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-<h1>How to Download Guitar Rig 6 Full Version</h1>
- <p>If you are looking for a way to create realistic and professional guitar tones on your computer, you might have heard of <strong>Guitar Rig 6</strong>, the latest version of the popular amp simulator and multi-effects rack from Native Instruments. But how can you download Guitar Rig 6 full version for free or at a discounted price? And how can you install and use it to get the most out of your guitar playing and recording?</p>
-<h2>download guitar rig 6 full version</h2><br /><p><b><b>DOWNLOAD</b> >> <a href="https://byltly.com/2uKvLD">https://byltly.com/2uKvLD</a></b></p><br /><br />
- <p>In this article, we will answer all these questions and more. We will explain what Guitar Rig 6 is and why you need it, what features and benefits it offers, what system requirements and compatibility it has, how to download it for free or at a low cost, how to install and activate it on your computer, and how to use it to create amazing guitar tones. By the end of this article, you will have everything you need to know about downloading Guitar Rig 6 full version and using it to enhance your guitar sound.</p>
- <h2>What is Guitar Rig 6 and why you need it</h2>
- <p><strong>Guitar Rig 6</strong> is a software program that simulates the sound of various guitar amps, cabinets, pedals, effects, and tools. It allows you to plug your guitar into your computer and process your signal with a wide range of components that emulate real hardware devices. You can also use it as a standalone application or as a plugin in your digital audio workstation (DAW).</p>
- <p>Guitar Rig 6 is designed for guitarists of all levels and styles, from beginners to professionals, from rock to metal, from blues to jazz. Whether you want to practice, record, perform, or experiment with different sounds, Guitar Rig 6 can help you achieve your goals. You can use it to create realistic and authentic tones that match your favorite artists and genres, or you can use it to craft your own unique sounds that express your personality and creativity.</p>
- <h3>Guitar Rig 6 features and benefits</h3>
- <p>Guitar Rig 6 comes with a host of features and benefits that make it one of the best guitar effects software on the market. Here are some of them:</p>
-<p></p>
- <ul>
-<li><strong>Drag and drop interface</strong>: Guitar Rig 6 has a simple and intuitive interface that lets you easily build your own custom rigs by dragging and dropping components to the rack. You can also adjust the settings of each component with knobs, sliders, switches, and menus.</li>
-<li><strong>21 amp models with matching cabinets</strong>: Guitar Rig 6 offers a wide selection of amp models that cover various eras, styles, and sounds. From vintage classics to modern high-gain monsters, from clean and warm to crunchy and distorted, from British to American, you can find an amp that suits your taste. Each amp also comes with a matching cabinet that complements its tone.</li>
-<li><strong>68 effects models, tools, and modifiers</strong>: Guitar Rig 6 also provides a huge collection of effects models that emulate popular pedals, rack units, studio processors, and more. You can add effects such as distortion, overdrive, fuzz, compression, modulation, delay, reverb, pitch shifting, filtering, EQ, noise gate, looper, tuner, metronome, etc. You can also use tools such as splitters, mix ers, crossovers, and modifiers to shape and control your signal flow and dynamics.</li>
-<li><strong>New amps and effects powered by Intelligent Circuit Modeling</strong>: Guitar Rig 6 introduces a new technology called <strong>Intelligent Circuit Modeling</strong> that uses artificial intelligence to analyze and recreate the behavior of real analog circuits. This results in more realistic and responsive sounds that capture the nuances and character of the original hardware. Guitar Rig 6 features three new amps and 16 new effects based on this technology, such as the Chicago, Bass Invader, Fire Breather, Harmonic Synthesizer, Grain Delay, Choral Reef, etc.</li>
-<li><strong>Over 300 presets and styles</strong>: Guitar Rig 6 also comes with over 300 presets that are ready to use or tweak to your liking. These presets are organized by styles, such as rock, metal, blues, jazz, pop, etc., and by artists, such as Jimi Hendrix, Slash, John Frusciante, David Gilmour, etc. You can also create your own presets and save them for later use.</li>
-<li><strong>Flexible routing and sidechaining</strong>: Guitar Rig 6 allows you to route your signal in various ways to create complex and creative sounds. You can use up to eight parallel racks to process different parts of your signal separately, or you can use sidechaining to modulate one component with another. For example, you can use a compressor to duck the volume of your guitar when you sing into a microphone, or you can use an envelope follower to control the filter cutoff of a synth with your guitar.</li>
-<li><strong>MIDI control and automation</strong>: Guitar Rig 6 also supports MIDI control and automation, which means you can use external MIDI devices such as footswitches, pedals, keyboards, controllers, etc., to control the parameters of Guitar Rig 6 in real time. You can also record and edit automation data in your DAW to automate changes in your sound over time.</li>
-<li><strong>Integration with other Native Instruments products</strong>: Guitar Rig 6 is compatible with other Native Instruments products, such as Komplete Kontrol keyboards, Maschine grooveboxes, Traktor DJ software, etc. You can use these products to access and control Guitar Rig 6 features and functions more easily and intuitively. You can also use Guitar Rig 6 as an effect plugin for other Native Instruments instruments and sounds.</li>
-</ul>
- <h3>Guitar Rig 6 system requirements and compatibility</h3>
- <p>Guitar Rig 6 is compatible with Windows and Mac operating systems. Here are the minimum system requirements for running Guitar Rig 6:</p>
- <table>
-<tr>
-<th>Operating System</th>
-<th>Windows 10 (64-bit)</th>
-<th>macOS 10.14 or higher</th>
-</tr>
-<tr>
-<td>CPU</td>
-<td>Intel Core i5 or equivalent AMD processor</td>
-<td>Intel Core i5 or equivalent Apple processor</td>
-</tr>
-<tr>
-<td>RAM</td>
-<td>4 GB (8 GB recommended)</td>
-<td>4 GB (8 GB recommended)</td>
-</tr>
-<tr>
-<td>Disk Space</td>
-<td>1 GB for Guitar Rig 6 Player<br>3 GB for Guitar Rig 6 Pro</td>
-<td>1 GB for Guitar Rig 6 Player<br>3 GB for Guitar Rig 6 Pro</td>
-</tr>
-<tr>
-<td>Graphics Card</td>
-<td>NVIDIA GeForce GTX 600 series or higher<br>AMD Radeon HD 7000 series or higher<br>Intel HD Graphics 4000 or higher</td>
-<td>NVIDIA GeForce GTX 600 series or higher<br>AMD Radeon HD 7000 series or higher<br>Intel HD Graphics 4000 or higher</td>
-</tr>
-<tr>
-<td>Audio Interface</td>
-<td>A dedicated audio interface with ASIO driver support is recommended for optimal performance and low latency.</td>
-<td>A dedicated audio interface with Core Audio driver support is recommended for optimal performance and low latency.</td>
-</tr>
-<tr>
-<td>MIDI Device</td>
-<td>A MIDI device such as a footswitch, pedal, keyboard, controller, etc., is optional but recommended for controlling Guitar Rig 6 parameters in real time.</td>
-<td>A MIDI device such as a footswitch, pedal, keyboard, controller, etc., is optional but recommended for controlling Guitar Rig 6 parameters in real time.</td>
-</tr> <h2>How to download Guitar Rig 6 full version for free</h2>
- <p>Now that you know what Guitar Rig 6 is and what it can do for you, you might be wondering how to download it for free or at a low cost. There are three ways to get Guitar Rig 6 full version for free or at a discounted price:</p>
- <h3>Guitar Rig 6 Player: the free version with limited features</h3>
- <p>The first way to get Guitar Rig 6 full version for free is to download <strong>Guitar Rig 6 Player</strong>, the free version of Guitar Rig 6 that comes with limited features. Guitar Rig 6 Player is a great way to try out Guitar Rig 6 and see if you like it before buying the full version. Guitar Rig 6 Player includes:</p>
- <ul>
-<li>One amp model: Jump, based on the Marshall JMP Plexi</li>
-<li>One cabinet model: Matched Cabinet, based on the Marshall 1960A</li>
-<li>13 effects models: Skreamer, Demon Distortion, Cat, Mezcal, Citrus, AC Box, Gratifier, Treble Booster, Vintage Reverb, Quad Delay, Stereo Tune, Limiter, and Noise Reduction</li>
-<li>50 presets and styles</li>
-<li>The ability to use Guitar Rig 6 Player as a standalone application or as a plugin in your DAW</li>
-</ul>
- <p>To download Guitar Rig 6 Player for free, you need to create a free Native Instruments account and download the Native Access app. Native Access is a software that manages the installation and activation of Native Instruments products. Once you have Native Access installed, you can download Guitar Rig 6 Player from the Not Installed tab and install it on your computer.</p>
- <h3>Guitar Rig 6 Demo: the trial version with full features</h3>
- <p>The second way to get Guitar Rig 6 full version for free is to download <strong>Guitar Rig 6 Demo</strong>, the trial version of Guitar Rig 6 that comes with full features. Guitar Rig 6 Demo is a great way to test all the features and functions of Guitar Rig 6 and see if it meets your needs and expectations before buying the full version. Guitar Rig 6 Demo includes:</p>
- <ul>
-<li>All the features and benefits of Guitar Rig 6 Pro (see below)</li>
-<li>The ability to use Guitar Rig 6 Demo as a standalone application or as a plugin in your DAW</li>
-<li>A time limit of 30 minutes per session</li>
-<li>A noise burst every few minutes</li>
-<li>No saving or exporting of presets or sounds</li>
-</ul>
- <p>To download Guitar Rig 6 Demo for free, you need to create a free Native Instruments account and download the Native Access app. Once you have Native Access installed, you can download Guitar Rig 6 Demo from the Not Installed tab and install it on your computer.</p>
- <h3>Guitar Rig 6 Pro: the paid version with all features</h3>
- <p>The third way to get Guitar Rig 6 full version is to buy <strong>Guitar Rig 6 Pro</strong>, the paid version of Guitar Rig 6 that comes with all features. Guitar Rig 6 Pro is the ultimate guitar effects software that gives you unlimited creative possibilities and professional results. Guitar Rig 6 Pro includes:</p>
- <ul>
-<li>All the features and benefits of Guitar Rig 6 (see above)</li>
-<li>No time limit or noise burst</li>
-<li>The ability to save and export presets and sounds</li>
-<li>The ability to use Guitar Rig 6 Pro as a standalone application or as a plugin in your DAW</li>
-<li>Free updates and support from Native Instruments</li>
-</ul>
- <p>To buy Guitar Rig 6 Pro, you need to create a free Native Instruments account and download the Native Access app. Once you have Native Access installed, you can buy Guitar Rig 6 Pro from the Shop tab and install it on your computer. The price of Guitar Rig 6 Pro is $199 USD. However, there are some ways to get it at a discounted price:</p>
- <ul>
-<li>If you already own a previous version of Guitar Rig (Guitar Rig 1-5), you can upgrade to Guitar Rig 6 Pro for $99 USD.</li>
-<li>If you already own Komplete Start (a free collection of instruments and sounds from Native Instruments), you can crossgrade to Guitar Rig 6 Pro for $149 USD.</li>
-<li>If you already own Komplete Select (a curated collection of instruments and sounds from Native Instruments), you can crossgrade to Guitar Rig 6 Pro for $99 USD.</li>
-<li>If you already own Komplete (the ultimate production suite from Native Instruments), you can get Guitar Rig 6 Pro for free as part of your bundle.</li>
-</ul> <h2>How to install and activate Guitar Rig 6 full version</h2>
- <p>Once you have downloaded Guitar Rig 6 full version, either for free or for a price, you need to install and activate it on your computer. Here are the steps to do so:</p>
- <h3>How to install Guitar Rig 6 on your computer</h3>
- <p>To install Guitar Rig 6 on your computer, you need to use the Native Access app that you downloaded earlier. Here are the steps to install Guitar Rig 6 with Native Access:</p>
- <ol>
-<li>Open Native Access and log in with your Native Instruments account.</li>
-<li>Go to the Installed Products tab and find Guitar Rig 6 in the list.</li>
-<li>Click on the Install button and choose a location for the installation.</li>
-<li>Wait for the installation to complete and click on the Finish button.</li>
-<li>Guitar Rig 6 is now installed on your computer and ready to use.</li>
-</ol>
- <h3>How to activate Guitar Rig 6 with your license key or Native Access account</h3>
- <p>To activate Guitar Rig 6 on your computer, you need to use either your license key or your Native Access account. Here are the steps to activate Guitar Rig 6 with either method:</p>
- <ul>
-<li>If you bought Guitar Rig 6 Pro or upgraded from a previous version, you should have received a license key via email. To activate Guitar Rig 6 with your license key, follow these steps: <ol>
-<li>Open Native Access and log in with your Native Instruments account.</li>
-<li>Go to the Add a serial tab and enter your license key in the field.</li>
-<li>Click on the Add serial button and wait for the activation to complete.</li>
-<li>Guitar Rig 6 is now activated on your computer and ready to use.</li>
-</ol>
-</li>
-<li>If you downloaded Guitar Rig 6 Player or Guitar Rig 6 Demo, you don't need a license key. To activate Guitar Rig 6 with your Native Access account, follow these steps: <ol>
-<li>Open Native Access and log in with your Native Instruments account.</li>
-<li>Go to the Installed Products tab and find Guitar Rig 6 in the list.</li>
-<li>Click on the Activate button and wait for the activation to complete.</li>
-<li>Guitar Rig 6 is now activated on your computer and ready to use.</li>
-</ol>
-</li>
-</ul>
- <h2>How to use Guitar Rig 6 full version to create amazing guitar tones</h2>
- <p>Now that you have installed and activated Guitar Rig 6 full version on your computer, you can start using it to create amazing guitar tones. Here are some tips and tricks on how to use Guitar Rig 6 full version effectively and efficiently:</p>
- <h3>How to navigate the Guitar Rig 6 interface and browser</h3>
- <p>Guitar Rig 6 has a user-friendly interface that consists of four main sections: the header, the browser, the rack, and the footer. Here is a brief overview of each section:</p>
- <ul>
-<li>The header contains the menu bar, the toolbar, and the preset name. You can use the menu bar to access various options and settings, such as file, edit, view, help, etc. You can use the toolbar to access various functions and tools, such as tuner, metronome, tape deck, etc. You can also see and change the name of the current preset in the header.</li>
-<li>The browser contains the preset list, the style list, and the component list. You can use the preset list to browse, load, save, delete, or search presets. You can use the style list to filter presets by styles, such as rock, metal, blues, jazz, etc. You can use the component list to browse, load, or search components, such as amps, cabinets, effects, tools, etc.</li>
-<li>The rack contains the components that make up your guitar rig. You can see and adjust the settings of each component with knobs, sliders, switches, and menus. You can also drag and drop components to add, remove, or rearrange them in the rack. You can also use splitters, mixers, crossovers and modifiers to shape and control your signal flow and dynamics.</li>
-<li>The footer contains the master volume, the input level, the output level, and the CPU usage. You can use the master volume to adjust the overall volume of your guitar rig. You can also see and adjust the input level and the output level of your guitar rig. You can also see the CPU usage of your computer and optimize it if needed.</li>
-</ul>
- <p>To navigate the Guitar Rig 6 interface and browser, you can use your mouse, keyboard, or MIDI device. You can also use shortcuts and commands to access various functions and tools more quickly. For example, you can use the arrow keys to navigate the preset list, the style list, and the component list. You can also use the spacebar to bypass or enable a component, or use the delete key to remove a component from the rack. You can also use commands such as Ctrl+C to copy a component, Ctrl+V to paste a component, Ctrl+Z to undo an action, etc.</p>
- <h3>How to load and customize presets and components</h3>
- <p>Guitar Rig 6 comes with over 300 presets that are ready to use or tweak to your liking. You can also create your own presets and save them for later use. Here are some tips on how to load and customize presets and components:</p>
- <ul>
-<li>To load a preset, you can use the browser to find and select a preset from the preset list or the style list. You can also use the arrow keys or the mouse wheel to scroll through the presets. You can also use the search function to find a preset by name or keyword. Once you have found a preset that you like, you can double-click on it or press Enter to load it to the rack.</li>
-<li>To customize a preset, you can use the rack to adjust the settings of each component with knobs, sliders, switches, and menus. You can also drag and drop components to add, remove, or rearrange them in the rack. You can also use splitters, mixers, crossovers, and modifiers to shape and control your signal flow and dynamics. You can also use MIDI control and automation to control the parameters of Guitar Rig 6 in real time.</li>
-<li>To save a preset, you can click on the Save button in the header and enter a name for your preset. You can also choose a style for your preset from the style list. You can also add tags and comments to your preset for easier identification and organization. Once you have saved your preset, you can find it in the User folder in the browser.</li>
-<li>To load a component, you can use the browser to find and select a component from the component list. You can also use the arrow keys or the mouse wheel to scroll through the components. You can also use the search function to find a component by name or keyword. Once you have found a component that you like, you can drag and drop it to an empty slot in the rack or on top of an existing component to replace it.</li>
-<li>To customize a component, you can use the rack to adjust the settings of each component with knobs, sliders, switches, and menus. You can also drag and drop components to add, remove, or rearrange them in the rack. You can also use splitters, mixers, crossovers and modifiers to shape and control your signal flow and dynamics. You can also use MIDI control and automation to control the parameters of Guitar Rig 6 in real time.</li>
-</ul>
- <h3>How to use the new amps and effects powered by Intelligent Circuit Modeling</h3>
- <p>Guitar Rig 6 introduces a new technology called <strong>Intelligent Circuit Modeling</strong> that uses artificial intelligence to analyze and recreate the behavior of real analog circuits. This results in more realistic and responsive sounds that capture the nuances and character of the original hardware. Guitar Rig 6 features three new amps and 16 new effects based on this technology, such as the Chicago, Bass Invader, Fire Breather, Harmonic Synthesizer, Grain Delay, Choral Reef, etc. Here are some tips on how to use the new amps and effects powered by Intelligent Circuit Modeling:</p>
- <ul>
-<li>To load a new amp or effect, you can use the browser to find and select a component from the component list. You can also use the arrow keys or the mouse wheel to scroll through the components. You can also use the search function to find a component by name or keyword. Once you have found a component that you like, you can drag and drop it to an empty slot in the rack or on top of an existing component to replace it.</li>
