multiple videos at once or create playlists of videos to download later. For example, [TikTok Downloader] and [TikTok Video Downloader] are tools that allow you to download TikTok videos in bulk online or on your mobile devices.</p>
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<h3>Q5. How can I edit or convert downloaded TikTok videos?</h3>
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<p>A5. You can edit or convert downloaded TikTok videos by using tools that allow you to modify or change the format of the video after downloading it. For example, [TikTok Video Editor] and [TikTok Video Converter] are tools that allow you to edit or convert downloaded TikTok videos online or on your mobile devices.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Brick by Brick How to Create Stunning Masonry Projects.md
DELETED
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@@ -1,126 +0,0 @@
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| 1 |
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<br />
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| 2 |
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<h1>Bricks: Definition, History, Types, Manufacturing Process, Uses, Advantages and Disadvantages</h1>
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| 3 |
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<p>Bricks are one of the oldest and most versatile building materials, used for centuries in various cultures and climates. Bricks can be made of different materials, such as clay, concrete, sand, lime, or fly ash, and have different shapes, sizes, colors, and textures. Bricks can be used for various purposes, such as structural, aesthetic, fire-resistant, sound-insulating, or thermal-regulating. Bricks also have some advantages and disadvantages compared to other materials, depending on the context and the type of brick.</p>
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<h2>brick</h2><br /><p><b><b>Download Zip</b> ->->->-> <a href="https://jinyurl.com/2uNP8k">https://jinyurl.com/2uNP8k</a></b></p><br /><br />
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<p>In this article, we will provide you with an overview of the main topics related to bricks, such as their definition, history, types, manufacturing process, uses, advantages and disadvantages. We will also include some images of bricks and brick structures to illustrate the concepts and examples. We hope you will find this article informative and useful.</p>
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| 6 |
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<h2>Definition of Brick</h2>
|
| 7 |
-
<p>A brick is a small rectangular block typically made of fired or sun-dried clay or other materials that are used in masonry construction. The term brick can also refer to any unit of similar shape and size that is joined with mortar or cement when used in construction.</p>
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| 8 |
-
<p>The dimensions of bricks vary from country to country, but the most common size is about 8 inches long and 4 inches wide. The thickness of bricks can range from 2 to 4 inches. The weight of a standard brick is about 5 pounds.</p>
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<p>Bricks are usually red or brown in color due to the presence of iron oxide in the clay or other materials. However, bricks can also be made in different colors by adding pigments or using different firing temperatures.</p>
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<h2>History of Brick Making</h2>
|
| 51 |
-
<p>The history of brick making dates back to ancient times when people used mud or clay to make simple structures for shelter or storage. The first bricks were sun-dried mud bricks that were shaped by hand or with wooden molds.</p>
|
| 52 |
-
<p>The earliest evidence of brick making was found in southern Turkey and around Jericho dating back to 7000 BC. The ancient Egyptians also used bricks made of clay mixed with straw for building pyramids and tombs around 3000 BC.</p>
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| 53 |
-
<p>The invention of fired bricks was a major breakthrough that occurred around 3500 BC in Mesopotamia (now Iraq). By heating the clay bricks in a kiln or oven at high temperatures, they became stronger, harder, and more durable than sun-dried bricks. The fired bricks were also resistant to water damage and fire.</p>
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| 54 |
-
<p>The Romans were the first to use bricks extensively throughout their empire from Britain to North Africa. They developed various techniques to make bricks of different shapes and sizes. They also used bricks for decorative purposes by creating patterns with different colors or textures.</p>
|
| 55 |
-
<p>After the fall of the Roman Empire, brick making declined in Europe until the Middle Ages when bricks were revived as a cheaper and more convenient alternative to stone. The Gothic and Renaissance styles of architecture used bricks extensively for churches, castles, and palaces.</p>
|
| 56 |
-
<p>The Industrial Revolution in the 18th and 19th centuries brought significant changes to the brick making industry. The introduction of steam engines, mechanized molding machines, and tunnel kilns increased the production and quality of bricks. The development of new materials, such as concrete, sand-lime, and fly ash, also expanded the variety and applications of bricks.</p>
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| 57 |
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<p>In the 20th and 21st centuries, bricks have continued to be used for various purposes, such as housing, commercial buildings, industrial structures, roads, bridges, and monuments. Bricks have also been adapted to modern design trends and environmental concerns by incorporating features such as insulation, ventilation, solar panels, or recycled materials.</p>
|
| 58 |
-
<h2>Types of Brick</h2>
|
| 59 |
-
<p>There are many types of bricks that can be classified based on their material, shape, size, color, texture, or function. Some of the most common types of bricks are:</p>
|
| 60 |
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<ul>
|
| 61 |
-
<li><strong>Clay bricks:</strong> These are the traditional type of bricks made of clay or shale that are fired in a kiln at high temperatures. Clay bricks are usually red or brown in color and have a smooth or rough surface. Clay bricks can be further divided into categories such as common bricks, engineering bricks, facing bricks, or firebricks.</li>
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| 62 |
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<li><strong>Concrete bricks:</strong> These are bricks made of concrete that are molded and cured under pressure. Concrete bricks are usually gray or white in color and have a uniform texture. Concrete bricks can be used for structural or decorative purposes.</li>
|
| 63 |
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<li><strong>Sand-lime bricks:</strong> These are bricks made of sand and lime that are hardened by chemical reaction under pressure. Sand-lime bricks are usually yellow or gray in color and have a smooth surface. Sand-lime bricks are mainly used for aesthetic purposes.</li>
|
| 64 |
-
<li><strong>Fly ash bricks:</strong> These are bricks made of fly ash (a by-product of coal combustion) mixed with cement and water that are cured by steam. Fly ash bricks are usually light gray or brown in color and have a fine texture. Fly ash bricks are environmentally friendly and have good strength and durability.</li>
|
| 65 |
-
<li><strong>Hollow bricks:</strong> These are bricks that have hollow spaces inside them to reduce their weight and improve their insulation properties. Hollow bricks can be made of any material, such as clay, concrete, sand-lime, or fly ash. Hollow bricks can be used for structural or non-structural purposes.</li>
|
| 66 |
-
<li><strong>Paving bricks:</strong> These are bricks that are specially designed for paving roads, sidewalks, driveways, or patios. Paving bricks can be made of any material, such as clay, concrete, sand-lime, or fly ash. Paving bricks can have different shapes, sizes, colors, or patterns to create various effects.</li>
|
| 67 |
-
</ul>
|
| 68 |
-
<p>The following table summarizes some of the characteristics and uses of different types of bricks:</p>
|
| 69 |
-
<table>
|
| 70 |
-
<tr>
|
| 71 |
-
<th>Type</th>
|
| 72 |
-
<th>Material</th>
|
| 73 |
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<th>Color</th>
|
| 74 |
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<th>Texture</th>
|
| 75 |
-
<th>Use</th>
|
| 76 |
-
</tr>
|
| 77 |
-
<tr>
|
| 78 |
-
<td>Clay brick</td>
|
| 79 |
-
<td>Clay or shale</td>
|
| 80 |
-
<td>Red or brown</td>
|
| 81 |
-
<td>Smooth or rough</td>
|
| 82 |
-
<td>Structural or aesthetic</td>
|
| 83 |
-
</tr>
|
| 84 |
-
<tr>
|
| 85 |
-
<td>Concrete brick</td>
|
| 86 |
-
<td>Concrete</td>
|
| 87 |
-
<td>Gray or white</td>
|
| 88 |
-
<td>Uniform</td>
|
| 89 |
-
<td>Structural or decorative</td>
|
| 90 |
-
</tr>
|
| 91 |
-
<tr>
|
| 92 |
-
<td>Sand-lime brick</td>
|
| 93 |
-
<td>Sand and lime</td>
|
| 94 |
-
<td>Yellow or gray</td>
|
| 95 |
-
<td>Smooth</td>
|
| 96 |
-
<td>Aesthetic</td>
|
| 97 |
-
</tr>
|
| 98 |
-
<tr>
|
| 99 |
-
<td>Fly ash brick</td>
|
| 100 |
-
<td>Fly ash, cement, water</td>
|
| 101 |
-
<td>Light gray or brown</td>
|
| 102 |
-
<td>Fine</td>
|
| 103 |
-
<td>Eco-friendly, strong, durable</td>
|
| 104 |
-
</tr>
|
| 105 |
-
<tr>
|
| 106 |
-
<td>Hollow brick</td>
|
| 107 |
-
<td>Any material with hollow spaces</td>
|
| 108 |
-
<td>Any color depending on material</td>
|
| 109 |
-
<td>Any texture depending on material</td>
|
| 110 |
-
<td>Lightweight, insulating</td> contact us or leave a comment below. We would love to hear from you and answer your queries.</p>
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| 111 |
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<h2>FAQs</h2>
|
| 112 |
-
<p>Here are some of the frequently asked questions about bricks:</p>
|
| 113 |
-
<ol>
|
| 114 |
-
<li><strong>What is the difference between bricks and blocks?</strong></li>
|
| 115 |
-
<p>Bricks and blocks are both rectangular units used in masonry construction, but they have some differences. Bricks are usually smaller and lighter than blocks, and are made of clay or other materials that are fired in a kiln. Blocks are usually larger and heavier than bricks, and are made of concrete or other materials that are molded and cured under pressure.</p>
|
| 116 |
-
<li><strong>How many bricks are in a square foot?</strong></li>
|
| 117 |
-
<p>The number of bricks in a square foot depends on the size of the bricks and the thickness of the mortar joints. However, a general rule of thumb is that one standard brick (8 inches by 4 inches by 2.5 inches) covers about 0.22 square feet of wall area. Therefore, to cover one square foot of wall area, you would need about 4.5 bricks.</p>
|
| 118 |
-
<li><strong>How long do bricks last?</strong></li>
|
| 119 |
-
<p>The lifespan of bricks depends on the quality of the material, the type of brick, the exposure to weather conditions, and the maintenance practices. However, bricks are generally very durable and can last for hundreds of years if properly installed and cared for.</p>
|
| 120 |
-
<li><strong>How do you clean bricks?</strong></li>
|
| 121 |
-
<p>To clean bricks, you need to use a mild detergent or soap and water, and a soft brush or cloth. You can also use a pressure washer or a hose to rinse off the dirt and grime. However, you should avoid using harsh chemicals or abrasives that can damage the surface or color of the bricks.</p>
|
| 122 |
-
<li><strong>How do you paint bricks?</strong></li>
|
| 123 |
-
<p>To paint bricks, you need to prepare the surface by cleaning it and removing any loose or flaking paint. You also need to apply a primer that is suitable for masonry surfaces. Then, you can use a paint that is specially formulated for bricks, such as acrylic latex or elastomeric paint. You can use a roller, a brush, or a sprayer to apply the paint evenly and smoothly.</p>
|
| 124 |
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</ol></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Download Blue Eyes by Yo Yo Honey Singh - The Blockbuster Song of 2013.md
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<h1>Blue Eyes Song Download: How to Listen to Yo Yo Honey Singh's Hit Song Online</h1>
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| 3 |
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<h2>Introduction</h2>
|
| 4 |
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<p>If you are a fan of Indian rap music, you must have heard of Yo Yo Honey Singh, one of the most popular and influential rap artists in India. He has produced many hit songs that have topped the charts and won millions of hearts. One of his most famous songs is Blue Eyes, which was released in 2013 and became an instant sensation. In this article, we will tell you everything you need to know about Blue Eyes song, why it is so popular, and how you can download or stream it online.</p>
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<h2>blue eyes song download</h2><br /><p><b><b>Download File</b> ★★★★★ <a href="https://jinyurl.com/2uNKhf">https://jinyurl.com/2uNKhf</a></b></p><br /><br />
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<h3>What is Blue Eyes song?</h3>
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<p>Blue Eyes is a Hindi rap song by Yo Yo Honey Singh, which was released as a single on November 8, 2013. The song is composed by Honey Singh himself, and the lyrics are written by him and Lill Gollu. The song is about a girl with blue eyes who mesmerizes the singer with her beauty and charm. The song has a catchy tune, a groovy beat, and a catchy chorus that goes like this:</p>
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<blockquote>
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<p>"Blue eyes, hypnotize teri kardi ai mennu<br>
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I swear! chhoti dress mein bomb lagdi mennu<br>
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Glossy lips, uff yeah tricks<br>
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Baby lagdi ai killer<br>
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Oh yeah oh yeah<br>
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Katal kare tera bomb figure"</p>
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</blockquote>
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<p>The song also features a rap verse by Honey Singh, where he praises the girl's features and expresses his desire to be with her. The song has a duration of 3 minutes and 30 seconds, and it belongs to the genre of pop rap.</p>
|
| 17 |
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<h3>Why is Blue Eyes song popular?</h3>
|
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<p>Blue Eyes song became a huge hit as soon as it was released, and it received positive reviews from critics and fans alike. The song has been viewed over 400 million times on YouTube, making it one of the most watched Indian music videos ever. The song also topped the charts on various music platforms, such as iTunes, JioSaavn, Gaana, Wynk Music, etc. The song also won several awards, such as the Most Popular Song of the Year at the Mirchi Music Awards in 2014.</p>
|
| 19 |
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<p>Some of the reasons why Blue Eyes song is so popular are:</p>
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<ul>
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<li>The song has a catchy and upbeat tune that makes people want to dance and sing along.</li>
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<li>The song has a unique and appealing theme of blue eyes, which is rare in Indian music.</li>
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<li>The song showcases Honey Singh's rap skills and charisma, which have made him a star in the Indian music industry.</li>
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-
<li>The song has a stunning music video that features Honey Singh and a model named Chitrangada Singh, who plays the role of the blue-eyed girl. The video has high-quality production values, exotic locations, and stylish outfits.</li>
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-
<li>The song has a universal appeal that transcends language barriers and cultural differences. The song can be enjoyed by anyone who likes rap music or pop music.</li>
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-
</ul>
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<h2>How to download Blue Eyes song?</h2>
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| 28 |
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<p>If you want to download Blue Eyes song and listen to it offline, you have several options to choose from. Here are some of the ways you can download Blue Eyes song:</p>
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<h3>Download from JioSaavn</h3>
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<p>JioSaavn is one of the most popular music streaming services in India, which offers a huge collection of songs in various languages and genres. You can download Blue Eyes song from JioSaavn by following these steps:</p>
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| 31 |
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<ol>
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<li>Go to [JioSaavn](^1^) website or app on your device.</li>
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<li <li>Search for Blue Eyes song by Yo Yo Honey Singh in the search bar.</li>
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<li>Select the song from the results and tap on the download icon.</li>
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<li>Choose the quality of the download and wait for the song to be downloaded.</li>
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<li>Enjoy listening to Blue Eyes song offline on your device.</li>
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</ol>
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<p>Note: You need to have a JioSaavn Pro subscription to download songs from JioSaavn. You can get a free trial or a paid plan from their website or app.</p>
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<h3>Download from Archive.org</h3>
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<p>Archive.org is a website that provides free access to millions of digital files, such as books, music, videos, etc. You can download Blue Eyes song from Archive.org by following these steps:</p>
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<ol>
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<li>Go to [Archive.org] website on your device.</li>
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<li>Search for Blue Eyes song by Yo Yo Honey Singh in the search bar.</li>
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<li>Select the song from the results and click on the download options button.</li>
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<li>Choose the format of the download, such as MP3, OGG, etc. and click on the download link.</li>
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<p>Note: You do not need to have an account or a subscription to download songs from Archive.org. However, you should respect the copyright and fair use policies of the website and the artists.</p>
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<h3>Download from Wynk Music</h3>
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<p>Wynk Music is another popular music streaming service in India, which offers a wide range of songs in various languages and genres. You can download Blue Eyes song from Wynk Music by following these steps:</p>
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| 100 |
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<ol>
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<li>Go to [Wynk Music] website or app on your device.</li>
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<li>Search for Blue Eyes song by Yo Yo Honey Singh in the search bar.</li>
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<li>Select the song from the results and tap on the download icon.</li>
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<li>Choose the quality of the download and wait for the song to be downloaded.</li>
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<li>Enjoy listening to Blue Eyes song offline on your device.</li>
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</ol>
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<p>Note: You need to have a Wynk Music subscription to download songs from Wynk Music. You can get a free trial or a paid plan from their website or app.</p>
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<h2>How to stream Blue Eyes song online?</h2>
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<p>If you do not want to download Blue Eyes song and prefer to stream it online, you have several options to choose from. Here are some of the ways you can stream Blue Eyes song online:</p>
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<h3>Stream from YouTube</h3>
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<p>YouTube is one of the most popular and widely used video streaming platforms in the world, which offers a huge variety of content, including music videos. You can stream Blue Eyes song from YouTube by following these steps:</p>
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<ol>
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<li>Go to [YouTube] website or app on your device.</li>
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<li>Search for Blue Eyes song by Yo Yo Honey Singh in the search bar.</li>
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<li>Select the official music video of the song from the results and click on the play button.</li>
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<li>Enjoy watching and listening to Blue Eyes song online on your device.</li>
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</ol>
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<p>Note: You do not need to have an account or a subscription to stream songs from YouTube. However, you may encounter some ads or interruptions while streaming. You can also use YouTube Music app or website for a better music streaming experience.</p>
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<h3>Stream from Spotify</h3>
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<p>Spotify is one of the most popular and widely used music streaming platforms in the world, which offers a huge collection of songs in various languages and genres. You can stream Blue Eyes song from Spotify by following these steps:</p>
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<ol>
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<li>Go to [Spotify] website or app on your device.</li>
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<li <li>Search for Blue Eyes song by Yo Yo Honey Singh in the search bar.</li>
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<li>Select the song from the results and click on the play button.</li>
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<li>Enjoy listening to Blue Eyes song online on your device.</li>
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</ol>
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<p>Note: You need to have a Spotify account and a subscription to stream songs from Spotify. You can get a free trial or a paid plan from their website or app.</p>
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<h3>Stream from Gaana</h3>
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<p>Gaana is another popular music streaming service in India, which offers a wide range of songs in various languages and genres. You can stream Blue Eyes song from Gaana by following these steps:</p>
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<ol>
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<li>Go to [Gaana] website or app on your device.</li>
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<li>Search for Blue Eyes song by Yo Yo Honey Singh in the search bar.</li>
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<li>Select the song from the results and click on the play button.</li>
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<li>Enjoy listening to Blue Eyes song online on your device.</li>
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</ol>
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<p>Note: You do not need to have a Gaana account or a subscription to stream songs from Gaana. However, you may encounter some ads or interruptions while streaming. You can also use Gaana Plus app or website for a better music streaming experience.</p>
|
| 137 |
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<h2>Conclusion</h2>
|
| 138 |
-
<p>In this article, we have given you all the information you need to know about Blue Eyes song by Yo Yo Honey Singh, one of the most popular and influential rap artists in India. We have told you what the song is about, why it is so popular, and how you can download or stream it online. We hope you enjoyed reading this article and found it useful. If you are a fan of Honey Singh or rap music, you should definitely check out Blue Eyes song and listen to it on your device. You will surely love it and get hooked to it.</p>
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| 139 |
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<p>If you liked this article, please share it with your friends and family who are also interested in music. Also, let us know your feedback and suggestions in the comments section below. We would love to hear from you and improve our content. Thank you for reading!</p>
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| 140 |
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<h3>Call to action</h3>
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<p>If you want to listen to more songs by Yo Yo Honey Singh or other rap artists, you can visit [Rap Music] website, where you can find a huge collection of rap songs in various languages and genres. You can also download or stream them online on your device. Rap Music is the ultimate destination for rap music lovers. So, what are you waiting for? Visit Rap Music today and enjoy the best rap music ever!</p>
|
| 142 |
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<h2>Frequently Asked Questions</h2>
|
| 143 |
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<h4>Q: Who is Yo Yo Honey Singh?</h4>
|
| 144 |
-
<p>A: Yo Yo Honey Singh is an Indian rapper, singer, composer, producer, and actor, who is widely regarded as one of the most popular and influential rap artists in India. He has produced many hit songs that have topped the charts and won millions of hearts. Some of his famous songs are Dheere Dheere, Lungi Dance, Brown Rang, Love Dose, etc.</p>
|
| 145 |
-
<h4>Q: What is the meaning of Blue Eyes song?</h4>
|
| 146 |
-
<p>A: Blue Eyes song is a Hindi rap song by Yo Yo Honey Singh, which is about a girl with blue eyes who mesmerizes the singer with her beauty and charm. The song has a catchy tune, a groovy beat, and a catchy chorus that praises the girl's features and expresses the singer's desire to be with her.</p>
|
| 147 |
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<h4>Q: How can I download Blue Eyes song for free?</h4>
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<p>A: You can download Blue Eyes song for free from Archive.org website, which provides free access to millions of digital files, such as books, music, videos, etc. You can also download Blue Eyes song from JioSaavn or Wynk Music websites or apps if you have a subscription to their services.</p>
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| 149 |
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<h4>Q: How can I watch Blue Eyes song video online?</h4>
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<p>A: You can watch Blue Eyes song video online on YouTube website or app, where you can find the official music video of the song that has been viewed over 400 million times. You can also watch Blue Eyes song video on other video streaming platforms, such as Vimeo, Dailymotion, etc.</p>
|
| 151 |
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<h4>Q: What are some other rap songs by Yo Yo Honey Singh?</h4>
|
| 152 |
-
<p>A: Some other rap songs by Yo Yo Honey Singh are:</p>
|
| 153 |
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<ul>
|
| 154 |
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<li>Dheere Dheere: A romantic rap song that features Hrithik Roshan and Sonam Kapoor in the music video.</li>
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<li>Lungi Dance: A tribute rap song to Rajinikanth that features Shah Rukh Khan and Deepika Padukone in the music video.</li>
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spaces/1phancelerku/anime-remove-background/Download JioTV from Google Play Store and Never Miss Your Favourite Shows.md
DELETED
|
@@ -1,135 +0,0 @@
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<h1>Jio TV Download Google Play Store: How to Watch Live TV on Your Smartphone</h1>
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<h2>What is Jio TV and why you should download it</h2>
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<p>Jio TV is an app that lets you watch live TV on your smartphone using your Jio SIM. It is one of the most comprehensive options for anyone who wishes to watch live TV online in India. You can enjoy a range of live sports, including cricket, football, hockey, tennis, basketball, and more, with events like UEFA Champions League, FA Cup, Serie A, Europa League, EFL Carabao Cup Final, International Friendlies 2021, WWE SmackDown, WWE Raw, UFC Fights, MOTO GP, NBA, PGA Tour, AFC Champions League, Euro Qualifiers and much more with Jio TV app. You can also watch exclusive sports on Jio Sports channels.</p>
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<p>Not only sports, but you can also watch the latest TV shows like The Kapil Sharma Show, Taarak Mehta Ka Ooltah Chashmah, Parineeti, Super Star Singer 2, Friends, Two And Half Men, Brooklyn Nine-nine, The Big Bang Theory, Man vs Wild, Wild Frank & many more. You can also watch movies from various genres and languages on Jio TV app. You can explore 15,000+ on-demand movies for non-stop binging. You can also listen to your favorite tunes on the go with Jio Music channels.</p>
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<p>Jio TV app also lets you connect spiritually with live darshans, poojas, aartis & more from various temples and shrines across India. You can also play exciting games and have unlimited fun with Jio Games channels. You can also watch educational content from various sources like Million Lights, TV Teacher, Birla Brainiacs, Dnyanganga, Top Tutor, Kite Victers on Jio Education channels.</p>
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-
<h3>Jio TV features and benefits</h3>
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<p>Some of the awesome features and benefits of Jio TV app are:</p>
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| 11 |
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<ul>
|
| 12 |
-
<li>Never miss a program with the 7 days catch up of your favorite channels</li>
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| 13 |
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<li>Pause & play live TV channels at your convenience</li>
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| 14 |
-
<li>Find all the popular & trending shows in the ‘Featured’ tab</li>
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| 15 |
-
<li>Browse through the top stories of the day in our ‘News’ tab</li>
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-
<li>Mark channel/programs as favorites to never miss them</li>
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-
<li>One tap for all sports live/highlights in our ‘Sports’ tab</li>
|
| 18 |
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<li>Set a reminder for your favourite shows and never miss them</li>
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-
<li>Multiple audio and language support</li>
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-
<li>Record your favourite shows & watch them later</li>
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<li>Choose the quality at which you want the video to be played</li>
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-
<li>Watch LIVE TV and browse the app simultaneously by just dragging and docking the player</li>
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| 23 |
-
</ul> <h3>Jio TV drawbacks and limitations</h3>
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| 24 |
-
<p>While Jio TV app is a great option for watching live TV on your smartphone, it also has some drawbacks and limitations that you should be aware of. Some of them are:</p>
|
| 25 |
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<ul>
|
| 26 |
-
<li>You need to have a Jio SIM and a valid Jio plan to use Jio TV app. You cannot use it with any other network or Wi-Fi connection.</li>
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| 27 |
-
<li>You may face buffering issues or low video quality depending on your network speed and coverage.</li>
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-
<li>You may not find all the channels or programs that you want to watch on Jio TV app. Some channels may be geo-restricted or not available in your region.</li>
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-
<li>You may have to pay extra charges for some premium content or channels on Jio TV app.</li>
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-
<li>You may encounter some bugs or glitches while using Jio TV app. Sometimes, the app may crash or freeze unexpectedly.</li>
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</ul>
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| 32 |
-
<h2>How to download Jio TV app from Google Play Store</h2>
|
| 33 |
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<p>If you want to download Jio TV app from Google Play Store, you need to follow these simple steps:</p>
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| 34 |
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<h3>Step-by-step guide with screenshots</h3>
|
| 35 |
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<ol>
|
| 36 |
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<li>Open Google Play Store on your smartphone and search for "Jio TV" in the search bar.</li>
|
| 37 |
-
<li>Tap on the "JioTV – News, Movies, Entertainment, LIVE TV" app from the search results. It should have the logo of a blue play button with "Jio" written on it.</li>
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| 38 |
-
<li>Tap on the "Install" button to download and install the app on your smartphone. You may have to grant some permissions to the app during the installation process.</li>
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| 39 |
-
<li>Once the app is installed, you can find it on your home screen or app drawer. Tap on the app icon to open it.</li>
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-
</ol>
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<h3>How to sign in and use Jio TV app</h3>
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-
<ol>
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| 43 |
-
<li>When you open the app for the first time, you will see a welcome screen with some instructions. Tap on the "Skip" button to proceed.</li>
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| 44 |
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<li>You will be asked to enter your Jio number and password to sign in. If you don't have a password, you can tap on the "Get Password" option and follow the steps to create one.</li>
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| 45 |
-
<li>Once you sign in, you will see the home screen of the app with various tabs and categories. You can swipe left or right to browse through them.</li>
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-
<li>You can also tap on the menu icon at the top left corner of the screen to access more options and settings.</li>
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| 47 |
-
</ol> <h2>How to watch live TV channels on Jio TV app</h2>
|
| 48 |
-
<p>Once you have downloaded and signed in to Jio TV app, you can start watching live TV channels on your smartphone. Here is how you can do that:</p>
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<p>How to download JioTV app from Google Play Store<br />
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JioTV - Watch live TV channels and shows on Google Play<br />
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JioTV vs other streaming apps on Google Play Store<br />
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JioTV app features and benefits on Google Play Store<br />
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JioTV app reviews and ratings on Google Play Store<br />
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JioTV app update and bug fixes on Google Play Store<br />
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JioTV app alternatives and competitors on Google Play Store<br />
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JioTV app download link and QR code on Google Play Store<br />
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JioTV app installation and activation guide on Google Play Store<br />
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JioTV app permissions and privacy policy on Google Play Store<br />
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JioTV app categories and genres on Google Play Store<br />
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JioTV app quality and speed on Google Play Store<br />
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JioTV app catch-up and pause feature on Google Play Store<br />
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JioTV app favorites and reminders feature on Google Play Store<br />
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JioTV app sports and news channels on Google Play Store<br />
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JioTV app free trial and subscription plans on Google Play Store<br />
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How to watch Paramount library content with JioTv App from google play store</p>
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<h3>How to browse and select channels</h3>
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<ol>
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| 97 |
-
<li>On the home screen of the app, you will see various tabs and categories, such as Featured, Sports, Movies, News, Music, Entertainment, etc. You can swipe left or right to browse through them.</li>
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-
<li>You can also tap on the "All Channels" option at the bottom of the screen to see the complete list of channels available on Jio TV app. You can filter the channels by language, genre, or HD quality.</li>
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-
<li>You can also use the search icon at the top right corner of the screen to search for any channel or program by name or keyword.</li>
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<li>Once you find the channel or program that you want to watch, simply tap on it to start streaming it live on your smartphone.</li>
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</ol>
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<h3>How to pause, play, rewind, and record live TV</h3>
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<ol>
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| 104 |
-
<li>While watching live TV on Jio TV app, you can use the player controls at the bottom of the screen to pause, play, rewind, and record live TV.</li>
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-
<li>You can tap on the pause button to pause the live stream. You can tap on it again to resume it.</li>
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| 106 |
-
<li>You can tap on the rewind button to go back up to 30 seconds in the live stream. You can tap on it multiple times to go back further.</li>
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-
<li>You can tap on the record button to record the live stream. You can choose the duration and quality of the recording. You can also schedule a recording for a future program.</li>
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-
<li>You can access your recorded programs from the "My Recordings" option in the menu.</li>
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| 109 |
-
</ol>
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<h2>Jio TV app alternatives and competitors</h2>
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| 111 |
-
<p>Jio TV app is not the only option for watching live TV on your smartphone. There are some other apps that offer similar or better features and services. Here are some of them:</p>
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| 112 |
-
<h3>Airtel Xstream TV</h3>
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-
<p>Airtel Xstream TV is an app that allows you to watch live TV, movies, shows, and more on your smartphone using your Airtel SIM or broadband connection. You can watch over 400+ live TV channels, including 60+ HD channels, in 15+ languages. You can also enjoy over 10,000+ movies and shows from various platforms like ZEE5, Eros Now, Hungama Play, Shemaroo Me, Hoichoi and more. You can also access Airtel Xstream Box and Airtel Xstream Stick for a seamless viewing experience on your TV.</p>
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<h3>Disney+ Hotstar</h3>
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-
<p>Disney+ Hotstar is an app that lets you watch live sports, movies, shows, news, and more on your smartphone using your mobile data or Wi-Fi connection. You can watch live cricket matches, IPL 2021, Premier League football matches, Formula 1 races, and more with Disney+ Hotstar VIP subscription. You can also watch exclusive Disney+ originals, Marvel movies and shows, Star Wars movies and shows, Pixar movies and shows, National Geographic documentaries and more with Disney+ Hotstar Premium subscription. You can also watch popular Indian movies and shows from various languages and genres.</p>
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<h3>Vodafone Play</h3>
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| 117 |
-
<p>Vodafone Play is an app that lets you watch live TV, movies, shows, web series, and more on your smartphone using your Vodafone SIM or Wi-Fi connection. You can watch over 450+ live TV channels in 16+ languages. You can also enjoy over 15,000+ movies and shows from various platforms like ZEE5, SonyLIV, Lionsgate Play, Eros Now, Shemaroo Me, Sun NXT and more. You can also access Vodafone Play Box for a better viewing experience on your TV.</p>
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| 118 |
-
<h2>Conclusion and FAQs</h2>
|
| 119 |
-
<p>Jio TV app is a great way to watch live TV on your smartphone using your Jio SIM. It offers a wide range of channels and programs in various languages and genres. It also has some cool features like catch up TV, pause and play live TV, record live TV, etc. However, it also has some drawbacks like network dependency, limited channel availability, extra charges for premium content etc. If you are looking for some alternatives or competitors for Jio TV app, you can try Airtel Xstream TV , Disney+ Hotstar , or Vodafone Play apps.</p>
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| 120 |
-
<p>Here are some FAQs that you may have about Jio TV app:</p>
|
| 121 |
-
<ol>
|
| 122 |
-
<li><b>Is Jio TV app free?</ <b>Is Jio TV app free?</b></li>
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| 123 |
-
<li><p>Jio TV app is free for Jio SIM users who have an active Jio plan. However, you may have to pay extra charges for some premium content or channels on Jio TV app. You can check the details of your Jio plan and the applicable charges on the MyJio app or the Jio website.</p></li>
|
| 124 |
-
<li><b>How much data does Jio TV app consume?</b></li>
|
| 125 |
-
<li><p>Jio TV app consumes data depending on the quality and duration of the video that you watch. You can choose the quality at which you want the video to be played from low, medium, high, or auto. The auto option will adjust the quality according to your network speed and coverage. You can also check the data usage of Jio TV app on the MyJio app or the Jio website.</p></li>
|
| 126 |
-
<li><b>Can I watch Jio TV app on my laptop or PC?</b></li>
|
| 127 |
-
<li><p>Jio TV app is currently available only for Android and iOS smartphones. You cannot watch Jio TV app on your laptop or PC directly. However, you can use some third-party software or tools to mirror your smartphone screen to your laptop or PC and watch Jio TV app on it. You can also use some Android emulators to run Jio TV app on your laptop or PC.</p></li>
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| 128 |
-
<li><b>Can I watch Jio TV app on my smart TV?</b></li>
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| 129 |
-
<li><p>Jio TV app is not compatible with smart TVs directly. However, you can use some devices like Chromecast, Firestick, or Jio Media Cable to connect your smartphone to your smart TV and watch Jio TV app on it. You can also use some smart TVs that have Android OS and Google Play Store to download and install Jio TV app on them.</p></li>
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| 130 |
-
<li><b>Can I watch Jio TV app offline?</b></li>
|
| 131 |
-
<li><p>Jio TV app requires an internet connection to stream live TV channels and programs. You cannot watch Jio TV app offline. However, you can record some live TV programs and watch them later offline from the "My Recordings" option in the menu.</p></li>
|
| 132 |
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</ol>
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-
<p>I hope this article has helped you to understand how to download and use Jio TV app from Google Play Store and how to watch live TV on your smartphone. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p> 401be4b1e0<br />
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<p>Bus Simulator Ultimate is a simulation game developed by Zuuks Games that lets you drive various buses across realistic roads and huge city maps inspired by cities across the United States, Russia, Italy, France, Brazil, Azerbaijan, Turkey, The Netherlands, and Spain! You can pick up passengers at every stop in your route, follow traffic rules, listen to radio stations, deal with different weather conditions, manage your bus company, hire drivers, <p>Bus Simulator Ultimate is not only a realistic and fun bus driving game, but also a social game where you can chat with other players, join multiplayer events, create your own routes, and share your feedback with the developers. You can also customize your buses with different skins, stickers, accessories, and horns. You can even create your own radio station and play your favorite music while driving!</p>
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<h2>How to Download Bus Simulator Ultimate on Windows 11</h2>
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<p>An emulator is a software that allows you to run Android apps and games on your PC. There are many emulators available for Windows 11, but we will focus on three of the most popular ones: BlueStacks, LDPlayer, and GameLoop. Here are the steps to download and install Bus Simulator Ultimate on Windows 11 using any of these emulators:</p>
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| 44 |
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bus simulator ultimate amazing coach buses and designs for windows 11<br />
|
| 45 |
-
bus simulator ultimate original terminals and offices for windows 11<br />
|
| 46 |
-
bus simulator ultimate used buses market and prices for windows 11<br />
|
| 47 |
-
bus simulator ultimate detailed cockpits and interiors for windows 11<br />
|
| 48 |
-
bus simulator ultimate passengers reviews and feedbacks for windows 11<br />
|
| 49 |
-
bus simulator ultimate radio stations and music for windows 11<br />
|
| 50 |
-
bus simulator ultimate highway toll roads and traffic system for windows 11<br />
|
| 51 |
-
bus simulator ultimate realistic weather and seasons for windows 11<br />
|
| 52 |
-
bus simulator ultimate realistic sound effects and voices for windows 11<br />
|
| 53 |
-
bus simulator ultimate host service and announcements for windows 11<br />
|
| 54 |
-
bus simulator ultimate easy controls and steering options for windows 11 <br />
|
| 55 |
-
bus simulator ultimate language support and translations for windows 11 <br />
|
| 56 |
-
bus simulator ultimate zuuks games developer and publisher for windows 11 <br />
|
| 57 |
-
bus simulation games like bus simulator ultimate for windows 11 <br />
|
| 58 |
-
alternatives to bus simulator ultimate for windows 11</p>
|
| 59 |
-
<h3>BlueStacks</h3>
|
| 60 |
-
<h4>Download and install BlueStacks on your PC</h4>
|
| 61 |
-
<p>BlueStacks is one of the most widely used emulators for Windows 11. It has over 500 million users and supports thousands of Android games and apps. It also offers enhanced graphics, macros, multi-instance, and other features that make your gaming experience more enjoyable.</p>
|
| 62 |
-
<p>To download and install BlueStacks on your PC, follow these steps:</p>
|
| 63 |
-
<ol>
|
| 64 |
-
<li>Go to the official website of BlueStacks and click on the "Download BlueStacks" button.</li>
|
| 65 |
-
<li>Once the download is complete, run the installer and follow the instructions on the screen.</li>
|
| 66 |
-
<li>After the installation is done, launch BlueStacks and wait for it to initialize.</li>
|
| 67 |
-
</ol>
|
| 68 |
-
<p>Note: BlueStacks requires at least 2 GB of RAM, 5 GB of disk space, and an updated graphics driver to run smoothly. You can check the system requirements and FAQs on the website for more information.</p>
|
| 69 |
-
<h4>Launch BlueStacks and sign in with Google account</h4>
|
| 70 |
-
<p>To access the Play Store and download Bus Simulator Ultimate, you need to sign in with a Google account on BlueStacks. Here's how:</p>
|
| 71 |
-
<ol>
|
| 72 |
-
<li>On the home screen of BlueStacks, click on the "Google Sign-in" button.</li>
|
| 73 |
-
<li>Enter your Google account credentials or create a new one if you don't have one.</li>
|
| 74 |
-
<li>Agree to the terms and conditions and complete the setup.</li>
|
| 75 |
-
</ol>
|
| 76 |
-
<p>Now you can access the Play Store and other Google services on BlueStacks.</p>
|
| 77 |
-
<h4>Search for Bus Simulator Ultimate in the Play Store and install it</h4>
|
| 78 |
-
<p>To download and install Bus Simulator Ultimate on BlueStacks, follow these steps:</p>
|
| 79 |
-
<ol>
|
| 80 |
-
<li>On the home screen of BlueStacks, click on the "Play Store" icon.</li>
|
| 81 |
-
<li>In the search bar, type "Bus Simulator Ultimate" and hit enter.</li>
|
| 82 |
-
<li>From the search results, click on the game icon that has the developer name "Zuuks Games".</li>
|
| 83 |
-
<li>On the game page, click on the "Install" button.</li>
|
| 84 |
-
<li>Wait for the installation to finish.</li>
|
| 85 |
-
</ol>
|
| 86 |
-
<p>Congratulations! You have successfully installed Bus Simulator Ultimate on BlueStacks.</p>
|
| 87 |
-
<h4>Start the game and enjoy</h4>
|
| 88 |
-
<p>To start playing Bus Simulator Ultimate on BlueStacks, follow these steps:</p>
|
| 89 |
-
<ol>
|
| 90 |
-
<li>On the home screen of BlueStacks, click on the game icon that says "Bus Simulator Ultimate".</li>
|
| 91 |
-
<li>Wait for the game to load and accept the permissions.</li>
|
| 92 |
-
<li>Select your language and agree to the terms of service.</li>
|
| 93 |
-
<li>Create your profile name and choose your avatar.</li>
|
| 94 |
-
<li>Select your country and city from the map.</li>
|
| 95 |
-
<li>Pick your first bus from the garage.</li>
|
| 96 |
-
<li>Select a route from the list or create your own.</li>
|
| 97 |
-
<li>Start driving!</li>
|
| 98 |
-
</ol>
|
| 99 |
-
<p>You can use the keyboard and mouse to control your bus. You can also customize the settings according to your preference. For example, you can change the camera angle, adjust the volume, enable or disable traffic lights, etc. You can also use macros to automate certain actions or use multi-instance to play multiple games at once.</p>
|
| 100 |
-
<h3>LDPlayer</h3>
|
| 101 |
-
<h4>Download and install LDPlayer on your PC</h4>
|
| 102 |
-
<p>LDPlayer is another popular emulator for Windows 11. It has over 100 million users and supports a wide range of Android games and apps <p>It also offers high performance, keyboard mapping, script, and other features that make your gaming experience more smooth and convenient.</p>
|
| 103 |
-
<p>To download and install LDPlayer on your PC, follow these steps:</p>
|
| 104 |
-
<ol>
|
| 105 |
-
<li>Go to the official website of LDPlayer and click on the "Download LDPlayer" button.</li>
|
| 106 |
-
<li>Once the download is complete, run the installer and follow the instructions on the screen.</li>
|
| 107 |
-
<li>After the installation is done, launch LDPlayer and wait for it to initialize.</li>
|
| 108 |
-
</ol>
|
| 109 |
-
<p>Note: LDPlayer requires at least 2 GB of RAM, 36 GB of disk space, and an updated graphics driver to run smoothly. You can check the system requirements and FAQs on the website for more information.</p>
|
| 110 |
-
<h4>Launch LDPlayer and sign in with Google account</h4>
|
| 111 |
-
<p>To access the Play Store and download Bus Simulator Ultimate, you need to sign in with a Google account on LDPlayer. Here's how:</p>
|
| 112 |
-
<ol>
|
| 113 |
-
<li>On the home screen of LDPlayer, click on the "Google Play" icon.</li>
|
| 114 |
-
<li>Enter your Google account credentials or create a new one if you don't have one.</li>
|
| 115 |
-
<li>Agree to the terms and conditions and complete the setup.</li>
|
| 116 |
-
</ol>
|
| 117 |
-
<p>Now you can access the Play Store and other Google services on LDPlayer.</p>
|
| 118 |
-
<h4>Search for Bus Simulator Ultimate in the Play Store and install it</h4>
|
| 119 |
-
<p>To download and install Bus Simulator Ultimate on LDPlayer, follow these steps:</p>
|
| 120 |
-
<ol>
|
| 121 |
-
<li>On the home screen of LDPlayer, click on the "Play Store" icon.</li>
|
| 122 |
-
<li>In the search bar, type "Bus Simulator Ultimate" and hit enter.</li>
|
| 123 |
-
<li>From the search results, click on the game icon that has the developer name "Zuuks Games".</li>
|
| 124 |
-
<li>On the game page, click on the "Install" button.</li>
|
| 125 |
-
<li>Wait for the installation to finish.</li>
|
| 126 |
-
</ol>
|
| 127 |
-
<p>Congratulations! You have successfully installed Bus Simulator Ultimate on LDPlayer.</p>
|
| 128 |
-
<h4>Start the game and enjoy</h4>
|
| 129 |
-
<p>To start playing Bus Simulator Ultimate on LDPlayer, follow these steps:</p>
|
| 130 |
-
<ol>
|
| 131 |
-
<li>On the home screen of LDPlayer, click on the game icon that says "Bus Simulator Ultimate".</li>
|
| 132 |
-
<li>Wait for the game to load and accept the permissions.</li>
|
| 133 |
-
<li>Select your language and agree to the terms of service.</li>
|
| 134 |
-
<li>Create your profile name and choose your avatar.</li>
|
| 135 |
-
<li>Select your country and city from the map.</li>
|
| 136 |
-
<li>Pick your first bus from the garage.</li>
|
| 137 |
-
<li>Select a route from the list or create your own.</li>
|
| 138 |
-
<li>Start driving!</li>
|
| 139 |
-
</ol>
|
| 140 |
-
<p>You can use the keyboard and mouse to control your bus. You can also customize the settings according to your preference. For example, you can change the camera angle, adjust the volume, enable or disable traffic lights, etc. You can also use keyboard mapping to assign keys to specific actions or use script to automate certain tasks.</p>
|
| 141 |
-
<h3>GameLoop</h3>
|
| 142 |
-
<h4>Download and install GameLoop on your PC</h4>
|
| 143 |
-
<p>GameLoop is another popular emulator for Windows 11. It has over 50 million users and supports a wide range of Android games and apps. It also offers smooth gameplay, exclusive features, social network, and other features that make your gaming experience more fun and interactive.</p>
|
| 144 |
-
<p>To download and install GameLoop on your PC, follow these steps:</p>
|
| 145 |
-
<ol>
|
| 146 |
-
<li>Go to the official website of GameLoop and click on the "Download GameLoop" button.</li>
|
| 147 |
-
<li>Once the download is complete, run the installer and follow the instructions on the screen.</li>
|
| 148 |
-
<li>After the installation is done, launch GameLoop and wait for it to initialize.</li>
|
| 149 |
-
</ol>
|
| 150 |
-
<p>Note: GameLoop requires at least 2 GB of RAM, 1.5 GB of disk space, and an updated graphics driver to run smoothly. You can check the system requirements and FAQs on the website for more information.</p>
|
| 151 |
-
<h4>Launch GameLoop and sign in with Google account</h4>
|
| 152 |
-
<p>To access the Play Store and download Bus Simulator Ultimate, you need to sign in with a Google account on GameLoop. Here's how:</p>
|
| 153 |
-
<ol>
|
| 154 |
-
<li>On the home screen of GameLoop, click on the "Google Installer" icon.</li>
|
| 155 |
-
<li>Follow the instructions to install Google services on GameLoop.</li>
|
| 156 |
-
<li>Once the installation is complete, click on the "Play Store" icon.</li>
|
| 157 |
-
<li>Enter your Google account credentials or create a new one if you don't have one.</li>
|
| 158 |
-
<li>Agree to the terms and conditions and complete the setup.</li>
|
| 159 |
-
</ol>
|
| 160 |
-
<p>Now you can access the Play Store and other Google services on GameLoop.</p>
|
| 161 |
-
<h4>Search for Bus Simulator Ultimate in the Play Store and install it</h4>
|
| 162 |
-
<p>To download and install Bus Simulator Ultimate on GameLoop, follow these steps:</p>
|
| 163 |
-
<ol>
|
| 164 |
-
<li>On the home screen of GameLoop, click on the "Play Store" icon.</li>
|
| 165 |
-
<li>In the search bar, type "Bus Simulator Ultimate" and hit enter.</li>
|
| 166 |
-
<li>From the search results, click on the game icon that has the developer name "Zuuks Games".</li>
|
| 167 |
-
<li>On the game page, click on the "Install" button.</li>
|
| 168 |
-
<li>Wait for the installation to finish.</li>
|
| 169 |
-
</ol>
|
| 170 |
-
<p>Congratulations! You have successfully installed Bus Simulator Ultimate on GameLoop.</p>
|
| 171 |
-
<h4>Start the game and enjoy</h4>
|
| 172 |
-
<p>To start playing Bus Simulator Ultimate on GameLoop, follow these steps:</p>
|
| 173 |
-
<ol>
|
| 174 |
-
<li>On the home screen of GameLoop, click on the game icon that says "Bus Simulator Ultimate".</li>
|
| 175 |
-
<li>Wait for the game to load and accept the permissions.</li>
|
| 176 |
-
<li>Select your language and agree to the terms of service.</li>
|
| 177 |
-
<li>Create your profile name and choose your avatar.</li>
|
| 178 |
-
<li>Select your country and city from the map.</li>
|
| 179 |
-
<li>Pick your first bus from the garage.</li>
|
| 180 |
-
<li>Select a route from the list or create your own.</li>
|
| 181 |
-
<li>Start driving!</li>
|
| 182 |
-
</ol>
|
| 183 |
-
<p>You can use the keyboard and mouse to control your bus. You can also customize the settings according to your preference. For example, you can change the camera angle, adjust the volume, enable or disable traffic lights, etc. You can also use exclusive features such as Turbo GPU, Game Center, Live Stream, etc. to enhance your gaming experience.</p>
|
| 184 |
-
<h2>Tips and Tricks for Playing Bus Simulator Ultimate on Windows 11</h2>
|
| 185 |
-
<p>Now that you know how to download and play Bus Simulator Ultimate on Windows 11 using an emulator, here are some useful tips and tricks that will help you become a better bus driver and a successful bus company owner:</p>
|
| 186 |
-
<ul>
|
| 187 |
-
<li>Drive safely and follow traffic rules. Avoid speeding, running red lights, crashing into other vehicles or pedestrians, etc. These will reduce your reputation and income.</li>
|
| 188 |
-
<li>Manage your bus company wisely. Hire drivers, buy new buses, upgrade your garage, expand your routes, etc. These will increase your reputation and income.</li>
|
| 189 |
-
<li>Earn more money by completing missions, achievements, events, etc. You can also watch ads or use in-app purchases to get more coins or gems.</li>
|
| 190 |
-
<li>Upgrade your buses with better engines, brakes, tires, etc. These will improve your performance and fuel efficiency.</li>
|
| 191 |
-
<li>Customize your buses with different skins, stickers, accessories, horns, etc. These will make your buses more attractive and unique.</li>
|
| 192 |
-
<li>Create your own routes by selecting different stops and destinations. You can also share your routes with other players or download their routes.</li>
|
| 193 |
-
<li>Chat with other players using text or voice messages. You can also join multiplayer events or create your own events.</li>
|
| 194 |
-
<li>Create your own radio station and play your favorite music while driving. You can also listen to other radio stations or podcasts.</li>
|
| 195 |
-
</ul>
|
| 196 |
-
<h2>Conclusion</h2>
|
| 197 |
-
<p>Bus Simulator Ultimate is a realistic and fun bus driving game that lets you experience what it's like to be a bus driver in different countries and cities. You can also run your own bus company and become a successful entrepreneur. You can download and play Bus Simulator Ultimate on Windows 11 using an emulator of your choice: BlueStacks, LDPlayer, or GameLoop. Each emulator has its own advantages and features that will make your gaming experience more enjoyable. So what are you waiting for? Try out Bus Simulator Ultimate on Windows 11 today!</p>
|
| 198 |
-
<h2>Frequently Asked Questions</h2>
|
| 199 |
-
<p>Here are some of the most frequently asked questions about Bus Simulator Ultimate:</p>
|
| 200 |
-
<ol>
|
| 201 |
-
<li><b>Is Bus Simulator Ultimate free to play?</b></li>
|
| 202 |
-
<p>Yes, Bus Simulator Ultimate is free to play. However, it contains ads and in-app purchases that can enhance your gameplay or remove ads. You can also earn coins and gems by playing the game or watching ads.</p>
|
| 203 |
-
<li><b>Can I play Bus Simulator Ultimate offline?</b></li>
|
| 204 |
-
<p>Yes, you can play Bus Simulator Ultimate offline. However, some features such as multiplayer, radio, events, etc. will not be available. You will also need an internet connection to download and update the game.</p>
|
| 205 |
-
<li><b>How can I contact the developers of Bus Simulator Ultimate?</b></li>
|
| 206 |
-
<p>You can contact the developers of Bus Simulator Ultimate by sending an email to [email protected] or by visiting their website . You can also follow them on Facebook , Twitter , Instagram , and YouTube for the latest news and updates.</p>
|
| 207 |
-
<li><b>What are the minimum system requirements for Bus Simulator Ultimate?</b></li>
|
| 208 |
-
<p>The minimum system requirements for Bus Simulator Ultimate are as follows:</p>
|
| 209 |
-
<ul>
|
| 210 |
-
<li>Android version: 5.0 or higher</li>
|
| 211 |
-
<li>RAM: 2 GB or higher</li>
|
| 212 |
-
<li>Storage: 1 GB or higher</li>
|
| 213 |
-
<li>Internet connection: Required for some features</li>
|
| 214 |
-
</ul>
|
| 215 |
-
<p>If you want to play Bus Simulator Ultimate on Windows 11 using an emulator, you will also need to meet the system requirements of the emulator you choose. You can check the websites of BlueStacks , LDPlayer , and GameLoop for more information.</p>
|
| 216 |
-
<li><b>How can I update Bus Simulator Ultimate?</b></li>
|
| 217 |
-
<p>You can update Bus Simulator Ultimate by following these steps:</p>
|
| 218 |
-
<ol>
|
| 219 |
-
<li>Open the Play Store app on your device or emulator.</li>
|
| 220 |
-
<li>Tap on the menu icon and select "My apps & games".</li>
|
| 221 |
-
<li>Find Bus Simulator Ultimate in the list and tap on the "Update" button.</li>
|
| 222 |
-
<li>Wait for the update to finish and enjoy the new features and improvements.</li>
|
| 223 |
-
</ol>
|
| 224 |
-
<p>Note: You can also enable auto-update for Bus Simulator Ultimate by tapping on the menu icon on the game page and selecting "Enable auto-update". This way, you will always have the latest version of the game.</p> 197e85843d<br />
|
| 225 |
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spaces/1phancelerku/anime-remove-background/Free Download Ben 10 Omniverse Rise Of Heroes Game For Pc.md
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## Free Download Ben 10 Omniverse Rise Of Heroes Game For Pc
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**CLICK HERE >>>>> [https://www.google.com/url?q=https%3A%2F%2Fbyltly.com%2F2txiBj&sa=D&sntz=1&usg=AOvVaw2ZptWE8b7GtYPRxXxLqv\_S](https://www.google.com/url?q=https%3A%2F%2Fbyltly.com%2F2txiBj&sa=D&sntz=1&usg=AOvVaw2ZptWE8b7GtYPRxXxLqv_S)**
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# How to Free Download Ben 10 Omniverse Rise Of Heroes Game For Pc
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If you are a fan of Ben 10, you might be interested in playing Ben 10 Omniverse Rise Of Heroes, a 3D action-adventure game based on the popular animated series. In this game, you can control Ben and his alien forms as you fight against the evil forces of Khyber, Malware, and Albedo. You can also team up with other players online and explore the vast Omniverse.
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But how can you get this game for free on your PC? Well, there are some websites that claim to offer free downloads of Ben 10 Omniverse Rise Of Heroes Game For Pc, but you should be careful as some of them might contain viruses or malware that can harm your computer. To avoid any risks, we recommend you to follow these steps:
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1. Go to the official website of Cartoon Network Games at [https://www.cartoonnetwork.com/games/index.html](https://www.cartoonnetwork.com/games/index.html).
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2. Search for Ben 10 Omniverse Rise Of Heroes in the search bar or browse through the categories.
|
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3. Click on the game icon and then click on the "Play Now" button.
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4. You will be redirected to a new page where you can play the game online using your browser. You will need to have Adobe Flash Player installed and enabled on your browser.
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5. If you want to download the game for offline play, you can click on the "Download" button at the bottom of the page. You will need to have a Cartoon Network account or create one for free.
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6. After downloading the game file, you can run it on your PC and enjoy playing Ben 10 Omniverse Rise Of Heroes.
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We hope this guide helped you to free download Ben 10 Omniverse Rise Of Heroes Game For Pc. Have fun and share your feedback with us!
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Ben 10 Omniverse Rise Of Heroes is not the only Ben 10 game that you can play for free on your PC. There are many other games that you can find on the Cartoon Network website, such as Ben 10 Alien Force, Ben 10 Ultimate Alien, Ben 10 Omniverse, and more. You can also check out some other games based on your favorite Cartoon Network shows, such as Adventure Time, The Amazing World of Gumball, Teen Titans Go, and more.
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Playing online games is a great way to have fun and relax, but you should also be aware of some tips to keep your PC safe and secure. Here are some of them:
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- Always use a trusted antivirus software and scan your PC regularly for any threats.
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- Never click on suspicious links or pop-ups that claim to offer free downloads or prizes.
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- Never share your personal or financial information with anyone online.
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- Always update your browser and Flash Player to the latest version.
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- Limit your screen time and take breaks to rest your eyes and body.
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By following these tips, you can enjoy playing Ben 10 Omniverse Rise Of Heroes and other online games without any worries. Have a blast!
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If you are looking for more challenges and adventures in Ben 10 Omniverse Rise Of Heroes, you can also try some of the game modes and features that are available. Here are some of them:
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<dl>
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<dt>Story Mode</dt>
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<dd>In this mode, you can follow the storyline of the game and complete various missions and quests. You can also unlock new alien forms and upgrade your abilities as you progress.</dd>
|
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<dt>Multiplayer Mode</dt>
|
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<dd>In this mode, you can join other players online and team up or compete with them in different modes, such as co-op, versus, or capture the flag. You can also chat with them and make new friends.</dd>
|
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<dt>Customization Mode</dt>
|
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<dd>In this mode, you can customize your character and your alien forms by changing their appearance, outfits, accessories, and more. You can also create your own levels and share them with other players.</dd>
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</dl>
|
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Ben 10 Omniverse Rise Of Heroes is a game that offers a lot of fun and excitement for Ben 10 fans and gamers alike. You can download it for free on your PC and play it anytime you want. Don't miss this opportunity and join the action now!
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spaces/1phancelerku/anime-remove-background/Free Download Galaxy Attack Alien Shooter - Join the Battle and Defend the Earth from Alien Threats.md
DELETED
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<h1>Galaxy Attack: Alien Shooter Free Download - A Classic Arcade Game with a Modern Twist</h1>
|
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Do you love shooting games that test your reflexes and skills? Do you enjoy blasting away alien invaders in space? Do you want to relive the nostalgia of playing Galaga, the legendary arcade game? If you answered yes to any of these questions, then you should try Galaxy Attack: Alien Shooter, a free-to-play game that combines the best of both worlds. In this article, we will tell you everything you need to know about this game, including what it is, how to download it, how to play it, and some tips and tricks to help you save the universe. <h2>What is Galaxy Attack: Alien Shooter?</h2>
|
| 4 |
-
<h3>A nostalgic space shooter game inspired by Galaga</h3>
|
| 5 |
-
Galaxy Attack: Alien Shooter is a classic arcade game that pays homage to Galaga, one of the most popular and influential games of all time. Like Galaga, Galaxy Attack: Alien Shooter is a space shooter game where you take control of a lone spaceship and protect Earth from alien swarms. Your goal is to shoot down as many enemies as possible while avoiding their attacks and collecting items along the way. The game features a simple and intuitive control scheme, a retro-style graphics, and a catchy soundtrack that will make you feel like you are playing in an arcade. <h3>A challenging and addictive gameplay with various modes and features</h3>
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| 6 |
-
Galaxy Attack: Alien Shooter is not just a mindless shooter game. It also offers a variety of modes and features that will keep you hooked for hours. The game has over 160 levels on different difficulties, each with its own objectives and challenges. You will face an increasingly large number of enemies, some of which have special abilities and behaviors. You will also encounter multiple boss battles that will test your skills and strategies. The game also has a multiplayer mode where you can compete with other players online in 1v1 or 1v3 matches. You can also join events and missions that offer exclusive rewards and bonuses. The game also has a talent system that lets you customize your spaceship with different skills and perks. <h3>A colorful and vibrant graphics with immersive sound effects</h3>
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| 7 |
-
Galaxy Attack: Alien Shooter is not just a fun game to play, but also a feast for the eyes and ears. The game boasts a colorful and vibrant graphics that create a stunning contrast between your spaceship and the dark background of space. The game also has a smooth animation and a realistic physics that make the gameplay more dynamic and exciting. The game also has an immersive sound effects that enhance the atmosphere of the game. You will hear the sound of your lasers, explosions, power-ups, and enemies as you play. The game also has a catchy soundtrack that matches the mood of each level. <h2>How to Download Galaxy Attack: Alien Shooter for Free?</h2>
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| 8 |
-
<h3>Download from the official website or app stores</h3>
|
| 9 |
-
The easiest and safest way to download Galaxy Attack: Alien Shooter for free is to visit the official website of the game or the app stores of your device. The game is available for both Android and iOS devices, as well as for web browsers (desktop and mobile). You can find the links to download the game below: - [Official website](^1^) - [Google Play Store](^2^) - [Apple App Store](^1 - [Web browser] <h3>Download from third-party sources (not recommended)</h3>
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| 10 |
-
Another way to download Galaxy Attack: Alien Shooter for free is to use third-party sources, such as APK files or modded versions of the game. However, this method is not recommended, as it may expose your device to malware, viruses, or other security risks. Moreover, you may not be able to access the latest updates, features, and events of the game. Therefore, it is better to stick to the official sources and avoid any potential problems. <h3>Download from online emulators (optional)</h3>
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| 11 |
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A third option to download Galaxy Attack: Alien Shooter for free is to use online emulators, such as Bluestacks or Nox Player. These are software that allow you to run Android apps on your PC or Mac. This way, you can enjoy the game on a bigger screen and with better controls. However, this option is optional, as it may require some extra steps and resources to set up. You can find the links to download the emulators below: - [Bluestacks] - [Nox Player] <h2>How to Play Galaxy Attack: Alien Shooter?</h2>
|
| 12 |
-
<h3>Control your spaceship with touch screen or mouse</h3>
|
| 13 |
-
The game has a simple and intuitive control scheme that anyone can learn quickly. You can control your spaceship with either your touch screen or your mouse, depending on your device. To move your spaceship, just drag it left or right on the screen or move your mouse cursor. To shoot, just tap or click anywhere on the screen. Your spaceship will automatically fire at the enemies. To pause the game, just tap or click on the pause button at the top right corner of the screen. <h3>Collect items and power-ups to upgrade your weapons and abilities</h3>
|
| 14 |
-
As you play, you will encounter various items and power-ups that will help you in your mission. These include: - Gold coins: These are the main currency of the game that you can use to buy and upgrade your ships, skills, and drones. - Crystals: These are the premium currency of the game that you can use to buy special items and features. - Power-ups: These are temporary boosts that enhance your weapons and abilities. They include: - Laser: This gives you a powerful laser beam that can pierce through multiple enemies. - Shield: This protects you from one enemy attack. - Bomb: This destroys all enemies on the screen. - Speed: This increases your movement speed. - Double: This doubles your firepower. - Magnet: This attracts all gold coins on the screen. <h3>Defeat waves of alien enemies and bosses</h3>
|
| 15 |
-
The game has over 160 levels on different difficulties, each with its own objectives and challenges. You will face an increasingly large number of enemies, some of which have special abilities and behaviors. You will also encounter multiple boss battles that will test your skills and strategies. To complete a level, you have to defeat all enemies and bosses without losing all your lives. You can earn up to three stars per level depending on your performance. <h2>Tips and Tricks to Master Galaxy Attack: Alien Shooter</h2>
|
| 16 |
-
<h3>Spend your gold and crystals wisely</h3>
|
| 17 |
-
Gold and crystals are the two currencies of the game that you can use to buy and upgrade various things. However, they are not easy to come by, so you have to spend them wisely. Here are some tips on how to do that: - Save up your gold and crystals for buying new ships and skills. These are more important than upgrading your existing ones, as they offer more benefits and variety. - Don't waste your gold and crystals on buying drones. Drones are helpful companions that assist you in battle, but they are not essential. You can get them for free by watching ads or completing missions. - Don't spend your crystals on reviving or continuing a level. It is better to retry a level than to waste your precious crystals on a temporary advantage. <h3>Understand and upgrade your ships</h3>
|
| 18 |
-
The game has over 30 different ships that you can unlock and use in battle. Each ship has its own stats, skills, and appearance. Some ships are better suited for certain levels or modes than others. Therefore, it is important to understand and upgrade your ships accordingly. Here are some tips on how to do that: - Check the stats of each ship before buying or using them. The stats include: - Damage: The amount of damage your ship can deal per shot. - Fire rate: The speed at which your ship can fire shots. - HP: The amount of health your ship has. - Speed: The speed at which your ship can move. - Check the skills of each ship before buying or using them. The skills include: - Active skill: A special ability that you can activate by tapping or clicking on the skill icon at the bottom of the screen. Each skill has a cooldown time and a different effect, such as healing, freezing, or stunning enemies. - Passive skill: A permanent ability that is always active and gives you a bonus, such as extra damage, fire rate, or HP. - Upgrade your ships with gold and crystals to improve their stats and skills. You can upgrade each ship up to 10 times, with each upgrade costing more than the previous one. - Experiment with different ships and find the ones that suit your playstyle and preferences. You can switch your ship before starting a level or a match. <h3>Use active skills and drones strategically</h3>
|
| 19 |
-
The game also has active skills and drones that can help you in battle. Active skills are special abilities that you can activate by tapping or clicking on the skill icon at the bottom of the screen. Drones are helpful companions that assist you in battle. However, both of them have limited uses and cooldown times, so you have to use them strategically. Here are some tips on how to do that: - Use your active skills when you need them most, such as when you are surrounded by enemies, facing a boss, or low on health. Don't waste them on easy enemies or when you have full health. - Choose your active skills wisely before starting a level or a match. You can equip up to two active skills per ship, and each skill has a different effect and cooldown time. Some skills are more useful than others depending on the situation, such as healing, freezing, or stunning enemies. - Use your drones wisely as well. You can equip up to two drones per ship, and each drone has a different ability and fire rate. Some drones are more effective than others depending on the enemy type, such as laser, missile, or plasma drones. - Upgrade your active skills and drones with gold and crystals to improve their effects and cooldown times. You can upgrade each skill and drone up to 10 times, with each upgrade costing more than the previous one. <h3>Join multiplayer mode and events for more rewards and fun</h3>
|
| 20 |
-
The game also has a multiplayer mode where you can compete with other players online in 1v1 or 1v3 matches. You can also join events and missions that offer exclusive rewards and bonuses. Here are some tips on how to enjoy these features: - Join multiplayer mode to test your skills and earn more gold and crystals. You can choose between two modes: PvP (player versus player) or Co-op (player versus enemy). In PvP mode, you can challenge other players in 1v1 or 1v3 matches and try to score more points than them by shooting down enemies and avoiding their attacks. In Co-op mode, you can team up with other players in 1v3 matches and try to survive as long as possible against waves of enemies. - Join events and missions to get more rewards and fun. The game regularly hosts events and missions that offer exclusive rewards and bonuses for completing certain tasks or objectives. For example, you can get special ships, skills, drones, or items by participating in seasonal events, daily missions, weekly missions, or special missions. <h2>Conclusion</h2>
|
| 21 |
-
Galaxy Attack: Alien Shooter is a classic arcade game that will bring back the nostalgia of playing Galaga while offering a modern twist with various modes and features. The game is free to download and play for both Android and iOS devices, as well as for web browsers. The game is easy to learn but hard to master, as it requires quick reflexes and smart strategies to defeat waves of alien enemies and bosses. The game also has a colorful and vibrant graphics with immersive sound effects that will make you feel like you are in an arcade. If you are looking for a fun and addictive space shooter game that will keep you entertained for hours, then Galaxy Attack: Alien Shooter is the game for you. <h2>FAQs</h2>
|
| 22 |
-
Q: How do I get more gold and crystals? A: You can get more gold and crystals by playing the game regularly, completing levels, joining multiplayer mode, participating in events and missions, watching ads, or buying them with real money. Q: How do I unlock new ships? A: You can unlock new ships by buying them with gold or crystals from the shop, or by getting them from events or missions. Q: How do I change my ship? A: You can change your ship before starting a level or a match by tapping or clicking on the ship icon at the top left corner of the screen. Q: How do I reset my progress? A: You can reset your progress by going to the settings menu (the gear icon at the top right corner of the screen) and tapping or clicking on the reset button. Q: How do I contact the developers? A: You can contact the developers by going to the settings menu (the gear icon at the top right corner of the screen) and tapping or clicking on the feedback button. I</p>
|
| 23 |
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<h2>galaxy attack alien shooter free download</h2><br /><p><b><b>Download Zip</b> →→→ <a href="https://jinyurl.com/2uNR7E">https://jinyurl.com/2uNR7E</a></b></p><br /><br /> 401be4b1e0<br />
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spaces/44ov41za8i/FreeVC/speaker_encoder/compute_embed.py
DELETED
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|
| 1 |
-
from speaker_encoder import inference as encoder
|
| 2 |
-
from multiprocessing.pool import Pool
|
| 3 |
-
from functools import partial
|
| 4 |
-
from pathlib import Path
|
| 5 |
-
# from utils import logmmse
|
| 6 |
-
# from tqdm import tqdm
|
| 7 |
-
# import numpy as np
|
| 8 |
-
# import librosa
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
def embed_utterance(fpaths, encoder_model_fpath):
|
| 12 |
-
if not encoder.is_loaded():
|
| 13 |
-
encoder.load_model(encoder_model_fpath)
|
| 14 |
-
|
| 15 |
-
# Compute the speaker embedding of the utterance
|
| 16 |
-
wav_fpath, embed_fpath = fpaths
|
| 17 |
-
wav = np.load(wav_fpath)
|
| 18 |
-
wav = encoder.preprocess_wav(wav)
|
| 19 |
-
embed = encoder.embed_utterance(wav)
|
| 20 |
-
np.save(embed_fpath, embed, allow_pickle=False)
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
def create_embeddings(outdir_root: Path, wav_dir: Path, encoder_model_fpath: Path, n_processes: int):
|
| 24 |
-
|
| 25 |
-
wav_dir = outdir_root.joinpath("audio")
|
| 26 |
-
metadata_fpath = synthesizer_root.joinpath("train.txt")
|
| 27 |
-
assert wav_dir.exists() and metadata_fpath.exists()
|
| 28 |
-
embed_dir = synthesizer_root.joinpath("embeds")
|
| 29 |
-
embed_dir.mkdir(exist_ok=True)
|
| 30 |
-
|
| 31 |
-
# Gather the input wave filepath and the target output embed filepath
|
| 32 |
-
with metadata_fpath.open("r") as metadata_file:
|
| 33 |
-
metadata = [line.split("|") for line in metadata_file]
|
| 34 |
-
fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata]
|
| 35 |
-
|
| 36 |
-
# TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here.
|
| 37 |
-
# Embed the utterances in separate threads
|
| 38 |
-
func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath)
|
| 39 |
-
job = Pool(n_processes).imap(func, fpaths)
|
| 40 |
-
list(tqdm(job, "Embedding", len(fpaths), unit="utterances"))
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spaces/7hao/bingo/src/components/chat-list.tsx
DELETED
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@@ -1,28 +0,0 @@
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|
| 1 |
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import React from 'react'
|
| 2 |
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|
| 3 |
-
import { Separator } from '@/components/ui/separator'
|
| 4 |
-
import { ChatMessage } from '@/components/chat-message'
|
| 5 |
-
import { ChatMessageModel } from '@/lib/bots/bing/types'
|
| 6 |
-
|
| 7 |
-
export interface ChatList {
|
| 8 |
-
messages: ChatMessageModel[]
|
| 9 |
-
}
|
| 10 |
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|
| 11 |
-
export function ChatList({ messages }: ChatList) {
|
| 12 |
-
if (!messages.length) {
|
| 13 |
-
return null
|
| 14 |
-
}
|
| 15 |
-
|
| 16 |
-
return (
|
| 17 |
-
<div className="chat-container relative flex flex-col">
|
| 18 |
-
{messages.map((message, index) => (
|
| 19 |
-
<React.Fragment key={index}>
|
| 20 |
-
<ChatMessage message={message} />
|
| 21 |
-
{index < messages.length - 1 && (
|
| 22 |
-
<Separator className="my-2" />
|
| 23 |
-
)}
|
| 24 |
-
</React.Fragment>
|
| 25 |
-
))}
|
| 26 |
-
</div>
|
| 27 |
-
)
|
| 28 |
-
}
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spaces/AI-Hobbyist/Hoyo-RVC/docs/faq_en.md
DELETED
|
@@ -1,95 +0,0 @@
|
|
| 1 |
-
## Q1:ffmpeg error/utf8 error.
|
| 2 |
-
It is most likely not a FFmpeg issue, but rather an audio path issue;
|
| 3 |
-
|
| 4 |
-
FFmpeg may encounter an error when reading paths containing special characters like spaces and (), which may cause an FFmpeg error; and when the training set's audio contains Chinese paths, writing it into filelist.txt may cause a utf8 error.<br>
|
| 5 |
-
|
| 6 |
-
## Q2:Cannot find index file after "One-click Training".
|
| 7 |
-
If it displays "Training is done. The program is closed," then the model has been trained successfully, and the subsequent errors are fake;
|
| 8 |
-
|
| 9 |
-
The lack of an 'added' index file after One-click training may be due to the training set being too large, causing the addition of the index to get stuck; this has been resolved by using batch processing to add the index, which solves the problem of memory overload when adding the index. As a temporary solution, try clicking the "Train Index" button again.<br>
|
| 10 |
-
|
| 11 |
-
## Q3:Cannot find the model in “Inferencing timbre” after training
|
| 12 |
-
Click “Refresh timbre list” and check again; if still not visible, check if there are any errors during training and send screenshots of the console, web UI, and logs/experiment_name/*.log to the developers for further analysis.<br>
|
| 13 |
-
|
| 14 |
-
## Q4:How to share a model/How to use others' models?
|
| 15 |
-
The pth files stored in rvc_root/logs/experiment_name are not meant for sharing or inference, but for storing the experiment checkpoits for reproducibility and further training. The model to be shared should be the 60+MB pth file in the weights folder;
|
| 16 |
-
|
| 17 |
-
In the future, weights/exp_name.pth and logs/exp_name/added_xxx.index will be merged into a single weights/exp_name.zip file to eliminate the need for manual index input; so share the zip file, not the pth file, unless you want to continue training on a different machine;
|
| 18 |
-
|
| 19 |
-
Copying/sharing the several hundred MB pth files from the logs folder to the weights folder for forced inference may result in errors such as missing f0, tgt_sr, or other keys. You need to use the ckpt tab at the bottom to manually or automatically (if the information is found in the logs/exp_name), select whether to include pitch infomation and target audio sampling rate options and then extract the smaller model. After extraction, there will be a 60+ MB pth file in the weights folder, and you can refresh the voices to use it.<br>
|
| 20 |
-
|
| 21 |
-
## Q5:Connection Error.
|
| 22 |
-
You may have closed the console (black command line window).<br>
|
| 23 |
-
|
| 24 |
-
## Q6:WebUI popup 'Expecting value: line 1 column 1 (char 0)'.
|
| 25 |
-
Please disable system LAN proxy/global proxy and then refresh.<br>
|
| 26 |
-
|
| 27 |
-
## Q7:How to train and infer without the WebUI?
|
| 28 |
-
Training script:<br>
|
| 29 |
-
You can run training in WebUI first, and the command-line versions of dataset preprocessing and training will be displayed in the message window.<br>
|
| 30 |
-
|
| 31 |
-
Inference script:<br>
|
| 32 |
-
https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/myinfer.py<br>
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
e.g.<br>
|
| 36 |
-
|
| 37 |
-
runtime\python.exe myinfer.py 0 "E:\codes\py39\RVC-beta\todo-songs\1111.wav" "E:\codes\py39\logs\mi-test\added_IVF677_Flat_nprobe_7.index" harvest "test.wav" "weights/mi-test.pth" 0.6 cuda:0 True<br>
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
f0up_key=sys.argv[1]<br>
|
| 41 |
-
input_path=sys.argv[2]<br>
|
| 42 |
-
index_path=sys.argv[3]<br>
|
| 43 |
-
f0method=sys.argv[4]#harvest or pm<br>
|
| 44 |
-
opt_path=sys.argv[5]<br>
|
| 45 |
-
model_path=sys.argv[6]<br>
|
| 46 |
-
index_rate=float(sys.argv[7])<br>
|
| 47 |
-
device=sys.argv[8]<br>
|
| 48 |
-
is_half=bool(sys.argv[9])<br>
|
| 49 |
-
|
| 50 |
-
## Q8:Cuda error/Cuda out of memory.
|
| 51 |
-
There is a small chance that there is a problem with the CUDA configuration or the device is not supported; more likely, there is not enough memory (out of memory).<br>
|
| 52 |
-
|
| 53 |
-
For training, reduce the batch size (if reducing to 1 is still not enough, you may need to change the graphics card); for inference, adjust the x_pad, x_query, x_center, and x_max settings in the config.py file as needed. 4G or lower memory cards (e.g. 1060(3G) and various 2G cards) can be abandoned, while 4G memory cards still have a chance.<br>
|
| 54 |
-
|
| 55 |
-
## Q9:How many total_epoch are optimal?
|
| 56 |
-
If the training dataset's audio quality is poor and the noise floor is high, 20-30 epochs are sufficient. Setting it too high won't improve the audio quality of your low-quality training set.<br>
|
| 57 |
-
|
| 58 |
-
If the training set audio quality is high, the noise floor is low, and there is sufficient duration, you can increase it. 200 is acceptable (since training is fast, and if you're able to prepare a high-quality training set, your GPU likely can handle a longer training duration without issue).<br>
|
| 59 |
-
|
| 60 |
-
## Q10:How much training set duration is needed?
|
| 61 |
-
|
| 62 |
-
A dataset of around 10min to 50min is recommended.<br>
|
| 63 |
-
|
| 64 |
-
With guaranteed high sound quality and low bottom noise, more can be added if the dataset's timbre is uniform.<br>
|
| 65 |
-
|
| 66 |
-
For a high-level training set (lean + distinctive tone), 5min to 10min is fine.<br>
|
| 67 |
-
|
| 68 |
-
There are some people who have trained successfully with 1min to 2min data, but the success is not reproducible by others and is not very informative. <br>This requires that the training set has a very distinctive timbre (e.g. a high-frequency airy anime girl sound) and the quality of the audio is high;
|
| 69 |
-
Data of less than 1min duration has not been successfully attempted so far. This is not recommended.<br>
|
| 70 |
-
|
| 71 |
-
|
| 72 |
-
## Q11:What is the index rate for and how to adjust it?
|
| 73 |
-
If the tone quality of the pre-trained model and inference source is higher than that of the training set, they can bring up the tone quality of the inference result, but at the cost of a possible tone bias towards the tone of the underlying model/inference source rather than the tone of the training set, which is generally referred to as "tone leakage".<br>
|
| 74 |
-
|
| 75 |
-
The index rate is used to reduce/resolve the timbre leakage problem. If the index rate is set to 1, theoretically there is no timbre leakage from the inference source and the timbre quality is more biased towards the training set. If the training set has a lower sound quality than the inference source, then a higher index rate may reduce the sound quality. Turning it down to 0 does not have the effect of using retrieval blending to protect the training set tones.<br>
|
| 76 |
-
|
| 77 |
-
If the training set has good audio quality and long duration, turn up the total_epoch, when the model itself is less likely to refer to the inferred source and the pretrained underlying model, and there is little "tone leakage", the index_rate is not important and you can even not create/share the index file.<br>
|
| 78 |
-
|
| 79 |
-
## Q12:How to choose the gpu when inferring?
|
| 80 |
-
In the config.py file, select the card number after "device cuda:".<br>
|
| 81 |
-
|
| 82 |
-
The mapping between card number and graphics card can be seen in the graphics card information section of the training tab.<br>
|
| 83 |
-
|
| 84 |
-
## Q13:How to use the model saved in the middle of training?
|
| 85 |
-
Save via model extraction at the bottom of the ckpt processing tab.
|
| 86 |
-
|
| 87 |
-
## Q14:File/memory error(when training)?
|
| 88 |
-
Too many processes and your memory is not enough. You may fix it by:
|
| 89 |
-
|
| 90 |
-
1、decrease the input in field "Threads of CPU".
|
| 91 |
-
|
| 92 |
-
2、pre-cut trainset to shorter audio files.