-<li>To customize a new amp or effect, you can use the rack to adjust the settings of each component with knobs, sliders, switches, and menus. You can also drag and drop components to add, remove, or rearrange them in the rack. You can also use splitters, mixers, crossovers, and modifiers to shape and control your signal flow and dynamics. You can also use MIDI control and automation to control the parameters of Guitar Rig 6 in real time.</li>
-<li>To get the best sound from a new amp or effect, you need to match it with a suitable cabinet or speaker. You can use the browser to find and select a cabinet or speaker from the component list. You can also use the arrow keys or the mouse wheel to scroll through the components. You can also use the search function to find a component by name or keyword. Once you have found a component that you like, you can drag and drop it to an empty slot in the rack or on top of an existing component to replace it.</li>
-<li>To experiment with different sounds from a new amp or effect, you can use the presets that come with each component. You can use the browser to find and select a preset from the preset list or the style list. You can also use the arrow keys or the mouse wheel to scroll through the presets. You can also use the search function to find a preset by name or keyword. Once you have found a preset that you like, you can double-click on it or press Enter to load it to the rack.</li>
-</ul>
- <h2>Conclusion and FAQs</h2>
- <p>Guitar Rig 6 is a powerful and versatile guitar effects software that can help you create realistic and professional guitar tones on your computer. It offers a wide range of features and benefits that make it one of the best guitar effects software on the market. It also comes with three ways to get Guitar Rig 6 full version for free or at a discounted price: Guitar Rig 6 Player, Guitar Rig 6 Demo, and Guitar Rig 6 Pro.</p>
- <p>In this article, we have explained what Guitar Rig 6 is and why you need it, what features and benefits it offers, what system requirements and compatibility it has, how to download it for free or at a low cost, how to install and activate it on your computer, and how to use it to create amazing guitar tones. We hope that this article has helped you learn everything you need to know about downloading Guitar Rig 6 full version and using it to enhance your guitar sound.</p>
- <p>If you have any questions or doubts about Guitar Rig 6 full version, here are some frequently asked questions (FAQs) that might help you:</p>
- <h4>Q: Can I use Guitar Rig 6 with any guitar?</h4>
-<p>A: Yes, you can use Guitar Rig 6 with any electric guitar, acoustic guitar, bass guitar, or any other instrument that has a pickup or a microphone. You just need to connect your instrument to your computer via an audio interface with an instrument input.</p>
- <h4>Q: Can I use Guitar Rig 6 with any DAW?</h4>
-<p>A: Yes, you can use Guitar Rig 6 with any DAW that supports VST, AU, or AAX plugin formats. You just need to load Guitar Rig 6 as an effect plugin in your DAW's track or bus.</p>
- <h4>Q: Can I use Guitar Rig 6 offline?</h4>
-<p>A: Yes, you can use Guitar Rig 6 offline as a standalone application without an internet connection. However, you need an internet connection to download, install, and activate Guitar Rig 6 for the first time. You also need an internet connection to access the online features and updates of Guitar Rig 6.</p>
- <h4>Q: Can I use Guitar Rig 6 with other guitar effects software or hardware?</h4>
-<p>A: Yes, you can use Guitar Rig 6 with other guitar effects software or hardware, as long as they are compatible and do not cause any conflicts or issues. You can use Guitar Rig 6 as an effect plugin in your DAW and combine it with other plugins, or you can use Guitar Rig 6 as a standalone application and route it to other software or hardware via an audio interface or a virtual cable.</p>
- <h4>Q: Can I share my Guitar Rig 6 presets and sounds with others?</h4>
-<p>A: Yes, you can share your Guitar Rig 6 presets and sounds with others, as long as you respect the intellectual property rights of Native Instruments and the original creators of the components and presets. You can export your presets and sounds as files and send them to others via email, social media, cloud storage, etc. You can also import presets and sounds from others and load them to your Guitar Rig 6.</p> b2dd77e56b<br />
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-<p>Age of Empires 3 is not just about building and managing your economy; it is also about fighting and conquering your enemies. The combat system in Age of Empires 3 is based on a rock-paper-scissors model, where each unit type has strengths and weaknesses against other unit types. For example, infantry units are good against cavalry units, cavalry units are good against artillery units, and artillery units are good against infantry units.</p>
-<p>To master the combat system in Age of Empires 3, you need to know the different unit types and their counters, as well as how to use formations, stances, and special abilities. You also need to pay attention to the terrain, the weather, and the line of sight, as they can affect the performance and visibility of your units.</p>
-<p>Here are some general tips and tricks for combat in Age of Empires 3:</p>
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-<li>Always scout your enemy's base and army composition before attacking. This will help you plan your strategy and choose the right units for the battle.</li>
-<li>Try to have a balanced army with a mix of unit types. This will allow you to adapt to different situations and counter different threats.</li>
-<li>Use formations to organize your army and give them bonuses. For example, the staggered formation gives your ranged units more firing arc, while the box formation protects your artillery units from cavalry charges.</li>
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-<li>Use siege weapons to destroy enemy buildings and defenses. Siege weapons like cannons or mortars can deal massive damage to buildings and walls, but they are slow and vulnerable to enemy fire.</li>
-<li>Use ships to support your land army or attack from the sea. Ships can transport units across water, bombard enemy positions from afar, or engage in naval battles with other ships.</li>
-</ul>
-<h2>How to Enjoy the Campaign Mode in Age of Empires 3</h2>
-<p>If you are looking for a more story-driven and cinematic experience in Age of Empires 3, you might want to try the campaign mode. The campaign mode consists of three acts that follow the adventures of the Black family through different historical periods and continents.</p>
-<p>The first act is called Blood, Ice, and Steel, and it takes place during the colonization of America in the 16th and 17th centuries. You will play as Morgan Black, a knight of Malta who fights against the Spanish conquistadors and their allies.</p>
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-<p>The campaign mode in Age of Empires 3 offers a rich and varied gameplay experience that will appeal to both casual and hardcore strategy fans. You will explore and colonize new lands, trade and fight with other factions, build and manage your economy and military, research new technologies and upgrades, and customize your home city that provides you with bonuses and shipments.</p>
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-<p>If you want to enjoy the campaign mode in Age of Empires 3, here are some tips and tricks:</p>
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-<li>Play on a difficulty level that suits your skill level. The campaign mode has four difficulty levels: Easy, Moderate, Hard, and Expert. The higher the difficulty level, the more challenging the enemies will be.</li>
-<li>Watch the cutscenes and listen to the dialogue. They will provide you with important information about the story, characters, objectives, hints, tips, etc.</li>
-<li>Read the objectives carefully. They will tell you what you need to do to complete each mission. Some objectives are mandatory (marked with a star), while others are optional (marked with a circle).</li>
-<li>Check the map often. It will show you where you are, where your allies and enemies are, where your objectives are located, etc.</li>
-<li>Save your game frequently. You never know when something might go wrong or when you might want to try a different strategy.</li>
-<li>Have fun! The campaign mode is designed to be entertaining and engaging for all kinds of players. Don't worry too much about winning or losing; just enjoy the journey!</li>
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-<p>Adobe Reader X is a free software that allows you to view, print, and comment on PDF files. PDF stands for Portable Document Format, a file format that preserves the layout, fonts, images, and hyperlinks of any document. PDF files are widely used for sharing information across different platforms and devices.</p>
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-<h1>How to Download and Play CarX Street on PC</h1>
-<p>CarX Street is a racing game developed by CarX Technologies, LLC. It is an open-world street racer that lets you explore the large city and its surroundings, from busy city streets to spiral mountain roads and mesmerizing coastal highways. You can race to collect legendary racing cars and display them in your garage, or challenge other players in real network races. You can also build the car of your dreams using part tuning that unlocks all the physics of CarX Technology car behavior.</p>
-<p>If you are a fan of racing games, you might want to play CarX Street on your PC instead of your mobile device. Playing on PC has many advantages, such as a larger screen, better graphics, smoother performance, and more comfortable controls. In this article, we will show you how to download and install CarX Street on your PC using different emulators. We will also give you some tips and tricks to help you enjoy the game more.</p>
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- <h2>What is CarX Street?</h2>
-<p>CarX Street is a simulation racing video game that offers realistic car physics and high-speed drifting. The game also features different map types from around the world, and players can choose from several different game modes. Players can compete against other players, or participate in races and events.</p>
- <h3>Features of CarX Street</h3>
-<p>Some of the features of CarX Street are:</p>
-<ul>
-<li>Open world: You can get on your vehicle and explore the entire virtual world. You can find hidden spots, shortcuts, and secrets.</li>
-<li>Free to play: You can download and play CarX Street for free. You can also earn in-game currency by completing tasks and challenges.</li>
-<li>Buying gas: You need to fuel up your car with the right gas for the next race at city gas stations. Different types of gas have different effects on your car's performance.</li>
-<li>Houses and garages: You can buy houses for your cars and assemble collections for every race mode. You can also customize your garage with various decorations.</li>
-<li>In-game shop: You can buy over 50 official vehicles from the best automakers in the world. You can also buy parts, accessories, paints, stickers, and more.</li>
-<li>Many types of vehicles: You can choose from different types of vehicles, such as sports cars, muscle cars, supercars, hypercars, SUVs, trucks, and more.</li>
-<li>Car customization: You can customize your car with a detailed car-building system. You can swap parts and trick out your car for a specific race. You can also upgrade the engine, transmission, body, suspension, and tires.</li>
-<li>In-game free currency: You can earn free currency by watching ads, completing tasks, or participating in events. You can use the free currency to buy items or unlock features.</li>
-</ul>
- <h3>Benefits of playing CarX Street on PC</h3>
-<p>Playing CarX Street on PC has many benefits, such as:</p>
-<ul>
-<li>Larger screen: You can enjoy the stunning graphics and details of the game on a bigger screen. You can also see more of the map and the surroundings.</li>
-<li>Better graphics: You can adjust the graphics settings to suit your PC's specifications. You can also experience higher resolution, frame rate, and quality.</li>
-<li>Smoother performance: You can avoid lagging, crashing, or overheating issues that might occur on mobile devices. You can also save battery life and storage space.</li>
-<li>More comfortable controls: You can use your keyboard and mouse to control your car more easily and precisely. You can also customize your key mapping according to your preference.</li>
-</ul>
- <h2>How to download <h2>How to download and install CarX Street on PC</h2>
-<p>If you want to play CarX Street on your PC, you will need to use an Android emulator. An emulator is a software that mimics the Android operating system on your computer, allowing you to run Android apps and games. There are many emulators available, but we will show you how to use three of the most popular ones: BlueStacks, NoxPlayer, and LDPlayer.</p>
- <h3>Using BlueStacks emulator</h3>
-<p>BlueStacks is one of the most widely used Android emulators, with over 500 million users worldwide. It is compatible with both Windows and Mac operating systems, and it has a user-friendly interface and advanced features. Here are the steps to download and install CarX Street on PC using BlueStacks:</p>
-<ol>
-<li>Download and install BlueStacks on your PC from [1](https://www.bluestacks.com/).</li>
-<li>Complete Google sign-in to access the Play Store, or do it later.</li>
-<li>Look for CarX Street in the search bar at the top right corner.</li>
-<li>Click to install CarX Street from the search results.</li>
-<li>Complete Google sign-in (if you skipped step 2) to install CarX Street.</li>
-<li>Click the CarX Street icon on the home screen to start playing.</li>
-</ol>
- <h3>Using NoxPlayer emulator</h3>
-<p>NoxPlayer is another popular Android emulator, with over 150 million users worldwide. It is also compatible with both Windows and Mac operating systems, and it has a simple and fast interface and performance. Here are the steps to download and install CarX Street on PC using NoxPlayer:</p>
-<ol>
-<li>Download and install NoxPlayer on your PC from [5](https://www.bignox.com/).</li>
-<li>Run the installation package and complete the installation.</li>
-<li>Open NoxPlayer and search for CarX Street in the Google Play Store.</li>
-<li>Install the game and launch it to start playing.</li>
-</ol>
- <h3>Using LDPlayer emulator</h3>
-<p>LDPlayer is a newer Android emulator, but it has gained popularity among gamers for its high performance and compatibility. It is also compatible with both Windows and Mac operating systems, and it has a smooth and stable interface and features. Here are the steps to download and install CarX Street on PC using LDPlayer:</p>
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-<ol>
-<li>Download and install LDPlayer on your PC from [6](https://www.ldplayer.net/).</li>
-<li>Open LDPlayer and search for CarX Street in the LD Store or Google Play Store.</li>
-<li>Install the game and launch it to start playing.</li>
-</ol>
- <h2>Tips and tricks for CarX Street</h2>
-<p>Now that you know how to play CarX Street on your PC, you might want some tips and tricks to help you improve your skills and enjoy the game more. Here are some of them:</p>
- <h3>Follow the tutorial</h3>
-<p>The game has a tutorial that will teach you the basics of driving, racing, drifting, tuning, and more. It is highly recommended that you follow the tutorial before jumping into the action, as it will help you get familiar with the game mechanics and controls. You can also revisit the tutorial anytime from the settings menu if you need a refresher.</p>
- <h3>Roam through the city for more rewards</h3>
-<p>The game has an open world that you can explore at your own pace. You can find hidden spots, shortcuts, secrets, and rewards by roaming through the city. You can also encounter random events, challenges, and races that will give you more money, reputation, or items. Roaming through the city is also a good way to practice your driving skills and test your car's performance.</p>
- <h3>Take part in sprints and clubs</h3>
-<p>The game has two main modes: sprints and clubs. Sprints are short races that last under a minute, where you have to reach the finish line as fast as possible. Clubs are longer, story-driven competitions where you have to join a club, defeat its boss, and prove yourself as the best driver in the city. Both modes offer different rewards and challenges, so try them both out and see which one suits your style more.</p>
- <h3>Go for the best cars and customize them</h3>
-<p>The game has over 50 official vehicles from the best automakers in the world. You can buy them with in-game currency or real money, or earn them by completing tasks or events. You can also customize your car with a detailed car-building system that lets you swap parts, upgrade components, paint colors, add stickers, and more. You can also customize your garage with various decorations and display your car collection. Go for the best cars and make them your own.</p>
- <h2>Conclusion</h2>
-<p>CarX Street is a fun and realistic racing game that lets you experience the thrill of street racing. You can explore the open world, collect and customize your cars, and compete with other players. You can also play CarX Street on your PC using an Android emulator, which will give you many benefits such as a larger screen, better graphics, smoother performance, and more comfortable controls. If you are looking for a racing game that will keep you entertained and challenged, you should give CarX Street a try.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about CarX Street:</p>
-<ul>
-<li><b>Q: How do I drift in CarX Street?</b></li>
-<li>A: Drifting is an essential skill in CarX Street, as it will help you take corners faster and earn more points. To drift, you need to press the brake button while turning the steering wheel. You can also use the handbrake button to initiate a drift. You can adjust the sensitivity and angle of the steering wheel in the settings menu.</li>
-<li><b>Q: How do I get more money in CarX Street?</b></li>
-<li>A: Money is the main currency in CarX Street, which you can use to buy cars, parts, gas, and more. You can earn money by completing races, events, tasks, or challenges. You can also watch ads or use real money to get more money.</li>
-<li><b>Q: How do I join a club in CarX Street?</b></li>
-<li>A: Clubs are groups of racers that compete for territory and reputation in the city. You can join a club by completing its entry race and defeating its boss. You can also create your own club or join an existing one from the club menu.</li>
-<li><b>Q: How do I upgrade my car in CarX Street?</b></li>
-<li>A: You can upgrade your car by buying and installing new parts from the shop or the garage. You can also tune your car by adjusting the engine, transmission, body, suspension, and tires parameters. Upgrading and tuning your car will improve its performance and handling.</li>
-<li><b>Q: How do I play with friends in CarX Street?</b></li>
-<li>A: You can play with friends in CarX Street by inviting them to join your club or your race. You can also chat with them using the in-game chat feature or voice chat feature. You can also add friends from the social menu or search for them by their nickname or ID.</li>
-</ul></p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/CapCut Edit Videos like a Pro with TikToks Official Video Editor and Video Maker - Free Download.md DELETED Viewed