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
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|
spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/model.py
DELETED
|
@@ -1,936 +0,0 @@
|
|
| 1 |
-
""" CLAP Model
|
| 2 |
-
|
| 3 |
-
Adapted from CLIP: https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
|
| 4 |
-
Adapted to the Audio Task.
|
| 5 |
-
"""
|
| 6 |
-
|
| 7 |
-
from collections import OrderedDict
|
| 8 |
-
from dataclasses import dataclass
|
| 9 |
-
from email.mime import audio
|
| 10 |
-
from typing import Tuple, Union, Callable, Optional
|
| 11 |
-
|
| 12 |
-
import numpy as np
|
| 13 |
-
import torch
|
| 14 |
-
import torch.nn.functional as F
|
| 15 |
-
from torch import nn
|
| 16 |
-
|
| 17 |
-
from .timm_model import TimmModel
|
| 18 |
-
import logging
|
| 19 |
-
from .utils import freeze_batch_norm_2d
|
| 20 |
-
|
| 21 |
-
from .pann_model import create_pann_model
|
| 22 |
-
from .htsat import create_htsat_model
|
| 23 |
-
from transformers import BertModel, RobertaModel, BartModel
|
| 24 |
-
from transformers.tokenization_utils_base import BatchEncoding
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
class MLPLayers(nn.Module):
|
| 28 |
-
def __init__(self, units=[512, 512, 512], nonlin=nn.ReLU(), dropout=0.1):
|
| 29 |
-
super(MLPLayers, self).__init__()
|
| 30 |
-
self.nonlin = nonlin
|
| 31 |
-
self.dropout = dropout
|
| 32 |
-
|
| 33 |
-
sequence = []
|
| 34 |
-
for u0, u1 in zip(units[:-1], units[1:]):
|
| 35 |
-
sequence.append(nn.Linear(u0, u1))
|
| 36 |
-
sequence.append(self.nonlin)
|
| 37 |
-
sequence.append(nn.Dropout(self.dropout))
|
| 38 |
-
sequence = sequence[:-2]
|
| 39 |
-
|
| 40 |
-
self.sequential = nn.Sequential(*sequence)
|
| 41 |
-
|
| 42 |
-
def forward(self, X):
|
| 43 |
-
X = self.sequential(X)
|
| 44 |
-
return X
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
class Bottleneck(nn.Module):
|
| 48 |
-
expansion = 4
|
| 49 |
-
|
| 50 |
-
def __init__(self, inplanes, planes, stride=1):
|
| 51 |
-
super().__init__()
|
| 52 |
-
|
| 53 |
-
# all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
|
| 54 |
-
self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
|
| 55 |
-
self.bn1 = nn.BatchNorm2d(planes)
|
| 56 |
-
|
| 57 |
-
self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
|
| 58 |
-
self.bn2 = nn.BatchNorm2d(planes)
|
| 59 |
-
|
| 60 |
-
self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
|
| 61 |
-
|
| 62 |
-
self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
|
| 63 |
-
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
|
| 64 |
-
|
| 65 |
-
self.relu = nn.ReLU(inplace=True)
|
| 66 |
-
self.downsample = None
|
| 67 |
-
self.stride = stride
|
| 68 |
-
|
| 69 |
-
if stride > 1 or inplanes != planes * Bottleneck.expansion:
|
| 70 |
-
# downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
|
| 71 |
-
self.downsample = nn.Sequential(
|
| 72 |
-
OrderedDict(
|
| 73 |
-
[
|
| 74 |
-
("-1", nn.AvgPool2d(stride)),
|
| 75 |
-
(
|
| 76 |
-
"0",
|
| 77 |
-
nn.Conv2d(
|
| 78 |
-
inplanes,
|
| 79 |
-
planes * self.expansion,
|
| 80 |
-
1,
|
| 81 |
-
stride=1,
|
| 82 |
-
bias=False,
|
| 83 |
-
),
|
| 84 |
-
),
|
| 85 |
-
("1", nn.BatchNorm2d(planes * self.expansion)),
|
| 86 |
-
]
|
| 87 |
-
)
|
| 88 |
-
)
|
| 89 |
-
|
| 90 |
-
def forward(self, x: torch.Tensor):
|
| 91 |
-
identity = x
|
| 92 |
-
|
| 93 |
-
out = self.relu(self.bn1(self.conv1(x)))
|
| 94 |
-
out = self.relu(self.bn2(self.conv2(out)))
|
| 95 |
-
out = self.avgpool(out)
|
| 96 |
-
out = self.bn3(self.conv3(out))
|
| 97 |
-
|
| 98 |
-
if self.downsample is not None:
|
| 99 |
-
identity = self.downsample(x)
|
| 100 |
-
|
| 101 |
-
out += identity
|
| 102 |
-
out = self.relu(out)
|
| 103 |
-
return out
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
class AttentionPool2d(nn.Module):
|
| 107 |
-
def __init__(
|
| 108 |
-
self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None
|
| 109 |
-
):
|
| 110 |
-
super().__init__()
|
| 111 |
-
self.positional_embedding = nn.Parameter(
|
| 112 |
-
torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5
|
| 113 |
-
)
|
| 114 |
-
self.k_proj = nn.Linear(embed_dim, embed_dim)
|
| 115 |
-
self.q_proj = nn.Linear(embed_dim, embed_dim)
|
| 116 |
-
self.v_proj = nn.Linear(embed_dim, embed_dim)
|
| 117 |
-
self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
|
| 118 |
-
self.num_heads = num_heads
|
| 119 |
-
|
| 120 |
-
def forward(self, x):
|
| 121 |
-
x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(
|
| 122 |
-
2, 0, 1
|
| 123 |
-
) # NCHW -> (HW)NC
|
| 124 |
-
x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
|
| 125 |
-
x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
|
| 126 |
-
x, _ = F.multi_head_attention_forward(
|
| 127 |
-
query=x,
|
| 128 |
-
key=x,
|
| 129 |
-
value=x,
|
| 130 |
-
embed_dim_to_check=x.shape[-1],
|
| 131 |
-
num_heads=self.num_heads,
|
| 132 |
-
q_proj_weight=self.q_proj.weight,
|
| 133 |
-
k_proj_weight=self.k_proj.weight,
|
| 134 |
-
v_proj_weight=self.v_proj.weight,
|
| 135 |
-
in_proj_weight=None,
|
| 136 |
-
in_proj_bias=torch.cat(
|
| 137 |
-
[self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]
|
| 138 |
-
),
|
| 139 |
-
bias_k=None,
|
| 140 |
-
bias_v=None,
|
| 141 |
-
add_zero_attn=False,
|
| 142 |
-
dropout_p=0,
|
| 143 |
-
out_proj_weight=self.c_proj.weight,
|
| 144 |
-
out_proj_bias=self.c_proj.bias,
|
| 145 |
-
use_separate_proj_weight=True,
|
| 146 |
-
training=self.training,
|
| 147 |
-
need_weights=False,
|
| 148 |
-
)
|
| 149 |
-
|
| 150 |
-
return x[0]
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
class ModifiedResNet(nn.Module):
|
| 154 |
-
"""
|
| 155 |
-
A ResNet class that is similar to torchvision's but contains the following changes:
|
| 156 |
-
- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
|
| 157 |
-
- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
|
| 158 |
-
- The final pooling layer is a QKV attention instead of an average pool
|
| 159 |
-
"""
|
| 160 |
-
|
| 161 |
-
def __init__(self, layers, output_dim, heads, image_size=224, width=64):
|
| 162 |
-
super().__init__()
|
| 163 |
-
self.output_dim = output_dim
|
| 164 |
-
self.image_size = image_size
|
| 165 |
-
|
| 166 |
-
# the 3-layer stem
|
| 167 |
-
self.conv1 = nn.Conv2d(
|
| 168 |
-
3, width // 2, kernel_size=3, stride=2, padding=1, bias=False
|
| 169 |
-
)
|
| 170 |
-
self.bn1 = nn.BatchNorm2d(width // 2)
|
| 171 |
-
self.conv2 = nn.Conv2d(
|
| 172 |
-
width // 2, width // 2, kernel_size=3, padding=1, bias=False
|
| 173 |
-
)
|
| 174 |
-
self.bn2 = nn.BatchNorm2d(width // 2)
|
| 175 |
-
self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
|
| 176 |
-
self.bn3 = nn.BatchNorm2d(width)
|
| 177 |
-
self.avgpool = nn.AvgPool2d(2)
|
| 178 |
-
self.relu = nn.ReLU(inplace=True)
|
| 179 |
-
|
| 180 |
-
# residual layers
|
| 181 |
-
self._inplanes = width # this is a *mutable* variable used during construction
|
| 182 |
-
self.layer1 = self._make_layer(width, layers[0])
|
| 183 |
-
self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
|
| 184 |
-
self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
|
| 185 |
-
self.layer4 = self._make_layer(width * 8, layers[3], stride=2)
|
| 186 |
-
|
| 187 |
-
embed_dim = width * 32 # the ResNet feature dimension
|
| 188 |
-
self.attnpool = AttentionPool2d(image_size // 32, embed_dim, heads, output_dim)
|
| 189 |
-
|
| 190 |
-
self.init_parameters()
|
| 191 |
-
|
| 192 |
-
def _make_layer(self, planes, blocks, stride=1):
|
| 193 |
-
layers = [Bottleneck(self._inplanes, planes, stride)]
|
| 194 |
-
|
| 195 |
-
self._inplanes = planes * Bottleneck.expansion
|
| 196 |
-
for _ in range(1, blocks):
|
| 197 |
-
layers.append(Bottleneck(self._inplanes, planes))
|
| 198 |
-
|
| 199 |
-
return nn.Sequential(*layers)
|
| 200 |
-
|
| 201 |
-
def init_parameters(self):
|
| 202 |
-
if self.attnpool is not None:
|
| 203 |
-
std = self.attnpool.c_proj.in_features**-0.5
|
| 204 |
-
nn.init.normal_(self.attnpool.q_proj.weight, std=std)
|
| 205 |
-
nn.init.normal_(self.attnpool.k_proj.weight, std=std)
|
| 206 |
-
nn.init.normal_(self.attnpool.v_proj.weight, std=std)
|
| 207 |
-
nn.init.normal_(self.attnpool.c_proj.weight, std=std)
|
| 208 |
-
|
| 209 |
-
for resnet_block in [self.layer1, self.layer2, self.layer3, self.layer4]:
|
| 210 |
-
for name, param in resnet_block.named_parameters():
|
| 211 |
-
if name.endswith("bn3.weight"):
|
| 212 |
-
nn.init.zeros_(param)
|
| 213 |
-
|
| 214 |
-
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
|
| 215 |
-
assert (
|
| 216 |
-
unlocked_groups == 0
|
| 217 |
-
), "partial locking not currently supported for this model"
|
| 218 |
-
for param in self.parameters():
|
| 219 |
-
param.requires_grad = False
|
| 220 |
-
if freeze_bn_stats:
|
| 221 |
-
freeze_batch_norm_2d(self)
|
| 222 |
-
|
| 223 |
-
def stem(self, x):
|
| 224 |
-
for conv, bn in [
|
| 225 |
-
(self.conv1, self.bn1),
|
| 226 |
-
(self.conv2, self.bn2),
|
| 227 |
-
(self.conv3, self.bn3),
|
| 228 |
-
]:
|
| 229 |
-
x = self.relu(bn(conv(x)))
|
| 230 |
-
x = self.avgpool(x)
|
| 231 |
-
return x
|
| 232 |
-
|
| 233 |
-
def forward(self, x):
|
| 234 |
-
x = self.stem(x)
|
| 235 |
-
x = self.layer1(x)
|
| 236 |
-
x = self.layer2(x)
|
| 237 |
-
x = self.layer3(x)
|
| 238 |
-
x = self.layer4(x)
|
| 239 |
-
x = self.attnpool(x)
|
| 240 |
-
|
| 241 |
-
return x
|
| 242 |
-
|
| 243 |
-
|
| 244 |
-
class LayerNorm(nn.LayerNorm):
|
| 245 |
-
"""Subclass torch's LayerNorm to handle fp16."""
|
| 246 |
-
|
| 247 |
-
def forward(self, x: torch.Tensor):
|
| 248 |
-
orig_type = x.dtype
|
| 249 |
-
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
|
| 250 |
-
return x.to(orig_type)
|
| 251 |
-
|
| 252 |
-
|
| 253 |
-
class QuickGELU(nn.Module):
|
| 254 |
-
# NOTE This is slower than nn.GELU or nn.SiLU and uses more GPU memory
|
| 255 |
-
def forward(self, x: torch.Tensor):
|
| 256 |
-
return x * torch.sigmoid(1.702 * x)
|
| 257 |
-
|
| 258 |
-
|
| 259 |
-
class ResidualAttentionBlock(nn.Module):
|
| 260 |
-
def __init__(self, d_model: int, n_head: int, act_layer: Callable = nn.GELU):
|
| 261 |
-
super().__init__()
|
| 262 |
-
|
| 263 |
-
self.attn = nn.MultiheadAttention(d_model, n_head)
|
| 264 |
-
self.ln_1 = LayerNorm(d_model)
|
| 265 |
-
self.mlp = nn.Sequential(
|
| 266 |
-
OrderedDict(
|
| 267 |
-
[
|
| 268 |
-
("c_fc", nn.Linear(d_model, d_model * 4)),
|
| 269 |
-
("gelu", act_layer()),
|
| 270 |
-
("c_proj", nn.Linear(d_model * 4, d_model)),
|
| 271 |
-
]
|
| 272 |
-
)
|
| 273 |
-
)
|
| 274 |
-
self.ln_2 = LayerNorm(d_model)
|
| 275 |
-
|
| 276 |
-
def attention(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
|
| 277 |
-
return self.attn(x, x, x, need_weights=False, attn_mask=attn_mask)[0]
|
| 278 |
-
|
| 279 |
-
def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
|
| 280 |
-
x = x + self.attention(self.ln_1(x), attn_mask=attn_mask)
|
| 281 |
-
x = x + self.mlp(self.ln_2(x))
|
| 282 |
-
return x
|
| 283 |
-
|
| 284 |
-
|
| 285 |
-
class Transformer(nn.Module):
|
| 286 |
-
def __init__(
|
| 287 |
-
self, width: int, layers: int, heads: int, act_layer: Callable = nn.GELU
|
| 288 |
-
):
|
| 289 |
-
super().__init__()
|
| 290 |
-
self.width = width
|
| 291 |
-
self.layers = layers
|
| 292 |
-
self.resblocks = nn.ModuleList(
|
| 293 |
-
[
|
| 294 |
-
ResidualAttentionBlock(width, heads, act_layer=act_layer)
|
| 295 |
-
for _ in range(layers)
|
| 296 |
-
]
|
| 297 |
-
)
|
| 298 |
-
|
| 299 |
-
def forward(self, x: torch.Tensor, attn_mask: Optional[torch.Tensor] = None):
|
| 300 |
-
for r in self.resblocks:
|
| 301 |
-
x = r(x, attn_mask=attn_mask)
|
| 302 |
-
return x
|
| 303 |
-
|
| 304 |
-
|
| 305 |
-
class VisualTransformer(nn.Module):
|
| 306 |
-
def __init__(
|
| 307 |
-
self,
|
| 308 |
-
image_size: int,
|
| 309 |
-
patch_size: int,
|
| 310 |
-
width: int,
|
| 311 |
-
layers: int,
|
| 312 |
-
heads: int,
|
| 313 |
-
output_dim: int,
|
| 314 |
-
act_layer: Callable = nn.GELU,
|
| 315 |
-
):
|
| 316 |
-
super().__init__()
|
| 317 |
-
self.image_size = image_size
|
| 318 |
-
self.output_dim = output_dim
|
| 319 |
-
self.conv1 = nn.Conv2d(
|
| 320 |
-
in_channels=3,
|
| 321 |
-
out_channels=width,
|
| 322 |
-
kernel_size=patch_size,
|
| 323 |
-
stride=patch_size,
|
| 324 |
-
bias=False,
|
| 325 |
-
)
|
| 326 |
-
|
| 327 |
-
scale = width**-0.5
|
| 328 |
-
self.class_embedding = nn.Parameter(scale * torch.randn(width))
|
| 329 |
-
self.positional_embedding = nn.Parameter(
|
| 330 |
-
scale * torch.randn((image_size // patch_size) ** 2 + 1, width)
|
| 331 |
-
)
|
| 332 |
-
self.ln_pre = LayerNorm(width)
|
| 333 |
-
|
| 334 |
-
self.text_branch = Transformer(width, layers, heads, act_layer=act_layer)
|
| 335 |
-
|
| 336 |
-
self.ln_post = LayerNorm(width)
|
| 337 |
-
self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
|
| 338 |
-
|
| 339 |
-
def lock(self, unlocked_groups=0, freeze_bn_stats=False):
|
| 340 |
-
assert (
|
| 341 |
-
unlocked_groups == 0
|
| 342 |
-
), "partial locking not currently supported for this model"
|
| 343 |
-
for param in self.parameters():
|
| 344 |
-
param.requires_grad = False
|
| 345 |
-
|
| 346 |
-
def forward(self, x: torch.Tensor):
|
| 347 |
-
x = self.conv1(x) # shape = [*, width, grid, grid]
|
| 348 |
-
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
|
| 349 |
-
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
|
| 350 |
-
x = torch.cat(
|
| 351 |
-
[
|
| 352 |
-
self.class_embedding.to(x.dtype)
|
| 353 |
-
+ torch.zeros(
|
| 354 |
-
x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device
|
| 355 |
-
),
|
| 356 |
-
x,
|
| 357 |
-
],
|
| 358 |
-
dim=1,
|
| 359 |
-
) # shape = [*, grid ** 2 + 1, width]
|
| 360 |
-
x = x + self.positional_embedding.to(x.dtype)
|
| 361 |
-
x = self.ln_pre(x)
|
| 362 |
-
|
| 363 |
-
x = x.permute(1, 0, 2) # NLD -> LND
|
| 364 |
-
x = self.text_branch(x)
|
| 365 |
-
x = x.permute(1, 0, 2) # LND -> NLD
|
| 366 |
-
|
| 367 |
-
x = self.ln_post(x[:, 0, :])
|
| 368 |
-
|
| 369 |
-
if self.proj is not None:
|
| 370 |
-
x = x @ self.proj
|
| 371 |
-
|
| 372 |
-
return x
|
| 373 |
-
|
| 374 |
-
|
| 375 |
-
@dataclass
|
| 376 |
-
class CLAPVisionCfg:
|
| 377 |
-
layers: Union[Tuple[int, int, int, int], int] = 12
|
| 378 |
-
width: int = 768
|
| 379 |
-
patch_size: int = 16
|
| 380 |
-
image_size: Union[Tuple[int, int], int] = 224
|
| 381 |
-
timm_model_name: str = (
|
| 382 |
-
None # a valid model name overrides layers, width, patch_size
|
| 383 |
-
)
|
| 384 |
-
timm_model_pretrained: bool = (
|
| 385 |
-
False # use (imagenet) pretrained weights for named model
|
| 386 |
-
)
|
| 387 |
-
timm_pool: str = (
|
| 388 |
-
"avg" # feature pooling for timm model ('abs_attn', 'rot_attn', 'avg', '')
|
| 389 |
-
)
|
| 390 |
-
timm_proj: str = (
|
| 391 |
-
"linear" # linear projection for timm model output ('linear', 'mlp', '')
|
| 392 |
-
)
|
| 393 |
-
|
| 394 |
-
|
| 395 |
-
# Audio Config Class
|
| 396 |
-
@dataclass
|
| 397 |
-
class CLAPAudioCfp:
|
| 398 |
-
model_type: str = "PANN"
|
| 399 |
-
model_name: str = "Cnn14"
|
| 400 |
-
sample_rate: int = 48000
|
| 401 |
-
# Param
|
| 402 |
-
audio_length: int = 1024
|
| 403 |
-
window_size: int = 1024
|
| 404 |
-
hop_size: int = 1024
|
| 405 |
-
fmin: int = 50
|
| 406 |
-
fmax: int = 14000
|
| 407 |
-
class_num: int = 527
|
| 408 |
-
mel_bins: int = 64
|
| 409 |
-
clip_samples: int = 480000
|
| 410 |
-
|
| 411 |
-
|
| 412 |
-
@dataclass
|
| 413 |
-
class CLAPTextCfg:
|
| 414 |
-
context_length: int
|
| 415 |
-
vocab_size: int
|
| 416 |
-
width: int
|
| 417 |
-
heads: int
|
| 418 |
-
layers: int
|
| 419 |
-
model_type: str
|
| 420 |
-
|
| 421 |
-
|
| 422 |
-
class CLAP(nn.Module):
|
| 423 |
-
def __init__(
|
| 424 |
-
self,
|
| 425 |
-
embed_dim: int,
|
| 426 |
-
audio_cfg: CLAPAudioCfp,
|
| 427 |
-
text_cfg: CLAPTextCfg,
|
| 428 |
-
quick_gelu: bool = False,
|
| 429 |
-
enable_fusion: bool = False,
|
| 430 |
-
fusion_type: str = "None",
|
| 431 |
-
joint_embed_shape: int = 512,
|
| 432 |
-
mlp_act: str = "relu",
|
| 433 |
-
):
|
| 434 |
-
super().__init__()
|
| 435 |
-
if isinstance(audio_cfg, dict):
|
| 436 |
-
audio_cfg = CLAPAudioCfp(**audio_cfg)
|
| 437 |
-
if isinstance(text_cfg, dict):
|
| 438 |
-
text_cfg = CLAPTextCfg(**text_cfg)
|
| 439 |
-
|
| 440 |
-
self.audio_cfg = audio_cfg
|
| 441 |
-
self.text_cfg = text_cfg
|
| 442 |
-
self.enable_fusion = enable_fusion
|
| 443 |
-
self.fusion_type = fusion_type
|
| 444 |
-
self.joint_embed_shape = joint_embed_shape
|
| 445 |
-
self.mlp_act = mlp_act
|
| 446 |
-
|
| 447 |
-
self.context_length = text_cfg.context_length
|
| 448 |
-
|
| 449 |
-
# OpenAI models are pretrained w/ QuickGELU but native nn.GELU is both faster and more
|
| 450 |
-
# memory efficient in recent PyTorch releases (>= 1.10).
|
| 451 |
-
# NOTE: timm models always use native GELU regardless of quick_gelu flag.
|
| 452 |
-
act_layer = QuickGELU if quick_gelu else nn.GELU
|
| 453 |
-
|
| 454 |
-
if mlp_act == "relu":
|
| 455 |
-
mlp_act_layer = nn.ReLU()
|
| 456 |
-
elif mlp_act == "gelu":
|
| 457 |
-
mlp_act_layer = nn.GELU()
|
| 458 |
-
else:
|
| 459 |
-
raise NotImplementedError
|
| 460 |
-
|
| 461 |
-
# audio branch
|
| 462 |
-
# audio branch parameters
|
| 463 |
-
if audio_cfg.model_type == "PANN":
|
| 464 |
-
self.audio_branch = create_pann_model(audio_cfg, enable_fusion, fusion_type)
|
| 465 |
-
elif audio_cfg.model_type == "HTSAT":
|
| 466 |
-
self.audio_branch = create_htsat_model(
|
| 467 |
-
audio_cfg, enable_fusion, fusion_type
|
| 468 |
-
)
|
| 469 |
-
else:
|
| 470 |
-
logging.error(f"Model config for {audio_cfg.model_type} not found")
|
| 471 |
-
raise RuntimeError(f"Model config for {audio_cfg.model_type} not found.")
|
| 472 |
-
|
| 473 |
-
# text branch
|
| 474 |
-
# text branch parameters
|
| 475 |
-
if text_cfg.model_type == "transformer":
|
| 476 |
-
self.text_branch = Transformer(
|
| 477 |
-
width=text_cfg.width,
|
| 478 |
-
layers=text_cfg.layers,
|
| 479 |
-
heads=text_cfg.heads,
|
| 480 |
-
act_layer=act_layer,
|
| 481 |
-
)
|
| 482 |
-
self.vocab_size = text_cfg.vocab_size
|
| 483 |
-
self.token_embedding = nn.Embedding(text_cfg.vocab_size, text_cfg.width)
|
| 484 |
-
self.positional_embedding = nn.Parameter(
|
| 485 |
-
torch.empty(self.context_length, text_cfg.width)
|
| 486 |
-
)
|
| 487 |
-
self.ln_final = LayerNorm(text_cfg.width)
|
| 488 |
-
self.text_transform = MLPLayers(
|
| 489 |
-
units=[
|
| 490 |
-
self.joint_embed_shape,
|
| 491 |
-
self.joint_embed_shape,
|
| 492 |
-
self.joint_embed_shape,
|
| 493 |
-
],
|
| 494 |
-
dropout=0.1,
|
| 495 |
-
)
|
| 496 |
-
self.text_projection = nn.Sequential(
|
| 497 |
-
nn.Linear(text_cfg.width, self.joint_embed_shape),
|
| 498 |
-
mlp_act_layer,
|
| 499 |
-
nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
|
| 500 |
-
)
|
| 501 |
-
elif text_cfg.model_type == "bert":
|
| 502 |
-
self.text_branch = BertModel.from_pretrained("bert-base-uncased")
|
| 503 |
-
self.text_transform = MLPLayers(
|
| 504 |
-
units=[
|
| 505 |
-
self.joint_embed_shape,
|
| 506 |
-
self.joint_embed_shape,
|
| 507 |
-
self.joint_embed_shape,
|
| 508 |
-
],
|
| 509 |
-
dropout=0.1,
|
| 510 |
-
)
|
| 511 |
-
self.text_projection = nn.Sequential(
|
| 512 |
-
nn.Linear(768, self.joint_embed_shape),
|
| 513 |
-
mlp_act_layer,
|
| 514 |
-
nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
|
| 515 |
-
)
|
| 516 |
-
elif text_cfg.model_type == "roberta":
|
| 517 |
-
self.text_branch = RobertaModel.from_pretrained("roberta-base")
|
| 518 |
-
self.text_transform = MLPLayers(
|
| 519 |
-
units=[
|
| 520 |
-
self.joint_embed_shape,
|
| 521 |
-
self.joint_embed_shape,
|
| 522 |
-
self.joint_embed_shape,
|
| 523 |
-
],
|
| 524 |
-
dropout=0.1,
|
| 525 |
-
)
|
| 526 |
-
self.text_projection = nn.Sequential(
|
| 527 |
-
nn.Linear(768, self.joint_embed_shape),
|
| 528 |
-
mlp_act_layer,
|
| 529 |
-
nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
|
| 530 |
-
)
|
| 531 |
-
elif text_cfg.model_type == "bart":
|
| 532 |
-
self.text_branch = BartModel.from_pretrained("facebook/bart-base")
|
| 533 |
-
self.text_transform = MLPLayers(
|
| 534 |
-
units=[
|
| 535 |
-
self.joint_embed_shape,
|
| 536 |
-
self.joint_embed_shape,
|
| 537 |
-
self.joint_embed_shape,
|
| 538 |
-
],
|
| 539 |
-
dropout=0.1,
|
| 540 |
-
)
|
| 541 |
-
self.text_projection = nn.Sequential(
|
| 542 |
-
nn.Linear(768, self.joint_embed_shape),
|
| 543 |
-
mlp_act_layer,
|
| 544 |
-
nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
|
| 545 |
-
)
|
| 546 |
-
else:
|
| 547 |
-
logging.error(f"Model config for {text_cfg.model_type} not found")
|
| 548 |
-
raise RuntimeError(f"Model config for {text_cfg.model_type} not found.")
|
| 549 |
-
self.text_branch_type = text_cfg.model_type
|
| 550 |
-
# text branch parameters
|
| 551 |
-
|
| 552 |
-
# audio branch parameters
|
| 553 |
-
self.audio_transform = MLPLayers(
|
| 554 |
-
units=[
|
| 555 |
-
self.joint_embed_shape,
|
| 556 |
-
self.joint_embed_shape,
|
| 557 |
-
self.joint_embed_shape,
|
| 558 |
-
],
|
| 559 |
-
dropout=0.1,
|
| 560 |
-
)
|
| 561 |
-
|
| 562 |
-
# below here is text branch parameters
|
| 563 |
-
|
| 564 |
-
# ============================================================================================================
|
| 565 |
-
self.audio_projection = nn.Sequential(
|
| 566 |
-
nn.Linear(embed_dim, self.joint_embed_shape),
|
| 567 |
-
mlp_act_layer,
|
| 568 |
-
nn.Linear(self.joint_embed_shape, self.joint_embed_shape),
|
| 569 |
-
)
|
| 570 |
-
|
| 571 |
-
self.logit_scale_a = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
| 572 |
-
self.logit_scale_t = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
|
| 573 |
-
self.register_buffer("attn_mask", self.build_attention_mask(), persistent=False)
|
| 574 |
-
|
| 575 |
-
self.init_text_branch_parameters()
|
| 576 |
-
|
| 577 |
-
def init_text_branch_parameters(self):
|
| 578 |
-
if self.text_branch_type == "transformer":
|
| 579 |
-
nn.init.normal_(self.token_embedding.weight, std=0.02)
|
| 580 |
-
nn.init.normal_(self.positional_embedding, std=0.01)
|
| 581 |
-
proj_std = (self.text_branch.width**-0.5) * (
|
| 582 |
-
(2 * self.text_branch.layers) ** -0.5
|
| 583 |
-
)
|
| 584 |
-
attn_std = self.text_branch.width**-0.5
|
| 585 |
-
fc_std = (2 * self.text_branch.width) ** -0.5
|
| 586 |
-
for block in self.text_branch.resblocks:
|
| 587 |
-
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
|
| 588 |
-
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
|
| 589 |
-
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
|
| 590 |
-
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
|
| 591 |
-
if self.text_branch_type == "bert" or self.text_branch_type == "roberta":
|
| 592 |
-
width = self.text_branch.embeddings.word_embeddings.weight.shape[-1]
|
| 593 |
-
elif self.text_branch_type == "bart":
|
| 594 |
-
width = self.text_branch.shared.weight.shape[-1]
|
| 595 |
-
else:
|
| 596 |
-
width = self.text_branch.width
|
| 597 |
-
nn.init.constant_(self.logit_scale_a, np.log(1 / 0.07))
|
| 598 |
-
nn.init.constant_(self.logit_scale_t, np.log(1 / 0.07))
|
| 599 |
-
|
| 600 |
-
# deprecated
|
| 601 |
-
# if hasattr(self.visual, 'init_parameters'):
|
| 602 |
-
# self.visual.init_parameters()
|
| 603 |
-
|
| 604 |
-
# if self.text_projection is not None:
|
| 605 |
-
# nn.init.normal_(self.text_projection, std=width**-0.5)
|
| 606 |
-
|
| 607 |
-
def build_attention_mask(self):
|
| 608 |
-
# lazily create causal attention mask, with full attention between the vision tokens
|
| 609 |
-
# pytorch uses additive attention mask; fill with -inf
|
| 610 |
-
mask = torch.empty(self.context_length, self.context_length)
|
| 611 |
-
mask.fill_(float("-inf"))
|
| 612 |
-
mask.triu_(1) # zero out the lower diagonal
|
| 613 |
-
return mask
|
| 614 |
-
|
| 615 |
-
def encode_audio(self, audio, device):
|
| 616 |
-
return self.audio_branch(
|
| 617 |
-
audio, mixup_lambda=None, device=device
|
| 618 |
-
) # mix lambda needs to add
|
| 619 |
-
|
| 620 |
-
# def list_of_dict_of_tensor2dict_of_tensor(self, x, device):
|
| 621 |
-
# tmp = {}
|
| 622 |
-
# for k in x[0].keys():
|
| 623 |
-
# tmp[k] = []
|
| 624 |
-
# for i in range(len(x)):
|
| 625 |
-
# tmp[k].append(x[i][k][:77])
|
| 626 |
-
# for k in x[0].keys():
|
| 627 |
-
# tmp[k] = torch.tensor(tmp[k]).to(device=device, non_blocking=True)
|
| 628 |
-
# return tmp
|
| 629 |
-
|
| 630 |
-
def encode_text(self, text, device):
|
| 631 |
-
if self.text_branch_type == "transformer":
|
| 632 |
-
text = text.to(device=device, non_blocking=True)
|
| 633 |
-
x = self.token_embedding(text) # [batch_size, n_ctx, d_model]
|
| 634 |
-
|
| 635 |
-
x = x + self.positional_embedding
|
| 636 |
-
x = x.permute(1, 0, 2) # NLD -> LND
|
| 637 |
-
x = self.text_branch(x, attn_mask=self.attn_mask)
|
| 638 |
-
x = x.permute(1, 0, 2) # LND -> NLD
|
| 639 |
-
x = self.ln_final(x)
|
| 640 |
-
|
| 641 |
-
# x.shape = [batch_size, n_ctx, transformer.width]
|
| 642 |
-
# take features from the eot embedding (eot_token is the highest number in each sequence)
|
| 643 |
-
x = self.text_projection(x[torch.arange(x.shape[0]), text.argmax(dim=-1)])
|
| 644 |
-
elif self.text_branch_type == "bert":
|
| 645 |
-
# text = self.list_of_dict_of_tensor2dict_of_tensor(text, device)
|
| 646 |
-
# text = BatchEncoding(text)
|
| 647 |
-
x = self.text_branch(
|
| 648 |
-
input_ids=text["input_ids"].to(device=device, non_blocking=True),
|
| 649 |
-
attention_mask=text["attention_mask"].to(
|
| 650 |
-
device=device, non_blocking=True
|
| 651 |
-
),
|
| 652 |
-
token_type_ids=text["token_type_ids"].to(
|
| 653 |
-
device=device, non_blocking=True
|
| 654 |
-
),
|
| 655 |
-
)["pooler_output"]
|
| 656 |
-
x = self.text_projection(x)
|
| 657 |
-
elif self.text_branch_type == "roberta":
|
| 658 |
-
x = self.text_branch(
|
| 659 |
-
input_ids=text["input_ids"].to(device=device, non_blocking=True),
|
| 660 |
-
attention_mask=text["attention_mask"].to(
|
| 661 |
-
device=device, non_blocking=True
|
| 662 |
-
),
|
| 663 |
-
)["pooler_output"]
|
| 664 |
-
x = self.text_projection(x)
|
| 665 |
-
elif self.text_branch_type == "bart":
|
| 666 |
-
x = torch.mean(
|
| 667 |
-
self.text_branch(
|
| 668 |
-
input_ids=text["input_ids"].to(device=device, non_blocking=True),
|
| 669 |
-
attention_mask=text["attention_mask"].to(
|
| 670 |
-
device=device, non_blocking=True
|
| 671 |
-
),
|
| 672 |
-
)["encoder_last_hidden_state"],
|
| 673 |
-
axis=1,
|
| 674 |
-
)
|
| 675 |
-
x = self.text_projection(x)
|
| 676 |
-
else:
|
| 677 |
-
logging.error(f"Model type {self.text_branch_type} not found")
|
| 678 |
-
raise RuntimeError(f"Model type {self.text_branch_type} not found.")
|
| 679 |
-
return x
|
| 680 |
-
|
| 681 |
-
def forward(self, audio, text, device=None):
|
| 682 |
-
"""Forward audio and text into the CLAP
|
| 683 |
-
|
| 684 |
-
Parameters
|
| 685 |
-
----------
|
| 686 |
-
audio: torch.Tensor (batch_size, audio_length)
|
| 687 |
-
the time-domain audio input / the batch of mel_spec and longer list.
|
| 688 |
-
text: torch.Tensor () // need to add
|
| 689 |
-
the text token input
|
| 690 |
-
"""
|
| 691 |
-
if device is None:
|
| 692 |
-
if audio is not None:
|
| 693 |
-
device = audio.device
|
| 694 |
-
elif text is not None:
|
| 695 |
-
device = text.device
|
| 696 |
-
if audio is None and text is None:
|
| 697 |
-
# a hack to get the logit scale
|
| 698 |
-
return self.logit_scale_a.exp(), self.logit_scale_t.exp()
|
| 699 |
-
elif audio is None:
|
| 700 |
-
return self.encode_text(text, device=device)
|
| 701 |
-
elif text is None:
|
| 702 |
-
return self.audio_projection(
|
| 703 |
-
self.encode_audio(audio, device=device)["embedding"]
|
| 704 |
-
)
|
| 705 |
-
audio_features = self.audio_projection(
|
| 706 |
-
self.encode_audio(audio, device=device)["embedding"]
|
| 707 |
-
)
|
| 708 |
-
audio_features = F.normalize(audio_features, dim=-1)
|
| 709 |
-
|
| 710 |
-
text_features = self.encode_text(text, device=device)
|
| 711 |
-
# print("text_features", text_features)
|
| 712 |
-
# print("text_features.shape", text_features.shape)
|
| 713 |
-
# print("text_features.type", type(text_features))
|
| 714 |
-
text_features = F.normalize(text_features, dim=-1)
|
| 715 |
-
|
| 716 |
-
audio_features_mlp = self.audio_transform(audio_features)
|
| 717 |
-
text_features_mlp = self.text_transform(text_features)
|
| 718 |
-
# Four outputs: audio features (basic & MLP), text features (basic & MLP)
|
| 719 |
-
return (
|
| 720 |
-
audio_features,
|
| 721 |
-
text_features,
|
| 722 |
-
audio_features_mlp,
|
| 723 |
-
text_features_mlp,
|
| 724 |
-
self.logit_scale_a.exp(),
|
| 725 |
-
self.logit_scale_t.exp(),
|
| 726 |
-
)
|
| 727 |
-
|
| 728 |
-
def get_logit_scale(self):
|
| 729 |
-
return self.logit_scale_a.exp(), self.logit_scale_t.exp()
|
| 730 |
-
|
| 731 |
-
def get_text_embedding(self, data):
|
| 732 |
-
"""Get the text embedding from the model
|
| 733 |
-
|
| 734 |
-
Parameters
|
| 735 |
-
----------
|
| 736 |
-
data: torch.Tensor
|
| 737 |
-
a tensor of text embedding
|
| 738 |
-
|
| 739 |
-
Returns
|
| 740 |
-
----------
|
| 741 |
-
text_embed: torch.Tensor
|
| 742 |
-
a tensor of text_embeds (N, D)
|
| 743 |
-
|
| 744 |
-
"""
|
| 745 |
-
device = next(self.parameters()).device
|
| 746 |
-
for k in data:
|
| 747 |
-
data[k] = data[k].to(device)
|
| 748 |
-
if(len(data[k].size()) < 2):
|
| 749 |
-
data[k] = data[k].unsqueeze(0)
|
| 750 |
-
text_embeds = self.encode_text(data, device=device)
|
| 751 |
-
text_embeds = F.normalize(text_embeds, dim=-1)
|
| 752 |
-
|
| 753 |
-
return text_embeds
|
| 754 |
-
|
| 755 |
-
def get_audio_embedding(self, data):
|
| 756 |
-
"""Get the audio embedding from the model
|
| 757 |
-
|
| 758 |
-
Parameters
|
| 759 |
-
----------
|
| 760 |
-
data: a list of dict
|
| 761 |
-
the audio input dict list from 'get_audio_feature' method
|
| 762 |
-
|
| 763 |
-
Returns
|
| 764 |
-
----------
|
| 765 |
-
audio_embed: torch.Tensor
|
| 766 |
-
a tensor of audio_embeds (N, D)
|
| 767 |
-
|
| 768 |
-
"""
|
| 769 |
-
device = next(self.parameters()).device
|
| 770 |
-
input_dict = {}
|
| 771 |
-
keys = data[0].keys()
|
| 772 |
-
for k in keys:
|
| 773 |
-
input_dict[k] = torch.cat([d[k].unsqueeze(0) for d in data], dim=0).to(
|
| 774 |
-
device
|
| 775 |
-
)
|
| 776 |
-
|
| 777 |
-
audio_embeds = self.audio_projection(
|
| 778 |
-
self.encode_audio(input_dict, device=device)["embedding"]
|
| 779 |
-
)
|
| 780 |
-
audio_embeds = F.normalize(audio_embeds, dim=-1)
|
| 781 |
-
|
| 782 |
-
return audio_embeds
|
| 783 |
-
|
| 784 |
-
def audio_infer(self, audio, hopsize=None, device=None):
|
| 785 |
-
"""Forward one audio and produce the audio embedding
|
| 786 |
-
|
| 787 |
-
Parameters
|
| 788 |
-
----------
|
| 789 |
-
audio: (audio_length)
|
| 790 |
-
the time-domain audio input, notice that it must be only one input
|
| 791 |
-
hopsize: int
|
| 792 |
-
the overlap hopsize as the sliding window
|
| 793 |
-
|
| 794 |
-
Returns
|
| 795 |
-
----------
|
| 796 |
-
output_dict: {
|
| 797 |
-
key: [n, (embedding_shape)] if "HTS-AT"
|
| 798 |
-
or
|
| 799 |
-
key: [(embedding_shape)] if "PANN"
|
| 800 |
-
}
|
| 801 |
-
the list of key values of the audio branch
|
| 802 |
-
|
| 803 |
-
"""
|
| 804 |
-
|
| 805 |
-
assert not self.training, "the inference mode must be run at eval stage"
|
| 806 |
-
output_dict = {}
|
| 807 |
-
# PANN
|
| 808 |
-
if self.audio_cfg.model_type == "PANN":
|
| 809 |
-
audio_input = audio.unsqueeze(dim=0)
|
| 810 |
-
output_dict[key] = self.encode_audio(audio_input, device=device)[
|
| 811 |
-
key
|
| 812 |
-
].squeeze(dim=0)
|
| 813 |
-
elif self.audio_cfg.model_type == "HTSAT":
|
| 814 |
-
# repeat
|
| 815 |
-
audio_len = len(audio)
|
| 816 |
-
k = self.audio_cfg.clip_samples // audio_len
|
| 817 |
-
if k > 1:
|
| 818 |
-
audio = audio.repeat(k)
|
| 819 |
-
audio_len = len(audio)
|
| 820 |
-
|
| 821 |
-
if hopsize is None:
|
| 822 |
-
hopsize = min(hopsize, audio_len)
|
| 823 |
-
|
| 824 |
-
if audio_len > self.audio_cfg.clip_samples:
|
| 825 |
-
audio_input = [
|
| 826 |
-
audio[pos : pos + self.audio_cfg.clip_samples].clone()
|
| 827 |
-
for pos in range(
|
| 828 |
-
0, audio_len - self.audio_cfg.clip_samples, hopsize
|
| 829 |
-
)
|
| 830 |
-
]
|
| 831 |
-
audio_input.append(audio[-self.audio_cfg.clip_samples :].clone())
|
| 832 |
-
audio_input = torch.stack(audio_input)
|
| 833 |
-
output_dict[key] = self.encode_audio(audio_input, device=device)[key]
|
| 834 |
-
else:
|
| 835 |
-
audio_input = audio.unsqueeze(dim=0)
|
| 836 |
-
output_dict[key] = self.encode_audio(audio_input, device=device)[
|
| 837 |
-
key
|
| 838 |
-
].squeeze(dim=0)
|
| 839 |
-
|
| 840 |
-
return output_dict
|
| 841 |
-
|
| 842 |
-
|
| 843 |
-
def convert_weights_to_fp16(model: nn.Module):
|
| 844 |
-
"""Convert applicable model parameters to fp16"""
|
| 845 |
-
|
| 846 |
-
def _convert_weights_to_fp16(l):
|
| 847 |
-
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
|
| 848 |
-
l.weight.data = l.weight.data.half()
|
| 849 |
-
if l.bias is not None:
|
| 850 |
-
l.bias.data = l.bias.data.half()
|
| 851 |
-
|
| 852 |
-
if isinstance(l, nn.MultiheadAttention):
|
| 853 |
-
for attr in [
|
| 854 |
-
*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]],
|
| 855 |
-
"in_proj_bias",
|
| 856 |
-
"bias_k",
|
| 857 |
-
"bias_v",
|
| 858 |
-
]:
|
| 859 |
-
tensor = getattr(l, attr)
|
| 860 |
-
if tensor is not None:
|
| 861 |
-
tensor.data = tensor.data.half()
|
| 862 |
-
|
| 863 |
-
for name in ["text_projection", "proj"]:
|
| 864 |
-
if hasattr(l, name):
|
| 865 |
-
attr = getattr(l, name)
|
| 866 |
-
if attr is not None:
|
| 867 |
-
attr.data = attr.data.half()
|
| 868 |
-
|
| 869 |
-
model.apply(_convert_weights_to_fp16)
|
| 870 |
-
|
| 871 |
-
|
| 872 |
-
# Ignore the state dict of the vision part
|
| 873 |
-
def build_model_from_openai_state_dict(
|
| 874 |
-
state_dict: dict, model_cfg, enable_fusion: bool = False, fusion_type: str = "None"
|
| 875 |
-
):
|
| 876 |
-
|
| 877 |
-
embed_dim = model_cfg["embed_dim"]
|
| 878 |
-
audio_cfg = model_cfg["audio_cfg"]
|
| 879 |
-
text_cfg = model_cfg["text_cfg"]
|
| 880 |
-
context_length = state_dict["positional_embedding"].shape[0]
|
| 881 |
-
vocab_size = state_dict["token_embedding.weight"].shape[0]
|
| 882 |
-
transformer_width = state_dict["ln_final.weight"].shape[0]
|
| 883 |
-
transformer_heads = transformer_width // 64
|
| 884 |
-
transformer_layers = len(
|
| 885 |
-
set(
|
| 886 |
-
k.split(".")[2]
|
| 887 |
-
for k in state_dict
|
| 888 |
-
if k.startswith(f"transformer.resblocks")
|
| 889 |
-
)
|
| 890 |
-
)
|
| 891 |
-
|
| 892 |
-
audio_cfg = CLAPAudioCfp(**audio_cfg)
|
| 893 |
-
text_cfg = CLAPTextCfg(**text_cfg)
|
| 894 |
-
|
| 895 |
-
model = CLAP(
|
| 896 |
-
embed_dim,
|
| 897 |
-
audio_cfg=audio_cfg,
|
| 898 |
-
text_cfg=text_cfg,
|
| 899 |
-
quick_gelu=True, # OpenAI models were trained with QuickGELU
|
| 900 |
-
enable_fusion=enable_fusion,
|
| 901 |
-
fusion_type=fusion_type,
|
| 902 |
-
)
|
| 903 |
-
state_dict["logit_scale_a"] = state_dict["logit_scale"]
|
| 904 |
-
state_dict["logit_scale_t"] = state_dict["logit_scale"]
|
| 905 |
-
pop_keys = list(state_dict.keys())[::]
|
| 906 |
-
# pop the visual branch saved weights
|
| 907 |
-
for key in pop_keys:
|
| 908 |
-
if key.startswith("visual."):
|
| 909 |
-
state_dict.pop(key, None)
|
| 910 |
-
|
| 911 |
-
for key in ["logit_scale", "input_resolution", "context_length", "vocab_size"]:
|
| 912 |
-
state_dict.pop(key, None)
|
| 913 |
-
|
| 914 |
-
# not use fp16
|
| 915 |
-
# convert_weights_to_fp16(model)
|
| 916 |
-
model.load_state_dict(state_dict, strict=False)
|
| 917 |
-
return model.eval()
|
| 918 |
-
|
| 919 |
-
|
| 920 |
-
def trace_model(model, batch_size=256, device=torch.device("cpu")):
|
| 921 |
-
model.eval()
|
| 922 |
-
audio_length = model.audio_cfg.audio_length
|
| 923 |
-
example_audio = torch.ones((batch_size, audio_length), device=device)
|
| 924 |
-
example_text = torch.zeros(
|
| 925 |
-
(batch_size, model.context_length), dtype=torch.int, device=device
|
| 926 |
-
)
|
| 927 |
-
model = torch.jit.trace_module(
|
| 928 |
-
model,
|
| 929 |
-
inputs=dict(
|
| 930 |
-
forward=(example_audio, example_text),
|
| 931 |
-
encode_text=(example_text,),
|
| 932 |
-
encode_image=(example_audio,),
|
| 933 |
-
),
|
| 934 |
-
)
|
| 935 |
-
model.audio_cfg.audio_length = audio_length # Question: what does this do?
|
| 936 |
-
return model
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|
spaces/ASJMO/freegpt/g4f/Provider/Providers/Fakeopen.py
DELETED
|
@@ -1,54 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import json
|
| 3 |
-
import requests
|
| 4 |
-
from typing import Dict, get_type_hints
|
| 5 |
-
|
| 6 |
-
url = 'https://ai.fakeopen.com/v1/'
|
| 7 |
-
model = [
|
| 8 |
-
'gpt-3.5-turbo',
|
| 9 |
-
'gpt-3.5-turbo-0613',
|
| 10 |
-
'gpt-3.5-turbo-16k',
|
| 11 |
-
'gpt-3.5-turbo-16k-0613',
|
| 12 |
-
]
|
| 13 |
-
|
| 14 |
-
supports_stream = True
|
| 15 |
-
needs_auth = False
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
def _create_completion(model: str, messages: list, stream: bool, **kwargs):
|
| 19 |
-
|
| 20 |
-
headers = {
|
| 21 |
-
'Content-Type': 'application/json',
|
| 22 |
-
'accept': 'text/event-stream',
|
| 23 |
-
'Cache-Control': 'no-cache',
|
| 24 |
-
'Proxy-Connection': 'keep-alive',
|
| 25 |
-
'Authorization': f"Bearer {os.environ.get('FAKE_OPEN_KEY', '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')}",
|
| 26 |
-
}
|
| 27 |
-
|
| 28 |
-
json_data = {
|
| 29 |
-
'messages': messages,
|
| 30 |
-
'temperature': 1.0,
|
| 31 |
-
'model': model,
|
| 32 |
-
'stream': stream,
|
| 33 |
-
}
|
| 34 |
-
|
| 35 |
-
response = requests.post(
|
| 36 |
-
'https://ai.fakeopen.com/v1/chat/completions', headers=headers, json=json_data, stream=True
|
| 37 |
-
)
|
| 38 |
-
|
| 39 |
-
for token in response.iter_lines():
|
| 40 |
-
decoded = token.decode('utf-8')
|
| 41 |
-
if decoded == '[DONE]':
|
| 42 |
-
break
|
| 43 |
-
if decoded.startswith('data: '):
|
| 44 |
-
data_str = decoded.replace('data: ', '')
|
| 45 |
-
if data_str != '[DONE]':
|
| 46 |
-
data = json.loads(data_str)
|
| 47 |
-
if 'choices' in data and 'delta' in data['choices'][0] and 'content' in data['choices'][0]['delta']:
|
| 48 |
-
yield data['choices'][0]['delta']['content']
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + '(%s)' % ', '.join(
|
| 54 |
-
[f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
|
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|
|
spaces/Abdullahw72/bark-voice-cloning/README.md
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Bark Voice Cloning
|
| 3 |
-
emoji: 🐶
|
| 4 |
-
colorFrom: blue
|
| 5 |
-
colorTo: green
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.29.0
|
| 8 |
-
python_version: 3.10.11
|
| 9 |
-
app_file: app.py
|
| 10 |
-
models:
|
| 11 |
-
- facebook/hubert-base-ls960
|
| 12 |
-
- GitMylo/bark-voice-cloning
|
| 13 |
-
pinned: false
|
| 14 |
-
license: mit
|
| 15 |
-
duplicated_from: GitMylo/bark-voice-cloning
|
| 16 |
-
---
|
|
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|
|
spaces/Adapter/T2I-Adapter/ldm/models/diffusion/__init__.py
DELETED
|
File without changes
|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fixwidthbuttons/FixWidthButtons.js
DELETED
|
@@ -1,87 +0,0 @@
|
|
| 1 |
-
import FixWidthSizer from '../fixwidthsizer/FixWidthSizer.js';
|
| 2 |
-
import AddChildMethods from './AddChildMethods.js';
|
| 3 |
-
import RemoveChildMethods from './RemoveChildMethods.js';
|
| 4 |
-
import ButtonGroup from '../utils/buttongroup/ButtonGroup.js';
|
| 5 |
-
import ButtonMethods from '../utils/buttongroup/ButtonMethods.js';
|
| 6 |
-
import ButtonStateMethods from '../utils/buttongroup/ButtonStateMethods.js';
|
| 7 |
-
|
| 8 |
-
const GetValue = Phaser.Utils.Objects.GetValue;
|
| 9 |
-
|
| 10 |
-
class Buttons extends FixWidthSizer {
|
| 11 |
-
constructor(scene, config) {
|
| 12 |
-
if (config === undefined) {
|
| 13 |
-
config = {};
|
| 14 |
-
}
|
| 15 |
-
|
| 16 |
-
var buttonSpace = config.space;
|
| 17 |
-
if (typeof (buttonSpace) === 'number') {
|
| 18 |
-
config.space = { item: buttonSpace, line: buttonSpace };
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
// Create
|
| 22 |
-
super(scene, config);
|
| 23 |
-
this.type = 'rexFixWidthButtons';
|
| 24 |
-
this.buttonGroup = new ButtonGroup({
|
| 25 |
-
parent: this,
|
| 26 |
-
eventEmitter: GetValue(config, 'eventEmitter', this),
|
| 27 |
-
groupName: GetValue(config, 'groupName', undefined),
|
| 28 |
-
clickConfig: GetValue(config, 'click', undefined)
|
| 29 |
-
})
|
| 30 |
-
.setButtonsType(config);
|
| 31 |
-
|
| 32 |
-
// Add elements
|
| 33 |
-
var background = GetValue(config, 'background', undefined);
|
| 34 |
-
var buttons = GetValue(config, 'buttons', undefined);
|
| 35 |
-
|
| 36 |
-
// Buttons properties
|
| 37 |
-
this.buttonsAlign = GetValue(config, 'align', undefined);
|
| 38 |
-
|
| 39 |
-
if (background) {
|
| 40 |
-
this.addBackground(background);
|
| 41 |
-
}
|
| 42 |
-
|
| 43 |
-
if (buttons) {
|
| 44 |
-
this.addButtons(buttons);
|
| 45 |
-
}
|
| 46 |
-
|
| 47 |
-
this.addChildrenMap('background', background);
|
| 48 |
-
this.addChildrenMap('buttons', this.buttonGroup.buttons);
|
| 49 |
-
}
|
| 50 |
-
|
| 51 |
-
destroy(fromScene) {
|
| 52 |
-
// This Game Object has already been destroyed
|
| 53 |
-
if (!this.scene || this.ignoreDestroy) {
|
| 54 |
-
return;
|
| 55 |
-
}
|
| 56 |
-
|
| 57 |
-
super.destroy(fromScene);
|
| 58 |
-
this.buttonGroup.destroy();
|
| 59 |
-
this.buttonGroup = undefined;
|
| 60 |
-
}
|
| 61 |
-
|
| 62 |
-
get buttons() {
|
| 63 |
-
return this.buttonGroup.buttons;
|
| 64 |
-
}
|
| 65 |
-
|
| 66 |
-
get groupName() {
|
| 67 |
-
return this.buttonGroup.groupName;
|
| 68 |
-
}
|
| 69 |
-
|
| 70 |
-
set groupName(value) {
|
| 71 |
-
this.buttonGroup.groupName = value;
|
| 72 |
-
}
|
| 73 |
-
|
| 74 |
-
get eventEmitter() {
|
| 75 |
-
return this.buttonGroup.eventEmitter;
|
| 76 |
-
}
|
| 77 |
-
}
|
| 78 |
-
|
| 79 |
-
Object.assign(
|
| 80 |
-
Buttons.prototype,
|
| 81 |
-
AddChildMethods,
|
| 82 |
-
RemoveChildMethods,
|
| 83 |
-
ButtonMethods,
|
| 84 |
-
ButtonStateMethods
|
| 85 |
-
);
|
| 86 |
-
|
| 87 |
-
export default Buttons;
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/overlapsizer/Factory.d.ts
DELETED
|
@@ -1,16 +0,0 @@
|
|
| 1 |
-
import OverlapSizer from './OverlapSizer';
|
| 2 |
-
|
| 3 |
-
export default function (
|
| 4 |
-
config?: OverlapSizer.IConfig
|
| 5 |
-
): OverlapSizer;
|
| 6 |
-
|
| 7 |
-
export default function (
|
| 8 |
-
x: number, y: number,
|
| 9 |
-
config?: OverlapSizer.IConfig
|
| 10 |
-
): OverlapSizer;
|
| 11 |
-
|
| 12 |
-
export default function (
|
| 13 |
-
x: number, y: number,
|
| 14 |
-
width: number, height: number,
|
| 15 |
-
config?: OverlapSizer.IConfig
|
| 16 |
-
): OverlapSizer;
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
spaces/AlekseyCalvin/Make_Putin_Queer_Please-use-trp-token/app.py
DELETED
|
@@ -1,137 +0,0 @@
|
|
| 1 |
-
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
|
| 2 |
-
import gradio as gr
|
| 3 |
-
import torch
|
| 4 |
-
from PIL import Image
|
| 5 |
-
|
| 6 |
-
model_id = 'AlekseyCalvin/Make_Putin_Queer_Please'
|
| 7 |
-
prefix = 'trp' or 'trp person'
|
| 8 |
-
|
| 9 |
-
scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
|
| 10 |
-
|
| 11 |
-
pipe = StableDiffusionPipeline.from_pretrained(
|
| 12 |
-
model_id,
|
| 13 |
-
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
| 14 |
-
scheduler=scheduler)
|
| 15 |
-
|
| 16 |
-
pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
|
| 17 |
-
model_id,
|
| 18 |
-
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
|
| 19 |
-
scheduler=scheduler)
|
| 20 |
-
|
| 21 |
-
if torch.cuda.is_available():
|
| 22 |
-
pipe = pipe.to("cuda")
|
| 23 |
-
pipe_i2i = pipe_i2i.to("cuda")
|
| 24 |
-
|
| 25 |
-
def error_str(error, title="Error"):
|
| 26 |
-
return f"""#### {title}
|
| 27 |
-
{error}""" if error else ""
|
| 28 |
-
|
| 29 |
-
def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
|
| 30 |
-
|
| 31 |
-
generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
|
| 32 |
-
prompt = f"{prefix} {prompt}" if auto_prefix else prompt
|
| 33 |
-
|
| 34 |
-
try:
|
| 35 |
-
if img is not None:
|
| 36 |
-
return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
|
| 37 |
-
else:
|
| 38 |
-
return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
|
| 39 |
-
except Exception as e:
|
| 40 |
-
return None, error_str(e)
|
| 41 |
-
|
| 42 |
-
def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
|
| 43 |
-
|
| 44 |
-
result = pipe(
|
| 45 |
-
prompt,
|
| 46 |
-
negative_prompt = neg_prompt,
|
| 47 |
-
num_inference_steps = int(steps),
|
| 48 |
-
guidance_scale = guidance,
|
| 49 |
-
width = width,
|
| 50 |
-
height = height,
|
| 51 |
-
generator = generator)
|
| 52 |
-
|
| 53 |
-
return result.images[0]
|
| 54 |
-
|
| 55 |
-
def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
|
| 56 |
-
|
| 57 |
-
ratio = min(height / img.height, width / img.width)
|
| 58 |
-
img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
|
| 59 |
-
result = pipe_i2i(
|
| 60 |
-
prompt,
|
| 61 |
-
negative_prompt = neg_prompt,
|
| 62 |
-
init_image = img,
|
| 63 |
-
num_inference_steps = int(steps),
|
| 64 |
-
strength = strength,
|
| 65 |
-
guidance_scale = guidance,
|
| 66 |
-
width = width,
|
| 67 |
-
height = height,
|
| 68 |
-
generator = generator)
|
| 69 |
-
|
| 70 |
-
return result.images[0]
|
| 71 |
-
|
| 72 |
-
css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
|
| 73 |
-
"""
|
| 74 |
-
with gr.Blocks(css=css) as demo:
|
| 75 |
-
gr.HTML(
|
| 76 |
-
f"""
|
| 77 |
-
<div class="main-div">
|
| 78 |
-
<div>
|
| 79 |
-
<h1>Make Putin Queer, Please! Use trp person in prompts.</h1>
|
| 80 |
-
</div>
|
| 81 |
-
<p>
|
| 82 |
-
A gradio interface for <a href="https://huggingface.co/AlekseyCalvin/Make_Putin_Queer_Please">Make Putin Queer Please</a> Stable Diffusion model.<br>
|
| 83 |
-
{"Add the following tokens to your prompts for the model to work properly: <b>'trp'</b> if prefix else" }
|
| 84 |
-
</p>
|
| 85 |
-
Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/AlekseyCalvin/Make_Putin_Queer_Please-use-trp-token/settings'>Settings</a></b>"} after duplicating the space<br><br>
|
| 86 |
-
<a style="display:inline-block" href="https://huggingface.co/spaces/AlekseyCalvin/Make_Putin_Queer_Please-use-trp-token?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
|
| 87 |
-
</div>
|
| 88 |
-
"""
|
| 89 |
-
)
|
| 90 |
-
with gr.Row():
|
| 91 |
-
|
| 92 |
-
with gr.Column(scale=55):
|
| 93 |
-
with gr.Group():
|
| 94 |
-
with gr.Row():
|
| 95 |
-
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
|
| 96 |
-
generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
|
| 97 |
-
|
| 98 |
-
image_out = gr.Image(height=512)
|
| 99 |
-
error_output = gr.Markdown()
|
| 100 |
-
|
| 101 |
-
with gr.Column(scale=45):
|
| 102 |
-
with gr.Tab("Options"):
|
| 103 |
-
with gr.Group():
|
| 104 |
-
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
|
| 105 |
-
auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ('trp' or 'trp person')", value=prefix, visible=prefix)
|
| 106 |
-
|
| 107 |
-
with gr.Row():
|
| 108 |
-
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
|
| 109 |
-
steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
|
| 110 |
-
|
| 111 |
-
with gr.Row():
|
| 112 |
-
width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
|
| 113 |
-
height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
|
| 114 |
-
|
| 115 |
-
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
|
| 116 |
-
|
| 117 |
-
with gr.Tab("Image to image"):
|
| 118 |
-
with gr.Group():
|
| 119 |
-
image = gr.Image(label="Image", height=256, tool="editor", type="pil")
|
| 120 |
-
strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
|
| 121 |
-
|
| 122 |
-
auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
|
| 123 |
-
|
| 124 |
-
inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
|
| 125 |
-
outputs = [image_out, error_output]
|
| 126 |
-
prompt.submit(inference, inputs=inputs, outputs=outputs)
|
| 127 |
-
generate.click(inference, inputs=inputs, outputs=outputs)
|
| 128 |
-
|
| 129 |
-
gr.HTML("""
|
| 130 |
-
<div style="border-top: 1px solid #303030;">
|
| 131 |
-
<br>
|
| 132 |
-
<p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
|
| 133 |
-
</div>
|
| 134 |
-
""")
|
| 135 |
-
|
| 136 |
-
demo.queue(concurrency_count=1)
|
| 137 |
-
demo.launch()
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|
spaces/Alpaca233/SadTalker/src/face3d/models/arcface_torch/utils/utils_logging.py
DELETED
|
@@ -1,41 +0,0 @@
|
|
| 1 |
-
import logging
|
| 2 |
-
import os
|
| 3 |
-
import sys
|
| 4 |
-
|
| 5 |
-
|
| 6 |
-
class AverageMeter(object):
|
| 7 |
-
"""Computes and stores the average and current value
|
| 8 |
-
"""
|
| 9 |
-
|
| 10 |
-
def __init__(self):
|
| 11 |
-
self.val = None
|
| 12 |
-
self.avg = None
|
| 13 |
-
self.sum = None
|
| 14 |
-
self.count = None
|
| 15 |
-
self.reset()
|
| 16 |
-
|
| 17 |
-
def reset(self):
|
| 18 |
-
self.val = 0
|
| 19 |
-
self.avg = 0
|
| 20 |
-
self.sum = 0
|
| 21 |
-
self.count = 0
|
| 22 |
-
|
| 23 |
-
def update(self, val, n=1):
|
| 24 |
-
self.val = val
|
| 25 |
-
self.sum += val * n
|
| 26 |
-
self.count += n
|
| 27 |
-
self.avg = self.sum / self.count
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
def init_logging(rank, models_root):
|
| 31 |
-
if rank == 0:
|
| 32 |
-
log_root = logging.getLogger()
|
| 33 |
-
log_root.setLevel(logging.INFO)
|
| 34 |
-
formatter = logging.Formatter("Training: %(asctime)s-%(message)s")
|
| 35 |
-
handler_file = logging.FileHandler(os.path.join(models_root, "training.log"))
|
| 36 |
-
handler_stream = logging.StreamHandler(sys.stdout)
|
| 37 |
-
handler_file.setFormatter(formatter)
|
| 38 |
-
handler_stream.setFormatter(formatter)
|
| 39 |
-
log_root.addHandler(handler_file)
|
| 40 |
-
log_root.addHandler(handler_stream)
|
| 41 |
-
log_root.info('rank_id: %d' % rank)
|
|
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|
spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py
DELETED
|
@@ -1,744 +0,0 @@
|
|
| 1 |
-
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
| 4 |
-
# you may not use this file except in compliance with the License.
|
| 5 |
-
# You may obtain a copy of the License at
|
| 6 |
-
#
|
| 7 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
| 8 |
-
#
|
| 9 |
-
# Unless required by applicable law or agreed to in writing, software
|
| 10 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
| 11 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 12 |
-
# See the License for the specific language governing permissions and
|
| 13 |
-
# limitations under the License.
|
| 14 |
-
|
| 15 |
-
import inspect
|
| 16 |
-
from typing import List, Optional, Tuple, Union
|
| 17 |
-
|
| 18 |
-
import torch
|
| 19 |
-
import torch.nn as nn
|
| 20 |
-
import torch.utils.checkpoint
|
| 21 |
-
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
|
| 22 |
-
from transformers.activations import ACT2FN
|
| 23 |
-
from transformers.modeling_outputs import BaseModelOutput
|
| 24 |
-
from transformers.utils import logging
|
| 25 |
-
|
| 26 |
-
from ...models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
|
| 27 |
-
from ...schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
|
| 28 |
-
from ...utils import randn_tensor
|
| 29 |
-
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
class LDMTextToImagePipeline(DiffusionPipeline):
|
| 33 |
-
r"""
|
| 34 |
-
Pipeline for text-to-image generation using latent diffusion.
|
| 35 |
-
|
| 36 |
-
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
|
| 37 |
-
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
|
| 38 |
-
|
| 39 |
-
Parameters:
|
| 40 |
-
vqvae ([`VQModel`]):
|
| 41 |
-
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
|
| 42 |
-
bert ([`LDMBertModel`]):
|
| 43 |
-
Text-encoder model based on [`~transformers.BERT`].
|
| 44 |
-
tokenizer ([`~transformers.BertTokenizer`]):
|
| 45 |
-
A `BertTokenizer` to tokenize text.
|
| 46 |
-
unet ([`UNet2DConditionModel`]):
|
| 47 |
-
A `UNet2DConditionModel` to denoise the encoded image latents.
|
| 48 |
-
scheduler ([`SchedulerMixin`]):
|
| 49 |
-
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
|
| 50 |
-
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
|
| 51 |
-
"""
|
| 52 |
-
|
| 53 |
-
def __init__(
|
| 54 |
-
self,
|
| 55 |
-
vqvae: Union[VQModel, AutoencoderKL],
|
| 56 |
-
bert: PreTrainedModel,
|
| 57 |
-
tokenizer: PreTrainedTokenizer,
|
| 58 |
-
unet: Union[UNet2DModel, UNet2DConditionModel],
|
| 59 |
-
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
|
| 60 |
-
):
|
| 61 |
-
super().__init__()
|
| 62 |
-
self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
|
| 63 |
-
self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
|
| 64 |
-
|
| 65 |
-
@torch.no_grad()
|
| 66 |
-
def __call__(
|
| 67 |
-
self,
|
| 68 |
-
prompt: Union[str, List[str]],
|
| 69 |
-
height: Optional[int] = None,
|
| 70 |
-
width: Optional[int] = None,
|
| 71 |
-
num_inference_steps: Optional[int] = 50,
|
| 72 |
-
guidance_scale: Optional[float] = 1.0,
|
| 73 |
-
eta: Optional[float] = 0.0,
|
| 74 |
-
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
|
| 75 |
-
latents: Optional[torch.FloatTensor] = None,
|
| 76 |
-
output_type: Optional[str] = "pil",
|
| 77 |
-
return_dict: bool = True,
|
| 78 |
-
**kwargs,
|
| 79 |
-
) -> Union[Tuple, ImagePipelineOutput]:
|
| 80 |
-
r"""
|
| 81 |
-
The call function to the pipeline for generation.
|
| 82 |
-
|
| 83 |
-
Args:
|
| 84 |
-
prompt (`str` or `List[str]`):
|
| 85 |
-
The prompt or prompts to guide the image generation.
|
| 86 |
-
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
| 87 |
-
The height in pixels of the generated image.
|
| 88 |
-
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
| 89 |
-
The width in pixels of the generated image.
|
| 90 |
-
num_inference_steps (`int`, *optional*, defaults to 50):
|
| 91 |
-
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
| 92 |
-
expense of slower inference.
|
| 93 |
-
guidance_scale (`float`, *optional*, defaults to 1.0):
|
| 94 |
-
A higher guidance scale value encourages the model to generate images closely linked to the text
|
| 95 |
-
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
| 96 |
-
generator (`torch.Generator`, *optional*):
|
| 97 |
-
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
|
| 98 |
-
generation deterministic.
|
| 99 |
-
latents (`torch.FloatTensor`, *optional*):
|
| 100 |
-
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
|
| 101 |
-
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
|
| 102 |
-
tensor is generated by sampling using the supplied random `generator`.
|
| 103 |
-
output_type (`str`, *optional*, defaults to `"pil"`):
|
| 104 |
-
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
| 105 |
-
return_dict (`bool`, *optional*, defaults to `True`):
|
| 106 |
-
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple.
|
| 107 |
-
|
| 108 |
-
Example:
|
| 109 |
-
|
| 110 |
-
```py
|
| 111 |
-
>>> from diffusers import DiffusionPipeline
|
| 112 |
-
|
| 113 |
-
>>> # load model and scheduler
|
| 114 |
-
>>> ldm = DiffusionPipeline.from_pretrained("CompVis/ldm-text2im-large-256")
|
| 115 |
-
|
| 116 |
-
>>> # run pipeline in inference (sample random noise and denoise)
|
| 117 |
-
>>> prompt = "A painting of a squirrel eating a burger"
|
| 118 |
-
>>> images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6).images
|
| 119 |
-
|
| 120 |
-
>>> # save images
|
| 121 |
-
>>> for idx, image in enumerate(images):
|
| 122 |
-
... image.save(f"squirrel-{idx}.png")
|
| 123 |
-
```
|
| 124 |
-
|
| 125 |
-
Returns:
|
| 126 |
-
[`~pipelines.ImagePipelineOutput`] or `tuple`:
|
| 127 |
-
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
|
| 128 |
-
returned where the first element is a list with the generated images.
|
| 129 |
-
"""
|
| 130 |
-
# 0. Default height and width to unet
|
| 131 |
-
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
| 132 |
-
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
| 133 |
-
|
| 134 |
-
if isinstance(prompt, str):
|
| 135 |
-
batch_size = 1
|
| 136 |
-
elif isinstance(prompt, list):
|
| 137 |
-
batch_size = len(prompt)
|
| 138 |
-
else:
|
| 139 |
-
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
|
| 140 |
-
|
| 141 |
-
if height % 8 != 0 or width % 8 != 0:
|
| 142 |
-
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
| 143 |
-
|
| 144 |
-
# get unconditional embeddings for classifier free guidance
|
| 145 |
-
if guidance_scale != 1.0:
|
| 146 |
-
uncond_input = self.tokenizer(
|
| 147 |
-
[""] * batch_size, padding="max_length", max_length=77, truncation=True, return_tensors="pt"
|
| 148 |
-
)
|
| 149 |
-
negative_prompt_embeds = self.bert(uncond_input.input_ids.to(self._execution_device))[0]
|
| 150 |
-
|
| 151 |
-
# get prompt text embeddings
|
| 152 |
-
text_input = self.tokenizer(prompt, padding="max_length", max_length=77, truncation=True, return_tensors="pt")
|
| 153 |
-
prompt_embeds = self.bert(text_input.input_ids.to(self._execution_device))[0]
|
| 154 |
-
|
| 155 |
-
# get the initial random noise unless the user supplied it
|
| 156 |
-
latents_shape = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
|
| 157 |
-
if isinstance(generator, list) and len(generator) != batch_size:
|
| 158 |
-
raise ValueError(
|
| 159 |
-
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
|
| 160 |
-
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
|
| 161 |
-
)
|
| 162 |
-
|
| 163 |
-
if latents is None:
|
| 164 |
-
latents = randn_tensor(
|
| 165 |
-
latents_shape, generator=generator, device=self._execution_device, dtype=prompt_embeds.dtype
|
| 166 |
-
)
|
| 167 |
-
else:
|
| 168 |
-
if latents.shape != latents_shape:
|
| 169 |
-
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
|
| 170 |
-
latents = latents.to(self._execution_device)
|
| 171 |
-
|
| 172 |
-
self.scheduler.set_timesteps(num_inference_steps)
|
| 173 |
-
|
| 174 |
-
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
| 175 |
-
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
| 176 |
-
|
| 177 |
-
extra_kwargs = {}
|
| 178 |
-
if accepts_eta:
|
| 179 |
-
extra_kwargs["eta"] = eta
|
| 180 |
-
|
| 181 |
-
for t in self.progress_bar(self.scheduler.timesteps):
|
| 182 |
-
if guidance_scale == 1.0:
|
| 183 |
-
# guidance_scale of 1 means no guidance
|
| 184 |
-
latents_input = latents
|
| 185 |
-
context = prompt_embeds
|
| 186 |
-
else:
|
| 187 |
-
# For classifier free guidance, we need to do two forward passes.
|
| 188 |
-
# Here we concatenate the unconditional and text embeddings into a single batch
|
| 189 |
-
# to avoid doing two forward passes
|
| 190 |
-
latents_input = torch.cat([latents] * 2)
|
| 191 |
-
context = torch.cat([negative_prompt_embeds, prompt_embeds])
|
| 192 |
-
|
| 193 |
-
# predict the noise residual
|
| 194 |
-
noise_pred = self.unet(latents_input, t, encoder_hidden_states=context).sample
|
| 195 |
-
# perform guidance
|
| 196 |
-
if guidance_scale != 1.0:
|
| 197 |
-
noise_pred_uncond, noise_prediction_text = noise_pred.chunk(2)
|
| 198 |
-
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
|
| 199 |
-
|
| 200 |
-
# compute the previous noisy sample x_t -> x_t-1
|
| 201 |
-
latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample
|
| 202 |
-
|
| 203 |
-
# scale and decode the image latents with vae
|
| 204 |
-
latents = 1 / self.vqvae.config.scaling_factor * latents
|
| 205 |
-
image = self.vqvae.decode(latents).sample
|
| 206 |
-
|
| 207 |
-
image = (image / 2 + 0.5).clamp(0, 1)
|
| 208 |
-
image = image.cpu().permute(0, 2, 3, 1).numpy()
|
| 209 |
-
if output_type == "pil":
|
| 210 |
-
image = self.numpy_to_pil(image)
|
| 211 |
-
|
| 212 |
-
if not return_dict:
|
| 213 |
-
return (image,)
|
| 214 |
-
|
| 215 |
-
return ImagePipelineOutput(images=image)
|
| 216 |
-
|
| 217 |
-
|
| 218 |
-
################################################################################
|
| 219 |
-
# Code for the text transformer model
|
| 220 |
-
################################################################################
|
| 221 |
-
""" PyTorch LDMBERT model."""
|
| 222 |
-
|
| 223 |
-
|
| 224 |
-
logger = logging.get_logger(__name__)
|
| 225 |
-
|
| 226 |
-
LDMBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
|
| 227 |
-
"ldm-bert",
|
| 228 |
-
# See all LDMBert models at https://huggingface.co/models?filter=ldmbert
|
| 229 |
-
]
|
| 230 |
-
|
| 231 |
-
|
| 232 |
-
LDMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
| 233 |
-
"ldm-bert": "https://huggingface.co/valhalla/ldm-bert/blob/main/config.json",
|
| 234 |
-
}
|
| 235 |
-
|
| 236 |
-
|
| 237 |
-
""" LDMBERT model configuration"""
|
| 238 |
-
|
| 239 |
-
|
| 240 |
-
class LDMBertConfig(PretrainedConfig):
|
| 241 |
-
model_type = "ldmbert"
|
| 242 |
-
keys_to_ignore_at_inference = ["past_key_values"]
|
| 243 |
-
attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
|
| 244 |
-
|
| 245 |
-
def __init__(
|
| 246 |
-
self,
|
| 247 |
-
vocab_size=30522,
|
| 248 |
-
max_position_embeddings=77,
|
| 249 |
-
encoder_layers=32,
|
| 250 |
-
encoder_ffn_dim=5120,
|
| 251 |
-
encoder_attention_heads=8,
|
| 252 |
-
head_dim=64,
|
| 253 |
-
encoder_layerdrop=0.0,
|
| 254 |
-
activation_function="gelu",
|
| 255 |
-
d_model=1280,
|
| 256 |
-
dropout=0.1,
|
| 257 |
-
attention_dropout=0.0,
|
| 258 |
-
activation_dropout=0.0,
|
| 259 |
-
init_std=0.02,
|
| 260 |
-
classifier_dropout=0.0,
|
| 261 |
-
scale_embedding=False,
|
| 262 |
-
use_cache=True,
|
| 263 |
-
pad_token_id=0,
|
| 264 |
-
**kwargs,
|
| 265 |
-
):
|
| 266 |
-
self.vocab_size = vocab_size
|
| 267 |
-
self.max_position_embeddings = max_position_embeddings
|
| 268 |
-
self.d_model = d_model
|
| 269 |
-
self.encoder_ffn_dim = encoder_ffn_dim
|
| 270 |
-
self.encoder_layers = encoder_layers
|
| 271 |
-
self.encoder_attention_heads = encoder_attention_heads
|
| 272 |
-
self.head_dim = head_dim
|
| 273 |
-
self.dropout = dropout
|
| 274 |
-
self.attention_dropout = attention_dropout
|
| 275 |
-
self.activation_dropout = activation_dropout
|
| 276 |
-
self.activation_function = activation_function
|
| 277 |
-
self.init_std = init_std
|
| 278 |
-
self.encoder_layerdrop = encoder_layerdrop
|
| 279 |
-
self.classifier_dropout = classifier_dropout
|
| 280 |
-
self.use_cache = use_cache
|
| 281 |
-
self.num_hidden_layers = encoder_layers
|
| 282 |
-
self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True
|
| 283 |
-
|
| 284 |
-
super().__init__(pad_token_id=pad_token_id, **kwargs)
|
| 285 |
-
|
| 286 |
-
|
| 287 |
-
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
| 288 |
-
"""
|
| 289 |
-
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
| 290 |
-
"""
|
| 291 |
-
bsz, src_len = mask.size()
|
| 292 |
-
tgt_len = tgt_len if tgt_len is not None else src_len
|
| 293 |
-
|
| 294 |
-
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
| 295 |
-
|
| 296 |
-
inverted_mask = 1.0 - expanded_mask
|
| 297 |
-
|
| 298 |
-
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
| 299 |
-
|
| 300 |
-
|
| 301 |
-
# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->LDMBert
|
| 302 |
-
class LDMBertAttention(nn.Module):
|
| 303 |
-
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
| 304 |
-
|
| 305 |
-
def __init__(
|
| 306 |
-
self,
|
| 307 |
-
embed_dim: int,
|
| 308 |
-
num_heads: int,
|
| 309 |
-
head_dim: int,
|
| 310 |
-
dropout: float = 0.0,
|
| 311 |
-
is_decoder: bool = False,
|
| 312 |
-
bias: bool = False,
|
| 313 |
-
):
|
| 314 |
-
super().__init__()
|
| 315 |
-
self.embed_dim = embed_dim
|
| 316 |
-
self.num_heads = num_heads
|
| 317 |
-
self.dropout = dropout
|
| 318 |
-
self.head_dim = head_dim
|
| 319 |
-
self.inner_dim = head_dim * num_heads
|
| 320 |
-
|
| 321 |
-
self.scaling = self.head_dim**-0.5
|
| 322 |
-
self.is_decoder = is_decoder
|
| 323 |
-
|
| 324 |
-
self.k_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
|
| 325 |
-
self.v_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
|
| 326 |
-
self.q_proj = nn.Linear(embed_dim, self.inner_dim, bias=bias)
|
| 327 |
-
self.out_proj = nn.Linear(self.inner_dim, embed_dim)
|
| 328 |
-
|
| 329 |
-
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
| 330 |
-
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
| 331 |
-
|
| 332 |
-
def forward(
|
| 333 |
-
self,
|
| 334 |
-
hidden_states: torch.Tensor,
|
| 335 |
-
key_value_states: Optional[torch.Tensor] = None,
|
| 336 |
-
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
| 337 |
-
attention_mask: Optional[torch.Tensor] = None,
|
| 338 |
-
layer_head_mask: Optional[torch.Tensor] = None,
|
| 339 |
-
output_attentions: bool = False,
|
| 340 |
-
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
| 341 |
-
"""Input shape: Batch x Time x Channel"""
|
| 342 |
-
|
| 343 |
-
# if key_value_states are provided this layer is used as a cross-attention layer
|
| 344 |
-
# for the decoder
|
| 345 |
-
is_cross_attention = key_value_states is not None
|
| 346 |
-
|
| 347 |
-
bsz, tgt_len, _ = hidden_states.size()
|
| 348 |
-
|
| 349 |
-
# get query proj
|
| 350 |
-
query_states = self.q_proj(hidden_states) * self.scaling
|
| 351 |
-
# get key, value proj
|
| 352 |
-
if is_cross_attention and past_key_value is not None:
|
| 353 |
-
# reuse k,v, cross_attentions
|
| 354 |
-
key_states = past_key_value[0]
|
| 355 |
-
value_states = past_key_value[1]
|
| 356 |
-
elif is_cross_attention:
|
| 357 |
-
# cross_attentions
|
| 358 |
-
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
|
| 359 |
-
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
|
| 360 |
-
elif past_key_value is not None:
|
| 361 |
-
# reuse k, v, self_attention
|
| 362 |
-
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
|
| 363 |
-
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
|
| 364 |
-
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
| 365 |
-
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
| 366 |
-
else:
|
| 367 |
-
# self_attention
|
| 368 |
-
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
|
| 369 |
-
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
|
| 370 |
-
|
| 371 |
-
if self.is_decoder:
|
| 372 |
-
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
|
| 373 |
-
# Further calls to cross_attention layer can then reuse all cross-attention
|
| 374 |
-
# key/value_states (first "if" case)
|
| 375 |
-
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
|
| 376 |
-
# all previous decoder key/value_states. Further calls to uni-directional self-attention
|
| 377 |
-
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
|
| 378 |
-
# if encoder bi-directional self-attention `past_key_value` is always `None`
|
| 379 |
-
past_key_value = (key_states, value_states)
|
| 380 |
-
|
| 381 |
-
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
|
| 382 |
-
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
|
| 383 |
-
key_states = key_states.view(*proj_shape)
|
| 384 |
-
value_states = value_states.view(*proj_shape)
|
| 385 |
-
|
| 386 |
-
src_len = key_states.size(1)
|
| 387 |
-
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
|
| 388 |
-
|
| 389 |
-
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
|
| 390 |
-
raise ValueError(
|
| 391 |
-
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
|
| 392 |
-
f" {attn_weights.size()}"
|
| 393 |
-
)
|
| 394 |
-
|
| 395 |
-
if attention_mask is not None:
|
| 396 |
-
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
|
| 397 |
-
raise ValueError(
|
| 398 |
-
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
|
| 399 |
-
)
|
| 400 |
-
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
|
| 401 |
-
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
| 402 |
-
|
| 403 |
-
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
| 404 |
-
|
| 405 |
-
if layer_head_mask is not None:
|
| 406 |
-
if layer_head_mask.size() != (self.num_heads,):
|
| 407 |
-
raise ValueError(
|
| 408 |
-
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
|
| 409 |
-
f" {layer_head_mask.size()}"
|
| 410 |
-
)
|
| 411 |
-
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
| 412 |
-
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
| 413 |
-
|
| 414 |
-
if output_attentions:
|
| 415 |
-
# this operation is a bit awkward, but it's required to
|
| 416 |
-
# make sure that attn_weights keeps its gradient.
|
| 417 |
-
# In order to do so, attn_weights have to be reshaped
|
| 418 |
-
# twice and have to be reused in the following
|
| 419 |
-
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
| 420 |
-
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
|
| 421 |
-
else:
|
| 422 |
-
attn_weights_reshaped = None
|
| 423 |
-
|
| 424 |
-
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
|
| 425 |
-
|
| 426 |
-
attn_output = torch.bmm(attn_probs, value_states)
|
| 427 |
-
|
| 428 |
-
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
|
| 429 |
-
raise ValueError(
|
| 430 |
-
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
|
| 431 |
-
f" {attn_output.size()}"
|
| 432 |
-
)
|
| 433 |
-
|
| 434 |
-
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
|
| 435 |
-
attn_output = attn_output.transpose(1, 2)
|
| 436 |
-
|
| 437 |
-
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
|
| 438 |
-
# partitioned across GPUs when using tensor-parallelism.
|
| 439 |
-
attn_output = attn_output.reshape(bsz, tgt_len, self.inner_dim)
|
| 440 |
-
|
| 441 |
-
attn_output = self.out_proj(attn_output)
|
| 442 |
-
|
| 443 |
-
return attn_output, attn_weights_reshaped, past_key_value
|
| 444 |
-
|
| 445 |
-
|
| 446 |
-
class LDMBertEncoderLayer(nn.Module):
|
| 447 |
-
def __init__(self, config: LDMBertConfig):
|
| 448 |
-
super().__init__()
|
| 449 |
-
self.embed_dim = config.d_model
|
| 450 |
-
self.self_attn = LDMBertAttention(
|
| 451 |
-
embed_dim=self.embed_dim,
|
| 452 |
-
num_heads=config.encoder_attention_heads,
|
| 453 |
-
head_dim=config.head_dim,
|
| 454 |
-
dropout=config.attention_dropout,
|
| 455 |
-
)
|
| 456 |
-
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
|
| 457 |
-
self.dropout = config.dropout
|
| 458 |
-
self.activation_fn = ACT2FN[config.activation_function]
|
| 459 |
-
self.activation_dropout = config.activation_dropout
|
| 460 |
-
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
|
| 461 |
-
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
|
| 462 |
-
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
|
| 463 |
-
|
| 464 |
-
def forward(
|
| 465 |
-
self,
|
| 466 |
-
hidden_states: torch.FloatTensor,
|
| 467 |
-
attention_mask: torch.FloatTensor,
|
| 468 |
-
layer_head_mask: torch.FloatTensor,
|
| 469 |
-
output_attentions: Optional[bool] = False,
|
| 470 |
-
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
|
| 471 |
-
"""
|
| 472 |
-
Args:
|
| 473 |
-
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
|
| 474 |
-
attention_mask (`torch.FloatTensor`): attention mask of size
|
| 475 |
-
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
| 476 |
-
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
|
| 477 |
-
`(encoder_attention_heads,)`.
|
| 478 |
-
output_attentions (`bool`, *optional*):
|
| 479 |
-
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
| 480 |
-
returned tensors for more detail.