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-<h1>How to Download and Use CapCut Video Editor for TikTok</h1>
- <p>TikTok is one of the most popular social media platforms for creating and sharing short videos. Whether you want to make funny, educational, or inspirational videos, you need a good video editor to make them stand out. In this article, we will show you how to download and use CapCut, the official video editor and maker app for TikTok.</p>
- <h2>What is CapCut?</h2>
- <p>CapCut is a free video editor and maker app that is compatible with TikTok. It is developed by ByteDance, the same company that owns TikTok. CapCut allows you to edit videos on your mobile device with ease and fun. You can also use it to create videos for other social media platforms, such as YouTube, Instagram, Facebook, and WhatsApp.</p>
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- <h3>CapCut is a free video editor and maker app for TikTok</h3>
- <p>CapCut has everything you need to create stunning, high-quality videos. You can import your own videos and photos or record new ones in the app. You can also access a massive music library and exclusive TikTok songs. You can extract audio from videos or add your own voice-overs. You can also use AI tools to enhance your videos, such as auto captions, background removal, text-to-speech, motion tracking, and more.</p>
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- <p>CapCut has a user-friendly interface that lets you edit videos with simple gestures. You can trim, cut, merge, split, reverse, speed up, slow down, zoom in, zoom out, freeze, and animate your clips. You can also add text, stickers, filters, effects, transitions, and colors to your videos. You can use keyframe animation to customize every setting. You can also use chroma key to remove specific colors from videos. You can apply picture-in-picture (PIP) feature to add video and photo layers above the clip. You can also use the stabilizing feature to keep video footage steady.</p>
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- <p>CapCut lets you export your videos in custom resolutions and formats. You can export your videos in HD quality and support 4K 60fps exports and smart HDR. You can also adjust the format and share your creativity on TikTok and other social media platforms with one tap.</p>
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- <p>Downloading CapCut is easy and fast. Here are the steps to download CapCut for Android and iOS devices.</p>
- <h3>Download CapCut from Google Play Store or Apple App Store</h3>
- <p>You can download CapCut for free from Google Play Store or Apple App Store. Just search for "CapCut" in the store and tap Install or Get. The app size is about 100 MB.</p>
- <h3>Open CapCut and tap New Project to start editing</h3>
- <p>Once you have downloaded CapCut, open it on your device. You don't need a TikTok account or any other type of account to use CapCut. You can start editing right away by tapping New Project on the home screen.</p>
- <h3>Select a video or photos to edit and tap Add</h3>
- <p>You can select a video or photos from your device gallery or record a new one in the app. You can also use the search feature to find videos and photos online. You can select multiple files and tap Add to import them to your project. You can also rearrange, delete, or duplicate the clips in your timeline.</p>
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- <p>Editing videos with CapCut is fun and easy. Here are some tips on how to use CapCut to edit videos for TikTok.</p>
- <h3>Use the editing tools to trim, crop, reverse, speed up, and animate your clips</h3>
- <p>You can use the editing tools at the bottom of the screen to adjust your clips. You can tap Trim to cut out unwanted parts of your video. You can tap Crop to change the aspect ratio and zoom in or out of your video. You can tap Reverse to play your video backwards. You can tap Speed to change the playback speed of your video. You can tap Animate to add motion effects to your video.</p>
- <h3>Add text, stickers, filters, effects, and music to your videos</h3>
- <p>You can add text, stickers, filters, effects, and music to your videos by tapping the icons on the right side of the screen. You can tap Text to add captions, titles, or subtitles to your video. You can tap Sticker to add emojis, icons, or images to your video. You can tap Filter to apply different color presets to your video. You can tap Effect to add various visual effects to your video. You can tap Music to add songs, sound effects, or voice-overs to your video.</p>
- <h3>Use the templates and styles to enhance your videos</h3>
- <p>You can use the templates and styles to enhance your videos by tapping the icons on the left side of the screen. You can tap Template to apply pre-made themes and layouts to your video. You can tap Style to apply different artistic styles and filters to your video.</p>
- <h3>Tap Export to save and share your videos</h3>
- <p>When you are done editing your video, you can tap Export at the top right corner of the screen. You can choose the resolution, format, and quality of your video. You can also enable watermark removal if you want. Then you can tap Save or Share to save your video to your device or share it directly on TikTok or other social media platforms.</p>
- <h2>Benefits of Using CapCut for TikTok Videos</h2>
- <p>Using CapCut for TikTok videos has many benefits. Here are some of them.</p>
- <h3>CapCut is easy to use and versatile</h3>
- <p>CapCut is designed for beginners and professionals alike. It has a simple and intuitive interface that lets you edit videos with ease and fun. It also has a lot of features and options that let you customize your videos according to your preferences and needs.</p>
- <h3>CapCut has a large library of sounds and animations</h3>
- <p>CapCut has a large library of sounds and animations that you can use for free. You can access thousands of songs and sound effects that are updated regularly. You can also use exclusive TikTok songs that are popular and trending. You can also use hundreds of animations that are dynamic and creative.</p>
- <h3>CapCut can create stunning, high-quality videos</h3>
- <p>CapCut can create stunning, high-quality videos that will impress your audience. You can export your videos in HD quality and support 4K 60fps exports and smart HDR. You can also use AI tools that will enhance your videos automatically.</p>
- <h1>Conclusion</h1>
- <p>CapCut is a free video editor and maker app for TikTok that you can download and use on your Android or iOS device. It has everything you need to create stunning, high-quality videos with ease and fun. You can also use it to create videos for other social media platforms, such as YouTube, Instagram, Facebook, and WhatsApp. If you want to make amazing TikTok videos, download CapCut today!</p>
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- <h4>Is CapCut safe?</h4>
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-<p>You can delete CapCut by going to your device settings and tapping Apps or Applications. Then you can find CapCut and tap Uninstall or Delete. You can also delete CapCut by long-pressing the app icon and tapping Remove or Delete.</p>
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-<p>You can contact CapCut support by going to the app settings and tapping Feedback or Help. You can also email them at [email protected] or visit their website at https://www.capcut.net/.</p> 401be4b1e0<br />
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spaces/2023Liu2023/bingo/Dockerfile DELETED Viewed