|
| 481 |
-
"""
|
| 482 |
-
residual = hidden_states
|
| 483 |
-
hidden_states = self.self_attn_layer_norm(hidden_states)
|
| 484 |
-
hidden_states, attn_weights, _ = self.self_attn(
|
| 485 |
-
hidden_states=hidden_states,
|
| 486 |
-
attention_mask=attention_mask,
|
| 487 |
-
layer_head_mask=layer_head_mask,
|
| 488 |
-
output_attentions=output_attentions,
|
| 489 |
-
)
|
| 490 |
-
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
| 491 |
-
hidden_states = residual + hidden_states
|
| 492 |
-
|
| 493 |
-
residual = hidden_states
|
| 494 |
-
hidden_states = self.final_layer_norm(hidden_states)
|
| 495 |
-
hidden_states = self.activation_fn(self.fc1(hidden_states))
|
| 496 |
-
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
|
| 497 |
-
hidden_states = self.fc2(hidden_states)
|
| 498 |
-
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
| 499 |
-
hidden_states = residual + hidden_states
|
| 500 |
-
|
| 501 |
-
if hidden_states.dtype == torch.float16 and (
|
| 502 |
-
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
|
| 503 |
-
):
|
| 504 |
-
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
|
| 505 |
-
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
|
| 506 |
-
|
| 507 |
-
outputs = (hidden_states,)
|
| 508 |
-
|
| 509 |
-
if output_attentions:
|
| 510 |
-
outputs += (attn_weights,)
|
| 511 |
-
|
| 512 |
-
return outputs
|
| 513 |
-
|
| 514 |
-
|
| 515 |
-
# Copied from transformers.models.bart.modeling_bart.BartPretrainedModel with Bart->LDMBert
|
| 516 |
-
class LDMBertPreTrainedModel(PreTrainedModel):
|
| 517 |
-
config_class = LDMBertConfig
|
| 518 |
-
base_model_prefix = "model"
|
| 519 |
-
_supports_gradient_checkpointing = True
|
| 520 |
-
_keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]
|
| 521 |
-
|
| 522 |
-
def _init_weights(self, module):
|
| 523 |
-
std = self.config.init_std
|
| 524 |
-
if isinstance(module, nn.Linear):
|
| 525 |
-
module.weight.data.normal_(mean=0.0, std=std)
|
| 526 |
-
if module.bias is not None:
|
| 527 |
-
module.bias.data.zero_()
|
| 528 |
-
elif isinstance(module, nn.Embedding):
|
| 529 |
-
module.weight.data.normal_(mean=0.0, std=std)
|
| 530 |
-
if module.padding_idx is not None:
|
| 531 |
-
module.weight.data[module.padding_idx].zero_()
|
| 532 |
-
|
| 533 |
-
def _set_gradient_checkpointing(self, module, value=False):
|
| 534 |
-
if isinstance(module, (LDMBertEncoder,)):
|
| 535 |
-
module.gradient_checkpointing = value
|
| 536 |
-
|
| 537 |
-
@property
|
| 538 |
-
def dummy_inputs(self):
|
| 539 |
-
pad_token = self.config.pad_token_id
|
| 540 |
-
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
|
| 541 |
-
dummy_inputs = {
|
| 542 |
-
"attention_mask": input_ids.ne(pad_token),
|
| 543 |
-
"input_ids": input_ids,
|
| 544 |
-
}
|
| 545 |
-
return dummy_inputs
|
| 546 |
-
|
| 547 |
-
|
| 548 |
-
class LDMBertEncoder(LDMBertPreTrainedModel):
|
| 549 |
-
"""
|
| 550 |
-
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
|
| 551 |
-
[`LDMBertEncoderLayer`].
|
| 552 |
-
|
| 553 |
-
Args:
|
| 554 |
-
config: LDMBertConfig
|
| 555 |
-
embed_tokens (nn.Embedding): output embedding
|
| 556 |
-
"""
|
| 557 |
-
|
| 558 |
-
def __init__(self, config: LDMBertConfig):
|
| 559 |
-
super().__init__(config)
|
| 560 |
-
|
| 561 |
-
self.dropout = config.dropout
|
| 562 |
-
|
| 563 |
-
embed_dim = config.d_model
|
| 564 |
-
self.padding_idx = config.pad_token_id
|
| 565 |
-
self.max_source_positions = config.max_position_embeddings
|
| 566 |
-
|
| 567 |
-
self.embed_tokens = nn.Embedding(config.vocab_size, embed_dim)
|
| 568 |
-
self.embed_positions = nn.Embedding(config.max_position_embeddings, embed_dim)
|
| 569 |
-
self.layers = nn.ModuleList([LDMBertEncoderLayer(config) for _ in range(config.encoder_layers)])
|
| 570 |
-
self.layer_norm = nn.LayerNorm(embed_dim)
|
| 571 |
-
|
| 572 |
-
self.gradient_checkpointing = False
|
| 573 |
-
# Initialize weights and apply final processing
|
| 574 |
-
self.post_init()
|
| 575 |
-
|
| 576 |
-
def get_input_embeddings(self):
|
| 577 |
-
return self.embed_tokens
|
| 578 |
-
|
| 579 |
-
def set_input_embeddings(self, value):
|
| 580 |
-
self.embed_tokens = value
|
| 581 |
-
|
| 582 |
-
def forward(
|
| 583 |
-
self,
|
| 584 |
-
input_ids: torch.LongTensor = None,
|
| 585 |
-
attention_mask: Optional[torch.Tensor] = None,
|
| 586 |
-
position_ids: Optional[torch.LongTensor] = None,
|
| 587 |
-
head_mask: Optional[torch.Tensor] = None,
|
| 588 |
-
inputs_embeds: Optional[torch.FloatTensor] = None,
|
| 589 |
-
output_attentions: Optional[bool] = None,
|
| 590 |
-
output_hidden_states: Optional[bool] = None,
|
| 591 |
-
return_dict: Optional[bool] = None,
|
| 592 |
-
) -> Union[Tuple, BaseModelOutput]:
|
| 593 |
-
r"""
|
| 594 |
-
Args:
|
| 595 |
-
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
| 596 |
-
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
| 597 |
-
provide it.
|
| 598 |
-
|
| 599 |
-
Indices can be obtained using [`BartTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
| 600 |
-
[`PreTrainedTokenizer.__call__`] for details.
|
| 601 |
-
|
| 602 |
-
[What are input IDs?](../glossary#input-ids)
|
| 603 |
-
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
| 604 |
-
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
| 605 |
-
|
| 606 |
-
- 1 for tokens that are **not masked**,
|
| 607 |
-
- 0 for tokens that are **masked**.
|
| 608 |
-
|
| 609 |
-
[What are attention masks?](../glossary#attention-mask)
|
| 610 |
-
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
|
| 611 |
-
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
| 612 |
-
|
| 613 |
-
- 1 indicates the head is **not masked**,
|
| 614 |
-
- 0 indicates the head is **masked**.
|
| 615 |
-
|
| 616 |
-
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
| 617 |
-
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
| 618 |
-
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
| 619 |
-
than the model's internal embedding lookup matrix.
|
| 620 |
-
output_attentions (`bool`, *optional*):
|
| 621 |
-
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
| 622 |
-
returned tensors for more detail.
|
| 623 |
-
output_hidden_states (`bool`, *optional*):
|
| 624 |
-
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
| 625 |
-
for more detail.
|
| 626 |
-
return_dict (`bool`, *optional*):
|
| 627 |
-
Whether or not to return a [`~utils.BaseModelOutput`] instead of a plain tuple.
|
| 628 |
-
"""
|
| 629 |
-
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
| 630 |
-
output_hidden_states = (
|
| 631 |
-
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
| 632 |
-
)
|
| 633 |
-
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
| 634 |
-
|
| 635 |
-
# retrieve input_ids and inputs_embeds
|
| 636 |
-
if input_ids is not None and inputs_embeds is not None:
|
| 637 |
-
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
| 638 |
-
elif input_ids is not None:
|
| 639 |
-
input_shape = input_ids.size()
|
| 640 |
-
input_ids = input_ids.view(-1, input_shape[-1])
|
| 641 |
-
elif inputs_embeds is not None:
|
| 642 |
-
input_shape = inputs_embeds.size()[:-1]
|
| 643 |
-
else:
|
| 644 |
-
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
| 645 |
-
|
| 646 |
-
if inputs_embeds is None:
|
| 647 |
-
inputs_embeds = self.embed_tokens(input_ids)
|
| 648 |
-
|
| 649 |
-
seq_len = input_shape[1]
|
| 650 |
-
if position_ids is None:
|
| 651 |
-
position_ids = torch.arange(seq_len, dtype=torch.long, device=inputs_embeds.device).expand((1, -1))
|
| 652 |
-
embed_pos = self.embed_positions(position_ids)
|
| 653 |
-
|
| 654 |
-
hidden_states = inputs_embeds + embed_pos
|
| 655 |
-
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
| 656 |
-
|
| 657 |
-
# expand attention_mask
|
| 658 |
-
if attention_mask is not None:
|
| 659 |
-
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
| 660 |
-
attention_mask = _expand_mask(attention_mask, inputs_embeds.dtype)
|
| 661 |
-
|
| 662 |
-
encoder_states = () if output_hidden_states else None
|
| 663 |
-
all_attentions = () if output_attentions else None
|
| 664 |
-
|
| 665 |
-
# check if head_mask has a correct number of layers specified if desired
|
| 666 |
-
if head_mask is not None:
|
| 667 |
-
if head_mask.size()[0] != (len(self.layers)):
|
| 668 |
-
raise ValueError(
|
| 669 |
-
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
|
| 670 |
-
f" {head_mask.size()[0]}."
|
| 671 |
-
)
|
| 672 |
-
|
| 673 |
-
for idx, encoder_layer in enumerate(self.layers):
|
| 674 |
-
if output_hidden_states:
|
| 675 |
-
encoder_states = encoder_states + (hidden_states,)
|
| 676 |
-
if self.gradient_checkpointing and self.training:
|
| 677 |
-
|
| 678 |
-
def create_custom_forward(module):
|
| 679 |
-
def custom_forward(*inputs):
|
| 680 |
-
return module(*inputs, output_attentions)
|
| 681 |
-
|
| 682 |
-
return custom_forward
|
| 683 |
-
|
| 684 |
-
layer_outputs = torch.utils.checkpoint.checkpoint(
|
| 685 |
-
create_custom_forward(encoder_layer),
|
| 686 |
-
hidden_states,
|
| 687 |
-
attention_mask,
|
| 688 |
-
(head_mask[idx] if head_mask is not None else None),
|
| 689 |
-
)
|
| 690 |
-
else:
|
| 691 |
-
layer_outputs = encoder_layer(
|
| 692 |
-
hidden_states,
|
| 693 |
-
attention_mask,
|
| 694 |
-
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
| 695 |
-
output_attentions=output_attentions,
|
| 696 |
-
)
|
| 697 |
-
|
| 698 |
-
hidden_states = layer_outputs[0]
|
| 699 |
-
|
| 700 |
-
if output_attentions:
|
| 701 |
-
all_attentions = all_attentions + (layer_outputs[1],)
|
| 702 |
-
|
| 703 |
-
hidden_states = self.layer_norm(hidden_states)
|
| 704 |
-
|
| 705 |
-
if output_hidden_states:
|
| 706 |
-
encoder_states = encoder_states + (hidden_states,)
|
| 707 |
-
|
| 708 |
-
if not return_dict:
|
| 709 |
-
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
|
| 710 |
-
return BaseModelOutput(
|
| 711 |
-
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
|
| 712 |
-
)
|
| 713 |
-
|
| 714 |
-
|
| 715 |
-
class LDMBertModel(LDMBertPreTrainedModel):
|
| 716 |
-
_no_split_modules = []
|
| 717 |
-
|
| 718 |
-
def __init__(self, config: LDMBertConfig):
|
| 719 |
-
super().__init__(config)
|
| 720 |
-
self.model = LDMBertEncoder(config)
|
| 721 |
-
self.to_logits = nn.Linear(config.hidden_size, config.vocab_size)
|
| 722 |
-
|
| 723 |
-
def forward(
|
| 724 |
-
self,
|
| 725 |
-
input_ids=None,
|
| 726 |
-
attention_mask=None,
|
| 727 |
-
position_ids=None,
|
| 728 |
-
head_mask=None,
|
| 729 |
-
inputs_embeds=None,
|
| 730 |
-
output_attentions=None,
|
| 731 |
-
output_hidden_states=None,
|
| 732 |
-
return_dict=None,
|
| 733 |
-
):
|
| 734 |
-
outputs = self.model(
|
| 735 |
-
input_ids,
|
| 736 |
-
attention_mask=attention_mask,
|
| 737 |
-
position_ids=position_ids,
|
| 738 |
-
head_mask=head_mask,
|
| 739 |
-
inputs_embeds=inputs_embeds,
|
| 740 |
-
output_attentions=output_attentions,
|
| 741 |
-
output_hidden_states=output_hidden_states,
|
| 742 |
-
return_dict=return_dict,
|
| 743 |
-
)
|
| 744 |
-
return outputs
|
|
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|
spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r101-d8_769x769_40k_cityscapes.py
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
_base_ = './psanet_r50-d8_769x769_40k_cityscapes.py'
|
| 2 |
-
model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
|
|
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|
spaces/Ariharasudhan/YoloV5/utils/flask_rest_api/README.md
DELETED
|
@@ -1,73 +0,0 @@
|
|
| 1 |
-
# Flask REST API
|
| 2 |
-
|
| 3 |
-
[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are
|
| 4 |
-
commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API
|
| 5 |
-
created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
|
| 6 |
-
|
| 7 |
-
## Requirements
|
| 8 |
-
|
| 9 |
-
[Flask](https://palletsprojects.com/p/flask/) is required. Install with:
|
| 10 |
-
|
| 11 |
-
```shell
|
| 12 |
-
$ pip install Flask
|
| 13 |
-
```
|
| 14 |
-
|
| 15 |
-
## Run
|
| 16 |
-
|
| 17 |
-
After Flask installation run:
|
| 18 |
-
|
| 19 |
-
```shell
|
| 20 |
-
$ python3 restapi.py --port 5000
|
| 21 |
-
```
|
| 22 |
-
|
| 23 |
-
Then use [curl](https://curl.se/) to perform a request:
|
| 24 |
-
|
| 25 |
-
```shell
|
| 26 |
-
$ curl -X POST -F [email protected] 'http://localhost:5000/v1/object-detection/yolov5s'
|
| 27 |
-
```
|
| 28 |
-
|
| 29 |
-
The model inference results are returned as a JSON response:
|
| 30 |
-
|
| 31 |
-
```json
|
| 32 |
-
[
|
| 33 |
-
{
|
| 34 |
-
"class": 0,
|
| 35 |
-
"confidence": 0.8900438547,
|
| 36 |
-
"height": 0.9318675399,
|
| 37 |
-
"name": "person",
|
| 38 |
-
"width": 0.3264600933,
|
| 39 |
-
"xcenter": 0.7438579798,
|
| 40 |
-
"ycenter": 0.5207948685
|
| 41 |
-
},
|
| 42 |
-
{
|
| 43 |
-
"class": 0,
|
| 44 |
-
"confidence": 0.8440024257,
|
| 45 |
-
"height": 0.7155083418,
|
| 46 |
-
"name": "person",
|
| 47 |
-
"width": 0.6546785235,
|
| 48 |
-
"xcenter": 0.427829951,
|
| 49 |
-
"ycenter": 0.6334488392
|
| 50 |
-
},
|
| 51 |
-
{
|
| 52 |
-
"class": 27,
|
| 53 |
-
"confidence": 0.3771208823,
|
| 54 |
-
"height": 0.3902671337,
|
| 55 |
-
"name": "tie",
|
| 56 |
-
"width": 0.0696444362,
|
| 57 |
-
"xcenter": 0.3675483763,
|
| 58 |
-
"ycenter": 0.7991207838
|
| 59 |
-
},
|
| 60 |
-
{
|
| 61 |
-
"class": 27,
|
| 62 |
-
"confidence": 0.3527112305,
|
| 63 |
-
"height": 0.1540903747,
|
| 64 |
-
"name": "tie",
|
| 65 |
-
"width": 0.0336618312,
|
| 66 |
-
"xcenter": 0.7814827561,
|
| 67 |
-
"ycenter": 0.5065554976
|
| 68 |
-
}
|
| 69 |
-
]
|
| 70 |
-
```
|
| 71 |
-
|
| 72 |
-
An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given
|
| 73 |
-
in `example_request.py`
|
|
|
|
|
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|
|
spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/depends.py
DELETED
|
@@ -1,176 +0,0 @@
|
|
| 1 |
-
import sys
|
| 2 |
-
import marshal
|
| 3 |
-
import contextlib
|
| 4 |
-
import dis
|
| 5 |
-
|
| 6 |
-
from setuptools.extern.packaging import version
|
| 7 |
-
|
| 8 |
-
from ._imp import find_module, PY_COMPILED, PY_FROZEN, PY_SOURCE
|
| 9 |
-
from . import _imp
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
__all__ = [
|
| 13 |
-
'Require', 'find_module', 'get_module_constant', 'extract_constant'
|
| 14 |
-
]
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
class Require:
|
| 18 |
-
"""A prerequisite to building or installing a distribution"""
|
| 19 |
-
|
| 20 |
-
def __init__(
|
| 21 |
-
self, name, requested_version, module, homepage='',
|
| 22 |
-
attribute=None, format=None):
|
| 23 |
-
|
| 24 |
-
if format is None and requested_version is not None:
|
| 25 |
-
format = version.Version
|
| 26 |
-
|
| 27 |
-
if format is not None:
|
| 28 |
-
requested_version = format(requested_version)
|
| 29 |
-
if attribute is None:
|
| 30 |
-
attribute = '__version__'
|
| 31 |
-
|
| 32 |
-
self.__dict__.update(locals())
|
| 33 |
-
del self.self
|
| 34 |
-
|
| 35 |
-
def full_name(self):
|
| 36 |
-
"""Return full package/distribution name, w/version"""
|
| 37 |
-
if self.requested_version is not None:
|
| 38 |
-
return '%s-%s' % (self.name, self.requested_version)
|
| 39 |
-
return self.name
|
| 40 |
-
|
| 41 |
-
def version_ok(self, version):
|
| 42 |
-
"""Is 'version' sufficiently up-to-date?"""
|
| 43 |
-
return self.attribute is None or self.format is None or \
|
| 44 |
-
str(version) != "unknown" and self.format(version) >= self.requested_version
|
| 45 |
-
|
| 46 |
-
def get_version(self, paths=None, default="unknown"):
|
| 47 |
-
"""Get version number of installed module, 'None', or 'default'
|
| 48 |
-
|
| 49 |
-
Search 'paths' for module. If not found, return 'None'. If found,
|
| 50 |
-
return the extracted version attribute, or 'default' if no version
|
| 51 |
-
attribute was specified, or the value cannot be determined without
|
| 52 |
-
importing the module. The version is formatted according to the
|
| 53 |
-
requirement's version format (if any), unless it is 'None' or the
|
| 54 |
-
supplied 'default'.
|
| 55 |
-
"""
|
| 56 |
-
|
| 57 |
-
if self.attribute is None:
|
| 58 |
-
try:
|
| 59 |
-
f, p, i = find_module(self.module, paths)
|
| 60 |
-
if f:
|
| 61 |
-
f.close()
|
| 62 |
-
return default
|
| 63 |
-
except ImportError:
|
| 64 |
-
return None
|
| 65 |
-
|
| 66 |
-
v = get_module_constant(self.module, self.attribute, default, paths)
|
| 67 |
-
|
| 68 |
-
if v is not None and v is not default and self.format is not None:
|
| 69 |
-
return self.format(v)
|
| 70 |
-
|
| 71 |
-
return v
|
| 72 |
-
|
| 73 |
-
def is_present(self, paths=None):
|
| 74 |
-
"""Return true if dependency is present on 'paths'"""
|
| 75 |
-
return self.get_version(paths) is not None
|
| 76 |
-
|
| 77 |
-
def is_current(self, paths=None):
|
| 78 |
-
"""Return true if dependency is present and up-to-date on 'paths'"""
|
| 79 |
-
version = self.get_version(paths)
|
| 80 |
-
if version is None:
|
| 81 |
-
return False
|
| 82 |
-
return self.version_ok(str(version))
|
| 83 |
-
|
| 84 |
-
|
| 85 |
-
def maybe_close(f):
|
| 86 |
-
@contextlib.contextmanager
|
| 87 |
-
def empty():
|
| 88 |
-
yield
|
| 89 |
-
return
|
| 90 |
-
if not f:
|
| 91 |
-
return empty()
|
| 92 |
-
|
| 93 |
-
return contextlib.closing(f)
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
def get_module_constant(module, symbol, default=-1, paths=None):
|
| 97 |
-
"""Find 'module' by searching 'paths', and extract 'symbol'
|
| 98 |
-
|
| 99 |
-
Return 'None' if 'module' does not exist on 'paths', or it does not define
|
| 100 |
-
'symbol'. If the module defines 'symbol' as a constant, return the
|
| 101 |
-
constant. Otherwise, return 'default'."""
|
| 102 |
-
|
| 103 |
-
try:
|
| 104 |
-
f, path, (suffix, mode, kind) = info = find_module(module, paths)
|
| 105 |
-
except ImportError:
|
| 106 |
-
# Module doesn't exist
|
| 107 |
-
return None
|
| 108 |
-
|
| 109 |
-
with maybe_close(f):
|
| 110 |
-
if kind == PY_COMPILED:
|
| 111 |
-
f.read(8) # skip magic & date
|
| 112 |
-
code = marshal.load(f)
|
| 113 |
-
elif kind == PY_FROZEN:
|
| 114 |
-
code = _imp.get_frozen_object(module, paths)
|
| 115 |
-
elif kind == PY_SOURCE:
|
| 116 |
-
code = compile(f.read(), path, 'exec')
|
| 117 |
-
else:
|
| 118 |
-
# Not something we can parse; we'll have to import it. :(
|
| 119 |
-
imported = _imp.get_module(module, paths, info)
|
| 120 |
-
return getattr(imported, symbol, None)
|
| 121 |
-
|
| 122 |
-
return extract_constant(code, symbol, default)
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
def extract_constant(code, symbol, default=-1):
|
| 126 |
-
"""Extract the constant value of 'symbol' from 'code'
|
| 127 |
-
|
| 128 |
-
If the name 'symbol' is bound to a constant value by the Python code
|
| 129 |
-
object 'code', return that value. If 'symbol' is bound to an expression,
|
| 130 |
-
return 'default'. Otherwise, return 'None'.
|
| 131 |
-
|
| 132 |
-
Return value is based on the first assignment to 'symbol'. 'symbol' must
|
| 133 |
-
be a global, or at least a non-"fast" local in the code block. That is,
|
| 134 |
-
only 'STORE_NAME' and 'STORE_GLOBAL' opcodes are checked, and 'symbol'
|
| 135 |
-
must be present in 'code.co_names'.
|
| 136 |
-
"""
|
| 137 |
-
if symbol not in code.co_names:
|
| 138 |
-
# name's not there, can't possibly be an assignment
|
| 139 |
-
return None
|
| 140 |
-
|
| 141 |
-
name_idx = list(code.co_names).index(symbol)
|
| 142 |
-
|
| 143 |
-
STORE_NAME = 90
|
| 144 |
-
STORE_GLOBAL = 97
|
| 145 |
-
LOAD_CONST = 100
|
| 146 |
-
|
| 147 |
-
const = default
|
| 148 |
-
|
| 149 |
-
for byte_code in dis.Bytecode(code):
|
| 150 |
-
op = byte_code.opcode
|
| 151 |
-
arg = byte_code.arg
|
| 152 |
-
|
| 153 |
-
if op == LOAD_CONST:
|
| 154 |
-
const = code.co_consts[arg]
|
| 155 |
-
elif arg == name_idx and (op == STORE_NAME or op == STORE_GLOBAL):
|
| 156 |
-
return const
|
| 157 |
-
else:
|
| 158 |
-
const = default
|
| 159 |
-
|
| 160 |
-
|
| 161 |
-
def _update_globals():
|
| 162 |
-
"""
|
| 163 |
-
Patch the globals to remove the objects not available on some platforms.
|
| 164 |
-
|
| 165 |
-
XXX it'd be better to test assertions about bytecode instead.
|
| 166 |
-
"""
|
| 167 |
-
|
| 168 |
-
if not sys.platform.startswith('java') and sys.platform != 'cli':
|
| 169 |
-
return
|
| 170 |
-
incompatible = 'extract_constant', 'get_module_constant'
|
| 171 |
-
for name in incompatible:
|
| 172 |
-
del globals()[name]
|
| 173 |
-
__all__.remove(name)
|
| 174 |
-
|
| 175 |
-
|
| 176 |
-
_update_globals()
|
|
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|
spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/datasets/prepare_for_tests.sh
DELETED
|
@@ -1,31 +0,0 @@
|
|
| 1 |
-
#!/bin/bash -e
|
| 2 |
-
# Copyright (c) Facebook, Inc. and its affiliates.
|
| 3 |
-
|
| 4 |
-
# Download the mini dataset (coco val2017_100, with only 100 images)
|
| 5 |
-
# to be used in unittests & integration tests.
|
| 6 |
-
|
| 7 |
-
cd "${0%/*}"
|
| 8 |
-
|
| 9 |
-
BASE=https://dl.fbaipublicfiles.com/detectron2
|
| 10 |
-
ROOT=${DETECTRON2_DATASETS:-./}
|
| 11 |
-
ROOT=${ROOT/#\~/$HOME} # expand ~ to HOME
|
| 12 |
-
mkdir -p $ROOT/coco/annotations
|
| 13 |
-
|
| 14 |
-
for anno in instances_val2017_100 \
|
| 15 |
-
person_keypoints_val2017_100 ; do
|
| 16 |
-
|
| 17 |
-
dest=$ROOT/coco/annotations/$anno.json
|
| 18 |
-
[[ -s $dest ]] && {
|
| 19 |
-
echo "$dest exists. Skipping ..."
|
| 20 |
-
} || {
|
| 21 |
-
wget $BASE/annotations/coco/$anno.json -O $dest
|
| 22 |
-
}
|
| 23 |
-
done
|
| 24 |
-
|
| 25 |
-
dest=$ROOT/coco/val2017_100.tgz
|
| 26 |
-
[[ -d $ROOT/coco/val2017 ]] && {
|
| 27 |
-
echo "$ROOT/coco/val2017 exists. Skipping ..."
|
| 28 |
-
} || {
|
| 29 |
-
wget $BASE/annotations/coco/val2017_100.tgz -O $dest
|
| 30 |
-
tar xzf $dest -C $ROOT/coco/ && rm -f $dest
|
| 31 |
-
}
|
|
|
|
|
|
|
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|
|
|
spaces/Bart92/RVC_HF/infer/modules/ipex/hijacks.py
DELETED
|
@@ -1,196 +0,0 @@
|
|
| 1 |
-
import contextlib
|
| 2 |
-
import importlib
|
| 3 |
-
import torch
|
| 4 |
-
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
|
| 5 |
-
|
| 6 |
-
# pylint: disable=protected-access, missing-function-docstring, line-too-long, unnecessary-lambda, no-else-return
|
| 7 |
-
|
| 8 |
-
class CondFunc: # pylint: disable=missing-class-docstring
|
| 9 |
-
def __new__(cls, orig_func, sub_func, cond_func):
|
| 10 |
-
self = super(CondFunc, cls).__new__(cls)
|
| 11 |
-
if isinstance(orig_func, str):
|
| 12 |
-
func_path = orig_func.split('.')
|
| 13 |
-
for i in range(len(func_path)-1, -1, -1):
|
| 14 |
-
try:
|
| 15 |
-
resolved_obj = importlib.import_module('.'.join(func_path[:i]))
|
| 16 |
-
break
|
| 17 |
-
except ImportError:
|
| 18 |
-
pass
|
| 19 |
-
for attr_name in func_path[i:-1]:
|
| 20 |
-
resolved_obj = getattr(resolved_obj, attr_name)
|
| 21 |
-
orig_func = getattr(resolved_obj, func_path[-1])
|
| 22 |
-
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
|
| 23 |
-
self.__init__(orig_func, sub_func, cond_func)
|
| 24 |
-
return lambda *args, **kwargs: self(*args, **kwargs)
|
| 25 |
-
def __init__(self, orig_func, sub_func, cond_func):
|
| 26 |
-
self.__orig_func = orig_func
|
| 27 |
-
self.__sub_func = sub_func
|
| 28 |
-
self.__cond_func = cond_func
|
| 29 |
-
def __call__(self, *args, **kwargs):
|
| 30 |
-
if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
|
| 31 |
-
return self.__sub_func(self.__orig_func, *args, **kwargs)
|
| 32 |
-
else:
|
| 33 |
-
return self.__orig_func(*args, **kwargs)
|
| 34 |
-
|
| 35 |
-
_utils = torch.utils.data._utils
|
| 36 |
-
def _shutdown_workers(self):
|
| 37 |
-
if torch.utils.data._utils is None or torch.utils.data._utils.python_exit_status is True or torch.utils.data._utils.python_exit_status is None:
|
| 38 |
-
return
|
| 39 |
-
if hasattr(self, "_shutdown") and not self._shutdown:
|
| 40 |
-
self._shutdown = True
|
| 41 |
-
try:
|
| 42 |
-
if hasattr(self, '_pin_memory_thread'):
|
| 43 |
-
self._pin_memory_thread_done_event.set()
|
| 44 |
-
self._worker_result_queue.put((None, None))
|
| 45 |
-
self._pin_memory_thread.join()
|
| 46 |
-
self._worker_result_queue.cancel_join_thread()
|
| 47 |
-
self._worker_result_queue.close()
|
| 48 |
-
self._workers_done_event.set()
|
| 49 |
-
for worker_id in range(len(self._workers)):
|
| 50 |
-
if self._persistent_workers or self._workers_status[worker_id]:
|
| 51 |
-
self._mark_worker_as_unavailable(worker_id, shutdown=True)
|
| 52 |
-
for w in self._workers: # pylint: disable=invalid-name
|
| 53 |
-
w.join(timeout=torch.utils.data._utils.MP_STATUS_CHECK_INTERVAL)
|
| 54 |
-
for q in self._index_queues: # pylint: disable=invalid-name
|
| 55 |
-
q.cancel_join_thread()
|
| 56 |
-
q.close()
|
| 57 |
-
finally:
|
| 58 |
-
if self._worker_pids_set:
|
| 59 |
-
torch.utils.data._utils.signal_handling._remove_worker_pids(id(self))
|
| 60 |
-
self._worker_pids_set = False
|
| 61 |
-
for w in self._workers: # pylint: disable=invalid-name
|
| 62 |
-
if w.is_alive():
|
| 63 |
-
w.terminate()
|
| 64 |
-
|
| 65 |
-
class DummyDataParallel(torch.nn.Module): # pylint: disable=missing-class-docstring, unused-argument, too-few-public-methods
|
| 66 |
-
def __new__(cls, module, device_ids=None, output_device=None, dim=0): # pylint: disable=unused-argument
|
| 67 |
-
if isinstance(device_ids, list) and len(device_ids) > 1:
|
| 68 |
-
print("IPEX backend doesn't support DataParallel on multiple XPU devices")
|
| 69 |
-
return module.to("xpu")
|
| 70 |
-
|
| 71 |
-
def return_null_context(*args, **kwargs): # pylint: disable=unused-argument
|
| 72 |
-
return contextlib.nullcontext()
|
| 73 |
-
|
| 74 |
-
def check_device(device):
|
| 75 |
-
return bool((isinstance(device, torch.device) and device.type == "cuda") or (isinstance(device, str) and "cuda" in device) or isinstance(device, int))
|
| 76 |
-
|
| 77 |
-
def return_xpu(device):
|
| 78 |
-
return f"xpu:{device[-1]}" if isinstance(device, str) and ":" in device else f"xpu:{device}" if isinstance(device, int) else torch.device("xpu") if isinstance(device, torch.device) else "xpu"
|
| 79 |
-
|
| 80 |
-
def ipex_no_cuda(orig_func, *args, **kwargs):
|
| 81 |
-
torch.cuda.is_available = lambda: False
|
| 82 |
-
orig_func(*args, **kwargs)
|
| 83 |
-
torch.cuda.is_available = torch.xpu.is_available
|
| 84 |
-
|
| 85 |
-
original_autocast = torch.autocast
|
| 86 |
-
def ipex_autocast(*args, **kwargs):
|
| 87 |
-
if len(args) > 0 and args[0] == "cuda":
|
| 88 |
-
return original_autocast("xpu", *args[1:], **kwargs)
|
| 89 |
-
else:
|
| 90 |
-
return original_autocast(*args, **kwargs)
|
| 91 |
-
|
| 92 |
-
original_torch_cat = torch.cat
|
| 93 |
-
def torch_cat(tensor, *args, **kwargs):
|
| 94 |
-
if len(tensor) == 3 and (tensor[0].dtype != tensor[1].dtype or tensor[2].dtype != tensor[1].dtype):
|
| 95 |
-
return original_torch_cat([tensor[0].to(tensor[1].dtype), tensor[1], tensor[2].to(tensor[1].dtype)], *args, **kwargs)
|
| 96 |
-
else:
|
| 97 |
-
return original_torch_cat(tensor, *args, **kwargs)
|
| 98 |
-
|
| 99 |
-
original_interpolate = torch.nn.functional.interpolate
|
| 100 |
-
def interpolate(tensor, size=None, scale_factor=None, mode='nearest', align_corners=None, recompute_scale_factor=None, antialias=False): # pylint: disable=too-many-arguments
|
| 101 |
-
if antialias or align_corners is not None:
|
| 102 |
-
return_device = tensor.device
|
| 103 |
-
return_dtype = tensor.dtype
|
| 104 |
-
return original_interpolate(tensor.to("cpu", dtype=torch.float32), size=size, scale_factor=scale_factor, mode=mode,
|
| 105 |
-
align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias).to(return_device, dtype=return_dtype)
|
| 106 |
-
else:
|
| 107 |
-
return original_interpolate(tensor, size=size, scale_factor=scale_factor, mode=mode,
|
| 108 |
-
align_corners=align_corners, recompute_scale_factor=recompute_scale_factor, antialias=antialias)
|
| 109 |
-
|
| 110 |
-
original_linalg_solve = torch.linalg.solve
|
| 111 |
-
def linalg_solve(A, B, *args, **kwargs): # pylint: disable=invalid-name
|
| 112 |
-
if A.device != torch.device("cpu") or B.device != torch.device("cpu"):
|
| 113 |
-
return_device = A.device
|
| 114 |
-
return original_linalg_solve(A.to("cpu"), B.to("cpu"), *args, **kwargs).to(return_device)
|
| 115 |
-
else:
|
| 116 |
-
return original_linalg_solve(A, B, *args, **kwargs)
|
| 117 |
-
|
| 118 |
-
def ipex_hijacks():
|
| 119 |
-
CondFunc('torch.Tensor.to',
|
| 120 |
-
lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs),
|
| 121 |
-
lambda orig_func, self, device=None, *args, **kwargs: check_device(device))
|
| 122 |
-
CondFunc('torch.Tensor.cuda',
|
| 123 |
-
lambda orig_func, self, device=None, *args, **kwargs: orig_func(self, return_xpu(device), *args, **kwargs),
|
| 124 |
-
lambda orig_func, self, device=None, *args, **kwargs: check_device(device))
|
| 125 |
-
CondFunc('torch.empty',
|
| 126 |
-
lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
|
| 127 |
-
lambda orig_func, *args, device=None, **kwargs: check_device(device))
|
| 128 |
-
CondFunc('torch.load',
|
| 129 |
-
lambda orig_func, *args, map_location=None, **kwargs: orig_func(*args, return_xpu(map_location), **kwargs),
|
| 130 |
-
lambda orig_func, *args, map_location=None, **kwargs: map_location is None or check_device(map_location))
|
| 131 |
-
CondFunc('torch.randn',
|
| 132 |
-
lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
|
| 133 |
-
lambda orig_func, *args, device=None, **kwargs: check_device(device))
|
| 134 |
-
CondFunc('torch.ones',
|
| 135 |
-
lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
|
| 136 |
-
lambda orig_func, *args, device=None, **kwargs: check_device(device))
|
| 137 |
-
CondFunc('torch.zeros',
|
| 138 |
-
lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
|
| 139 |
-
lambda orig_func, *args, device=None, **kwargs: check_device(device))
|
| 140 |
-
CondFunc('torch.tensor',
|
| 141 |
-
lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
|
| 142 |
-
lambda orig_func, *args, device=None, **kwargs: check_device(device))
|
| 143 |
-
CondFunc('torch.linspace',
|
| 144 |
-
lambda orig_func, *args, device=None, **kwargs: orig_func(*args, device=return_xpu(device), **kwargs),
|
| 145 |
-
lambda orig_func, *args, device=None, **kwargs: check_device(device))
|
| 146 |
-
|
| 147 |
-
CondFunc('torch.Generator',
|
| 148 |
-
lambda orig_func, device=None: torch.xpu.Generator(device),
|
| 149 |
-
lambda orig_func, device=None: device is not None and device != torch.device("cpu") and device != "cpu")
|
| 150 |
-
|
| 151 |
-
CondFunc('torch.batch_norm',
|
| 152 |
-
lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
|
| 153 |
-
weight if weight is not None else torch.ones(input.size()[1], device=input.device),
|
| 154 |
-
bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
|
| 155 |
-
lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
|
| 156 |
-
CondFunc('torch.instance_norm',
|
| 157 |
-
lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(input,
|
| 158 |
-
weight if weight is not None else torch.ones(input.size()[1], device=input.device),
|
| 159 |
-
bias if bias is not None else torch.zeros(input.size()[1], device=input.device), *args, **kwargs),
|
| 160 |
-
lambda orig_func, input, *args, **kwargs: input.device != torch.device("cpu"))
|
| 161 |
-
|
| 162 |
-
#Functions with dtype errors:
|
| 163 |
-
CondFunc('torch.nn.modules.GroupNorm.forward',
|
| 164 |
-
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
|
| 165 |
-
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
|
| 166 |
-
CondFunc('torch.nn.modules.linear.Linear.forward',
|
| 167 |
-
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
|
| 168 |
-
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
|
| 169 |
-
CondFunc('torch.nn.modules.conv.Conv2d.forward',
|
| 170 |
-
lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
|
| 171 |
-
lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
|
| 172 |
-
CondFunc('torch.nn.functional.layer_norm',
|
| 173 |
-
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
|
| 174 |
-
orig_func(input.to(weight.data.dtype), normalized_shape, weight, *args, **kwargs),
|
| 175 |
-
lambda orig_func, input, normalized_shape=None, weight=None, *args, **kwargs:
|
| 176 |
-
weight is not None and input.dtype != weight.data.dtype)
|
| 177 |
-
|
| 178 |
-
#Diffusers Float64 (ARC GPUs doesn't support double or Float64):
|
| 179 |
-
if not torch.xpu.has_fp64_dtype():
|
| 180 |
-
CondFunc('torch.from_numpy',
|
| 181 |
-
lambda orig_func, ndarray: orig_func(ndarray.astype('float32')),
|
| 182 |
-
lambda orig_func, ndarray: ndarray.dtype == float)
|
| 183 |
-
|
| 184 |
-
#Broken functions when torch.cuda.is_available is True:
|
| 185 |
-
CondFunc('torch.utils.data.dataloader._BaseDataLoaderIter.__init__',
|
| 186 |
-
lambda orig_func, *args, **kwargs: ipex_no_cuda(orig_func, *args, **kwargs),
|
| 187 |
-
lambda orig_func, *args, **kwargs: True)
|
| 188 |
-
|
| 189 |
-
#Functions that make compile mad with CondFunc:
|
| 190 |
-
torch.utils.data.dataloader._MultiProcessingDataLoaderIter._shutdown_workers = _shutdown_workers
|
| 191 |
-
torch.nn.DataParallel = DummyDataParallel
|
| 192 |
-
torch.autocast = ipex_autocast
|
| 193 |
-
torch.cat = torch_cat
|
| 194 |
-
torch.linalg.solve = linalg_solve
|
| 195 |
-
torch.nn.functional.interpolate = interpolate
|
| 196 |
-
torch.backends.cuda.sdp_kernel = return_null_context
|
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|
spaces/Benebene/Chat-question-answering/utils.py
DELETED
|
@@ -1,44 +0,0 @@
|
|
| 1 |
-
from sentence_transformers import SentenceTransformer, util
|
| 2 |
-
import datasets as ds
|
| 3 |
-
|
| 4 |
-
import os
|
| 5 |
-
|
| 6 |
-
ERROR_MESSAGE = "We are sorry, we haven't found the answer to your request."