@@ -1,36 +0,0 @@
-FROM node:18
-ARG DEBIAN_FRONTEND=noninteractive
-ENV BING_HEADER ""
-# Set home to the user's home directory
-ENV HOME=/home/user \
-	PATH=/home/user/.local/bin:$PATH
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user && mkdir -p $HOME/app && chown -R user $HOME
-# Switch to the "user" user
-USER user
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app/
-RUN npm install
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app/
-RUN npm run build
-ENV PORT 7860
-EXPOSE 7860
-CMD npm start

spaces/2023Liu2023/bingo/src/app/page.tsx DELETED Viewed

@@ -1,15 +0,0 @@
-import dynamic from 'next/dynamic'
-const DynamicComponentWithNoSSR = dynamic(
-  () => import('../components/chat'),
-  { ssr: false }
-)
-export default function IndexPage() {
-  return (
-    <>
-      <div className="loading-spinner" />
-      <DynamicComponentWithNoSSR />
-    </>
-  )
-}

spaces/2023Liu2023/bingo/src/components/chat.tsx DELETED Viewed

@@ -1,93 +0,0 @@
-'use client'
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-export default function Chat({ className }: ChatProps) {
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}

spaces/AIFILMS/audioldm-text-to-audio-generation/audioldm/clap/open_clip/transform.py DELETED Viewed

@@ -1,45 +0,0 @@
-from torchvision.transforms import (
-    Normalize,
-    Compose,
-    RandomResizedCrop,
-    InterpolationMode,
-    ToTensor,
-    Resize,
-    CenterCrop,
-)
-def _convert_to_rgb(image):
-    return image.convert("RGB")
-def image_transform(
-    image_size: int,
-    is_train: bool,
-    mean=(0.48145466, 0.4578275, 0.40821073),
-    std=(0.26862954, 0.26130258, 0.27577711),
-):
-    normalize = Normalize(mean=mean, std=std)
-    if is_train:
-        return Compose(
-            [
-                RandomResizedCrop(
-                    image_size,
-                    scale=(0.9, 1.0),
-                    interpolation=InterpolationMode.BICUBIC,
-                ),
-                _convert_to_rgb,
-                ToTensor(),
-                normalize,
-            ]
-        )
-    else:
-        return Compose(
-            [
-                Resize(image_size, interpolation=InterpolationMode.BICUBIC),
-                CenterCrop(image_size),
-                _convert_to_rgb,
-                ToTensor(),
-                normalize,
-            ]
-        )

spaces/AIGC-Audio/AudioGPT/text_to_speech/tasks/tts/ps_adv.py DELETED Viewed

@@ -1,374 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-import torch.nn as nn
-import numpy as np
-from text_to_speech.modules.tts.portaspeech.portaspeech import PortaSpeech
-from text_to_speech.modules.tts.syntaspeech.multi_window_disc import Discriminator
-from tasks.tts.fs import FastSpeechTask
-from text_to_speech.utils.audio.align import mel2token_to_dur
-from text_to_speech.utils.commons.hparams import hparams
-from text_to_speech.utils.metrics.diagonal_metrics import get_focus_rate, get_phone_coverage_rate, get_diagonal_focus_rate
-from text_to_speech.utils.nn.model_utils import num_params
-from text_to_speech.utils.commons.tensor_utils import tensors_to_scalars
-from text_to_speech.utils.audio.pitch.utils import denorm_f0, norm_f0
-from text_to_speech.utils.audio.pitch_extractors import get_pitch
-from text_to_speech.utils.metrics.dtw import dtw as DTW
-from text_to_speech.utils.plot.plot import spec_to_figure
-from text_to_speech.utils.text.text_encoder import build_token_encoder
-class PortaSpeechAdvTask(FastSpeechTask):
-    def __init__(self):
-        super().__init__()
-        data_dir = hparams['binary_data_dir']
-        self.word_encoder = build_token_encoder(f'{data_dir}/word_set.json')
-        self.build_disc_model()
-        self.mse_loss_fn = torch.nn.MSELoss()
-    def build_tts_model(self):
-        ph_dict_size = len(self.token_encoder)
-        word_dict_size = len(self.word_encoder)
-        self.model = PortaSpeech(ph_dict_size, word_dict_size, hparams)
-        self.gen_params = [p for p in self.model.parameters() if p.requires_grad]
-        self.dp_params = [p for k, p in self.model.named_parameters() if (('dur_predictor' in k) and p.requires_grad)]
-        self.gen_params_except_dp = [p for k, p in self.model.named_parameters() if (('dur_predictor' not in k) and p.requires_grad)]
-        self.bert_params = [p for k, p in self.model.named_parameters() if (('bert' in k) and p.requires_grad)]
-        self.gen_params_except_bert_and_dp = [p for k, p in self.model.named_parameters() if ('dur_predictor' not in k) and ('bert' not in k) and p.requires_grad ]
-        self.use_bert = True if len(self.bert_params) > 0 else False
-    def build_disc_model(self):
-        disc_win_num = hparams['disc_win_num']
-        h = hparams['mel_disc_hidden_size']
-        self.mel_disc = Discriminator(
-            time_lengths=[32, 64, 128][:disc_win_num],
-            freq_length=80, hidden_size=h, kernel=(3, 3)
-        )
-        self.disc_params = list(self.mel_disc.parameters())
-    def on_train_start(self):
-        super().on_train_start()
-        for n, m in self.model.named_children():
-            num_params(m, model_name=n)
-        if hasattr(self.model, 'fvae'):
-            for n, m in self.model.fvae.named_children():
-                num_params(m, model_name=f'fvae.{n}')
-    def _training_step(self, sample, batch_idx, optimizer_idx):
-        loss_output = {}
-        loss_weights = {}
-        disc_start = self.global_step >= hparams["disc_start_steps"] and hparams['lambda_mel_adv'] > 0
-        if optimizer_idx == 0:
-            #######################
-            #      Generator      #
-            #######################
-            loss_output, model_out = self.run_model(sample, infer=False)
-            self.model_out_gt = self.model_out = \
-                {k: v.detach() for k, v in model_out.items() if isinstance(v, torch.Tensor)}
-            if disc_start:
-                mel_p = model_out['mel_out']
-                if hasattr(self.model, 'out2mel'):
-                    mel_p = self.model.out2mel(mel_p)
-                o_ = self.mel_disc(mel_p)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    loss_output['a'] = self.mse_loss_fn(p_, p_.new_ones(p_.size()))
-                    loss_weights['a'] = hparams['lambda_mel_adv']
-                if pc_ is not None:
-                    loss_output['ac'] = self.mse_loss_fn(pc_, pc_.new_ones(pc_.size()))
-                    loss_weights['ac'] = hparams['lambda_mel_adv']
-        else:
-            #######################
-            #    Discriminator    #
-            #######################
-            if disc_start and self.global_step % hparams['disc_interval'] == 0:
-                model_out = self.model_out_gt
-                mel_g = sample['mels']
-                mel_p = model_out['mel_out']
-                o = self.mel_disc(mel_g)
-                p, pc = o['y'], o['y_c']
-                o_ = self.mel_disc(mel_p)
-                p_, pc_ = o_['y'], o_['y_c']
-                if p_ is not None:
-                    loss_output["r"] = self.mse_loss_fn(p, p.new_ones(p.size()))
-                    loss_output["f"] = self.mse_loss_fn(p_, p_.new_zeros(p_.size()))
-                if pc_ is not None:
-                    loss_output["rc"] = self.mse_loss_fn(pc, pc.new_ones(pc.size()))
-                    loss_output["fc"] = self.mse_loss_fn(pc_, pc_.new_zeros(pc_.size()))
-        total_loss = sum([loss_weights.get(k, 1) * v for k, v in loss_output.items() if isinstance(v, torch.Tensor) and v.requires_grad])
-        loss_output['batch_size'] = sample['txt_tokens'].size()[0]
-        return total_loss, loss_output
-    def run_model(self, sample, infer=False, *args, **kwargs):
-        txt_tokens = sample['txt_tokens']
-        word_tokens = sample['word_tokens']
-        spk_embed = sample.get('spk_embed')
-        spk_id = sample.get('spk_ids')
-        if not infer:
-            output = self.model(txt_tokens, word_tokens,
-                                ph2word=sample['ph2word'],
-                                mel2word=sample['mel2word'],
-                                mel2ph=sample['mel2ph'],
-                                word_len=sample['word_lengths'].max(),
-                                tgt_mels=sample['mels'],
-                                pitch=sample.get('pitch'),
-                                spk_embed=spk_embed,
-                                spk_id=spk_id,
-                                infer=False,
-                                global_step=self.global_step,
-                                graph_lst=sample['graph_lst'],
-                                etypes_lst=sample['etypes_lst'],
-                                bert_feats=sample.get("bert_feats"),
-                                cl_feats=sample.get("cl_feats")
-                                )
-            losses = {}
-            losses['kl_v'] = output['kl'].detach()
-            losses_kl = output['kl']
-            losses_kl = torch.clamp(losses_kl, min=hparams['kl_min'])
-            losses_kl = min(self.global_step / hparams['kl_start_steps'], 1) * losses_kl
-            losses_kl = losses_kl * hparams['lambda_kl']
-            losses['kl'] = losses_kl
-            self.add_mel_loss(output['mel_out'], sample['mels'], losses)
-            if hparams['dur_level'] == 'word':
-                self.add_dur_loss(
-                    output['dur'], sample['mel2word'], sample['word_lengths'], sample['txt_tokens'], losses)
-                self.get_attn_stats(output['attn'], sample, losses)
-            else:
-                super(PortaSpeechAdvTask, self).add_dur_loss(output['dur'], sample['mel2ph'], sample['txt_tokens'], losses)
-            return losses, output
-        else:
-            use_gt_dur = kwargs.get('infer_use_gt_dur', hparams['use_gt_dur'])
-            output = self.model(
-                txt_tokens, word_tokens,
-                ph2word=sample['ph2word'],
-                word_len=sample['word_lengths'].max(),
-                pitch=sample.get('pitch'),
-                mel2ph=sample['mel2ph'] if use_gt_dur else None,
-                mel2word=sample['mel2word'] if use_gt_dur else None,
-                tgt_mels=sample['mels'],
-                infer=True,
-                spk_embed=spk_embed,
-                spk_id=spk_id,
-                graph_lst=sample['graph_lst'],
-                etypes_lst=sample['etypes_lst'],
-                bert_feats=sample.get("bert_feats"),
-                cl_feats=sample.get("cl_feats")
-            )
-            return output
-    def add_dur_loss(self, dur_pred, mel2token, word_len, txt_tokens, losses=None):
-        T = word_len.max()
-        dur_gt = mel2token_to_dur(mel2token, T).float()
-        nonpadding = (torch.arange(T).to(dur_pred.device)[None, :] < word_len[:, None]).float()
-        dur_pred = dur_pred * nonpadding
-        dur_gt = dur_gt * nonpadding
-        wdur = F.l1_loss((dur_pred + 1).log(), (dur_gt + 1).log(), reduction='none')
-        wdur = (wdur * nonpadding).sum() / nonpadding.sum()
-        if hparams['lambda_word_dur'] > 0:
-            losses['wdur'] = wdur * hparams['lambda_word_dur']
-        if hparams['lambda_sent_dur'] > 0:
-            sent_dur_p = dur_pred.sum(-1)
-            sent_dur_g = dur_gt.sum(-1)
-            sdur_loss = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean')
-            losses['sdur'] = sdur_loss.mean() * hparams['lambda_sent_dur']
-        with torch.no_grad():
-            # calculate word-level abs_dur_error in micro-second
-            abs_word_dur_error = F.l1_loss(dur_pred , dur_gt, reduction='none')
-            abs_word_dur_error = (abs_word_dur_error * nonpadding).sum() / nonpadding.sum()
-            abs_word_dur_error = abs_word_dur_error * hparams['hop_size'] / hparams['audio_sample_rate'] * 1000
-            losses['abs_word_dur_error'] = abs_word_dur_error
-            # calculate word-level abs_dur_error in second
-            sent_dur_p = dur_pred.sum(-1)
-            sent_dur_g = dur_gt.sum(-1)
-            abs_sent_dur_error = F.l1_loss(sent_dur_p, sent_dur_g, reduction='mean').mean()
-            abs_sent_dur_error = abs_sent_dur_error * hparams['hop_size'] / hparams['audio_sample_rate']
-            losses['abs_sent_dur_error'] = abs_sent_dur_error
-    def validation_step(self, sample, batch_idx):
-        outputs = {}
-        outputs['losses'] = {}
-        outputs['losses'], model_out = self.run_model(sample)
-        outputs['total_loss'] = sum(outputs['losses'].values())
-        outputs['nsamples'] = sample['nsamples']
-        outputs = tensors_to_scalars(outputs)
-        if self.global_step % hparams['valid_infer_interval'] == 0 \
-                and batch_idx < hparams['num_valid_plots']:
-            valid_results = self.save_valid_result(sample, batch_idx, model_out)
-            wav_gt = valid_results['wav_gt']
-            mel_gt = valid_results['mel_gt']
-            wav_pred = valid_results['wav_pred']
-            mel_pred = valid_results['mel_pred']
-            f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
-            f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams)
-            manhattan_distance = lambda x, y: np.abs(x - y)
-            dist, cost, acc, path = DTW(f0_pred_, f0_gt_, manhattan_distance)
-            outputs['losses']['f0_dtw'] = dist / len(f0_gt_)
-        return outputs
-    def save_valid_result(self, sample, batch_idx, model_out):
-        sr = hparams['audio_sample_rate']
-        f0_gt = None
-        mel_out = model_out['mel_out']
-        if sample.get('f0') is not None:
-            f0_gt = denorm_f0(sample['f0'][0].cpu(), sample['uv'][0].cpu())
-        self.plot_mel(batch_idx, sample['mels'], mel_out, f0s=f0_gt)
-        # if self.global_step > 0:
-        wav_pred = self.vocoder.spec2wav(mel_out[0].cpu(), f0=f0_gt)
-        self.logger.add_audio(f'wav_val_{batch_idx}', wav_pred, self.global_step, sr)
-        # with gt duration
-        model_out = self.run_model(sample, infer=True, infer_use_gt_dur=True)
-        dur_info = self.get_plot_dur_info(sample, model_out)
-        del dur_info['dur_pred']
-        wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
-        self.logger.add_audio(f'wav_gdur_{batch_idx}', wav_pred, self.global_step, sr)
-        self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_gdur_{batch_idx}',
-                        dur_info=dur_info, f0s=f0_gt)
-        # with pred duration
-        if not hparams['use_gt_dur']:
-            model_out = self.run_model(sample, infer=True, infer_use_gt_dur=False)
-            dur_info = self.get_plot_dur_info(sample, model_out)
-            self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'][0], f'mel_pdur_{batch_idx}',
-                            dur_info=dur_info, f0s=f0_gt)
-            wav_pred = self.vocoder.spec2wav(model_out['mel_out'][0].cpu(), f0=f0_gt)
-            self.logger.add_audio(f'wav_pdur_{batch_idx}', wav_pred, self.global_step, sr)
-        # gt wav
-        mel_gt = sample['mels'][0].cpu()
-        wav_gt = self.vocoder.spec2wav(mel_gt, f0=f0_gt)
-        if self.global_step <= hparams['valid_infer_interval']:
-            self.logger.add_audio(f'wav_gt_{batch_idx}', wav_gt, self.global_step, sr)
-        # add attn plot
-        if self.global_step > 0 and hparams['dur_level'] == 'word':
-            self.logger.add_figure(f'attn_{batch_idx}', spec_to_figure(model_out['attn'][0]), self.global_step)
-        return {'wav_gt': wav_gt, 'wav_pred': wav_pred, 'mel_gt': mel_gt, 'mel_pred': model_out['mel_out'][0].cpu()}
-    def get_attn_stats(self, attn, sample, logging_outputs, prefix=''):
-        # diagonal_focus_rate
-        txt_lengths = sample['txt_lengths'].float()
-        mel_lengths = sample['mel_lengths'].float()
-        src_padding_mask = sample['txt_tokens'].eq(0)
-        target_padding_mask = sample['mels'].abs().sum(-1).eq(0)
-        src_seg_mask = sample['txt_tokens'].eq(self.seg_idx)
-        attn_ks = txt_lengths.float() / mel_lengths.float()
-        focus_rate = get_focus_rate(attn, src_padding_mask, target_padding_mask).mean().data
-        phone_coverage_rate = get_phone_coverage_rate(
-            attn, src_padding_mask, src_seg_mask, target_padding_mask).mean()
-        diagonal_focus_rate, diag_mask = get_diagonal_focus_rate(
-            attn, attn_ks, mel_lengths, src_padding_mask, target_padding_mask)
-        logging_outputs[f'{prefix}fr'] = focus_rate.mean().data
-        logging_outputs[f'{prefix}pcr'] = phone_coverage_rate.mean().data
-        logging_outputs[f'{prefix}dfr'] = diagonal_focus_rate.mean().data
-    def get_plot_dur_info(self, sample, model_out):
-        if hparams['dur_level'] == 'word':
-            T_txt = sample['word_lengths'].max()
-            dur_gt = mel2token_to_dur(sample['mel2word'], T_txt)[0]
-            dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt
-            txt = sample['ph_words'][0].split(" ")
-        else:
-            T_txt = sample['txt_tokens'].shape[1]
-            dur_gt = mel2token_to_dur(sample['mel2ph'], T_txt)[0]
-            dur_pred = model_out['dur'] if 'dur' in model_out else dur_gt
-            txt = self.token_encoder.decode(sample['txt_tokens'][0].cpu().numpy())
-            txt = txt.split(" ")
-        return {'dur_gt': dur_gt, 'dur_pred': dur_pred, 'txt': txt}
-    def build_optimizer(self, model):
-        optimizer_gen = torch.optim.AdamW(
-            self.gen_params,
-            lr=hparams['lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            weight_decay=hparams['weight_decay'])
-        optimizer_disc = torch.optim.AdamW(
-            self.disc_params,
-            lr=hparams['disc_lr'],
-            betas=(hparams['optimizer_adam_beta1'], hparams['optimizer_adam_beta2']),
-            **hparams["discriminator_optimizer_params"]) if len(self.disc_params) > 0 else None
-        return [optimizer_gen, optimizer_disc]
-    def build_scheduler(self, optimizer):
-        return [
-            FastSpeechTask.build_scheduler(self, optimizer[0]), # Generator Scheduler
-            torch.optim.lr_scheduler.StepLR(optimizer=optimizer[1], # Discriminator Scheduler
-                **hparams["discriminator_scheduler_params"]),
-        ]
-    def on_before_optimization(self, opt_idx):
-        if opt_idx == 0:
-            nn.utils.clip_grad_norm_(self.dp_params, hparams['clip_grad_norm'])
-            if self.use_bert:
-                nn.utils.clip_grad_norm_(self.bert_params, hparams['clip_grad_norm'])
-                nn.utils.clip_grad_norm_(self.gen_params_except_bert_and_dp, hparams['clip_grad_norm'])
-            else:
-                nn.utils.clip_grad_norm_(self.gen_params_except_dp, hparams['clip_grad_norm'])
-        else:
-            nn.utils.clip_grad_norm_(self.disc_params, hparams["clip_grad_norm"])
-    def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
-        if self.scheduler is not None:
-            self.scheduler[0].step(self.global_step // hparams['accumulate_grad_batches'])
-            self.scheduler[1].step(self.global_step // hparams['accumulate_grad_batches'])
-    ############
-    # infer
-    ############
-    def test_start(self):
-        super().test_start()
-        if hparams.get('save_attn', False):
-            os.makedirs(f'{self.gen_dir}/attn', exist_ok=True)
-        self.model.store_inverse_all()
-    def test_step(self, sample, batch_idx):
-        assert sample['txt_tokens'].shape[0] == 1, 'only support batch_size=1 in inference'
-        outputs = self.run_model(sample, infer=True)
-        text = sample['text'][0]
-        item_name = sample['item_name'][0]
-        tokens = sample['txt_tokens'][0].cpu().numpy()
-        mel_gt = sample['mels'][0].cpu().numpy()
-        mel_pred = outputs['mel_out'][0].cpu().numpy()
-        mel2ph = sample['mel2ph'][0].cpu().numpy()
-        mel2ph_pred = None
-        str_phs = self.token_encoder.decode(tokens, strip_padding=True)
-        base_fn = f'[{batch_idx:06d}][{item_name.replace("%", "_")}][%s]'
-        if text is not None:
-            base_fn += text.replace(":", "$3A")[:80]
-        base_fn = base_fn.replace(' ', '_')
-        gen_dir = self.gen_dir
-        wav_pred = self.vocoder.spec2wav(mel_pred)
-        self.saving_result_pool.add_job(self.save_result, args=[
-            wav_pred, mel_pred, base_fn % 'P', gen_dir, str_phs, mel2ph_pred])
-        if hparams['save_gt']:
-            wav_gt = self.vocoder.spec2wav(mel_gt)
-            self.saving_result_pool.add_job(self.save_result, args=[
-                wav_gt, mel_gt, base_fn % 'G', gen_dir, str_phs, mel2ph])
-        if hparams.get('save_attn', False):
-            attn = outputs['attn'][0].cpu().numpy()
-            np.save(f'{gen_dir}/attn/{item_name}.npy', attn)
-        # save f0 for pitch dtw
-        f0_pred_, _ = get_pitch(wav_pred, mel_pred, hparams)
-        f0_gt_, _ = get_pitch(wav_gt, mel_gt, hparams)
-        np.save(f'{gen_dir}/f0/{item_name}.npy', f0_pred_)
-        np.save(f'{gen_dir}/f0/{item_name}_gt.npy', f0_gt_)
-        print(f"Pred_shape: {mel_pred.shape}, gt_shape: {mel_gt.shape}")
-        return {
-            'item_name': item_name,
-            'text': text,
-            'ph_tokens': self.token_encoder.decode(tokens.tolist()),
-            'wav_fn_pred': base_fn % 'P',
-            'wav_fn_gt': base_fn % 'G',
-        }

spaces/AIGText/GlyphControl/ldm/models/diffusion/__init__.py DELETED Viewed

File without changes

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/work_dirs/mobilevit-small_4xb32_2000e_3c_noF/mobilevit-small_4xb32_2000e_3c_noF.py DELETED Viewed

@@ -1,137 +0,0 @@
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(type='MobileViT', arch='small'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=7,
-        in_channels=640,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-        topk=(
-            1,
-            3,
-        )))
-dataset_type = 'CustomDataset'
-data_preprocessor = dict(
-    num_classes=6, mean=[
-        0,
-        0,
-        0,
-    ], std=[
-        255,
-        255,
-        255,
-    ], to_rgb=False)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='RandomResizedCrop', scale=224),
-    dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-    dict(type='PackInputs'),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='ResizeEdge', scale=288, edge='short'),
-    dict(type='CenterCrop', crop_size=256),
-    dict(type='PackInputs'),
-]
-train_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='data',
-        with_label=True,
-        ann_file='',
-        data_prefix='train',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='RandomResizedCrop', scale=224),
-            dict(type='RandomFlip', prob=0.5, direction='horizontal'),
-            dict(type='PackInputs'),
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=True))
-val_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='data',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='ResizeEdge', scale=288, edge='short'),
-            dict(type='CenterCrop', crop_size=256),
-            dict(type='PackInputs'),
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=False))
-val_evaluator = dict(
-    type='Accuracy', topk=(
-        1,
-        3,
-    ))
-test_dataloader = dict(
-    pin_memory=True,
-    persistent_workers=True,
-    collate_fn=dict(type='default_collate'),
-    batch_size=32,
-    num_workers=5,
-    dataset=dict(
-        type='CustomDataset',
-        data_root='data',
-        with_label=True,
-        ann_file='',
-        data_prefix='val',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='ResizeEdge', scale=288, edge='short'),
-            dict(type='CenterCrop', crop_size=256),
-            dict(type='PackInputs'),
-        ]),
-    sampler=dict(type='DefaultSampler', shuffle=False))
-test_evaluator = dict(
-    type='Accuracy', topk=(
-        1,
-        3,
-    ))
-default_scope = 'mmpretrain'
-default_hooks = dict(
-    timer=dict(type='IterTimerHook'),
-    logger=dict(type='LoggerHook', interval=10),
-    param_scheduler=dict(type='ParamSchedulerHook'),
-    checkpoint=dict(type='CheckpointHook', save_best='auto', interval=10),
-    sampler_seed=dict(type='DistSamplerSeedHook'),
-    visualization=dict(type='VisualizationHook', enable=False))
-env_cfg = dict(
-    cudnn_benchmark=False,
-    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
-    dist_cfg=dict(backend='nccl'))
-vis_backends = [
-    dict(type='LocalVisBackend'),
-]
-visualizer = dict(
-    type='UniversalVisualizer',
-    vis_backends=[
-        dict(type='LocalVisBackend'),
-        dict(type='WandbVisBackend'),
-    ])
-log_level = 'INFO'
-load_from = None
-resume = False
-randomness = dict(seed=None, deterministic=False)
-optim_wrapper = dict(
-    optimizer=dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001))
-param_scheduler = dict(type='StepLR', by_epoch=True, step_size=10, gamma=0.98)
-train_cfg = dict(by_epoch=True, max_epochs=2000, val_interval=10)
-val_cfg = dict()
-test_cfg = dict()
-auto_scale_lr = dict(base_batch_size=256)
-launcher = 'pytorch'
-work_dir = './work_dirs/mobilevit-small_4xb32_2000e_3c_noF'

spaces/AgProfile/GradioGenOpenAi/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: GradioGenOpenAi
-emoji: ⚡
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/maker/builders/CreateButtons.js DELETED Viewed