|
| 7 |
-
|
| 8 |
-
class Stuff:
|
| 9 |
-
|
| 10 |
-
def __init__(self):
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
self.datas = ds.load_from_disk(os.path.join("stackexchange_astronomy"))
|
| 14 |
-
self.model = SentenceTransformer('all-MiniLM-L6-v2')
|
| 15 |
-
self.embeddings = [self.model.encode(data['title_body']) for data in self.datas['train']]
|
| 16 |
-
|
| 17 |
-
|
| 18 |
-
def most_similar(self, question: str) -> int:
|
| 19 |
-
|
| 20 |
-
q = self.model.encode(question)
|
| 21 |
-
max_cos_sim = -1
|
| 22 |
-
|
| 23 |
-
for i, emb in enumerate(self.embeddings):
|
| 24 |
-
cos_sim = util.cos_sim(emb, q)
|
| 25 |
-
if cos_sim > max_cos_sim:
|
| 26 |
-
max_cos_sim = cos_sim
|
| 27 |
-
final_index = i
|
| 28 |
-
|
| 29 |
-
if max_cos_sim < 0.7:
|
| 30 |
-
return None
|
| 31 |
-
|
| 32 |
-
return final_index
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
def get_answer(self, question: str) -> str:
|
| 36 |
-
|
| 37 |
-
best_index = self.most_similar(question)
|
| 38 |
-
|
| 39 |
-
if best_index is None:
|
| 40 |
-
return ERROR_MESSAGE
|
| 41 |
-
|
| 42 |
-
return self.datas['train'][best_index]['upvoted_answer']
|
| 43 |
-
|
| 44 |
-
|
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|
spaces/Benson/text-generation/Examples/Descargar Fuente Clash Of Clans.md
DELETED
|
@@ -1,164 +0,0 @@
|
|
| 1 |
-
<br />
|
| 2 |
-
<h1>Cómo descargar e instalar el choque de fuentes de clanes</h1>
|
| 3 |
-
<p>Si eres un fan del juego de estrategia móvil <strong>Clash of Clans</strong>, es posible que te hayas preguntado cómo obtener la misma fuente que se utiliza para su logotipo y marca. En este artículo, te mostraremos cómo encontrar, descargar e instalar la fuente <strong>Clash of Clans</strong> en tu PC, para que puedas usarla para tus propios proyectos y diseños. </p>
|
| 4 |
-
<h2>descargar fuente clash of clans</h2><br /><p><b><b>Download Zip</b> 🗸 <a href="https://bltlly.com/2v6K25">https://bltlly.com/2v6K25</a></b></p><br /><br />
|
| 5 |
-
<h2>¿Qué es el choque de clanes y por qué necesita su fuente</h2>
|
| 6 |
-
<h3>Choque de clanes: Un popular juego de estrategia móvil</h3>
|
| 7 |
-
<p>Clash of Clans es un videojuego móvil de estrategia MMO desarrollado y publicado por Supercell, una compañía de videojuegos con sede en Helsinki, Finlandia. El juego fue lanzado en 2012 para dispositivos iOS y en 2013 para dispositivos Android. Desde entonces se ha convertido en uno de los juegos móviles más populares y rentables del mundo, con más de 500 millones de descargas y millones de jugadores activos. </p>
|
| 8 |
-
<p>El juego se desarrolla en un mundo de fantasía donde los jugadores construyen sus propias aldeas, entrenan a sus tropas y compiten con otros jugadores en las guerras de clanes. El juego también cuenta con un modo de campaña para un solo jugador, donde los jugadores pueden atacar pueblos goblin y ganar recursos. El juego es gratis, pero los jugadores también pueden comprar divisas y objetos con dinero real. </p>
|
| 9 |
-
<h3>La fuente utilizada para el logotipo y la marca de choque de clanes</h3>
|
| 10 |
-
<p>La fuente utilizada para el logo y la marca de Clash of Clans es probablemente <strong>You Blockhead</strong>. Diseñado por John Roshell, You Blockhead es un tipo de letra de bloque de cómic disponible en cuatro fuentes: regular y esquema, cada uno con una versión en mayúsculas. La fuente tiene una postura robusta y más de 100 pares de letras amistosos entrelazados, dándole un aspecto lúdico y dinámico. La fuente también se usó para el logo y la marca de otro juego de Supercell, <strong>Clash Royale</strong>. </p>
|
| 11 |
-
|
| 12 |
-
<h2>Cómo encontrar y descargar la fuente Clash of Clans</h2>
|
| 13 |
-
<h3>Los mejores sitios web para descargar fuentes de juegos gratis y premium</h3>
|
| 14 |
-
<p>Si desea descargar fuentes de juegos, puede usar la Microsoft Store o una fuente web. La tienda de Microsoft ofrece una variedad de fuentes que son compatibles con Windows 10 dispositivos. Para acceder a él, vaya a Configuración > Personalización > Fuentes > Obtener más fuentes en Microsoft Store. Elija una fuente y seleccione Obtener. La fuente se descargará e instalará automáticamente. </p>
|
| 15 |
-
<p></p>
|
| 16 |
-
<p>Si prefiere usar una fuente web, hay muchos sitios web que ofrecen fuentes de juegos gratuitas y premium. Algunos ejemplos son:</p>
|
| 17 |
-
<ul>
|
| 18 |
-
<li><a href="( 1 )">DaFont</a>: Un sitio web que cuenta con miles de fuentes gratuitas en varias categorías, incluyendo juegos. Puede navegar por categoría o tipo, o buscar por nombre o palabra clave. También puede previsualizar cómo se ven las fuentes antes de descargarlas. </li>
|
| 19 |
-
<li><a href="( 2 )">1001 Free Fonts </a></li>: Un sitio web que ofrece más de 10.000 fuentes gratuitas en varios estilos, incluyendo juegos. Puede navegar por categoría o alfabeto, o buscar por nombre o palabra clave. También puede personalizar el tamaño de la fuente, el color y el fondo antes de descargarlos. </li>
|
| 20 |
-
<li><a href="">FontSpace</a>: Un sitio web que cuenta con más de 32,000 fuentes gratuitas de diseñadores independientes, incluyendo juegos. Puede navegar por categoría o diseñador, o buscar por nombre o palabra clave. También puede filtrar por tipo de licencia, popularidad o fecha añadida antes de descargarlos. </li>
|
| 21 |
-
<li><a href="">Font Squirrel</a>: Un sitio web que ofrece solo fuentes gratuitas y de alta calidad que están autorizadas para uso comercial, incluyendo juegos. Puede navegar por categoría o etiqueta, o buscar por nombre o palabra clave. También puede usar la herramienta de identificación de fuente para encontrar fuentes de imágenes. </li>
|
| 22 |
-
|
| 23 |
-
</ul>
|
| 24 |
-
<h3>Cómo elegir el formato de fuente adecuado para su dispositivo</h3>
|
| 25 |
-
<p>Antes de descargar una fuente, debe asegurarse de que sea compatible con su dispositivo y software. Hay diferentes tipos de formatos de fuente, como TrueType (.ttf), OpenType (.otf), Web Open Font Format (.woff) y Embedded OpenType (.eot). Cada formato tiene sus propias ventajas y desventajas, dependiendo de la plataforma y la aplicación que esté utilizando. </p>
|
| 26 |
-
<p>En términos generales, TrueType y OpenType son los formatos de fuente más comunes y versátiles que funcionan en la mayoría de los dispositivos y software. Soportan una amplia gama de personajes y características, como kerning, ligaduras y alternantes. Web Open Font Format e Embedded OpenType se utilizan principalmente para el diseño y desarrollo web, ya que permiten incrustar y mostrar fuentes en navegadores web. </p>
|
| 27 |
-
<p>Para elegir el formato de fuente adecuado para su dispositivo, debe verificar la compatibilidad y los requisitos de su sistema operativo y software. Por ejemplo, Windows 10 es compatible con TrueType, OpenType, Web Open Font Format 2.0 y OpenType integrado; mientras que Mac OS X admite TrueType, OpenType y Web Open Font Format 1.0. Algunos programas también pueden tener formatos de fuente específicos que soportan o recomiendan. </p>
|
| 28 |
-
<h3>Cómo descargar la fuente de choque de clanes de una fuente de confianza</h3>
|
| 29 |
-
<p>Una vez que hayas encontrado la fuente Clash of Clans o una similar que te guste, necesitas descargarla de una fuente confiable. Una fuente confiable es un sitio web que ofrece fuentes legales y seguras que están libres de virus, malware o spyware. También debe leer los términos y condiciones de la licencia de fuentes antes de descargarla, ya que algunas fuentes pueden tener restricciones sobre cómo puede usarlas. </p>
|
| 30 |
-
<p>Para descargar la fuente Clash of Clans desde una fuente de confianza, sigue estos pasos:</p>
|
| 31 |
-
<ol>
|
| 32 |
-
<li>Ir a la página web donde la fuente está disponible y haga clic en el botón de descarga o enlace. </li>
|
| 33 |
-
<li> Elija una ubicación en su computadora donde desea guardar el archivo de fuente y haga clic en guardar. </li>
|
| 34 |
-
|
| 35 |
-
</ol>
|
| 36 |
-
<p>Aquí hay un ejemplo de cómo descargar la fuente Game Day de DaFont:</p>
|
| 37 |
-
<tabla>
|
| 38 |
-
<tr>
|
| 39 |
-
<th>Paso</th>
|
| 40 |
-
<th>Captura de pantalla</th>
|
| 41 |
-
<th>Descripción</th>
|
| 42 |
-
</tr>
|
| 43 |
-
<tr>
|
| 44 |
-
<td>1</td>
|
| 45 |
-
<td><img src="" alt="Ir al sitio web de DaFont"></td>
|
| 46 |
-
<td>Vaya a <a href=">DaFont</a> sitio web y escriba Game Day en el cuadro de búsqueda. </td>
|
| 47 |
-
</tr>
|
| 48 |
-
<tr>
|
| 49 |
-
<td>2</td>
|
| 50 |
-
<td><img src="" alt="Click on Game Day font"></td>
|
| 51 |
-
<td>Haz clic en la fuente Game Day de Iconian Fonts.</td>
|
| 52 |
-
</tr>
|
| 53 |
-
<tr>
|
| 54 |
-
<td>3</td>
|
| 55 |
-
<td><img src="" alt="Haga clic en el botón Descargar"></td>
|
| 56 |
-
<td>Haga clic en el botón Descargar en el lado derecho de la página. </td>
|
| 57 |
-
</tr>
|
| 58 |
-
<tr>
|
| 59 |
-
<td>4</td>
|
| 60 |
-
<td><img src="" alt="Elija una ubicación para guardar el archivo"></td>
|
| 61 |
-
<td>Elija una ubicación en su computadora donde desea guardar el archivo y haga clic en guardar. </td>
|
| 62 |
-
</tr>
|
| 63 |
-
<tr>
|
| 64 |
-
<td>5</td>
|
| 65 |
-
<td><img src="" alt="Comprueba si el archivo está en formato zip"></td>
|
| 66 |
-
<td>Compruebe si el archivo está en formato zip </td>
|
| 67 |
-
<td>Si el archivo está en formato zip, primero debe descomprimirlo antes de instalarlo. Puede usar un software como WinZip o 7-Zip para extraer los archivos. </td>
|
| 68 |
-
</tr>
|
| 69 |
-
</tabla>
|
| 70 |
-
<h2>Cómo instalar y utilizar el choque de fuentes de clanes en su PC</h2>
|
| 71 |
-
<h3>Cómo descomprimir los archivos de fuente y localizarlos en su computadora</h3>
|
| 72 |
-
<p>Después de haber descargado los archivos de fuente, debe descomprimirlos y localizarlos en su computadora. Para descomprimir los archivos de fuente, siga estos pasos:</p>
|
| 73 |
-
<ol>
|
| 74 |
-
<li>Haga clic derecho en el archivo zip y elija Extraer todo o Extraer aquí.</li>
|
| 75 |
-
<li>Elija una carpeta de destino donde desea extraer los archivos y haga clic en Extraer.</li>
|
| 76 |
-
<li>Espere a que la extracción se complete y abra la carpeta de destino. </li>
|
| 77 |
-
<li>Busque los archivos de fuente que tienen la extensión . ttf o .otf. Estos son los archivos que necesita instalar. </li>
|
| 78 |
-
</ol>
|
| 79 |
-
<p>Aquí hay un ejemplo de cómo descomprimir la fuente Game Day de DaFont:</p>
|
| 80 |
-
<tabla>
|
| 81 |
-
<tr>
|
| 82 |
-
<th>Paso</th>
|
| 83 |
-
<th>Captura de pantalla</th>
|
| 84 |
-
<th>Descripción</th>
|
| 85 |
-
</tr>
|
| 86 |
-
<tr>
|
| 87 |
-
<td>1</td>
|
| 88 |
-
<td><img src="" alt="Haga clic derecho en el archivo zip"></td>
|
| 89 |
-
|
| 90 |
-
</tr>
|
| 91 |
-
<tr>
|
| 92 |
-
<td>2</td>
|
| 93 |
-
<td><img src="" alt="Elija una carpeta de destino"></td>
|
| 94 |
-
<td>Elija una carpeta de destino donde desea extraer los archivos y haga clic en Extraer.</td>
|
| 95 |
-
</tr>
|
| 96 |
-
<tr>
|
| 97 |
-
<td>3</td>
|
| 98 |
-
<td><img src="" alt="Abre la carpeta de destino"></td>
|
| 99 |
-
<td>Abra la carpeta de destino y busque los archivos de fuente. </td>
|
| 100 |
-
</tr>
|
| 101 |
-
<tr>
|
| 102 |
-
<td>4</td>
|
| 103 |
-
<td><img src="" alt="Localizar los archivos de fuente"></td>
|
| 104 |
-
<td>Busque los archivos de fuente que tienen la extensión . ttf o .otf. Estos son los archivos que necesita instalar. </td>
|
| 105 |
-
</tr>
|
| 106 |
-
</tabla>
|
| 107 |
-
<h3>Cómo instalar el choque de fuentes de clanes en Windows 10</h3>
|
| 108 |
-
<p>Después de haber descomprimido y localizado los archivos de fuente, necesita instalarlos en su PC. Para instalar la fuente Clash of Clans en Windows 10, siga estos pasos:</p>
|
| 109 |
-
<ol>
|
| 110 |
-
<li> Seleccione todos los archivos de fuente que desea instalar y haga clic derecho sobre ellos. </li>
|
| 111 |
-
<li>Elegir Instalar para todos los usuarios o Instalar como administrador.</li>
|
| 112 |
-
<li>Espere a que se complete la instalación y compruebe si la fuente está disponible en su lista de fuentes. </li>
|
| 113 |
-
</ol>
|
| 114 |
-
<p>Aquí hay un ejemplo de cómo instalar la fuente Game Day en Windows 10:</p>
|
| 115 |
-
<tabla>
|
| 116 |
-
<tr>
|
| 117 |
-
<th>Paso</th>
|
| 118 |
-
<th>Captura de pantalla</th>
|
| 119 |
-
<th>Descripción</th>
|
| 120 |
-
</tr>
|
| 121 |
-
<tr>
|
| 122 |
-
<td>1</td>
|
| 123 |
-
<td><img src="" alt="Seleccione todos los archivos de fuente"></td>
|
| 124 |
-
<td>Seleccione todos los archivos de fuente que desea instalar y haga clic derecho sobre ellos. </td>
|
| 125 |
-
</tr>
|
| 126 |
-
<tr>
|
| 127 |
-
<td>2</td>
|
| 128 |
-
<td><img src="" alt="Elija Instalar para todos los usuarios"></td>
|
| 129 |
-
<td>Elija Instalar para todos los usuarios o Instalar como administrador.</td>
|
| 130 |
-
</tr>
|
| 131 |
-
<tr>
|
| 132 |
-
<td>3</td <td><img src="" alt="Comprueba si la fuente está disponible"></td>
|
| 133 |
-
<td>Compruebe si la fuente está disponible en su lista de fuentes abriendo un software como Word o Photoshop y buscándolo en el menú de fuentes. </td></tr></table>
|
| 134 |
-
<h3>Cómo cambiar la fuente por defecto en su PC al choque de fuentes de clanes</h3>
|
| 135 |
-
|
| 136 |
-
<ol><li>Ir a Configuración > Personalización > Fuentes.</li><li>Seleccionar la configuración de fuentes desde el panel izquierdo. </li><li>Seleccione Fuentes personalizadas en el menú desplegable. </li><li>Seleccione Clash of Clans o una fuente similar de la lista de fuentes. </li><li>Haga clic en Aplicar y OK.</li></ol>
|
| 137 |
-
<p>Aquí hay un ejemplo de cómo cambiar la fuente predeterminada en su PC a Game Day:</p>
|
| 138 |
-
<table><tr><th>Step</th><th><th>Screenshot</th><th>Description</th></tr><tr><td>1</td><td><img src="" alt="Ir a Settings"></td><td>Ir a Settings > Personalización > Fonts.<td>>>>>tr><>><td<img<td" </td></tr><tr><td>3</td><td><img src="" alt="Select Custom fonts"></td><td>Select Custom fonts from the drop-down menu. </td></tr><tr><td>4</td><td><img src="" alt="Select Game Day font"></td><td>Select Game Day or a similar font from the list of fonts. </td></tr><tr><td>5</td><td><img src="" alt="Click Apply and OK"></td><td>Click Apply and OK.</td></table>
|
| 139 |
-
<h2>Conclusión y preguntas frecuentes</h2>
|
| 140 |
-
<h3>Resumen de los principales puntos y beneficios de usar el choque de fuentes de clanes</h3>
|
| 141 |
-
<p>En conclusión, la fuente Clash of Clans es una tipografía de bloque de cómic que se utiliza para el logotipo y la marca del popular juego de estrategia móvil Clash of Clans. Tiene un aspecto lúdico y dinámico que se adapta al tema y estilo del juego. Puedes descargar e instalar la fuente Clash of Clans o una similar en tu PC siguiendo los pasos que hemos descrito en este artículo. Al usar la fuente Clash of Clans, puedes crear tus propios diseños y proyectos inspirados en el juego, como logotipos, banners, carteles, folletos, invitaciones, tarjetas, pegatinas, etiquetas y más. También puedes cambiar la fuente por defecto en tu PC a la fuente Clash of Clans si quieres darle a tu ordenador un cambio de imagen. </p>
|
| 142 |
-
<h3>Cinco preguntas frecuentes únicas sobre el choque de fuentes de clanes</h3>
|
| 143 |
-
<p>Aquí hay algunas preguntas frecuentes sobre la fuente Clash of Clans que puedes encontrar útiles:</p>
|
| 144 |
-
<ol>
|
| 145 |
-
|
| 146 |
-
<br>A: Depende del tipo de licencia de la fuente. Si ha adquirido una licencia de fuentes de su creador o distribuidor, puede utilizarla con fines comerciales de acuerdo con los términos y condiciones de la licencia. Si ha descargado una fuente gratuita de un sitio web, debe verificar si está autorizada para uso comercial o no. Algunas fuentes gratuitas pueden tener restricciones sobre cómo puede usarlas con fines comerciales, como exigir la atribución, limitar el número de copias o descargas, o prohibir modificaciones o derivaciones. </li>
|
| 147 |
-
<li><strong>P: ¿Cómo puedo asegurarme de que la fuente Clash of Clans sea segura de descargar e instalar? </strong>
|
| 148 |
-
<br>A: Para asegurarse de que la fuente Clash of Clans es seguro de descargar e instalar, es necesario descargarlo de una fuente de confianza. Una fuente confiable es un sitio web que ofrece fuentes legales y seguras que están libres de virus, malware o spyware. También debe escanear los archivos de fuente con un software antivirus antes de instalarlos en su PC.</li>
|
| 149 |
-
<li><strong>Q: ¿Cómo puedo desinstalar la fuente Clash of Clans desde mi PC? </strong>
|
| 150 |
-
<br>A: Para desinstalar la fuente Clash of Clans de tu PC, sigue estos pasos:</p>
|
| 151 |
-
<ol>
|
| 152 |
-
<li>Ir a Configuración > Personalización > Fuentes.</li>
|
| 153 |
-
<li>Seleccione Configuración de fuente desde el panel izquierdo. </li>
|
| 154 |
-
<li>Seleccione Clash of Clans o una fuente similar de la lista de fuentes. </li>
|
| 155 |
-
<li>Haga clic en desinstalar.</li>
|
| 156 |
-
<li>Confirma tu acción y espera a que se complete la desinstalación. </li>
|
| 157 |
-
</ol></li>
|
| 158 |
-
<li><strong>Q: ¿Cómo puedo usar la fuente Clash of Clans en mi dispositivo móvil? </strong>
|
| 159 |
-
<br>A: Para usar la fuente Clash of Clans en tu dispositivo móvil, necesitas tener una aplicación compatible que te permita importar y usar fuentes personalizadas. Algunos ejemplos son PicsArt, Phonto, iFont o FontFix. También necesita transferir los archivos de fuente desde su PC a su dispositivo móvil a través de un cable USB, Bluetooth, correo electrónico o almacenamiento en la nube. Luego, debe abrir la aplicación y seguir sus instrucciones sobre cómo importar y usar fuentes personalizadas. </li>
|
| 160 |
-
|
| 161 |
-
<br>A: Puedes encontrar más fuentes de juegos como Clash of Clans en sitios web que ofrecen fuentes de juegos gratuitas y premium. Algunos ejemplos son DaFont, 1001 Free Fonts , FontSpace, Font Squirrel o Creative Market. También puede utilizar los motores de búsqueda como Google o Bing para encontrar más fuentes de juegos escribiendo palabras clave como "fuentes de juegos", "fuentes de juegos gratis", o "mejores fuentes de juegos". </li>
|
| 162 |
-
</ol></p> 64aa2da5cf<br />
|
| 163 |
-
<br />
|
| 164 |
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spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/packaging/utils.py
DELETED
|
@@ -1,136 +0,0 @@
|
|
| 1 |
-
# This file is dual licensed under the terms of the Apache License, Version
|
| 2 |
-
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
|
| 3 |
-
# for complete details.
|
| 4 |
-
|
| 5 |
-
import re
|
| 6 |
-
from typing import FrozenSet, NewType, Tuple, Union, cast
|
| 7 |
-
|
| 8 |
-
from .tags import Tag, parse_tag
|
| 9 |
-
from .version import InvalidVersion, Version
|
| 10 |
-
|
| 11 |
-
BuildTag = Union[Tuple[()], Tuple[int, str]]
|
| 12 |
-
NormalizedName = NewType("NormalizedName", str)
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
class InvalidWheelFilename(ValueError):
|
| 16 |
-
"""
|
| 17 |
-
An invalid wheel filename was found, users should refer to PEP 427.
|
| 18 |
-
"""
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
class InvalidSdistFilename(ValueError):
|
| 22 |
-
"""
|
| 23 |
-
An invalid sdist filename was found, users should refer to the packaging user guide.
|
| 24 |
-
"""
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
_canonicalize_regex = re.compile(r"[-_.]+")
|
| 28 |
-
# PEP 427: The build number must start with a digit.
|
| 29 |
-
_build_tag_regex = re.compile(r"(\d+)(.*)")
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
def canonicalize_name(name: str) -> NormalizedName:
|
| 33 |
-
# This is taken from PEP 503.
|
| 34 |
-
value = _canonicalize_regex.sub("-", name).lower()
|
| 35 |
-
return cast(NormalizedName, value)
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
def canonicalize_version(version: Union[Version, str]) -> str:
|
| 39 |
-
"""
|
| 40 |
-
This is very similar to Version.__str__, but has one subtle difference
|
| 41 |
-
with the way it handles the release segment.
|
| 42 |
-
"""
|
| 43 |
-
if isinstance(version, str):
|
| 44 |
-
try:
|
| 45 |
-
parsed = Version(version)
|
| 46 |
-
except InvalidVersion:
|
| 47 |
-
# Legacy versions cannot be normalized
|
| 48 |
-
return version
|
| 49 |
-
else:
|
| 50 |
-
parsed = version
|
| 51 |
-
|
| 52 |
-
parts = []
|
| 53 |
-
|
| 54 |
-
# Epoch
|
| 55 |
-
if parsed.epoch != 0:
|
| 56 |
-
parts.append(f"{parsed.epoch}!")
|
| 57 |
-
|
| 58 |
-
# Release segment
|
| 59 |
-
# NB: This strips trailing '.0's to normalize
|
| 60 |
-
parts.append(re.sub(r"(\.0)+$", "", ".".join(str(x) for x in parsed.release)))
|
| 61 |
-
|
| 62 |
-
# Pre-release
|
| 63 |
-
if parsed.pre is not None:
|
| 64 |
-
parts.append("".join(str(x) for x in parsed.pre))
|
| 65 |
-
|
| 66 |
-
# Post-release
|
| 67 |
-
if parsed.post is not None:
|
| 68 |
-
parts.append(f".post{parsed.post}")
|
| 69 |
-
|
| 70 |
-
# Development release
|
| 71 |
-
if parsed.dev is not None:
|
| 72 |
-
parts.append(f".dev{parsed.dev}")
|
| 73 |
-
|
| 74 |
-
# Local version segment
|
| 75 |
-
if parsed.local is not None:
|
| 76 |
-
parts.append(f"+{parsed.local}")
|
| 77 |
-
|
| 78 |
-
return "".join(parts)
|
| 79 |
-
|
| 80 |
-
|
| 81 |
-
def parse_wheel_filename(
|
| 82 |
-
filename: str,
|
| 83 |
-
) -> Tuple[NormalizedName, Version, BuildTag, FrozenSet[Tag]]:
|
| 84 |
-
if not filename.endswith(".whl"):
|
| 85 |
-
raise InvalidWheelFilename(
|
| 86 |
-
f"Invalid wheel filename (extension must be '.whl'): {filename}"
|
| 87 |
-
)
|
| 88 |
-
|
| 89 |
-
filename = filename[:-4]
|
| 90 |
-
dashes = filename.count("-")
|
| 91 |
-
if dashes not in (4, 5):
|
| 92 |
-
raise InvalidWheelFilename(
|
| 93 |
-
f"Invalid wheel filename (wrong number of parts): {filename}"
|
| 94 |
-
)
|
| 95 |
-
|
| 96 |
-
parts = filename.split("-", dashes - 2)
|
| 97 |
-
name_part = parts[0]
|
| 98 |
-
# See PEP 427 for the rules on escaping the project name
|
| 99 |
-
if "__" in name_part or re.match(r"^[\w\d._]*$", name_part, re.UNICODE) is None:
|
| 100 |
-
raise InvalidWheelFilename(f"Invalid project name: {filename}")
|
| 101 |
-
name = canonicalize_name(name_part)
|
| 102 |
-
version = Version(parts[1])
|
| 103 |
-
if dashes == 5:
|
| 104 |
-
build_part = parts[2]
|
| 105 |
-
build_match = _build_tag_regex.match(build_part)
|
| 106 |
-
if build_match is None:
|
| 107 |
-
raise InvalidWheelFilename(
|
| 108 |
-
f"Invalid build number: {build_part} in '{filename}'"
|
| 109 |
-
)
|
| 110 |
-
build = cast(BuildTag, (int(build_match.group(1)), build_match.group(2)))
|
| 111 |
-
else:
|
| 112 |
-
build = ()
|
| 113 |
-
tags = parse_tag(parts[-1])
|
| 114 |
-
return (name, version, build, tags)
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
def parse_sdist_filename(filename: str) -> Tuple[NormalizedName, Version]:
|
| 118 |
-
if filename.endswith(".tar.gz"):
|
| 119 |
-
file_stem = filename[: -len(".tar.gz")]
|
| 120 |
-
elif filename.endswith(".zip"):
|
| 121 |
-
file_stem = filename[: -len(".zip")]
|
| 122 |
-
else:
|
| 123 |
-
raise InvalidSdistFilename(
|
| 124 |
-
f"Invalid sdist filename (extension must be '.tar.gz' or '.zip'):"
|
| 125 |
-
f" {filename}"
|
| 126 |
-
)
|
| 127 |
-
|
| 128 |
-
# We are requiring a PEP 440 version, which cannot contain dashes,
|
| 129 |
-
# so we split on the last dash.
|
| 130 |
-
name_part, sep, version_part = file_stem.rpartition("-")
|
| 131 |
-
if not sep:
|
| 132 |
-
raise InvalidSdistFilename(f"Invalid sdist filename: {filename}")
|
| 133 |
-
|
| 134 |
-
name = canonicalize_name(name_part)
|
| 135 |
-
version = Version(version_part)
|
| 136 |
-
return (name, version)
|
|
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/pkg_resources/_vendor/packaging/version.py
DELETED
|
@@ -1,504 +0,0 @@
|
|
| 1 |
-
# This file is dual licensed under the terms of the Apache License, Version
|
| 2 |
-
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
|
| 3 |
-
# for complete details.
|
| 4 |
-
|
| 5 |
-
import collections
|
| 6 |
-
import itertools
|
| 7 |
-
import re
|
| 8 |
-
import warnings
|
| 9 |
-
from typing import Callable, Iterator, List, Optional, SupportsInt, Tuple, Union
|
| 10 |
-
|
| 11 |
-
from ._structures import Infinity, InfinityType, NegativeInfinity, NegativeInfinityType
|
| 12 |
-
|
| 13 |
-
__all__ = ["parse", "Version", "LegacyVersion", "InvalidVersion", "VERSION_PATTERN"]
|
| 14 |
-
|
| 15 |
-
InfiniteTypes = Union[InfinityType, NegativeInfinityType]
|
| 16 |
-
PrePostDevType = Union[InfiniteTypes, Tuple[str, int]]
|
| 17 |
-
SubLocalType = Union[InfiniteTypes, int, str]
|
| 18 |
-
LocalType = Union[
|
| 19 |
-
NegativeInfinityType,
|
| 20 |
-
Tuple[
|
| 21 |
-
Union[
|
| 22 |
-
SubLocalType,
|
| 23 |
-
Tuple[SubLocalType, str],
|
| 24 |
-
Tuple[NegativeInfinityType, SubLocalType],
|
| 25 |
-
],
|
| 26 |
-
...,
|
| 27 |
-
],
|
| 28 |
-
]
|
| 29 |
-
CmpKey = Tuple[
|
| 30 |
-
int, Tuple[int, ...], PrePostDevType, PrePostDevType, PrePostDevType, LocalType
|
| 31 |
-
]
|
| 32 |
-
LegacyCmpKey = Tuple[int, Tuple[str, ...]]
|
| 33 |
-
VersionComparisonMethod = Callable[
|
| 34 |
-
[Union[CmpKey, LegacyCmpKey], Union[CmpKey, LegacyCmpKey]], bool
|
| 35 |
-
]
|
| 36 |
-
|
| 37 |
-
_Version = collections.namedtuple(
|
| 38 |
-
"_Version", ["epoch", "release", "dev", "pre", "post", "local"]
|
| 39 |
-
)
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
def parse(version: str) -> Union["LegacyVersion", "Version"]:
|
| 43 |
-
"""
|
| 44 |
-
Parse the given version string and return either a :class:`Version` object
|
| 45 |
-
or a :class:`LegacyVersion` object depending on if the given version is
|
| 46 |
-
a valid PEP 440 version or a legacy version.
|
| 47 |
-
"""
|
| 48 |
-
try:
|
| 49 |
-
return Version(version)
|
| 50 |
-
except InvalidVersion:
|
| 51 |
-
return LegacyVersion(version)
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
class InvalidVersion(ValueError):
|
| 55 |
-
"""
|
| 56 |
-
An invalid version was found, users should refer to PEP 440.
|
| 57 |
-
"""
|
| 58 |
-
|
| 59 |
-
|
| 60 |
-
class _BaseVersion:
|
| 61 |
-
_key: Union[CmpKey, LegacyCmpKey]
|
| 62 |
-
|
| 63 |
-
def __hash__(self) -> int:
|
| 64 |
-
return hash(self._key)
|
| 65 |
-
|
| 66 |
-
# Please keep the duplicated `isinstance` check
|
| 67 |
-
# in the six comparisons hereunder
|
| 68 |
-
# unless you find a way to avoid adding overhead function calls.
|
| 69 |
-
def __lt__(self, other: "_BaseVersion") -> bool:
|
| 70 |
-
if not isinstance(other, _BaseVersion):
|
| 71 |
-
return NotImplemented
|
| 72 |
-
|
| 73 |
-
return self._key < other._key
|
| 74 |
-
|
| 75 |
-
def __le__(self, other: "_BaseVersion") -> bool:
|
| 76 |
-
if not isinstance(other, _BaseVersion):
|
| 77 |
-
return NotImplemented
|
| 78 |
-
|
| 79 |
-
return self._key <= other._key
|
| 80 |
-
|
| 81 |
-
def __eq__(self, other: object) -> bool:
|
| 82 |
-
if not isinstance(other, _BaseVersion):
|
| 83 |
-
return NotImplemented
|
| 84 |
-
|
| 85 |
-
return self._key == other._key
|
| 86 |
-
|
| 87 |
-
def __ge__(self, other: "_BaseVersion") -> bool:
|
| 88 |
-
if not isinstance(other, _BaseVersion):
|
| 89 |
-
return NotImplemented
|
| 90 |
-
|
| 91 |
-
return self._key >= other._key
|
| 92 |
-
|
| 93 |
-
def __gt__(self, other: "_BaseVersion") -> bool:
|
| 94 |
-
if not isinstance(other, _BaseVersion):
|
| 95 |
-
return NotImplemented
|
| 96 |
-
|
| 97 |
-
return self._key > other._key
|
| 98 |
-
|
| 99 |
-
def __ne__(self, other: object) -> bool:
|
| 100 |
-
if not isinstance(other, _BaseVersion):
|
| 101 |
-
return NotImplemented
|
| 102 |
-
|
| 103 |
-
return self._key != other._key
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
class LegacyVersion(_BaseVersion):
|
| 107 |
-
def __init__(self, version: str) -> None:
|
| 108 |
-
self._version = str(version)
|
| 109 |
-
self._key = _legacy_cmpkey(self._version)
|
| 110 |
-
|
| 111 |
-
warnings.warn(
|
| 112 |
-
"Creating a LegacyVersion has been deprecated and will be "
|
| 113 |
-
"removed in the next major release",
|
| 114 |
-
DeprecationWarning,
|
| 115 |
-
)
|
| 116 |
-
|
| 117 |
-
def __str__(self) -> str:
|
| 118 |
-
return self._version
|
| 119 |
-
|
| 120 |
-
def __repr__(self) -> str:
|
| 121 |
-
return f"<LegacyVersion('{self}')>"
|
| 122 |
-
|
| 123 |
-
@property
|
| 124 |
-
def public(self) -> str:
|
| 125 |
-
return self._version
|
| 126 |
-
|
| 127 |
-
@property
|
| 128 |
-
def base_version(self) -> str:
|
| 129 |
-
return self._version
|
| 130 |
-
|
| 131 |
-
@property
|
| 132 |
-
def epoch(self) -> int:
|
| 133 |
-
return -1
|
| 134 |
-
|
| 135 |
-
@property
|
| 136 |
-
def release(self) -> None:
|
| 137 |
-
return None
|
| 138 |
-
|
| 139 |
-
@property
|
| 140 |
-
def pre(self) -> None:
|
| 141 |
-
return None
|
| 142 |
-
|
| 143 |
-
@property
|
| 144 |
-
def post(self) -> None:
|
| 145 |
-
return None
|
| 146 |
-
|
| 147 |
-
@property
|
| 148 |
-
def dev(self) -> None:
|
| 149 |
-
return None
|
| 150 |
-
|
| 151 |
-
@property
|
| 152 |
-
def local(self) -> None:
|
| 153 |
-
return None
|
| 154 |
-
|
| 155 |
-
@property
|
| 156 |
-
def is_prerelease(self) -> bool:
|
| 157 |
-
return False
|
| 158 |
-
|
| 159 |
-
@property
|
| 160 |
-
def is_postrelease(self) -> bool:
|
| 161 |
-
return False
|
| 162 |
-
|
| 163 |
-
@property
|
| 164 |
-
def is_devrelease(self) -> bool:
|
| 165 |
-
return False
|
| 166 |
-
|
| 167 |
-
|
| 168 |
-
_legacy_version_component_re = re.compile(r"(\d+ | [a-z]+ | \.| -)", re.VERBOSE)
|
| 169 |
-
|
| 170 |
-
_legacy_version_replacement_map = {
|
| 171 |
-
"pre": "c",
|
| 172 |
-
"preview": "c",
|
| 173 |
-
"-": "final-",
|
| 174 |
-
"rc": "c",
|
| 175 |
-
"dev": "@",
|
| 176 |
-
}
|
| 177 |
-
|
| 178 |
-
|
| 179 |
-
def _parse_version_parts(s: str) -> Iterator[str]:
|
| 180 |
-
for part in _legacy_version_component_re.split(s):
|
| 181 |
-
part = _legacy_version_replacement_map.get(part, part)
|
| 182 |
-
|
| 183 |
-
if not part or part == ".":
|
| 184 |
-
continue
|
| 185 |
-
|
| 186 |
-
if part[:1] in "0123456789":
|
| 187 |
-
# pad for numeric comparison
|
| 188 |
-
yield part.zfill(8)
|
| 189 |
-
else:
|
| 190 |
-
yield "*" + part
|
| 191 |
-
|
| 192 |
-
# ensure that alpha/beta/candidate are before final
|
| 193 |
-
yield "*final"
|
| 194 |
-
|
| 195 |
-
|
| 196 |
-
def _legacy_cmpkey(version: str) -> LegacyCmpKey:
|
| 197 |
-
|
| 198 |
-
# We hardcode an epoch of -1 here. A PEP 440 version can only have a epoch
|
| 199 |
-
# greater than or equal to 0. This will effectively put the LegacyVersion,
|
| 200 |
-
# which uses the defacto standard originally implemented by setuptools,
|
| 201 |
-
# as before all PEP 440 versions.
|
| 202 |
-
epoch = -1
|
| 203 |
-
|
| 204 |
-
# This scheme is taken from pkg_resources.parse_version setuptools prior to
|
| 205 |
-
# it's adoption of the packaging library.
|
| 206 |
-
parts: List[str] = []
|
| 207 |
-
for part in _parse_version_parts(version.lower()):
|
| 208 |
-
if part.startswith("*"):
|
| 209 |
-
# remove "-" before a prerelease tag
|
| 210 |
-
if part < "*final":
|
| 211 |
-
while parts and parts[-1] == "*final-":
|
| 212 |
-
parts.pop()
|
| 213 |
-
|
| 214 |
-
# remove trailing zeros from each series of numeric parts
|
| 215 |
-
while parts and parts[-1] == "00000000":
|
| 216 |
-
parts.pop()
|
| 217 |
-
|
| 218 |
-
parts.append(part)
|
| 219 |
-
|
| 220 |
-
return epoch, tuple(parts)
|
| 221 |
-
|
| 222 |
-
|
| 223 |
-
# Deliberately not anchored to the start and end of the string, to make it
|
| 224 |
-
# easier for 3rd party code to reuse
|
| 225 |
-
VERSION_PATTERN = r"""
|
| 226 |
-
v?
|
| 227 |
-
(?:
|
| 228 |
-
(?:(?P<epoch>[0-9]+)!)? # epoch
|
| 229 |
-
(?P<release>[0-9]+(?:\.[0-9]+)*) # release segment
|
| 230 |
-
(?P<pre> # pre-release
|
| 231 |
-
[-_\.]?
|
| 232 |
-
(?P<pre_l>(a|b|c|rc|alpha|beta|pre|preview))
|
| 233 |
-
[-_\.]?
|
| 234 |
-
(?P<pre_n>[0-9]+)?
|
| 235 |
-
)?
|
| 236 |
-
(?P<post> # post release
|
| 237 |
-
(?:-(?P<post_n1>[0-9]+))
|
| 238 |
-
|
|
| 239 |
-
(?:
|
| 240 |
-
[-_\.]?
|
| 241 |
-
(?P<post_l>post|rev|r)
|
| 242 |
-
[-_\.]?
|
| 243 |
-
(?P<post_n2>[0-9]+)?
|
| 244 |
-
)
|
| 245 |
-
)?
|
| 246 |
-
(?P<dev> # dev release
|
| 247 |
-
[-_\.]?
|
| 248 |
-
(?P<dev_l>dev)
|
| 249 |
-
[-_\.]?
|
| 250 |
-
(?P<dev_n>[0-9]+)?
|
| 251 |
-
)?
|
| 252 |
-
)
|
| 253 |
-
(?:\+(?P<local>[a-z0-9]+(?:[-_\.][a-z0-9]+)*))? # local version
|
| 254 |
-
"""
|
| 255 |
-
|
| 256 |
-
|
| 257 |
-
class Version(_BaseVersion):
|
| 258 |
-
|
| 259 |
-
_regex = re.compile(r"^\s*" + VERSION_PATTERN + r"\s*$", re.VERBOSE | re.IGNORECASE)
|
| 260 |
-
|
| 261 |
-
def __init__(self, version: str) -> None:
|
| 262 |
-
|
| 263 |
-
# Validate the version and parse it into pieces
|
| 264 |
-
match = self._regex.search(version)
|
| 265 |
-
if not match:
|
| 266 |
-
raise InvalidVersion(f"Invalid version: '{version}'")
|
| 267 |
-
|
| 268 |
-
# Store the parsed out pieces of the version
|
| 269 |
-
self._version = _Version(
|
| 270 |
-
epoch=int(match.group("epoch")) if match.group("epoch") else 0,
|
| 271 |
-
release=tuple(int(i) for i in match.group("release").split(".")),
|
| 272 |
-
pre=_parse_letter_version(match.group("pre_l"), match.group("pre_n")),
|
| 273 |
-
post=_parse_letter_version(
|
| 274 |
-
match.group("post_l"), match.group("post_n1") or match.group("post_n2")
|
| 275 |
-
),
|
| 276 |
-
dev=_parse_letter_version(match.group("dev_l"), match.group("dev_n")),
|
| 277 |
-
local=_parse_local_version(match.group("local")),
|
| 278 |
-
)
|
| 279 |
-
|
| 280 |
-
# Generate a key which will be used for sorting
|
| 281 |
-
self._key = _cmpkey(
|
| 282 |
-
self._version.epoch,
|
| 283 |
-
self._version.release,
|
| 284 |
-
self._version.pre,
|
| 285 |
-
self._version.post,
|
| 286 |
-
self._version.dev,
|
| 287 |
-
self._version.local,
|
| 288 |
-
)
|
| 289 |
-
|
| 290 |
-
def __repr__(self) -> str:
|
| 291 |
-
return f"<Version('{self}')>"
|
| 292 |
-
|
| 293 |
-
def __str__(self) -> str:
|
| 294 |
-
parts = []
|
| 295 |
-
|
| 296 |
-
# Epoch
|
| 297 |
-
if self.epoch != 0:
|
| 298 |
-
parts.append(f"{self.epoch}!")