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

spaces/Akmyradov/TurkmenTTSweSTT/uroman/bin/uroman-tsv.sh DELETED Viewed

@@ -1,28 +0,0 @@
-#!/usr/bin/env bash
-# Created by Thamme Gowda on June 17, 2019
-DIR=$(dirname "${BASH_SOURCE[0]}")  # get the directory name
-# DIR=$(realpath "${DIR}")    # resolve its full path if need be
-if [[ $# -lt 1 || $# -gt 2 ]]; then
-    >&2 echo "ERROR: invalid args"
-    >&2 echo "Usage: <input.tsv> [<output.tsv>]"
-    exit 2
-fi
-INP=$1
-OUT=$2
-CMD=$DIR/uroman.pl
-function romanize(){
-    paste <(cut -f1 $INP) <(cut -f2 $INP | $CMD)
-}
-if [[ -n $OUT ]]; then
-    romanize > $OUT
-else
-    romanize
-fi

spaces/AlexZou/Deploy_Restoration/net/utils.py DELETED Viewed

@@ -1,86 +0,0 @@
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from skimage.measure.simple_metrics import compare_psnr
-from torchvision import models
-def weights_init_kaiming(m):
-    classname = m.__class__.__name__
-    if classname.find('Conv') != -1:
-        nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
-    elif classname.find('Linear') != -1:
-        nn.init.kaiming_normal(m.weight.data, a=0, mode='fan_in')
-    elif classname.find('BatchNorm') != -1:
-        # nn.init.uniform(m.weight.data, 1.0, 0.02)
-        m.weight.data.normal_(mean=0, std=math.sqrt(2./9./64.)).clamp_(-0.025,0.025)
-        nn.init.constant(m.bias.data, 0.0)
-class VGG19_PercepLoss(nn.Module):
-    """ Calculates perceptual loss in vgg19 space
-    """
-    def __init__(self, _pretrained_=True):
-        super(VGG19_PercepLoss, self).__init__()
-        self.vgg = models.vgg19(pretrained=_pretrained_).features
-        for param in self.vgg.parameters():
-            param.requires_grad_(False)
-    def get_features(self, image, layers=None):
-        if layers is None:
-            layers = {'30': 'conv5_2'} # may add other layers
-        features = {}
-        x = image
-        for name, layer in self.vgg._modules.items():
-            x = layer(x)
-            if name in layers:
-                features[layers[name]] = x
-        return features
-    def forward(self, pred, true, layer='conv5_2'):
-        true_f = self.get_features(true)
-        pred_f = self.get_features(pred)
-        return torch.mean((true_f[layer]-pred_f[layer])**2)
-def batch_PSNR(img, imclean, data_range):
-    Img = img.data.cpu().numpy().astype(np.float32)
-    Iclean = imclean.data.cpu().numpy().astype(np.float32)
-    PSNR = 0
-    for i in range(Img.shape[0]):
-        PSNR += compare_psnr(Iclean[i,:,:,:], Img[i,:,:,:], data_range=data_range)
-    return (PSNR/Img.shape[0])
-def data_augmentation(image, mode):
-    out = np.transpose(image, (1,2,0))
-    #out = image
-    if mode == 0:
-        # original
-        out = out
-    elif mode == 1:
-        # flip up and down
-        out = np.flipud(out)
-    elif mode == 2:
-        # rotate counterwise 90 degree
-        out = np.rot90(out)
-    elif mode == 3:
-        # rotate 90 degree and flip up and down
-        out = np.rot90(out)
-        out = np.flipud(out)
-    elif mode == 4:
-        # rotate 180 degree
-        out = np.rot90(out, k=2)
-    elif mode == 5:
-        # rotate 180 degree and flip
-        out = np.rot90(out, k=2)
-        out = np.flipud(out)
-    elif mode == 6:
-        # rotate 270 degree
-        out = np.rot90(out, k=3)
-    elif mode == 7:
-        # rotate 270 degree and flip
-        out = np.rot90(out, k=3)
-        out = np.flipud(out)
-    return np.transpose(out, (2,0,1))
-    #return out

spaces/Amrrs/DragGan-Inversion/PTI/torch_utils/misc.py DELETED Viewed

@@ -1,262 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-_constant_cache = dict()
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-#----------------------------------------------------------------------------
-# Symbolic assert.
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-class suppress_tracer_warnings(warnings.catch_warnings):
-    def __enter__(self):
-        super().__enter__()
-        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
-        return self
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-    # Print table.
-    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-    print()
-    for row in rows:
-        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
-    print()
-    return outputs
-#----------------------------------------------------------------------------

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/using-diffusers/img2img.md DELETED Viewed

@@ -1,100 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-http://www.apache.org/licenses/LICENSE-2.0
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-# 텍스트 기반 image-to-image 생성
-[[Colab에서 열기]]
-[`StableDiffusionImg2ImgPipeline`]을 사용하면 텍스트 프롬프트와 시작 이미지를 전달하여 새 이미지 생성의 조건을 지정할 수 있습니다.
-시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
-```bash
-!pip install diffusers transformers ftfy accelerate
-```
-[`nitrosocke/Ghibli-Diffusion`](https://huggingface.co/nitrosocke/Ghibli-Diffusion)과 같은 사전학습된 stable diffusion 모델로 [`StableDiffusionImg2ImgPipeline`]을 생성하여 시작하세요.
-```python
-import torch
-import requests
-from PIL import Image
-from io import BytesIO
-from diffusers import StableDiffusionImg2ImgPipeline
-device = "cuda"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("nitrosocke/Ghibli-Diffusion", torch_dtype=torch.float16).to(
-    device
-)
-```
-초기 이미지를 다운로드하고 사전 처리하여 파이프라인에 전달할 수 있습니다:
-```python
-url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-response = requests.get(url)
-init_image = Image.open(BytesIO(response.content)).convert("RGB")
-init_image.thumbnail((768, 768))
-init_image
-```
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/image_2_image_using_diffusers_cell_8_output_0.jpeg"/>
-</div>
-<Tip>
-💡 `strength`는 입력 이미지에 추가되는 노이즈의 양을 제어하는 0.0에서 1.0 사이의 값입니다. 1.0에 가까운 값은 다양한 변형을 허용하지만 입력 이미지와 의미적으로 일치하지 않는 이미지를 생성합니다.
-</Tip>
-프롬프트를 정의하고(지브리 스타일(Ghibli-style)에 맞게 조정된 이 체크포인트의 경우 프롬프트 앞에 `ghibli style` 토큰을 붙여야 합니다) 파이프라인을 실행합니다:
-```python
-prompt = "ghibli style, a fantasy landscape with castles"
-generator = torch.Generator(device=device).manual_seed(1024)
-image = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
-image
-```
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ghibli-castles.png"/>
-</div>
-다른 스케줄러로 실험하여 출력에 어떤 영향을 미치는지 확인할 수도 있습니다:
-```python
-from diffusers import LMSDiscreteScheduler
-lms = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe.scheduler = lms
-generator = torch.Generator(device=device).manual_seed(1024)
-image = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5, generator=generator).images[0]
-image
-```
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/lms-ghibli.png"/>
-</div>
-아래 공백을 확인하고 `strength` 값을 다르게 설정하여 이미지를 생성해 보세요. `strength`를 낮게 설정하면 원본 이미지와 더 유사한 이미지가 생성되는 것을 확인할 수 있습니다.
-자유롭게 스케줄러를 [`LMSDiscreteScheduler`]로 전환하여 출력에 어떤 영향을 미치는지 확인해 보세요.
-<iframe
-	src="https://stevhliu-ghibli-img2img.hf.space"
-	frameborder="0"
-	width="850"
-	height="500"
-></iframe>

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/textual_inversion/textual_inversion.py DELETED Viewed