|
| 299 |
-
|
| 300 |
-
# Release segment
|
| 301 |
-
parts.append(".".join(str(x) for x in self.release))
|
| 302 |
-
|
| 303 |
-
# Pre-release
|
| 304 |
-
if self.pre is not None:
|
| 305 |
-
parts.append("".join(str(x) for x in self.pre))
|
| 306 |
-
|
| 307 |
-
# Post-release
|
| 308 |
-
if self.post is not None:
|
| 309 |
-
parts.append(f".post{self.post}")
|
| 310 |
-
|
| 311 |
-
# Development release
|
| 312 |
-
if self.dev is not None:
|
| 313 |
-
parts.append(f".dev{self.dev}")
|
| 314 |
-
|
| 315 |
-
# Local version segment
|
| 316 |
-
if self.local is not None:
|
| 317 |
-
parts.append(f"+{self.local}")
|
| 318 |
-
|
| 319 |
-
return "".join(parts)
|
| 320 |
-
|
| 321 |
-
@property
|
| 322 |
-
def epoch(self) -> int:
|
| 323 |
-
_epoch: int = self._version.epoch
|
| 324 |
-
return _epoch
|
| 325 |
-
|
| 326 |
-
@property
|
| 327 |
-
def release(self) -> Tuple[int, ...]:
|
| 328 |
-
_release: Tuple[int, ...] = self._version.release
|
| 329 |
-
return _release
|
| 330 |
-
|
| 331 |
-
@property
|
| 332 |
-
def pre(self) -> Optional[Tuple[str, int]]:
|
| 333 |
-
_pre: Optional[Tuple[str, int]] = self._version.pre
|
| 334 |
-
return _pre
|
| 335 |
-
|
| 336 |
-
@property
|
| 337 |
-
def post(self) -> Optional[int]:
|
| 338 |
-
return self._version.post[1] if self._version.post else None
|
| 339 |
-
|
| 340 |
-
@property
|
| 341 |
-
def dev(self) -> Optional[int]:
|
| 342 |
-
return self._version.dev[1] if self._version.dev else None
|
| 343 |
-
|
| 344 |
-
@property
|
| 345 |
-
def local(self) -> Optional[str]:
|
| 346 |
-
if self._version.local:
|
| 347 |
-
return ".".join(str(x) for x in self._version.local)
|
| 348 |
-
else:
|
| 349 |
-
return None
|
| 350 |
-
|
| 351 |
-
@property
|
| 352 |
-
def public(self) -> str:
|
| 353 |
-
return str(self).split("+", 1)[0]
|
| 354 |
-
|
| 355 |
-
@property
|
| 356 |
-
def base_version(self) -> str:
|
| 357 |
-
parts = []
|
| 358 |
-
|
| 359 |
-
# Epoch
|
| 360 |
-
if self.epoch != 0:
|
| 361 |
-
parts.append(f"{self.epoch}!")
|
| 362 |
-
|
| 363 |
-
# Release segment
|
| 364 |
-
parts.append(".".join(str(x) for x in self.release))
|
| 365 |
-
|
| 366 |
-
return "".join(parts)
|
| 367 |
-
|
| 368 |
-
@property
|
| 369 |
-
def is_prerelease(self) -> bool:
|
| 370 |
-
return self.dev is not None or self.pre is not None
|
| 371 |
-
|
| 372 |
-
@property
|
| 373 |
-
def is_postrelease(self) -> bool:
|
| 374 |
-
return self.post is not None
|
| 375 |
-
|
| 376 |
-
@property
|
| 377 |
-
def is_devrelease(self) -> bool:
|
| 378 |
-
return self.dev is not None
|
| 379 |
-
|
| 380 |
-
@property
|
| 381 |
-
def major(self) -> int:
|
| 382 |
-
return self.release[0] if len(self.release) >= 1 else 0
|
| 383 |
-
|
| 384 |
-
@property
|
| 385 |
-
def minor(self) -> int:
|
| 386 |
-
return self.release[1] if len(self.release) >= 2 else 0
|
| 387 |
-
|
| 388 |
-
@property
|
| 389 |
-
def micro(self) -> int:
|
| 390 |
-
return self.release[2] if len(self.release) >= 3 else 0
|
| 391 |
-
|
| 392 |
-
|
| 393 |
-
def _parse_letter_version(
|
| 394 |
-
letter: str, number: Union[str, bytes, SupportsInt]
|
| 395 |
-
) -> Optional[Tuple[str, int]]:
|
| 396 |
-
|
| 397 |
-
if letter:
|
| 398 |
-
# We consider there to be an implicit 0 in a pre-release if there is
|
| 399 |
-
# not a numeral associated with it.
|
| 400 |
-
if number is None:
|
| 401 |
-
number = 0
|
| 402 |
-
|
| 403 |
-
# We normalize any letters to their lower case form
|
| 404 |
-
letter = letter.lower()
|
| 405 |
-
|
| 406 |
-
# We consider some words to be alternate spellings of other words and
|
| 407 |
-
# in those cases we want to normalize the spellings to our preferred
|
| 408 |
-
# spelling.
|
| 409 |
-
if letter == "alpha":
|
| 410 |
-
letter = "a"
|
| 411 |
-
elif letter == "beta":
|
| 412 |
-
letter = "b"
|
| 413 |
-
elif letter in ["c", "pre", "preview"]:
|
| 414 |
-
letter = "rc"
|
| 415 |
-
elif letter in ["rev", "r"]:
|
| 416 |
-
letter = "post"
|
| 417 |
-
|
| 418 |
-
return letter, int(number)
|
| 419 |
-
if not letter and number:
|
| 420 |
-
# We assume if we are given a number, but we are not given a letter
|
| 421 |
-
# then this is using the implicit post release syntax (e.g. 1.0-1)
|
| 422 |
-
letter = "post"
|
| 423 |
-
|
| 424 |
-
return letter, int(number)
|
| 425 |
-
|
| 426 |
-
return None
|
| 427 |
-
|
| 428 |
-
|
| 429 |
-
_local_version_separators = re.compile(r"[\._-]")
|
| 430 |
-
|
| 431 |
-
|
| 432 |
-
def _parse_local_version(local: str) -> Optional[LocalType]:
|
| 433 |
-
"""
|
| 434 |
-
Takes a string like abc.1.twelve and turns it into ("abc", 1, "twelve").
|
| 435 |
-
"""
|
| 436 |
-
if local is not None:
|
| 437 |
-
return tuple(
|
| 438 |
-
part.lower() if not part.isdigit() else int(part)
|
| 439 |
-
for part in _local_version_separators.split(local)
|
| 440 |
-
)
|
| 441 |
-
return None
|
| 442 |
-
|
| 443 |
-
|
| 444 |
-
def _cmpkey(
|
| 445 |
-
epoch: int,
|
| 446 |
-
release: Tuple[int, ...],
|
| 447 |
-
pre: Optional[Tuple[str, int]],
|
| 448 |
-
post: Optional[Tuple[str, int]],
|
| 449 |
-
dev: Optional[Tuple[str, int]],
|
| 450 |
-
local: Optional[Tuple[SubLocalType]],
|
| 451 |
-
) -> CmpKey:
|
| 452 |
-
|
| 453 |
-
# When we compare a release version, we want to compare it with all of the
|
| 454 |
-
# trailing zeros removed. So we'll use a reverse the list, drop all the now
|
| 455 |
-
# leading zeros until we come to something non zero, then take the rest
|
| 456 |
-
# re-reverse it back into the correct order and make it a tuple and use
|
| 457 |
-
# that for our sorting key.
|
| 458 |
-
_release = tuple(
|
| 459 |
-
reversed(list(itertools.dropwhile(lambda x: x == 0, reversed(release))))
|
| 460 |
-
)
|
| 461 |
-
|
| 462 |
-
# We need to "trick" the sorting algorithm to put 1.0.dev0 before 1.0a0.
|
| 463 |
-
# We'll do this by abusing the pre segment, but we _only_ want to do this
|
| 464 |
-
# if there is not a pre or a post segment. If we have one of those then
|
| 465 |
-
# the normal sorting rules will handle this case correctly.
|
| 466 |
-
if pre is None and post is None and dev is not None:
|
| 467 |
-
_pre: PrePostDevType = NegativeInfinity
|
| 468 |
-
# Versions without a pre-release (except as noted above) should sort after
|
| 469 |
-
# those with one.
|
| 470 |
-
elif pre is None:
|
| 471 |
-
_pre = Infinity
|
| 472 |
-
else:
|
| 473 |
-
_pre = pre
|
| 474 |
-
|
| 475 |
-
# Versions without a post segment should sort before those with one.
|
| 476 |
-
if post is None:
|
| 477 |
-
_post: PrePostDevType = NegativeInfinity
|
| 478 |
-
|
| 479 |
-
else:
|
| 480 |
-
_post = post
|
| 481 |
-
|
| 482 |
-
# Versions without a development segment should sort after those with one.
|
| 483 |
-
if dev is None:
|
| 484 |
-
_dev: PrePostDevType = Infinity
|
| 485 |
-
|
| 486 |
-
else:
|
| 487 |
-
_dev = dev
|
| 488 |
-
|
| 489 |
-
if local is None:
|
| 490 |
-
# Versions without a local segment should sort before those with one.
|
| 491 |
-
_local: LocalType = NegativeInfinity
|
| 492 |
-
else:
|
| 493 |
-
# Versions with a local segment need that segment parsed to implement
|
| 494 |
-
# the sorting rules in PEP440.
|
| 495 |
-
# - Alpha numeric segments sort before numeric segments
|
| 496 |
-
# - Alpha numeric segments sort lexicographically
|
| 497 |
-
# - Numeric segments sort numerically
|
| 498 |
-
# - Shorter versions sort before longer versions when the prefixes
|
| 499 |
-
# match exactly
|
| 500 |
-
_local = tuple(
|
| 501 |
-
(i, "") if isinstance(i, int) else (NegativeInfinity, i) for i in local
|
| 502 |
-
)
|
| 503 |
-
|
| 504 |
-
return epoch, _release, _pre, _post, _dev, _local
|
|
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|
spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/config/_validate_pyproject/error_reporting.py
DELETED
|
@@ -1,318 +0,0 @@
|
|
| 1 |
-
import io
|
| 2 |
-
import json
|
| 3 |
-
import logging
|
| 4 |
-
import os
|
| 5 |
-
import re
|
| 6 |
-
from contextlib import contextmanager
|
| 7 |
-
from textwrap import indent, wrap
|
| 8 |
-
from typing import Any, Dict, Iterator, List, Optional, Sequence, Union, cast
|
| 9 |
-
|
| 10 |
-
from .fastjsonschema_exceptions import JsonSchemaValueException
|
| 11 |
-
|
| 12 |
-
_logger = logging.getLogger(__name__)
|
| 13 |
-
|
| 14 |
-
_MESSAGE_REPLACEMENTS = {
|
| 15 |
-
"must be named by propertyName definition": "keys must be named by",
|
| 16 |
-
"one of contains definition": "at least one item that matches",
|
| 17 |
-
" same as const definition:": "",
|
| 18 |
-
"only specified items": "only items matching the definition",
|
| 19 |
-
}
|
| 20 |
-
|
| 21 |
-
_SKIP_DETAILS = (
|
| 22 |
-
"must not be empty",
|
| 23 |
-
"is always invalid",
|
| 24 |
-
"must not be there",
|
| 25 |
-
)
|
| 26 |
-
|
| 27 |
-
_NEED_DETAILS = {"anyOf", "oneOf", "anyOf", "contains", "propertyNames", "not", "items"}
|
| 28 |
-
|
| 29 |
-
_CAMEL_CASE_SPLITTER = re.compile(r"\W+|([A-Z][^A-Z\W]*)")
|
| 30 |
-
_IDENTIFIER = re.compile(r"^[\w_]+$", re.I)
|
| 31 |
-
|
| 32 |
-
_TOML_JARGON = {
|
| 33 |
-
"object": "table",
|
| 34 |
-
"property": "key",
|
| 35 |
-
"properties": "keys",
|
| 36 |
-
"property names": "keys",
|
| 37 |
-
}
|
| 38 |
-
|
| 39 |
-
|
| 40 |
-
class ValidationError(JsonSchemaValueException):
|
| 41 |
-
"""Report violations of a given JSON schema.
|
| 42 |
-
|
| 43 |
-
This class extends :exc:`~fastjsonschema.JsonSchemaValueException`
|
| 44 |
-
by adding the following properties:
|
| 45 |
-
|
| 46 |
-
- ``summary``: an improved version of the ``JsonSchemaValueException`` error message
|
| 47 |
-
with only the necessary information)
|
| 48 |
-
|
| 49 |
-
- ``details``: more contextual information about the error like the failing schema
|
| 50 |
-
itself and the value that violates the schema.
|
| 51 |
-
|
| 52 |
-
Depending on the level of the verbosity of the ``logging`` configuration
|
| 53 |
-
the exception message will be only ``summary`` (default) or a combination of
|
| 54 |
-
``summary`` and ``details`` (when the logging level is set to :obj:`logging.DEBUG`).
|
| 55 |
-
"""
|
| 56 |
-
|
| 57 |
-
summary = ""
|
| 58 |
-
details = ""
|
| 59 |
-
_original_message = ""
|
| 60 |
-
|
| 61 |
-
@classmethod
|
| 62 |
-
def _from_jsonschema(cls, ex: JsonSchemaValueException):
|
| 63 |
-
formatter = _ErrorFormatting(ex)
|
| 64 |
-
obj = cls(str(formatter), ex.value, formatter.name, ex.definition, ex.rule)
|
| 65 |
-
debug_code = os.getenv("JSONSCHEMA_DEBUG_CODE_GENERATION", "false").lower()
|
| 66 |
-
if debug_code != "false": # pragma: no cover
|
| 67 |
-
obj.__cause__, obj.__traceback__ = ex.__cause__, ex.__traceback__
|
| 68 |
-
obj._original_message = ex.message
|
| 69 |
-
obj.summary = formatter.summary
|
| 70 |
-
obj.details = formatter.details
|
| 71 |
-
return obj
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
@contextmanager
|
| 75 |
-
def detailed_errors():
|
| 76 |
-
try:
|
| 77 |
-
yield
|
| 78 |
-
except JsonSchemaValueException as ex:
|
| 79 |
-
raise ValidationError._from_jsonschema(ex) from None
|
| 80 |
-
|
| 81 |
-
|
| 82 |
-
class _ErrorFormatting:
|
| 83 |
-
def __init__(self, ex: JsonSchemaValueException):
|
| 84 |
-
self.ex = ex
|
| 85 |
-
self.name = f"`{self._simplify_name(ex.name)}`"
|
| 86 |
-
self._original_message = self.ex.message.replace(ex.name, self.name)
|
| 87 |
-
self._summary = ""
|
| 88 |
-
self._details = ""
|
| 89 |
-
|
| 90 |
-
def __str__(self) -> str:
|
| 91 |
-
if _logger.getEffectiveLevel() <= logging.DEBUG and self.details:
|
| 92 |
-
return f"{self.summary}\n\n{self.details}"
|
| 93 |
-
|
| 94 |
-
return self.summary
|
| 95 |
-
|
| 96 |
-
@property
|
| 97 |
-
def summary(self) -> str:
|
| 98 |
-
if not self._summary:
|
| 99 |
-
self._summary = self._expand_summary()
|
| 100 |
-
|
| 101 |
-
return self._summary
|
| 102 |
-
|
| 103 |
-
@property
|
| 104 |
-
def details(self) -> str:
|
| 105 |
-
if not self._details:
|
| 106 |
-
self._details = self._expand_details()
|
| 107 |
-
|
| 108 |
-
return self._details
|
| 109 |
-
|
| 110 |
-
def _simplify_name(self, name):
|
| 111 |
-
x = len("data.")
|
| 112 |
-
return name[x:] if name.startswith("data.") else name
|
| 113 |
-
|
| 114 |
-
def _expand_summary(self):
|
| 115 |
-
msg = self._original_message
|
| 116 |
-
|
| 117 |
-
for bad, repl in _MESSAGE_REPLACEMENTS.items():
|
| 118 |
-
msg = msg.replace(bad, repl)
|
| 119 |
-
|
| 120 |
-
if any(substring in msg for substring in _SKIP_DETAILS):
|
| 121 |
-
return msg
|
| 122 |
-
|
| 123 |
-
schema = self.ex.rule_definition
|
| 124 |
-
if self.ex.rule in _NEED_DETAILS and schema:
|
| 125 |
-
summary = _SummaryWriter(_TOML_JARGON)
|
| 126 |
-
return f"{msg}:\n\n{indent(summary(schema), ' ')}"
|
| 127 |
-
|
| 128 |
-
return msg
|
| 129 |
-
|
| 130 |
-
def _expand_details(self) -> str:
|
| 131 |
-
optional = []
|
| 132 |
-
desc_lines = self.ex.definition.pop("$$description", [])
|
| 133 |
-
desc = self.ex.definition.pop("description", None) or " ".join(desc_lines)
|
| 134 |
-
if desc:
|
| 135 |
-
description = "\n".join(
|
| 136 |
-
wrap(
|
| 137 |
-
desc,
|
| 138 |
-
width=80,
|
| 139 |
-
initial_indent=" ",
|
| 140 |
-
subsequent_indent=" ",
|
| 141 |
-
break_long_words=False,
|
| 142 |
-
)
|
| 143 |
-
)
|
| 144 |
-
optional.append(f"DESCRIPTION:\n{description}")
|
| 145 |
-
schema = json.dumps(self.ex.definition, indent=4)
|
| 146 |
-
value = json.dumps(self.ex.value, indent=4)
|
| 147 |
-
defaults = [
|
| 148 |
-
f"GIVEN VALUE:\n{indent(value, ' ')}",
|
| 149 |
-
f"OFFENDING RULE: {self.ex.rule!r}",
|
| 150 |
-
f"DEFINITION:\n{indent(schema, ' ')}",
|
| 151 |
-
]
|
| 152 |
-
return "\n\n".join(optional + defaults)
|
| 153 |
-
|
| 154 |
-
|
| 155 |
-
class _SummaryWriter:
|
| 156 |
-
_IGNORE = {"description", "default", "title", "examples"}
|
| 157 |
-
|
| 158 |
-
def __init__(self, jargon: Optional[Dict[str, str]] = None):
|
| 159 |
-
self.jargon: Dict[str, str] = jargon or {}
|
| 160 |
-
# Clarify confusing terms
|
| 161 |
-
self._terms = {
|
| 162 |
-
"anyOf": "at least one of the following",
|
| 163 |
-
"oneOf": "exactly one of the following",
|
| 164 |
-
"allOf": "all of the following",
|
| 165 |
-
"not": "(*NOT* the following)",
|
| 166 |
-
"prefixItems": f"{self._jargon('items')} (in order)",
|
| 167 |
-
"items": "items",
|
| 168 |
-
"contains": "contains at least one of",
|
| 169 |
-
"propertyNames": (
|
| 170 |
-
f"non-predefined acceptable {self._jargon('property names')}"
|
| 171 |
-
),
|
| 172 |
-
"patternProperties": f"{self._jargon('properties')} named via pattern",
|
| 173 |
-
"const": "predefined value",
|
| 174 |
-
"enum": "one of",
|
| 175 |
-
}
|
| 176 |
-
# Attributes that indicate that the definition is easy and can be done
|
| 177 |
-
# inline (e.g. string and number)
|
| 178 |
-
self._guess_inline_defs = [
|
| 179 |
-
"enum",
|
| 180 |
-
"const",
|
| 181 |
-
"maxLength",
|
| 182 |
-
"minLength",
|
| 183 |
-
"pattern",
|
| 184 |
-
"format",
|
| 185 |
-
"minimum",
|
| 186 |
-
"maximum",
|
| 187 |
-
"exclusiveMinimum",
|
| 188 |
-
"exclusiveMaximum",
|
| 189 |
-
"multipleOf",
|
| 190 |
-
]
|
| 191 |
-
|
| 192 |
-
def _jargon(self, term: Union[str, List[str]]) -> Union[str, List[str]]:
|
| 193 |
-
if isinstance(term, list):
|
| 194 |
-
return [self.jargon.get(t, t) for t in term]
|
| 195 |
-
return self.jargon.get(term, term)
|
| 196 |
-
|
| 197 |
-
def __call__(
|
| 198 |
-
self,
|
| 199 |
-
schema: Union[dict, List[dict]],
|
| 200 |
-
prefix: str = "",
|
| 201 |
-
*,
|
| 202 |
-
_path: Sequence[str] = (),
|
| 203 |
-
) -> str:
|
| 204 |
-
if isinstance(schema, list):
|
| 205 |
-
return self._handle_list(schema, prefix, _path)
|
| 206 |
-
|
| 207 |
-
filtered = self._filter_unecessary(schema, _path)
|
| 208 |
-
simple = self._handle_simple_dict(filtered, _path)
|
| 209 |
-
if simple:
|
| 210 |
-
return f"{prefix}{simple}"
|
| 211 |
-
|
| 212 |
-
child_prefix = self._child_prefix(prefix, " ")
|
| 213 |
-
item_prefix = self._child_prefix(prefix, "- ")
|
| 214 |
-
indent = len(prefix) * " "
|
| 215 |
-
with io.StringIO() as buffer:
|
| 216 |
-
for i, (key, value) in enumerate(filtered.items()):
|
| 217 |
-
child_path = [*_path, key]
|
| 218 |
-
line_prefix = prefix if i == 0 else indent
|
| 219 |
-
buffer.write(f"{line_prefix}{self._label(child_path)}:")
|
| 220 |
-
# ^ just the first item should receive the complete prefix
|
| 221 |
-
if isinstance(value, dict):
|
| 222 |
-
filtered = self._filter_unecessary(value, child_path)
|
| 223 |
-
simple = self._handle_simple_dict(filtered, child_path)
|
| 224 |
-
buffer.write(
|
| 225 |
-
f" {simple}"
|
| 226 |
-
if simple
|
| 227 |
-
else f"\n{self(value, child_prefix, _path=child_path)}"
|
| 228 |
-
)
|
| 229 |
-
elif isinstance(value, list) and (
|
| 230 |
-
key != "type" or self._is_property(child_path)
|
| 231 |
-
):
|
| 232 |
-
children = self._handle_list(value, item_prefix, child_path)
|
| 233 |
-
sep = " " if children.startswith("[") else "\n"
|
| 234 |
-
buffer.write(f"{sep}{children}")
|
| 235 |
-
else:
|
| 236 |
-
buffer.write(f" {self._value(value, child_path)}\n")
|
| 237 |
-
return buffer.getvalue()
|
| 238 |
-
|
| 239 |
-
def _is_unecessary(self, path: Sequence[str]) -> bool:
|
| 240 |
-
if self._is_property(path) or not path: # empty path => instruction @ root
|
| 241 |
-
return False
|
| 242 |
-
key = path[-1]
|
| 243 |
-
return any(key.startswith(k) for k in "$_") or key in self._IGNORE
|
| 244 |
-
|
| 245 |
-
def _filter_unecessary(self, schema: dict, path: Sequence[str]):
|
| 246 |
-
return {
|
| 247 |
-
key: value
|
| 248 |
-
for key, value in schema.items()
|
| 249 |
-
if not self._is_unecessary([*path, key])
|
| 250 |
-
}
|
| 251 |
-
|
| 252 |
-
def _handle_simple_dict(self, value: dict, path: Sequence[str]) -> Optional[str]:
|
| 253 |
-
inline = any(p in value for p in self._guess_inline_defs)
|
| 254 |
-
simple = not any(isinstance(v, (list, dict)) for v in value.values())
|
| 255 |
-
if inline or simple:
|
| 256 |
-
return f"{{{', '.join(self._inline_attrs(value, path))}}}\n"
|
| 257 |
-
return None
|
| 258 |
-
|
| 259 |
-
def _handle_list(
|
| 260 |
-
self, schemas: list, prefix: str = "", path: Sequence[str] = ()
|
| 261 |
-
) -> str:
|
| 262 |
-
if self._is_unecessary(path):
|
| 263 |
-
return ""
|
| 264 |
-
|
| 265 |
-
repr_ = repr(schemas)
|
| 266 |
-
if all(not isinstance(e, (dict, list)) for e in schemas) and len(repr_) < 60:
|
| 267 |
-
return f"{repr_}\n"
|
| 268 |
-
|
| 269 |
-
item_prefix = self._child_prefix(prefix, "- ")
|
| 270 |
-
return "".join(
|
| 271 |
-
self(v, item_prefix, _path=[*path, f"[{i}]"]) for i, v in enumerate(schemas)
|
| 272 |
-
)
|
| 273 |
-
|
| 274 |
-
def _is_property(self, path: Sequence[str]):
|
| 275 |
-
"""Check if the given path can correspond to an arbitrarily named property"""
|
| 276 |
-
counter = 0
|
| 277 |
-
for key in path[-2::-1]:
|
| 278 |
-
if key not in {"properties", "patternProperties"}:
|
| 279 |
-
break
|
| 280 |
-
counter += 1
|
| 281 |
-
|
| 282 |
-
# If the counter if even, the path correspond to a JSON Schema keyword
|
| 283 |
-
# otherwise it can be any arbitrary string naming a property
|
| 284 |
-
return counter % 2 == 1
|
| 285 |
-
|
| 286 |
-
def _label(self, path: Sequence[str]) -> str:
|
| 287 |
-
*parents, key = path
|
| 288 |
-
if not self._is_property(path):
|
| 289 |
-
norm_key = _separate_terms(key)
|
| 290 |
-
return self._terms.get(key) or " ".join(self._jargon(norm_key))
|
| 291 |
-
|
| 292 |
-
if parents[-1] == "patternProperties":
|
| 293 |
-
return f"(regex {key!r})"
|
| 294 |
-
return repr(key) # property name
|
| 295 |
-
|
| 296 |
-
def _value(self, value: Any, path: Sequence[str]) -> str:
|
| 297 |
-
if path[-1] == "type" and not self._is_property(path):
|
| 298 |
-
type_ = self._jargon(value)
|
| 299 |
-
return (
|
| 300 |
-
f"[{', '.join(type_)}]" if isinstance(value, list) else cast(str, type_)
|
| 301 |
-
)
|
| 302 |
-
return repr(value)
|
| 303 |
-
|
| 304 |
-
def _inline_attrs(self, schema: dict, path: Sequence[str]) -> Iterator[str]:
|
| 305 |
-
for key, value in schema.items():
|
| 306 |
-
child_path = [*path, key]
|
| 307 |
-
yield f"{self._label(child_path)}: {self._value(value, child_path)}"
|
| 308 |
-
|
| 309 |
-
def _child_prefix(self, parent_prefix: str, child_prefix: str) -> str:
|
| 310 |
-
return len(parent_prefix) * " " + child_prefix
|
| 311 |
-
|
| 312 |
-
|
| 313 |
-
def _separate_terms(word: str) -> List[str]:
|
| 314 |
-
"""
|
| 315 |
-
>>> _separate_terms("FooBar-foo")
|
| 316 |
-
['foo', 'bar', 'foo']
|
| 317 |
-
"""
|
| 318 |
-
return [w.lower() for w in _CAMEL_CASE_SPLITTER.split(word) if w]
|
|
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spaces/CVPR/LIVE/thrust/thrust/detail/functional/value.h
DELETED
|
@@ -1,80 +0,0 @@
|
|
| 1 |
-
/*
|
| 2 |
-
* Copyright 2008-2013 NVIDIA Corporation
|
| 3 |
-
*
|
| 4 |
-
* Licensed under the Apache License, Version 2.0 (the "License");
|
| 5 |
-
* you may not use this file except in compliance with the License.
|
| 6 |
-
* You may obtain a copy of the License at
|
| 7 |
-
*
|
| 8 |
-
* http://www.apache.org/licenses/LICENSE-2.0
|
| 9 |
-
*
|
| 10 |
-
* Unless required by applicable law or agreed to in writing, software
|
| 11 |
-
* distributed under the License is distributed on an "AS IS" BASIS,
|
| 12 |
-
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 13 |
-
* See the License for the specific language governing permissions and
|
| 14 |
-
* limitations under the License.
|
| 15 |
-
*/
|
| 16 |
-
|
| 17 |
-
// Portions of this code are derived from
|
| 18 |
-
//
|
| 19 |
-
// Manjunath Kudlur's Carbon library
|
| 20 |
-
//
|
| 21 |
-
// and
|
| 22 |
-
//
|
| 23 |
-
// Based on Boost.Phoenix v1.2
|
| 24 |
-
// Copyright (c) 2001-2002 Joel de Guzman
|
| 25 |
-
|
| 26 |
-
#pragma once
|
| 27 |
-
|
| 28 |
-
#include <thrust/detail/config.h>
|
| 29 |
-
#include <thrust/detail/functional/actor.h>
|
| 30 |
-
|
| 31 |
-
namespace thrust
|
| 32 |
-
{
|
| 33 |
-
namespace detail
|
| 34 |
-
{
|
| 35 |
-
namespace functional
|
| 36 |
-
{
|
| 37 |
-
|
| 38 |
-
|
| 39 |
-
template<typename Eval> struct actor;
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
template<typename T>
|
| 43 |
-
class value
|
| 44 |
-
{
|
| 45 |
-
public:
|
| 46 |
-
|
| 47 |
-
template<typename Env>
|
| 48 |
-
struct result
|
| 49 |
-
{
|
| 50 |
-
typedef T type;
|
| 51 |
-
};
|
| 52 |
-
|
| 53 |
-
__host__ __device__
|
| 54 |
-
value(const T &arg)
|
| 55 |
-
: m_val(arg)
|
| 56 |
-
{}
|
| 57 |
-
|
| 58 |
-
template<typename Env>
|
| 59 |
-
__host__ __device__
|
| 60 |
-
T eval(const Env &) const
|
| 61 |
-
{
|
| 62 |
-
return m_val;
|
| 63 |
-
}
|
| 64 |
-
|
| 65 |
-
private:
|
| 66 |
-
T m_val;
|
| 67 |
-
}; // end value
|
| 68 |
-
|
| 69 |
-
template<typename T>
|
| 70 |
-
__host__ __device__
|
| 71 |
-
actor<value<T> > val(const T &x)
|
| 72 |
-
{
|
| 73 |
-
return value<T>(x);
|
| 74 |
-
} // end val()
|
| 75 |
-
|
| 76 |
-
|
| 77 |
-
} // end functional
|
| 78 |
-
} // end detail
|
| 79 |
-
} // end thrust
|
| 80 |
-
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spaces/CVPR/Text2Human/Text2Human/models/archs/transformer_arch.py
DELETED
|
@@ -1,273 +0,0 @@
|
|
| 1 |
-
import math
|
| 2 |
-
|
| 3 |
-
import numpy as np
|
| 4 |
-
import torch
|
| 5 |
-
import torch.nn as nn
|
| 6 |
-
import torch.nn.functional as F
|
| 7 |
-
|
| 8 |
-
|
| 9 |
-
class CausalSelfAttention(nn.Module):
|
| 10 |
-
"""
|
| 11 |
-
A vanilla multi-head masked self-attention layer with a projection at the end.
|
| 12 |
-
It is possible to use torch.nn.MultiheadAttention here but I am including an
|
| 13 |
-
explicit implementation here to show that there is nothing too scary here.
|
| 14 |
-
"""
|
| 15 |
-
|
| 16 |
-
def __init__(self, bert_n_emb, bert_n_head, attn_pdrop, resid_pdrop,
|
| 17 |
-
latent_shape, sampler):
|
| 18 |
-
super().__init__()
|
| 19 |
-
assert bert_n_emb % bert_n_head == 0
|
| 20 |
-
# key, query, value projections for all heads
|
| 21 |
-
self.key = nn.Linear(bert_n_emb, bert_n_emb)
|
| 22 |
-
self.query = nn.Linear(bert_n_emb, bert_n_emb)
|
| 23 |
-
self.value = nn.Linear(bert_n_emb, bert_n_emb)
|
| 24 |
-
# regularization
|
| 25 |
-
self.attn_drop = nn.Dropout(attn_pdrop)
|
| 26 |
-
self.resid_drop = nn.Dropout(resid_pdrop)
|
| 27 |
-
# output projection
|
| 28 |
-
self.proj = nn.Linear(bert_n_emb, bert_n_emb)
|
| 29 |
-
self.n_head = bert_n_head
|
| 30 |
-
self.causal = True if sampler == 'autoregressive' else False
|
| 31 |
-
if self.causal:
|
| 32 |
-
block_size = np.prod(latent_shape)
|
| 33 |
-
mask = torch.tril(torch.ones(block_size, block_size))
|
| 34 |
-
self.register_buffer("mask", mask.view(1, 1, block_size,
|
| 35 |
-
block_size))
|
| 36 |
-
|
| 37 |
-
def forward(self, x, layer_past=None):
|
| 38 |
-
B, T, C = x.size()
|
| 39 |
-
|
| 40 |
-
# calculate query, key, values for all heads in batch and move head forward to be the batch dim
|
| 41 |
-
k = self.key(x).view(B, T, self.n_head,
|
| 42 |
-
C // self.n_head).transpose(1,
|
| 43 |
-
2) # (B, nh, T, hs)
|
| 44 |
-
q = self.query(x).view(B, T, self.n_head,
|
| 45 |
-
C // self.n_head).transpose(1,
|
| 46 |
-
2) # (B, nh, T, hs)
|
| 47 |
-
v = self.value(x).view(B, T, self.n_head,
|
| 48 |
-
C // self.n_head).transpose(1,
|
| 49 |
-
2) # (B, nh, T, hs)
|
| 50 |
-
|
| 51 |
-
present = torch.stack((k, v))
|
| 52 |
-
if self.causal and layer_past is not None:
|
| 53 |
-
past_key, past_value = layer_past
|
| 54 |
-
k = torch.cat((past_key, k), dim=-2)
|
| 55 |
-
v = torch.cat((past_value, v), dim=-2)
|
| 56 |
-
|
| 57 |
-
# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
|
| 58 |
-
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
|
| 59 |
-
|
| 60 |
-
if self.causal and layer_past is None:
|
| 61 |
-
att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float('-inf'))
|
| 62 |
-
|
| 63 |
-
att = F.softmax(att, dim=-1)
|
| 64 |
-
att = self.attn_drop(att)
|
| 65 |
-
y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
|
| 66 |
-
# re-assemble all head outputs side by side
|
| 67 |
-
y = y.transpose(1, 2).contiguous().view(B, T, C)
|
| 68 |
-
|
| 69 |
-
# output projection
|
| 70 |
-
y = self.resid_drop(self.proj(y))
|
| 71 |
-
return y, present
|
| 72 |
-
|
| 73 |
-
|
| 74 |
-
class Block(nn.Module):
|
| 75 |
-
""" an unassuming Transformer block """
|
| 76 |
-
|
| 77 |
-
def __init__(self, bert_n_emb, resid_pdrop, bert_n_head, attn_pdrop,
|
| 78 |
-
latent_shape, sampler):
|
| 79 |
-
super().__init__()
|
| 80 |
-
self.ln1 = nn.LayerNorm(bert_n_emb)
|
| 81 |
-
self.ln2 = nn.LayerNorm(bert_n_emb)
|
| 82 |
-
self.attn = CausalSelfAttention(bert_n_emb, bert_n_head, attn_pdrop,
|
| 83 |
-
resid_pdrop, latent_shape, sampler)
|
| 84 |
-
self.mlp = nn.Sequential(
|
| 85 |
-
nn.Linear(bert_n_emb, 4 * bert_n_emb),
|
| 86 |
-
nn.GELU(), # nice
|
| 87 |
-
nn.Linear(4 * bert_n_emb, bert_n_emb),
|
| 88 |
-
nn.Dropout(resid_pdrop),
|
| 89 |
-
)
|
| 90 |
-
|
| 91 |
-
def forward(self, x, layer_past=None, return_present=False):
|
| 92 |
-
|
| 93 |
-
attn, present = self.attn(self.ln1(x), layer_past)
|
| 94 |
-
x = x + attn
|
| 95 |
-
x = x + self.mlp(self.ln2(x))
|
| 96 |
-
|
| 97 |
-
if layer_past is not None or return_present:
|
| 98 |
-
return x, present
|
| 99 |
-
return x
|
| 100 |
-
|
| 101 |
-
|
| 102 |
-
class Transformer(nn.Module):
|
| 103 |
-
""" the full GPT language model, with a context size of block_size """
|
| 104 |
-
|
| 105 |
-
def __init__(self,
|
| 106 |
-
codebook_size,
|
| 107 |
-
segm_codebook_size,
|
| 108 |
-
bert_n_emb,
|
| 109 |
-
bert_n_layers,
|
| 110 |
-
bert_n_head,
|
| 111 |
-
block_size,
|
| 112 |
-
latent_shape,
|
| 113 |
-
embd_pdrop,
|
| 114 |
-
resid_pdrop,
|
| 115 |
-
attn_pdrop,
|
| 116 |
-
sampler='absorbing'):
|
| 117 |
-
super().__init__()
|
| 118 |
-
|
| 119 |
-
self.vocab_size = codebook_size + 1
|
| 120 |
-
self.n_embd = bert_n_emb
|
| 121 |
-
self.block_size = block_size
|
| 122 |
-
self.n_layers = bert_n_layers
|
| 123 |
-
self.codebook_size = codebook_size
|
| 124 |
-
self.segm_codebook_size = segm_codebook_size
|
| 125 |
-
self.causal = sampler == 'autoregressive'
|
| 126 |
-
if self.causal:
|
| 127 |
-
self.vocab_size = codebook_size
|
| 128 |
-
|
| 129 |
-
self.tok_emb = nn.Embedding(self.vocab_size, self.n_embd)
|
| 130 |
-
self.pos_emb = nn.Parameter(
|
| 131 |
-
torch.zeros(1, self.block_size, self.n_embd))
|
| 132 |
-
self.segm_emb = nn.Embedding(self.segm_codebook_size, self.n_embd)
|
| 133 |
-
self.start_tok = nn.Parameter(torch.zeros(1, 1, self.n_embd))
|
| 134 |
-
self.drop = nn.Dropout(embd_pdrop)
|
| 135 |
-
|
| 136 |
-
# transformer
|
| 137 |
-
self.blocks = nn.Sequential(*[
|
| 138 |
-
Block(bert_n_emb, resid_pdrop, bert_n_head, attn_pdrop,
|
| 139 |
-
latent_shape, sampler) for _ in range(self.n_layers)
|
| 140 |
-
])
|
| 141 |
-
# decoder head
|
| 142 |
-
self.ln_f = nn.LayerNorm(self.n_embd)
|
| 143 |
-
self.head = nn.Linear(self.n_embd, self.codebook_size, bias=False)
|
| 144 |
-
|
| 145 |
-
def get_block_size(self):
|
| 146 |
-
return self.block_size
|
| 147 |
-
|
| 148 |
-
def _init_weights(self, module):
|
| 149 |
-
if isinstance(module, (nn.Linear, nn.Embedding)):
|
| 150 |
-
module.weight.data.normal_(mean=0.0, std=0.02)
|
| 151 |
-
if isinstance(module, nn.Linear) and module.bias is not None:
|
| 152 |
-
module.bias.data.zero_()
|
| 153 |
-
elif isinstance(module, nn.LayerNorm):
|
| 154 |
-
module.bias.data.zero_()
|
| 155 |
-
module.weight.data.fill_(1.0)
|
| 156 |
-
|
| 157 |
-
def forward(self, idx, segm_tokens, t=None):
|
| 158 |
-
# each index maps to a (learnable) vector
|
| 159 |
-
token_embeddings = self.tok_emb(idx)
|
| 160 |
-
|
| 161 |
-
segm_embeddings = self.segm_emb(segm_tokens)
|
| 162 |
-
|
| 163 |
-
if self.causal:
|
| 164 |
-
token_embeddings = torch.cat((self.start_tok.repeat(
|
| 165 |
-
token_embeddings.size(0), 1, 1), token_embeddings),
|
| 166 |
-
dim=1)
|
| 167 |
-
|
| 168 |
-
t = token_embeddings.shape[1]
|
| 169 |
-
assert t <= self.block_size, "Cannot forward, model block size is exhausted."
|
| 170 |
-
# each position maps to a (learnable) vector
|
| 171 |
-
|
| 172 |
-
position_embeddings = self.pos_emb[:, :t, :]
|
| 173 |
-
|
| 174 |
-
x = token_embeddings + position_embeddings + segm_embeddings
|
| 175 |
-
x = self.drop(x)
|
| 176 |
-
for block in self.blocks:
|
| 177 |
-
x = block(x)
|
| 178 |
-
x = self.ln_f(x)
|
| 179 |
-
logits = self.head(x)
|
| 180 |
-
|
| 181 |
-
return logits
|
| 182 |
-
|
| 183 |
-
|
| 184 |
-
class TransformerMultiHead(nn.Module):
|
| 185 |
-
""" the full GPT language model, with a context size of block_size """
|
| 186 |
-
|
| 187 |
-
def __init__(self,
|
| 188 |
-
codebook_size,
|
| 189 |
-
segm_codebook_size,
|
| 190 |
-
texture_codebook_size,
|
| 191 |
-
bert_n_emb,
|
| 192 |
-
bert_n_layers,
|
| 193 |
-
bert_n_head,
|
| 194 |
-
block_size,
|
| 195 |
-
latent_shape,
|
| 196 |
-
embd_pdrop,
|
| 197 |
-
resid_pdrop,
|
| 198 |
-
attn_pdrop,
|
| 199 |
-
num_head,
|
| 200 |
-
sampler='absorbing'):
|
| 201 |
-
super().__init__()
|
| 202 |
-
|
| 203 |
-
self.vocab_size = codebook_size + 1
|
| 204 |
-
self.n_embd = bert_n_emb
|
| 205 |
-
self.block_size = block_size
|
| 206 |
-
self.n_layers = bert_n_layers
|
| 207 |
-
self.codebook_size = codebook_size
|
| 208 |
-
self.segm_codebook_size = segm_codebook_size
|
| 209 |
-
self.texture_codebook_size = texture_codebook_size
|
| 210 |
-
self.causal = sampler == 'autoregressive'
|
| 211 |
-
if self.causal:
|
| 212 |
-
self.vocab_size = codebook_size
|
| 213 |
-
|
| 214 |
-
self.tok_emb = nn.Embedding(self.vocab_size, self.n_embd)
|
| 215 |
-
self.pos_emb = nn.Parameter(
|
| 216 |
-
torch.zeros(1, self.block_size, self.n_embd))
|
| 217 |
-
self.segm_emb = nn.Embedding(self.segm_codebook_size, self.n_embd)
|
| 218 |
-
self.texture_emb = nn.Embedding(self.texture_codebook_size,
|
| 219 |
-
self.n_embd)
|
| 220 |
-
self.start_tok = nn.Parameter(torch.zeros(1, 1, self.n_embd))
|
| 221 |
-
self.drop = nn.Dropout(embd_pdrop)
|
| 222 |
-
|
| 223 |
-
# transformer
|
| 224 |
-
self.blocks = nn.Sequential(*[
|
| 225 |
-
Block(bert_n_emb, resid_pdrop, bert_n_head, attn_pdrop,
|
| 226 |
-
latent_shape, sampler) for _ in range(self.n_layers)
|
| 227 |
-
])
|
| 228 |
-
# decoder head
|
| 229 |
-
self.num_head = num_head
|
| 230 |
-
self.head_class_num = codebook_size // self.num_head
|
| 231 |
-
self.ln_f = nn.LayerNorm(self.n_embd)
|
| 232 |
-
self.head_list = nn.ModuleList([
|
| 233 |
-
nn.Linear(self.n_embd, self.head_class_num, bias=False)
|
| 234 |
-
for _ in range(self.num_head)
|
| 235 |
-
])
|
| 236 |
-
|
| 237 |
-
def get_block_size(self):
|
| 238 |
-
return self.block_size
|
| 239 |
-
|
| 240 |
-
def _init_weights(self, module):
|
| 241 |
-
if isinstance(module, (nn.Linear, nn.Embedding)):
|
| 242 |
-
module.weight.data.normal_(mean=0.0, std=0.02)
|
| 243 |
-
if isinstance(module, nn.Linear) and module.bias is not None:
|
| 244 |
-
module.bias.data.zero_()
|
| 245 |
-
elif isinstance(module, nn.LayerNorm):
|
| 246 |
-
module.bias.data.zero_()
|
| 247 |
-
module.weight.data.fill_(1.0)
|
| 248 |
-
|
| 249 |
-
def forward(self, idx, segm_tokens, texture_tokens, t=None):
|
| 250 |
-
# each index maps to a (learnable) vector
|
| 251 |
-
token_embeddings = self.tok_emb(idx)
|
| 252 |
-
segm_embeddings = self.segm_emb(segm_tokens)
|
| 253 |
-
texture_embeddings = self.texture_emb(texture_tokens)
|
| 254 |
-
|
| 255 |
-
if self.causal:
|
| 256 |
-
token_embeddings = torch.cat((self.start_tok.repeat(
|
| 257 |
-
token_embeddings.size(0), 1, 1), token_embeddings),
|
| 258 |
-
dim=1)
|
| 259 |
-
|
| 260 |
-
t = token_embeddings.shape[1]
|
| 261 |
-
assert t <= self.block_size, "Cannot forward, model block size is exhausted."