@@ -1,959 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-import argparse
-import logging
-import math
-import os
-import random
-import shutil
-import warnings
-from pathlib import Path
-import numpy as np
-import PIL
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import create_repo, upload_folder
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-import diffusers
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    DiffusionPipeline,
-    DPMSolverMultistepScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-if is_wandb_available():
-    import wandb
-if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.Resampling.BILINEAR,
-        "bilinear": PIL.Image.Resampling.BILINEAR,
-        "bicubic": PIL.Image.Resampling.BICUBIC,
-        "lanczos": PIL.Image.Resampling.LANCZOS,
-        "nearest": PIL.Image.Resampling.NEAREST,
-    }
-else:
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.LINEAR,
-        "bilinear": PIL.Image.BILINEAR,
-        "bicubic": PIL.Image.BICUBIC,
-        "lanczos": PIL.Image.LANCZOS,
-        "nearest": PIL.Image.NEAREST,
-    }
-# ------------------------------------------------------------------------------
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-logger = get_logger(__name__)
-def save_model_card(repo_id: str, images=None, base_model=str, repo_folder=None):
-    img_str = ""
-    for i, image in enumerate(images):
-        image.save(os.path.join(repo_folder, f"image_{i}.png"))
-        img_str += f"![img_{i}](./image_{i}.png)\n"
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- textual_inversion
-inference: true
----
-    """
-    model_card = f"""
-# Textual inversion text2image fine-tuning - {repo_id}
-These are textual inversion adaption weights for {base_model}. You can find some example images in the following. \n
-{img_str}
-"""
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
-def log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):
-    logger.info(
-        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-        f" {args.validation_prompt}."
-    )
-    # create pipeline (note: unet and vae are loaded again in float32)
-    pipeline = DiffusionPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        text_encoder=accelerator.unwrap_model(text_encoder),
-        tokenizer=tokenizer,
-        unet=unet,
-        vae=vae,
-        safety_checker=None,
-        revision=args.revision,
-        torch_dtype=weight_dtype,
-    )
-    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=True)
-    # run inference
-    generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
-    images = []
-    for _ in range(args.num_validation_images):
-        with torch.autocast("cuda"):
-            image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
-        images.append(image)
-    for tracker in accelerator.trackers:
-        if tracker.name == "tensorboard":
-            np_images = np.stack([np.asarray(img) for img in images])
-            tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
-        if tracker.name == "wandb":
-            tracker.log(
-                {
-                    "validation": [
-                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
-                    ]
-                }
-            )
-    del pipeline
-    torch.cuda.empty_cache()
-    return images
-def save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path):
-    logger.info("Saving embeddings")
-    learned_embeds = (
-        accelerator.unwrap_model(text_encoder)
-        .get_input_embeddings()
-        .weight[min(placeholder_token_ids) : max(placeholder_token_ids) + 1]
-    )
-    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
-    torch.save(learned_embeds_dict, save_path)
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--save_steps",
-        type=int,
-        default=500,
-        help="Save learned_embeds.bin every X updates steps.",
-    )
-    parser.add_argument(
-        "--save_as_full_pipeline",
-        action="store_true",
-        help="Save the complete stable diffusion pipeline.",
-    )
-    parser.add_argument(
-        "--num_vectors",
-        type=int,
-        default=1,
-        help="How many textual inversion vectors shall be used to learn the concept.",
-    )
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
-    )
-    parser.add_argument(
-        "--placeholder_token",
-        type=str,
-        default=None,
-        required=True,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
-    )
-    parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
-    parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="text-inversion-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution."
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=5000,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_num_cycles",
-        type=int,
-        default=1,
-        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
-    )
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument(
-        "--validation_prompt",
-        type=str,
-        default=None,
-        help="A prompt that is used during validation to verify that the model is learning.",
-    )
-    parser.add_argument(
-        "--num_validation_images",
-        type=int,
-        default=4,
-        help="Number of images that should be generated during validation with `validation_prompt`.",
-    )
-    parser.add_argument(
-        "--validation_steps",
-        type=int,
-        default=100,
-        help=(
-            "Run validation every X steps. Validation consists of running the prompt"
-            " `args.validation_prompt` multiple times: `args.num_validation_images`"
-            " and logging the images."
-        ),
-    )
-    parser.add_argument(
-        "--validation_epochs",
-        type=int,
-        default=None,
-        help=(
-            "Deprecated in favor of validation_steps. Run validation every X epochs. Validation consists of running the prompt"
-            " `args.validation_prompt` multiple times: `args.num_validation_images`"
-            " and logging the images."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=("Max number of checkpoints to store."),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-    if args.train_data_dir is None:
-        raise ValueError("You must specify a train data directory.")
-    return args
-imagenet_templates_small = [
-    "a photo of a {}",
-    "a rendering of a {}",
-    "a cropped photo of the {}",
-    "the photo of a {}",
-    "a photo of a clean {}",
-    "a photo of a dirty {}",
-    "a dark photo of the {}",
-    "a photo of my {}",
-    "a photo of the cool {}",
-    "a close-up photo of a {}",
-    "a bright photo of the {}",
-    "a cropped photo of a {}",
-    "a photo of the {}",
-    "a good photo of the {}",
-    "a photo of one {}",
-    "a close-up photo of the {}",
-    "a rendition of the {}",
-    "a photo of the clean {}",
-    "a rendition of a {}",
-    "a photo of a nice {}",
-    "a good photo of a {}",
-    "a photo of the nice {}",
-    "a photo of the small {}",
-    "a photo of the weird {}",
-    "a photo of the large {}",
-    "a photo of a cool {}",
-    "a photo of a small {}",
-]
-imagenet_style_templates_small = [
-    "a painting in the style of {}",
-    "a rendering in the style of {}",
-    "a cropped painting in the style of {}",
-    "the painting in the style of {}",
-    "a clean painting in the style of {}",
-    "a dirty painting in the style of {}",
-    "a dark painting in the style of {}",
-    "a picture in the style of {}",
-    "a cool painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a bright painting in the style of {}",
-    "a cropped painting in the style of {}",
-    "a good painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a rendition in the style of {}",
-    "a nice painting in the style of {}",
-    "a small painting in the style of {}",
-    "a weird painting in the style of {}",
-    "a large painting in the style of {}",
-]
-class TextualInversionDataset(Dataset):
-    def __init__(
-        self,
-        data_root,
-        tokenizer,
-        learnable_property="object",  # [object, style]
-        size=512,
-        repeats=100,
-        interpolation="bicubic",
-        flip_p=0.5,
-        set="train",
-        placeholder_token="*",
-        center_crop=False,
-    ):
-        self.data_root = data_root
-        self.tokenizer = tokenizer
-        self.learnable_property = learnable_property
-        self.size = size
-        self.placeholder_token = placeholder_token
-        self.center_crop = center_crop
-        self.flip_p = flip_p
-        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
-        self.num_images = len(self.image_paths)
-        self._length = self.num_images
-        if set == "train":
-            self._length = self.num_images * repeats
-        self.interpolation = {
-            "linear": PIL_INTERPOLATION["linear"],
-            "bilinear": PIL_INTERPOLATION["bilinear"],
-            "bicubic": PIL_INTERPOLATION["bicubic"],
-            "lanczos": PIL_INTERPOLATION["lanczos"],
-        }[interpolation]
-        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
-        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
-    def __len__(self):
-        return self._length
-    def __getitem__(self, i):
-        example = {}
-        image = Image.open(self.image_paths[i % self.num_images])
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        placeholder_string = self.placeholder_token
-        text = random.choice(self.templates).format(placeholder_string)
-        example["input_ids"] = self.tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids[0]
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-        if self.center_crop:
-            crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
-                img.shape[0],
-                img.shape[1],
-            )
-            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
-        image = Image.fromarray(img)
-        image = image.resize((self.size, self.size), resample=self.interpolation)
-        image = self.flip_transform(image)
-        image = np.array(image).astype(np.uint8)
-        image = (image / 127.5 - 1.0).astype(np.float32)
-        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
-        return example
-def main():
-    args = parse_args()
-    logging_dir = os.path.join(args.output_dir, args.logging_dir)
-    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        project_config=accelerator_project_config,
-    )
-    if args.report_to == "wandb":
-        if not is_wandb_available():
-            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-    # Load tokenizer
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    text_encoder = CLIPTextModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
-    )
-    vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
-    )
-    # Add the placeholder token in tokenizer
-    placeholder_tokens = [args.placeholder_token]
-    if args.num_vectors < 1:
-        raise ValueError(f"--num_vectors has to be larger or equal to 1, but is {args.num_vectors}")
-    # add dummy tokens for multi-vector
-    additional_tokens = []
-    for i in range(1, args.num_vectors):
-        additional_tokens.append(f"{args.placeholder_token}_{i}")
-    placeholder_tokens += additional_tokens
-    num_added_tokens = tokenizer.add_tokens(placeholder_tokens)
-    if num_added_tokens != args.num_vectors:
-        raise ValueError(
-            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
-            " `placeholder_token` that is not already in the tokenizer."
-        )
-    # Convert the initializer_token, placeholder_token to ids
-    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
-    # Check if initializer_token is a single token or a sequence of tokens
-    if len(token_ids) > 1:
-        raise ValueError("The initializer token must be a single token.")
-    initializer_token_id = token_ids[0]
-    placeholder_token_ids = tokenizer.convert_tokens_to_ids(placeholder_tokens)
-    # Resize the token embeddings as we are adding new special tokens to the tokenizer
-    text_encoder.resize_token_embeddings(len(tokenizer))
-    # Initialise the newly added placeholder token with the embeddings of the initializer token
-    token_embeds = text_encoder.get_input_embeddings().weight.data
-    with torch.no_grad():
-        for token_id in placeholder_token_ids:
-            token_embeds[token_id] = token_embeds[initializer_token_id].clone()
-    # Freeze vae and unet
-    vae.requires_grad_(False)
-    unet.requires_grad_(False)
-    # Freeze all parameters except for the token embeddings in text encoder
-    text_encoder.text_model.encoder.requires_grad_(False)
-    text_encoder.text_model.final_layer_norm.requires_grad_(False)
-    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
-    if args.gradient_checkpointing:
-        # Keep unet in train mode if we are using gradient checkpointing to save memory.
-        # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
-        unet.train()
-        text_encoder.gradient_checkpointing_enable()
-        unet.enable_gradient_checkpointing()
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-    # Initialize the optimizer
-    optimizer = torch.optim.AdamW(
-        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-    # Dataset and DataLoaders creation:
-    train_dataset = TextualInversionDataset(
-        data_root=args.train_data_dir,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        placeholder_token=args.placeholder_token,
-        repeats=args.repeats,
-        learnable_property=args.learnable_property,
-        center_crop=args.center_crop,
-        set="train",
-    )
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
-    )
-    if args.validation_epochs is not None:
-        warnings.warn(
-            f"FutureWarning: You are doing logging with validation_epochs={args.validation_epochs}."
-            " Deprecated validation_epochs in favor of `validation_steps`"
-            f"Setting `args.validation_steps` to {args.validation_epochs * len(train_dataset)}",
-            FutureWarning,
-            stacklevel=2,
-        )
-        args.validation_steps = args.validation_epochs * len(train_dataset)
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-        num_training_steps=args.max_train_steps * accelerator.num_processes,
-        num_cycles=args.lr_num_cycles,
-    )
-    # Prepare everything with our `accelerator`.
-    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        text_encoder, optimizer, train_dataloader, lr_scheduler
-    )
-    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
-    # as these weights are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-    # Move vae and unet to device and cast to weight_dtype
-    unet.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("textual_inversion", config=vars(args))
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-            resume_global_step = global_step * args.gradient_accumulation_steps
-            first_epoch = global_step // num_update_steps_per_epoch
-            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-    # keep original embeddings as reference
-    orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
-    for epoch in range(first_epoch, args.num_train_epochs):
-        text_encoder.train()
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-            with accelerator.accumulate(text_encoder):
-                # Convert images to latent space
-                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
-                latents = latents * vae.config.scaling_factor
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-                # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
-                # Predict the noise residual
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-                accelerator.backward(loss)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-                # Let's make sure we don't update any embedding weights besides the newly added token
-                index_no_updates = torch.ones((len(tokenizer),), dtype=torch.bool)
-                index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False
-                with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
-                        index_no_updates
-                    ] = orig_embeds_params[index_no_updates]
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                images = []
-                progress_bar.update(1)
-                global_step += 1
-                if global_step % args.save_steps == 0:
-                    save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
-                    save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)
-                if accelerator.is_main_process:
-                    if global_step % args.checkpointing_steps == 0:
-                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
-                        if args.checkpoints_total_limit is not None:
-                            checkpoints = os.listdir(args.output_dir)
-                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
-                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
-                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
-                            if len(checkpoints) >= args.checkpoints_total_limit:
-                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
-                                removing_checkpoints = checkpoints[0:num_to_remove]
-                                logger.info(
-                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
-                                )
-                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
-                                for removing_checkpoint in removing_checkpoints:
-                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
-                                    shutil.rmtree(removing_checkpoint)
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
-                        images = log_validation(
-                            text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch
-                        )
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-            if global_step >= args.max_train_steps:
-                break
-    # Create the pipeline using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        if args.push_to_hub and not args.save_as_full_pipeline:
-            logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
-            save_full_model = True
-        else:
-            save_full_model = args.save_as_full_pipeline
-        if save_full_model:
-            pipeline = StableDiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path,
-                text_encoder=accelerator.unwrap_model(text_encoder),
-                vae=vae,
-                unet=unet,
-                tokenizer=tokenizer,
-            )
-            pipeline.save_pretrained(args.output_dir)
-        # Save the newly trained embeddings
-        save_path = os.path.join(args.output_dir, "learned_embeds.bin")
-        save_progress(text_encoder, placeholder_token_ids, accelerator, args, save_path)
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                images=images,
-                base_model=args.pretrained_model_name_or_path,
-                repo_folder=args.output_dir,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-    accelerator.end_training()
-if __name__ == "__main__":
-    main()

spaces/Andy1621/uniformer_image_detection/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py DELETED Viewed

@@ -1,10 +0,0 @@
-_base_ = './htc_hrnetv2p_w32_20e_coco.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w40',
-    backbone=dict(
-        type='HRNet',
-        extra=dict(
-            stage2=dict(num_channels=(40, 80)),
-            stage3=dict(num_channels=(40, 80, 160)),
-            stage4=dict(num_channels=(40, 80, 160, 320)))),
-    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))

spaces/Andy1621/uniformer_image_detection/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py DELETED Viewed

@@ -1,27 +0,0 @@
-from mmdet.models.builder import HEADS
-from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
-from .fused_semantic_head import FusedSemanticHead
-@HEADS.register_module()
-class SCNetSemanticHead(FusedSemanticHead):
-    """Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
-    Args:
-        conv_to_res (bool, optional): if True, change the conv layers to
-            ``SimplifiedBasicBlock``.
-    """
-    def __init__(self, conv_to_res=True, **kwargs):
-        super(SCNetSemanticHead, self).__init__(**kwargs)
-        self.conv_to_res = conv_to_res
-        if self.conv_to_res:
-            num_res_blocks = self.num_convs // 2
-            self.convs = ResLayer(
-                SimplifiedBasicBlock,
-                self.in_channels,
-                self.conv_out_channels,
-                num_res_blocks,
-                conv_cfg=self.conv_cfg,
-                norm_cfg=self.norm_cfg)
-            self.num_convs = num_res_blocks

spaces/Andy1621/uniformer_image_segmentation/configs/hrnet/fcn_hr48_480x480_80k_pascal_context_59.py DELETED Viewed

@@ -1,10 +0,0 @@
-_base_ = './fcn_hr18_480x480_80k_pascal_context_59.py'
-model = dict(
-    pretrained='open-mmlab://msra/hrnetv2_w48',
-    backbone=dict(
-        extra=dict(
-            stage2=dict(num_channels=(48, 96)),
-            stage3=dict(num_channels=(48, 96, 192)),
-            stage4=dict(num_channels=(48, 96, 192, 384)))),
-    decode_head=dict(
-        in_channels=[48, 96, 192, 384], channels=sum([48, 96, 192, 384])))

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv_custom/checkpoint.py DELETED Viewed