|
| 262 |
-
# each position maps to a (learnable) vector
|
| 263 |
-
|
| 264 |
-
position_embeddings = self.pos_emb[:, :t, :]
|
| 265 |
-
|
| 266 |
-
x = token_embeddings + position_embeddings + segm_embeddings + texture_embeddings
|
| 267 |
-
x = self.drop(x)
|
| 268 |
-
for block in self.blocks:
|
| 269 |
-
x = block(x)
|
| 270 |
-
x = self.ln_f(x)
|
| 271 |
-
logits_list = [self.head_list[i](x) for i in range(self.num_head)]
|
| 272 |
-
|
| 273 |
-
return logits_list
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|
spaces/CVPR/lama-example/bin/paper_runfiles/generate_test_paris_256.sh
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
#!/usr/bin/env bash
|
| 2 |
-
|
| 3 |
-
# paths to data are valid for mml-ws01
|
| 4 |
-
OUT_DIR="/media/inpainting/paper_data/Paris_StreetView_Dataset_val_256"
|
| 5 |
-
|
| 6 |
-
source "$(dirname $0)/env.sh"
|
| 7 |
-
|
| 8 |
-
for datadir in paris_eval_gt
|
| 9 |
-
do
|
| 10 |
-
for conf in random_thin_256 random_medium_256 random_thick_256 segm_256
|
| 11 |
-
do
|
| 12 |
-
"$BINDIR/gen_mask_dataset_hydra.py" -cn $conf datadir=$datadir location=mml-ws01-paris \
|
| 13 |
-
location.out_dir=$OUT_DIR cropping.out_square_crop=False cropping.out_min_size=256
|
| 14 |
-
|
| 15 |
-
"$BINDIR/calc_dataset_stats.py" --samples-n 20 "$OUT_DIR/$datadir/$conf" "$OUT_DIR/$datadir/${conf}_stats"
|
| 16 |
-
done
|
| 17 |
-
done
|
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|
spaces/CVPR/lama-example/saicinpainting/training/losses/__init__.py
DELETED
|
File without changes
|
spaces/Chirayuhumar/MyGenAIChatBot/app.py
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import gradio as gr
|
| 3 |
-
from langchain.chat_models import ChatOpenAI
|
| 4 |
-
from langchain import LLMChain, PromptTemplate
|
| 5 |
-
from langchain.memory import ConversationBufferMemory
|
| 6 |
-
|
| 7 |
-
OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
|
| 8 |
-
|
| 9 |
-
template = """Meet Riya, your youthful and witty personal assistant! At 21 years old, she's full of energy and always eager to help. Riya's goal is to assist you with any questions or problems you might have. Her enthusiasm shines through in every response, making interactions with her enjoyable and engaging.
|
| 10 |
-
{chat_history}
|
| 11 |
-
User: {user_message}
|
| 12 |
-
Chatbot:"""
|
| 13 |
-
|
| 14 |
-
prompt = PromptTemplate(
|
| 15 |
-
input_variables=["chat_history", "user_message"], template=template
|
| 16 |
-
)
|
| 17 |
-
|
| 18 |
-
memory = ConversationBufferMemory(memory_key="chat_history")
|
| 19 |
-
|
| 20 |
-
llm_chain = LLMChain(
|
| 21 |
-
llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
|
| 22 |
-
prompt=prompt,
|
| 23 |
-
verbose=True,
|
| 24 |
-
memory=memory,
|
| 25 |
-
)
|
| 26 |
-
|
| 27 |
-
def get_text_response(user_message,history):
|
| 28 |
-
response = llm_chain.predict(user_message = user_message)
|
| 29 |
-
return response
|
| 30 |
-
|
| 31 |
-
demo = gr.ChatInterface(get_text_response)
|
| 32 |
-
|
| 33 |
-
if __name__ == "__main__":
|
| 34 |
-
demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
|
|
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|
|
spaces/Cicooo/vits-uma-genshin-honkai/utils.py
DELETED
|
@@ -1,225 +0,0 @@
|
|
| 1 |
-
import os
|
| 2 |
-
import sys
|
| 3 |
-
import argparse
|
| 4 |
-
import logging
|
| 5 |
-
import json
|
| 6 |
-
import subprocess
|
| 7 |
-
import numpy as np
|
| 8 |
-
import librosa
|
| 9 |
-
import torch
|
| 10 |
-
|
| 11 |
-
MATPLOTLIB_FLAG = False
|
| 12 |
-
|
| 13 |
-
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
| 14 |
-
logger = logging
|
| 15 |
-
|
| 16 |
-
|
| 17 |
-
def load_checkpoint(checkpoint_path, model, optimizer=None):
|
| 18 |
-
assert os.path.isfile(checkpoint_path)
|
| 19 |
-
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
| 20 |
-
iteration = checkpoint_dict['iteration']
|
| 21 |
-
learning_rate = checkpoint_dict['learning_rate']
|
| 22 |
-
if optimizer is not None:
|
| 23 |
-
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
| 24 |
-
saved_state_dict = checkpoint_dict['model']
|
| 25 |
-
if hasattr(model, 'module'):
|
| 26 |
-
state_dict = model.module.state_dict()
|
| 27 |
-
else:
|
| 28 |
-
state_dict = model.state_dict()
|
| 29 |
-
new_state_dict= {}
|
| 30 |
-
for k, v in state_dict.items():
|
| 31 |
-
try:
|
| 32 |
-
new_state_dict[k] = saved_state_dict[k]
|
| 33 |
-
except:
|
| 34 |
-
logger.info("%s is not in the checkpoint" % k)
|
| 35 |
-
new_state_dict[k] = v
|
| 36 |
-
if hasattr(model, 'module'):
|
| 37 |
-
model.module.load_state_dict(new_state_dict)
|
| 38 |
-
else:
|
| 39 |
-
model.load_state_dict(new_state_dict)
|
| 40 |
-
logger.info("Loaded checkpoint '{}' (iteration {})" .format(
|
| 41 |
-
checkpoint_path, iteration))
|
| 42 |
-
return model, optimizer, learning_rate, iteration
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
def plot_spectrogram_to_numpy(spectrogram):
|
| 46 |
-
global MATPLOTLIB_FLAG
|
| 47 |
-
if not MATPLOTLIB_FLAG:
|
| 48 |
-
import matplotlib
|
| 49 |
-
matplotlib.use("Agg")
|
| 50 |
-
MATPLOTLIB_FLAG = True
|
| 51 |
-
mpl_logger = logging.getLogger('matplotlib')
|
| 52 |
-
mpl_logger.setLevel(logging.WARNING)
|
| 53 |
-
import matplotlib.pylab as plt
|
| 54 |
-
import numpy as np
|
| 55 |
-
|
| 56 |
-
fig, ax = plt.subplots(figsize=(10,2))
|
| 57 |
-
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
| 58 |
-
interpolation='none')
|
| 59 |
-
plt.colorbar(im, ax=ax)
|
| 60 |
-
plt.xlabel("Frames")
|
| 61 |
-
plt.ylabel("Channels")
|
| 62 |
-
plt.tight_layout()
|
| 63 |
-
|
| 64 |
-
fig.canvas.draw()
|
| 65 |
-
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
| 66 |
-
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
| 67 |
-
plt.close()
|
| 68 |
-
return data
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
def plot_alignment_to_numpy(alignment, info=None):
|
| 72 |
-
global MATPLOTLIB_FLAG
|
| 73 |
-
if not MATPLOTLIB_FLAG:
|
| 74 |
-
import matplotlib
|
| 75 |
-
matplotlib.use("Agg")
|
| 76 |
-
MATPLOTLIB_FLAG = True
|
| 77 |
-
mpl_logger = logging.getLogger('matplotlib')
|
| 78 |
-
mpl_logger.setLevel(logging.WARNING)
|
| 79 |
-
import matplotlib.pylab as plt
|
| 80 |
-
import numpy as np
|
| 81 |
-
|
| 82 |
-
fig, ax = plt.subplots(figsize=(6, 4))
|
| 83 |
-
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
| 84 |
-
interpolation='none')
|
| 85 |
-
fig.colorbar(im, ax=ax)
|
| 86 |
-
xlabel = 'Decoder timestep'
|
| 87 |
-
if info is not None:
|
| 88 |
-
xlabel += '\n\n' + info
|
| 89 |
-
plt.xlabel(xlabel)
|
| 90 |
-
plt.ylabel('Encoder timestep')
|
| 91 |
-
plt.tight_layout()
|
| 92 |
-
|
| 93 |
-
fig.canvas.draw()
|
| 94 |
-
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
| 95 |
-
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
| 96 |
-
plt.close()
|
| 97 |
-
return data
|
| 98 |
-
|
| 99 |
-
|
| 100 |
-
def load_audio_to_torch(full_path, target_sampling_rate):
|
| 101 |
-
audio, sampling_rate = librosa.load(full_path, sr=target_sampling_rate, mono=True)
|
| 102 |
-
return torch.FloatTensor(audio.astype(np.float32))
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
def load_filepaths_and_text(filename, split="|"):
|
| 106 |
-
with open(filename, encoding='utf-8') as f:
|
| 107 |
-
filepaths_and_text = [line.strip().split(split) for line in f]
|
| 108 |
-
return filepaths_and_text
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
def get_hparams(init=True):
|
| 112 |
-
parser = argparse.ArgumentParser()
|
| 113 |
-
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
|
| 114 |
-
help='JSON file for configuration')
|
| 115 |
-
parser.add_argument('-m', '--model', type=str, required=True,
|
| 116 |
-
help='Model name')
|
| 117 |
-
|
| 118 |
-
args = parser.parse_args()
|
| 119 |
-
model_dir = os.path.join("./logs", args.model)
|
| 120 |
-
|
| 121 |
-
if not os.path.exists(model_dir):
|
| 122 |
-
os.makedirs(model_dir)
|
| 123 |
-
|
| 124 |
-
config_path = args.config
|
| 125 |
-
config_save_path = os.path.join(model_dir, "config.json")
|
| 126 |
-
if init:
|
| 127 |
-
with open(config_path, "r") as f:
|
| 128 |
-
data = f.read()
|
| 129 |
-
with open(config_save_path, "w") as f:
|
| 130 |
-
f.write(data)
|
| 131 |
-
else:
|
| 132 |
-
with open(config_save_path, "r") as f:
|
| 133 |
-
data = f.read()
|
| 134 |
-
config = json.loads(data)
|
| 135 |
-
|
| 136 |
-
hparams = HParams(**config)
|
| 137 |
-
hparams.model_dir = model_dir
|
| 138 |
-
return hparams
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
def get_hparams_from_dir(model_dir):
|
| 142 |
-
config_save_path = os.path.join(model_dir, "config.json")
|
| 143 |
-
with open(config_save_path, "r") as f:
|
| 144 |
-
data = f.read()
|
| 145 |
-
config = json.loads(data)
|
| 146 |
-
|
| 147 |
-
hparams =HParams(**config)
|
| 148 |
-
hparams.model_dir = model_dir
|
| 149 |
-
return hparams
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
def get_hparams_from_file(config_path):
|
| 153 |
-
with open(config_path, "r") as f:
|
| 154 |
-
data = f.read()
|
| 155 |
-
config = json.loads(data)
|
| 156 |
-
|
| 157 |
-
hparams =HParams(**config)
|
| 158 |
-
return hparams
|
| 159 |
-
|
| 160 |
-
|
| 161 |
-
def check_git_hash(model_dir):
|
| 162 |
-
source_dir = os.path.dirname(os.path.realpath(__file__))
|
| 163 |
-
if not os.path.exists(os.path.join(source_dir, ".git")):
|
| 164 |
-
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
| 165 |
-
source_dir
|
| 166 |
-
))
|
| 167 |
-
return
|
| 168 |
-
|
| 169 |
-
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
| 170 |
-
|
| 171 |
-
path = os.path.join(model_dir, "githash")
|
| 172 |
-
if os.path.exists(path):
|
| 173 |
-
saved_hash = open(path).read()
|
| 174 |
-
if saved_hash != cur_hash:
|
| 175 |
-
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
| 176 |
-
saved_hash[:8], cur_hash[:8]))
|
| 177 |
-
else:
|
| 178 |
-
open(path, "w").write(cur_hash)
|
| 179 |
-
|
| 180 |
-
|
| 181 |
-
def get_logger(model_dir, filename="train.log"):
|
| 182 |
-
global logger
|
| 183 |
-
logger = logging.getLogger(os.path.basename(model_dir))
|
| 184 |
-
logger.setLevel(logging.DEBUG)
|
| 185 |
-
|
| 186 |
-
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
| 187 |
-
if not os.path.exists(model_dir):
|
| 188 |
-
os.makedirs(model_dir)
|
| 189 |
-
h = logging.FileHandler(os.path.join(model_dir, filename))
|
| 190 |
-
h.setLevel(logging.DEBUG)
|
| 191 |
-
h.setFormatter(formatter)
|
| 192 |
-
logger.addHandler(h)
|
| 193 |
-
return logger
|
| 194 |
-
|
| 195 |
-
|
| 196 |
-
class HParams():
|
| 197 |
-
def __init__(self, **kwargs):
|
| 198 |
-
for k, v in kwargs.items():
|
| 199 |
-
if type(v) == dict:
|
| 200 |
-
v = HParams(**v)
|
| 201 |
-
self[k] = v
|
| 202 |
-
|
| 203 |
-
def keys(self):
|
| 204 |
-
return self.__dict__.keys()
|
| 205 |
-
|
| 206 |
-
def items(self):
|
| 207 |
-
return self.__dict__.items()
|
| 208 |
-
|
| 209 |
-
def values(self):
|
| 210 |
-
return self.__dict__.values()
|
| 211 |
-
|
| 212 |
-
def __len__(self):
|
| 213 |
-
return len(self.__dict__)
|
| 214 |
-
|
| 215 |
-
def __getitem__(self, key):
|
| 216 |
-
return getattr(self, key)
|
| 217 |
-
|
| 218 |
-
def __setitem__(self, key, value):
|
| 219 |
-
return setattr(self, key, value)
|
| 220 |
-
|
| 221 |
-
def __contains__(self, key):
|
| 222 |
-
return key in self.__dict__
|
| 223 |
-
|
| 224 |
-
def __repr__(self):
|
| 225 |
-
return self.__dict__.__repr__()
|
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|
spaces/Cong723/gpt-academic-public/.github/ISSUE_TEMPLATE/bug_report.md
DELETED
|
@@ -1,25 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
name: Bug report
|
| 3 |
-
about: Create a report to help us improve
|
| 4 |
-
title: ''
|
| 5 |
-
labels: ''
|
| 6 |
-
assignees: ''
|
| 7 |
-
|
| 8 |
-
---
|
| 9 |
-
|
| 10 |
-
- **(1) Describe the bug 简述**
|
| 11 |
-
|
| 12 |
-
|
| 13 |
-
- **(2) Screen Shot 截图**
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
- **(3) Terminal Traceback 终端traceback（如有）**
|
| 17 |
-
|
| 18 |
-
|
| 19 |
-
- **(4) Material to Help Reproduce Bugs 帮助我们复现的测试材料样本（如有）**
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
Before submitting an issue 提交issue之前：
|
| 24 |
-
- Please try to upgrade your code. 如果您的代码不是最新的，建议您先尝试更新代码
|
| 25 |
-
- Please check project wiki for common problem solutions.项目[wiki](https://github.com/binary-husky/chatgpt_academic/wiki)有一些常见问题的解决方法
|
|
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|
|
spaces/DHEIVER/DICOM_to_JPG_Converter/app.py
DELETED
|
@@ -1,45 +0,0 @@
|
|
| 1 |
-
import streamlit as st
|
| 2 |
-
import pydicom
|
| 3 |
-
import matplotlib.pyplot as plt
|
| 4 |
-
import os
|
| 5 |
-
|
| 6 |
-
def visualize_dicom_sequence(file_path):
|
| 7 |
-
ds = pydicom.dcmread(file_path)
|
| 8 |
-
image_sequence = ds.pixel_array
|
| 9 |
-
|
| 10 |
-
if image_sequence.ndim == 2:
|
| 11 |
-
# Only one image in the sequence
|
| 12 |
-
fig, ax = plt.subplots()
|
| 13 |
-
ax.imshow(image_sequence, cmap=plt.cm.gray)
|
| 14 |
-
ax.axis('off')
|
| 15 |
-
st.pyplot(fig)
|
| 16 |
-
else:
|
| 17 |
-
# Multiple images in the sequence
|
| 18 |
-
for i, image in enumerate(image_sequence):
|
| 19 |
-
fig, ax = plt.subplots()
|
| 20 |
-
ax.imshow(image, cmap=plt.cm.gray)
|
| 21 |
-
ax.axis('off')
|
| 22 |
-
st.pyplot(fig)
|
| 23 |
-
|
| 24 |
-
def main():
|
| 25 |
-
st.title("Visualizador DICOM")
|
| 26 |
-
|
| 27 |
-
# Upload DICOM file
|
| 28 |
-
uploaded_file = st.file_uploader("Selecione um arquivo DICOM", type=".dcm")
|
| 29 |
-
|
| 30 |
-
if uploaded_file is not None:
|
| 31 |
-
# Convert uploaded file to bytes
|
| 32 |
-
file_bytes = uploaded_file.getvalue()
|
| 33 |
-
|
| 34 |
-
# Save the uploaded file to a temporary location
|
| 35 |
-
with open("temp.dcm", "wb") as f:
|
| 36 |
-
f.write(file_bytes)
|
| 37 |
-
|
| 38 |
-
# Visualize the DICOM image sequence
|
| 39 |
-
visualize_dicom_sequence("temp.dcm")
|
| 40 |
-
|
| 41 |
-
# Remove the temporary file
|
| 42 |
-
os.remove("temp.dcm")
|
| 43 |
-
|
| 44 |
-
if __name__ == "__main__":
|
| 45 |
-
main()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/attr/_compat.py
DELETED
|
@@ -1,185 +0,0 @@
|
|
| 1 |
-
# SPDX-License-Identifier: MIT
|
| 2 |
-
|
| 3 |
-
|
| 4 |
-
import inspect
|
| 5 |
-
import platform
|
| 6 |
-
import sys
|
| 7 |
-
import threading
|
| 8 |
-
import types
|
| 9 |
-
import warnings
|
| 10 |
-
|
| 11 |
-
from collections.abc import Mapping, Sequence # noqa
|
| 12 |
-
from typing import _GenericAlias
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
PYPY = platform.python_implementation() == "PyPy"
|
| 16 |
-
PY_3_9_PLUS = sys.version_info[:2] >= (3, 9)
|
| 17 |
-
PY310 = sys.version_info[:2] >= (3, 10)
|
| 18 |
-
PY_3_12_PLUS = sys.version_info[:2] >= (3, 12)
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
def just_warn(*args, **kw):
|
| 22 |
-
warnings.warn(
|
| 23 |
-
"Running interpreter doesn't sufficiently support code object "
|
| 24 |
-
"introspection. Some features like bare super() or accessing "
|
| 25 |
-
"__class__ will not work with slotted classes.",
|
| 26 |
-
RuntimeWarning,
|
| 27 |
-
stacklevel=2,
|
| 28 |
-
)
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
class _AnnotationExtractor:
|
| 32 |
-
"""
|
| 33 |
-
Extract type annotations from a callable, returning None whenever there
|
| 34 |
-
is none.
|
| 35 |
-
"""
|
| 36 |
-
|
| 37 |
-
__slots__ = ["sig"]
|
| 38 |
-
|
| 39 |
-
def __init__(self, callable):
|
| 40 |
-
try:
|
| 41 |
-
self.sig = inspect.signature(callable)
|
| 42 |
-
except (ValueError, TypeError): # inspect failed
|
| 43 |
-
self.sig = None
|
| 44 |
-
|
| 45 |
-
def get_first_param_type(self):
|
| 46 |
-
"""
|
| 47 |
-
Return the type annotation of the first argument if it's not empty.
|
| 48 |
-
"""
|
| 49 |
-
if not self.sig:
|
| 50 |
-
return None
|
| 51 |
-
|
| 52 |
-
params = list(self.sig.parameters.values())
|
| 53 |
-
if params and params[0].annotation is not inspect.Parameter.empty:
|
| 54 |
-
return params[0].annotation
|
| 55 |
-
|
| 56 |
-
return None
|
| 57 |
-
|
| 58 |
-
def get_return_type(self):
|
| 59 |
-
"""
|
| 60 |
-
Return the return type if it's not empty.
|
| 61 |
-
"""
|
| 62 |
-
if (
|
| 63 |
-
self.sig
|
| 64 |
-
and self.sig.return_annotation is not inspect.Signature.empty
|
| 65 |
-
):
|
| 66 |
-
return self.sig.return_annotation
|
| 67 |
-
|
| 68 |
-
return None
|
| 69 |
-
|
| 70 |
-
|
| 71 |
-
def make_set_closure_cell():
|
| 72 |
-
"""Return a function of two arguments (cell, value) which sets
|
| 73 |
-
the value stored in the closure cell `cell` to `value`.
|
| 74 |
-
"""
|
| 75 |
-
# pypy makes this easy. (It also supports the logic below, but
|
| 76 |
-
# why not do the easy/fast thing?)
|
| 77 |
-
if PYPY:
|
| 78 |
-
|
| 79 |
-
def set_closure_cell(cell, value):
|
| 80 |
-
cell.__setstate__((value,))
|
| 81 |
-
|
| 82 |
-
return set_closure_cell
|
| 83 |
-
|
| 84 |
-
# Otherwise gotta do it the hard way.
|
| 85 |
-
|
| 86 |
-
try:
|
| 87 |
-
if sys.version_info >= (3, 8):
|
| 88 |
-
|
| 89 |
-
def set_closure_cell(cell, value):
|
| 90 |
-
cell.cell_contents = value
|
| 91 |
-
|
| 92 |
-
else:
|
| 93 |
-
# Create a function that will set its first cellvar to `value`.
|
| 94 |
-
def set_first_cellvar_to(value):
|
| 95 |
-
x = value
|
| 96 |
-
return
|
| 97 |
-
|
| 98 |
-
# This function will be eliminated as dead code, but
|
| 99 |
-
# not before its reference to `x` forces `x` to be
|
| 100 |
-
# represented as a closure cell rather than a local.
|
| 101 |
-
def force_x_to_be_a_cell(): # pragma: no cover
|
| 102 |
-
return x
|
| 103 |
-
|
| 104 |
-
# Extract the code object and make sure our assumptions about
|
| 105 |
-
# the closure behavior are correct.
|
| 106 |
-
co = set_first_cellvar_to.__code__
|
| 107 |
-
if co.co_cellvars != ("x",) or co.co_freevars != ():
|
| 108 |
-
raise AssertionError # pragma: no cover
|
| 109 |
-
|
| 110 |
-
# Convert this code object to a code object that sets the
|
| 111 |
-
# function's first _freevar_ (not cellvar) to the argument.
|
| 112 |
-
args = [co.co_argcount]
|
| 113 |
-
args.append(co.co_kwonlyargcount)
|
| 114 |
-
args.extend(
|
| 115 |
-
[
|
| 116 |
-
co.co_nlocals,
|
| 117 |
-
co.co_stacksize,
|
| 118 |
-
co.co_flags,
|
| 119 |
-
co.co_code,
|
| 120 |
-
co.co_consts,
|
| 121 |
-
co.co_names,
|
| 122 |
-
co.co_varnames,
|
| 123 |
-
co.co_filename,
|
| 124 |
-
co.co_name,
|
| 125 |
-
co.co_firstlineno,
|
| 126 |
-
co.co_lnotab,
|
| 127 |
-
# These two arguments are reversed:
|
| 128 |
-
co.co_cellvars,
|
| 129 |
-
co.co_freevars,
|
| 130 |
-
]
|
| 131 |
-
)
|
| 132 |
-
set_first_freevar_code = types.CodeType(*args)
|
| 133 |
-
|
| 134 |
-
def set_closure_cell(cell, value):
|
| 135 |
-
# Create a function using the set_first_freevar_code,
|
| 136 |
-
# whose first closure cell is `cell`. Calling it will
|
| 137 |
-
# change the value of that cell.
|
| 138 |
-
setter = types.FunctionType(
|
| 139 |
-
set_first_freevar_code, {}, "setter", (), (cell,)
|
| 140 |
-
)
|
| 141 |
-
# And call it to set the cell.
|
| 142 |
-
setter(value)
|
| 143 |
-
|
| 144 |
-
# Make sure it works on this interpreter:
|
| 145 |
-
def make_func_with_cell():
|
| 146 |
-
x = None
|
| 147 |
-
|
| 148 |
-
def func():
|
| 149 |
-
return x # pragma: no cover
|
| 150 |
-
|
| 151 |
-
return func
|
| 152 |
-
|
| 153 |
-
cell = make_func_with_cell().__closure__[0]
|
| 154 |
-
set_closure_cell(cell, 100)
|
| 155 |
-
if cell.cell_contents != 100:
|
| 156 |
-
raise AssertionError # pragma: no cover
|
| 157 |
-
|
| 158 |
-
except Exception:
|
| 159 |
-
return just_warn
|
| 160 |
-
else:
|
| 161 |
-
return set_closure_cell
|
| 162 |
-
|
| 163 |
-
|
| 164 |
-
set_closure_cell = make_set_closure_cell()
|
| 165 |
-
|
| 166 |
-
# Thread-local global to track attrs instances which are already being repr'd.
|
| 167 |
-
# This is needed because there is no other (thread-safe) way to pass info
|
| 168 |
-
# about the instances that are already being repr'd through the call stack
|
| 169 |
-
# in order to ensure we don't perform infinite recursion.
|
| 170 |
-
#
|
| 171 |
-
# For instance, if an instance contains a dict which contains that instance,
|
| 172 |
-
# we need to know that we're already repr'ing the outside instance from within
|
| 173 |
-
# the dict's repr() call.
|
| 174 |
-
#
|
| 175 |
-
# This lives here rather than in _make.py so that the functions in _make.py
|
| 176 |
-
# don't have a direct reference to the thread-local in their globals dict.
|
| 177 |
-
# If they have such a reference, it breaks cloudpickle.
|
| 178 |
-
repr_context = threading.local()
|
| 179 |
-
|
| 180 |
-
|
| 181 |
-
def get_generic_base(cl):
|
| 182 |
-
"""If this is a generic class (A[str]), return the generic base for it."""
|
| 183 |
-
if cl.__class__ is _GenericAlias:
|
| 184 |
-
return cl.__origin__
|
| 185 |
-
return None
|
|
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|
spaces/Dave37/voicebot/README.md
DELETED
|
@@ -1,12 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
title: Voicebot
|
| 3 |
-
emoji: 🏆
|
| 4 |
-
colorFrom: pink
|
| 5 |
-
colorTo: pink
|
| 6 |
-
sdk: gradio
|
| 7 |
-
sdk_version: 3.39.0
|
| 8 |
-
app_file: app.py
|
| 9 |
-
pinned: false
|
| 10 |
-
---
|
| 11 |
-
|
| 12 |
-
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
spaces/DragGan/DragGan-Inversion/training/loss.py
DELETED
|
@@ -1,159 +0,0 @@
|
|
| 1 |
-
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
| 2 |
-
#
|
| 3 |
-
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
| 4 |
-
# and proprietary rights in and to this software, related documentation
|
| 5 |
-
# and any modifications thereto. Any use, reproduction, disclosure or
|
| 6 |
-
# distribution of this software and related documentation without an express
|
| 7 |
-
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
| 8 |
-
|
| 9 |
-
"""Loss functions."""
|
| 10 |
-
|
| 11 |
-
import numpy as np
|
| 12 |
-
import torch
|
| 13 |
-
from torch_utils import training_stats
|
| 14 |
-
from torch_utils.ops import conv2d_gradfix
|
| 15 |
-
from torch_utils.ops import upfirdn2d
|
| 16 |
-
|
| 17 |
-
# ----------------------------------------------------------------------------
|
| 18 |
-
|
| 19 |
-
|
| 20 |
-
class Loss:
|
| 21 |
-
# to be overridden by subclass
|
| 22 |
-
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg):
|
| 23 |
-
raise NotImplementedError()
|
| 24 |
-
|
| 25 |
-
# ----------------------------------------------------------------------------
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
class StyleGAN2Loss(Loss):
|
| 29 |
-
def __init__(self, device, G, D, augment_pipe=None, r1_gamma=10, style_mixing_prob=0, pl_weight=0, pl_batch_shrink=2, pl_decay=0.01, pl_no_weight_grad=False, blur_init_sigma=0, blur_fade_kimg=0):
|
| 30 |
-
super().__init__()
|
| 31 |
-
self.device = device
|
| 32 |
-
self.G = G
|
| 33 |
-
self.D = D
|
| 34 |
-
self.augment_pipe = augment_pipe
|
| 35 |
-
self.r1_gamma = r1_gamma
|
| 36 |
-
self.style_mixing_prob = style_mixing_prob
|
| 37 |
-
self.pl_weight = pl_weight
|
| 38 |
-
self.pl_batch_shrink = pl_batch_shrink
|
| 39 |
-
self.pl_decay = pl_decay
|
| 40 |
-
self.pl_no_weight_grad = pl_no_weight_grad
|
| 41 |
-
self.pl_mean = torch.zeros([], device=device)
|
| 42 |
-
self.blur_init_sigma = blur_init_sigma
|
| 43 |
-
self.blur_fade_kimg = blur_fade_kimg
|
| 44 |
-
|
| 45 |
-
def run_G(self, z, c, update_emas=False):
|
| 46 |
-
ws = self.G.mapping(z, c, update_emas=update_emas)
|
| 47 |
-
if self.style_mixing_prob > 0:
|
| 48 |
-
with torch.autograd.profiler.record_function('style_mixing'):
|
| 49 |
-
cutoff = torch.empty([], dtype=torch.int64,
|
| 50 |
-
device=ws.device).random_(1, ws.shape[1])
|
| 51 |
-
cutoff = torch.where(torch.rand(
|
| 52 |
-
[], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1]))
|
| 53 |
-
ws[:, cutoff:] = self.G.mapping(
|
| 54 |
-
torch.randn_like(z), c, update_emas=False)[:, cutoff:]
|
| 55 |
-
img = self.G.synthesis(ws, update_emas=update_emas)
|
| 56 |
-
return img, ws
|
| 57 |
-
|
| 58 |
-
def run_D(self, img, c, blur_sigma=0, update_emas=False):
|
| 59 |
-
blur_size = np.floor(blur_sigma * 3)
|
| 60 |
-
if blur_size > 0:
|
| 61 |
-
with torch.autograd.profiler.record_function('blur'):
|
| 62 |
-
f = torch.arange(-blur_size, blur_size + 1,
|
| 63 |
-
device=img.device).div(blur_sigma).square().neg().exp2()
|
| 64 |
-
img = upfirdn2d.filter2d(img, f / f.sum())
|
| 65 |
-
if self.augment_pipe is not None:
|
| 66 |
-
img = self.augment_pipe(img)
|
| 67 |
-
logits = self.D(img, c, update_emas=update_emas)
|
| 68 |
-
return logits
|
| 69 |
-
|
| 70 |
-
def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, gain, cur_nimg):
|
| 71 |
-
assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth']
|
| 72 |
-
if self.pl_weight == 0:
|
| 73 |
-
phase = {'Greg': 'none', 'Gboth': 'Gmain'}.get(phase, phase)
|
| 74 |
-
if self.r1_gamma == 0:
|
| 75 |
-
phase = {'Dreg': 'none', 'Dboth': 'Dmain'}.get(phase, phase)
|
| 76 |
-
blur_sigma = max(1 - cur_nimg / (self.blur_fade_kimg * 1e3), 0) * \
|
| 77 |
-
self.blur_init_sigma if self.blur_fade_kimg > 0 else 0
|
| 78 |
-
|
| 79 |
-
# Gmain: Maximize logits for generated images.
|
| 80 |
-
if phase in ['Gmain', 'Gboth']:
|
| 81 |
-
with torch.autograd.profiler.record_function('Gmain_forward'):
|
| 82 |
-
gen_img, _gen_ws = self.run_G(gen_z, gen_c)
|
| 83 |
-
gen_logits = self.run_D(gen_img, gen_c, blur_sigma=blur_sigma)
|
| 84 |
-
training_stats.report('Loss/scores/fake', gen_logits)
|
| 85 |
-
training_stats.report('Loss/signs/fake', gen_logits.sign())
|
| 86 |
-
# -log(sigmoid(gen_logits))
|
| 87 |
-
loss_Gmain = torch.nn.functional.softplus(-gen_logits)
|
| 88 |
-
training_stats.report('Loss/G/loss', loss_Gmain)
|
| 89 |
-
with torch.autograd.profiler.record_function('Gmain_backward'):
|
| 90 |
-
loss_Gmain.mean().mul(gain).backward()
|
| 91 |
-
|
| 92 |
-
# Gpl: Apply path length regularization.
|
| 93 |
-
if phase in ['Greg', 'Gboth']:
|
| 94 |
-
with torch.autograd.profiler.record_function('Gpl_forward'):
|
| 95 |
-
batch_size = gen_z.shape[0] // self.pl_batch_shrink
|
| 96 |
-
gen_img, gen_ws = self.run_G(
|
| 97 |
-
gen_z[:batch_size], gen_c[:batch_size])
|
| 98 |
-
pl_noise = torch.randn_like(
|
| 99 |
-
gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3])
|
| 100 |
-
with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients(self.pl_no_weight_grad):
|
| 101 |
-
pl_grads = torch.autograd.grad(outputs=[(
|
| 102 |
-
gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0]
|
| 103 |
-
pl_lengths = pl_grads.square().sum(2).mean(1).sqrt()
|
| 104 |
-
pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay)
|
| 105 |
-
self.pl_mean.copy_(pl_mean.detach())
|
| 106 |
-
pl_penalty = (pl_lengths - pl_mean).square()
|
| 107 |
-
training_stats.report('Loss/pl_penalty', pl_penalty)
|
| 108 |
-
loss_Gpl = pl_penalty * self.pl_weight
|
| 109 |
-
training_stats.report('Loss/G/reg', loss_Gpl)
|
| 110 |
-
with torch.autograd.profiler.record_function('Gpl_backward'):
|
| 111 |
-
loss_Gpl.mean().mul(gain).backward()
|
| 112 |
-
|
| 113 |
-
# Dmain: Minimize logits for generated images.
|
| 114 |
-
loss_Dgen = 0
|
| 115 |
-
if phase in ['Dmain', 'Dboth']:
|
| 116 |
-
with torch.autograd.profiler.record_function('Dgen_forward'):
|
| 117 |
-
gen_img, _gen_ws = self.run_G(gen_z, gen_c, update_emas=True)
|
| 118 |
-
gen_logits = self.run_D(
|
| 119 |
-
gen_img, gen_c, blur_sigma=blur_sigma, update_emas=True)
|
| 120 |
-
training_stats.report('Loss/scores/fake', gen_logits)
|
| 121 |
-
training_stats.report('Loss/signs/fake', gen_logits.sign())
|
| 122 |
-
loss_Dgen = torch.nn.functional.softplus(
|
| 123 |
-
gen_logits) # -log(1 - sigmoid(gen_logits))
|
| 124 |
-
with torch.autograd.profiler.record_function('Dgen_backward'):
|
| 125 |
-
loss_Dgen.mean().mul(gain).backward()
|
| 126 |
-
|
| 127 |
-
# Dmain: Maximize logits for real images.
|
| 128 |
-
# Dr1: Apply R1 regularization.
|
| 129 |
-
if phase in ['Dmain', 'Dreg', 'Dboth']:
|
| 130 |
-
name = 'Dreal' if phase == 'Dmain' else 'Dr1' if phase == 'Dreg' else 'Dreal_Dr1'
|
| 131 |
-
with torch.autograd.profiler.record_function(name + '_forward'):
|
| 132 |
-
real_img_tmp = real_img.detach().requires_grad_(
|
| 133 |
-
phase in ['Dreg', 'Dboth'])
|
| 134 |
-
real_logits = self.run_D(
|
| 135 |
-
real_img_tmp, real_c, blur_sigma=blur_sigma)
|
| 136 |
-
training_stats.report('Loss/scores/real', real_logits)
|
| 137 |
-
training_stats.report('Loss/signs/real', real_logits.sign())
|
| 138 |
-
|
| 139 |
-
loss_Dreal = 0
|
| 140 |
-
if phase in ['Dmain', 'Dboth']:
|
| 141 |
-
# -log(sigmoid(real_logits))
|
| 142 |
-
loss_Dreal = torch.nn.functional.softplus(-real_logits)
|
| 143 |
-
training_stats.report(
|
| 144 |
-
'Loss/D/loss', loss_Dgen + loss_Dreal)
|
| 145 |
-
|
| 146 |
-
loss_Dr1 = 0
|
| 147 |
-
if phase in ['Dreg', 'Dboth']:
|
| 148 |
-
with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients():
|
| 149 |
-
r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[
|
| 150 |
-
real_img_tmp], create_graph=True, only_inputs=True)[0]
|
| 151 |
-
r1_penalty = r1_grads.square().sum([1, 2, 3])
|
| 152 |
-
loss_Dr1 = r1_penalty * (self.r1_gamma / 2)
|
| 153 |
-
training_stats.report('Loss/r1_penalty', r1_penalty)
|
| 154 |
-
training_stats.report('Loss/D/reg', loss_Dr1)
|
| 155 |
-
|
| 156 |
-
with torch.autograd.profiler.record_function(name + '_backward'):
|
| 157 |
-
(loss_Dreal + loss_Dr1).mean().mul(gain).backward()
|
| 158 |
-
|
| 159 |
-
# ----------------------------------------------------------------------------
|
|
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|
spaces/ECCV2022/bytetrack/yolox/tracker/basetrack.py
DELETED
|
@@ -1,52 +0,0 @@
|
|
| 1 |
-
import numpy as np
|
| 2 |
-
from collections import OrderedDict
|
| 3 |
-
|
| 4 |
-
|
| 5 |
-
class TrackState(object):
|
| 6 |
-
New = 0
|
| 7 |
-
Tracked = 1
|
| 8 |
-
Lost = 2
|
| 9 |
-
Removed = 3
|
| 10 |
-
|
| 11 |
-
|
| 12 |
-
class BaseTrack(object):
|
| 13 |
-
_count = 0
|
| 14 |
-
|
| 15 |
-
track_id = 0
|
| 16 |
-
is_activated = False
|
| 17 |
-
state = TrackState.New
|
| 18 |
-
|
| 19 |
-
history = OrderedDict()
|
| 20 |
-
features = []
|
| 21 |
-
curr_feature = None
|
| 22 |
-
score = 0
|
| 23 |
-
start_frame = 0
|
| 24 |
-
frame_id = 0
|
| 25 |
-
time_since_update = 0
|
| 26 |
-
|
| 27 |
-
# multi-camera
|
| 28 |
-
location = (np.inf, np.inf)
|
| 29 |
-
|
| 30 |
-
@property
|
| 31 |
-
def end_frame(self):
|
| 32 |
-
return self.frame_id
|
| 33 |
-
|
| 34 |
-
@staticmethod
|
| 35 |
-
def next_id():
|
| 36 |
-
BaseTrack._count += 1
|
| 37 |
-
return BaseTrack._count
|
| 38 |
-
|
| 39 |
-
def activate(self, *args):
|
| 40 |
-
raise NotImplementedError
|
| 41 |
-
|
| 42 |
-
def predict(self):
|
| 43 |
-
raise NotImplementedError
|
| 44 |
-
|
| 45 |
-
def update(self, *args, **kwargs):
|
| 46 |
-
raise NotImplementedError
|
| 47 |
-
|
| 48 |
-
def mark_lost(self):
|
| 49 |
-
self.state = TrackState.Lost
|
| 50 |
-
|
| 51 |
-
def mark_removed(self):
|
| 52 |
-
self.state = TrackState.Removed
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