@@ -1,500 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-import io
-import os
-import os.path as osp
-import pkgutil
-import time
-import warnings
-from collections import OrderedDict
-from importlib import import_module
-from tempfile import TemporaryDirectory
-import torch
-import torchvision
-from torch.optim import Optimizer
-from torch.utils import model_zoo
-from torch.nn import functional as F
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.fileio import FileClient
-from annotator.uniformer.mmcv.fileio import load as load_file
-from annotator.uniformer.mmcv.parallel import is_module_wrapper
-from annotator.uniformer.mmcv.utils import mkdir_or_exist
-from annotator.uniformer.mmcv.runner import get_dist_info
-ENV_MMCV_HOME = 'MMCV_HOME'
-ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
-DEFAULT_CACHE_DIR = '~/.cache'
-def _get_mmcv_home():
-    mmcv_home = os.path.expanduser(
-        os.getenv(
-            ENV_MMCV_HOME,
-            os.path.join(
-                os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv')))
-    mkdir_or_exist(mmcv_home)
-    return mmcv_home
-def load_state_dict(module, state_dict, strict=False, logger=None):
-    """Load state_dict to a module.
-    This method is modified from :meth:`torch.nn.Module.load_state_dict`.
-    Default value for ``strict`` is set to ``False`` and the message for
-    param mismatch will be shown even if strict is False.
-    Args:
-        module (Module): Module that receives the state_dict.
-        state_dict (OrderedDict): Weights.
-        strict (bool): whether to strictly enforce that the keys
-            in :attr:`state_dict` match the keys returned by this module's
-            :meth:`~torch.nn.Module.state_dict` function. Default: ``False``.
-        logger (:obj:`logging.Logger`, optional): Logger to log the error
-            message. If not specified, print function will be used.
-    """
-    unexpected_keys = []
-    all_missing_keys = []
-    err_msg = []
-    metadata = getattr(state_dict, '_metadata', None)
-    state_dict = state_dict.copy()
-    if metadata is not None:
-        state_dict._metadata = metadata
-    # use _load_from_state_dict to enable checkpoint version control
-    def load(module, prefix=''):
-        # recursively check parallel module in case that the model has a
-        # complicated structure, e.g., nn.Module(nn.Module(DDP))
-        if is_module_wrapper(module):
-            module = module.module
-        local_metadata = {} if metadata is None else metadata.get(
-            prefix[:-1], {})
-        module._load_from_state_dict(state_dict, prefix, local_metadata, True,
-                                     all_missing_keys, unexpected_keys,
-                                     err_msg)
-        for name, child in module._modules.items():
-            if child is not None:
-                load(child, prefix + name + '.')
-    load(module)
-    load = None  # break load->load reference cycle
-    # ignore "num_batches_tracked" of BN layers
-    missing_keys = [
-        key for key in all_missing_keys if 'num_batches_tracked' not in key
-    ]
-    if unexpected_keys:
-        err_msg.append('unexpected key in source '
-                       f'state_dict: {", ".join(unexpected_keys)}\n')
-    if missing_keys:
-        err_msg.append(
-            f'missing keys in source state_dict: {", ".join(missing_keys)}\n')
-    rank, _ = get_dist_info()
-    if len(err_msg) > 0 and rank == 0:
-        err_msg.insert(
-            0, 'The model and loaded state dict do not match exactly\n')
-        err_msg = '\n'.join(err_msg)
-        if strict:
-            raise RuntimeError(err_msg)
-        elif logger is not None:
-            logger.warning(err_msg)
-        else:
-            print(err_msg)
-def load_url_dist(url, model_dir=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            checkpoint = model_zoo.load_url(url, model_dir=model_dir)
-    return checkpoint
-def load_pavimodel_dist(model_path, map_location=None):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    try:
-        from pavi import modelcloud
-    except ImportError:
-        raise ImportError(
-            'Please install pavi to load checkpoint from modelcloud.')
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    if rank == 0:
-        model = modelcloud.get(model_path)
-        with TemporaryDirectory() as tmp_dir:
-            downloaded_file = osp.join(tmp_dir, model.name)
-            model.download(downloaded_file)
-            checkpoint = torch.load(downloaded_file, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            model = modelcloud.get(model_path)
-            with TemporaryDirectory() as tmp_dir:
-                downloaded_file = osp.join(tmp_dir, model.name)
-                model.download(downloaded_file)
-                checkpoint = torch.load(
-                    downloaded_file, map_location=map_location)
-    return checkpoint
-def load_fileclient_dist(filename, backend, map_location):
-    """In distributed setting, this function only download checkpoint at local
-    rank 0."""
-    rank, world_size = get_dist_info()
-    rank = int(os.environ.get('LOCAL_RANK', rank))
-    allowed_backends = ['ceph']
-    if backend not in allowed_backends:
-        raise ValueError(f'Load from Backend {backend} is not supported.')
-    if rank == 0:
-        fileclient = FileClient(backend=backend)
-        buffer = io.BytesIO(fileclient.get(filename))
-        checkpoint = torch.load(buffer, map_location=map_location)
-    if world_size > 1:
-        torch.distributed.barrier()
-        if rank > 0:
-            fileclient = FileClient(backend=backend)
-            buffer = io.BytesIO(fileclient.get(filename))
-            checkpoint = torch.load(buffer, map_location=map_location)
-    return checkpoint
-def get_torchvision_models():
-    model_urls = dict()
-    for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__):
-        if ispkg:
-            continue
-        _zoo = import_module(f'torchvision.models.{name}')
-        if hasattr(_zoo, 'model_urls'):
-            _urls = getattr(_zoo, 'model_urls')
-            model_urls.update(_urls)
-    return model_urls
-def get_external_models():
-    mmcv_home = _get_mmcv_home()
-    default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
-    default_urls = load_file(default_json_path)
-    assert isinstance(default_urls, dict)
-    external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
-    if osp.exists(external_json_path):
-        external_urls = load_file(external_json_path)
-        assert isinstance(external_urls, dict)
-        default_urls.update(external_urls)
-    return default_urls
-def get_mmcls_models():
-    mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json')
-    mmcls_urls = load_file(mmcls_json_path)
-    return mmcls_urls
-def get_deprecated_model_names():
-    deprecate_json_path = osp.join(mmcv.__path__[0],
-                                   'model_zoo/deprecated.json')
-    deprecate_urls = load_file(deprecate_json_path)
-    assert isinstance(deprecate_urls, dict)
-    return deprecate_urls
-def _process_mmcls_checkpoint(checkpoint):
-    state_dict = checkpoint['state_dict']
-    new_state_dict = OrderedDict()
-    for k, v in state_dict.items():
-        if k.startswith('backbone.'):
-            new_state_dict[k[9:]] = v
-    new_checkpoint = dict(state_dict=new_state_dict)
-    return new_checkpoint
-def _load_checkpoint(filename, map_location=None):
-    """Load checkpoint from somewhere (modelzoo, file, url).
-    Args:
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str | None): Same as :func:`torch.load`. Default: None.
-    Returns:
-        dict | OrderedDict: The loaded checkpoint. It can be either an
-            OrderedDict storing model weights or a dict containing other
-            information, which depends on the checkpoint.
-    """
-    if filename.startswith('modelzoo://'):
-        warnings.warn('The URL scheme of "modelzoo://" is deprecated, please '
-                      'use "torchvision://" instead')
-        model_urls = get_torchvision_models()
-        model_name = filename[11:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('torchvision://'):
-        model_urls = get_torchvision_models()
-        model_name = filename[14:]
-        checkpoint = load_url_dist(model_urls[model_name])
-    elif filename.startswith('open-mmlab://'):
-        model_urls = get_external_models()
-        model_name = filename[13:]
-        deprecated_urls = get_deprecated_model_names()
-        if model_name in deprecated_urls:
-            warnings.warn(f'open-mmlab://{model_name} is deprecated in favor '
-                          f'of open-mmlab://{deprecated_urls[model_name]}')
-            model_name = deprecated_urls[model_name]
-        model_url = model_urls[model_name]
-        # check if is url
-        if model_url.startswith(('http://', 'https://')):
-            checkpoint = load_url_dist(model_url)
-        else:
-            filename = osp.join(_get_mmcv_home(), model_url)
-            if not osp.isfile(filename):
-                raise IOError(f'{filename} is not a checkpoint file')
-            checkpoint = torch.load(filename, map_location=map_location)
-    elif filename.startswith('mmcls://'):
-        model_urls = get_mmcls_models()
-        model_name = filename[8:]
-        checkpoint = load_url_dist(model_urls[model_name])
-        checkpoint = _process_mmcls_checkpoint(checkpoint)
-    elif filename.startswith(('http://', 'https://')):
-        checkpoint = load_url_dist(filename)
-    elif filename.startswith('pavi://'):
-        model_path = filename[7:]
-        checkpoint = load_pavimodel_dist(model_path, map_location=map_location)
-    elif filename.startswith('s3://'):
-        checkpoint = load_fileclient_dist(
-            filename, backend='ceph', map_location=map_location)
-    else:
-        if not osp.isfile(filename):
-            raise IOError(f'{filename} is not a checkpoint file')
-        checkpoint = torch.load(filename, map_location=map_location)
-    return checkpoint
-def load_checkpoint(model,
-                    filename,
-                    map_location='cpu',
-                    strict=False,
-                    logger=None):
-    """Load checkpoint from a file or URI.
-    Args:
-        model (Module): Module to load checkpoint.
-        filename (str): Accept local filepath, URL, ``torchvision://xxx``,
-            ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for
-            details.
-        map_location (str): Same as :func:`torch.load`.
-        strict (bool): Whether to allow different params for the model and
-            checkpoint.
-        logger (:mod:`logging.Logger` or None): The logger for error message.
-    Returns:
-        dict or OrderedDict: The loaded checkpoint.
-    """
-    checkpoint = _load_checkpoint(filename, map_location)
-    # OrderedDict is a subclass of dict
-    if not isinstance(checkpoint, dict):
-        raise RuntimeError(
-            f'No state_dict found in checkpoint file {filename}')
-    # get state_dict from checkpoint
-    if 'state_dict' in checkpoint:
-        state_dict = checkpoint['state_dict']
-    elif 'model' in checkpoint:
-        state_dict = checkpoint['model']
-    else:
-        state_dict = checkpoint
-    # strip prefix of state_dict
-    if list(state_dict.keys())[0].startswith('module.'):
-        state_dict = {k[7:]: v for k, v in state_dict.items()}
-    # for MoBY, load model of online branch
-    if sorted(list(state_dict.keys()))[0].startswith('encoder'):
-        state_dict = {k.replace('encoder.', ''): v for k, v in state_dict.items() if k.startswith('encoder.')}
-    # reshape absolute position embedding
-    if state_dict.get('absolute_pos_embed') is not None:
-        absolute_pos_embed = state_dict['absolute_pos_embed']
-        N1, L, C1 = absolute_pos_embed.size()
-        N2, C2, H, W = model.absolute_pos_embed.size()
-        if N1 != N2 or C1 != C2 or L != H*W:
-            logger.warning("Error in loading absolute_pos_embed, pass")
-        else:
-            state_dict['absolute_pos_embed'] = absolute_pos_embed.view(N2, H, W, C2).permute(0, 3, 1, 2)
-    # interpolate position bias table if needed
-    relative_position_bias_table_keys = [k for k in state_dict.keys() if "relative_position_bias_table" in k]
-    for table_key in relative_position_bias_table_keys:
-        table_pretrained = state_dict[table_key]
-        table_current = model.state_dict()[table_key]
-        L1, nH1 = table_pretrained.size()
-        L2, nH2 = table_current.size()
-        if nH1 != nH2:
-            logger.warning(f"Error in loading {table_key}, pass")
-        else:
-            if L1 != L2:
-                S1 = int(L1 ** 0.5)
-                S2 = int(L2 ** 0.5)
-                table_pretrained_resized = F.interpolate(
-                     table_pretrained.permute(1, 0).view(1, nH1, S1, S1),
-                     size=(S2, S2), mode='bicubic')
-                state_dict[table_key] = table_pretrained_resized.view(nH2, L2).permute(1, 0)
-    # load state_dict
-    load_state_dict(model, state_dict, strict, logger)
-    return checkpoint
-def weights_to_cpu(state_dict):
-    """Copy a model state_dict to cpu.
-    Args:
-        state_dict (OrderedDict): Model weights on GPU.
-    Returns:
-        OrderedDict: Model weights on GPU.
-    """
-    state_dict_cpu = OrderedDict()
-    for key, val in state_dict.items():
-        state_dict_cpu[key] = val.cpu()
-    return state_dict_cpu
-def _save_to_state_dict(module, destination, prefix, keep_vars):
-    """Saves module state to `destination` dictionary.
-    This method is modified from :meth:`torch.nn.Module._save_to_state_dict`.
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (dict): A dict where state will be stored.
-        prefix (str): The prefix for parameters and buffers used in this
-            module.
-    """
-    for name, param in module._parameters.items():
-        if param is not None:
-            destination[prefix + name] = param if keep_vars else param.detach()
-    for name, buf in module._buffers.items():
-        # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d
-        if buf is not None:
-            destination[prefix + name] = buf if keep_vars else buf.detach()
-def get_state_dict(module, destination=None, prefix='', keep_vars=False):
-    """Returns a dictionary containing a whole state of the module.
-    Both parameters and persistent buffers (e.g. running averages) are
-    included. Keys are corresponding parameter and buffer names.
-    This method is modified from :meth:`torch.nn.Module.state_dict` to
-    recursively check parallel module in case that the model has a complicated
-    structure, e.g., nn.Module(nn.Module(DDP)).
-    Args:
-        module (nn.Module): The module to generate state_dict.
-        destination (OrderedDict): Returned dict for the state of the
-            module.
-        prefix (str): Prefix of the key.
-        keep_vars (bool): Whether to keep the variable property of the
-            parameters. Default: False.
-    Returns:
-        dict: A dictionary containing a whole state of the module.
-    """
-    # recursively check parallel module in case that the model has a
-    # complicated structure, e.g., nn.Module(nn.Module(DDP))
-    if is_module_wrapper(module):
-        module = module.module
-    # below is the same as torch.nn.Module.state_dict()
-    if destination is None:
-        destination = OrderedDict()
-        destination._metadata = OrderedDict()
-    destination._metadata[prefix[:-1]] = local_metadata = dict(
-        version=module._version)
-    _save_to_state_dict(module, destination, prefix, keep_vars)
-    for name, child in module._modules.items():
-        if child is not None:
-            get_state_dict(
-                child, destination, prefix + name + '.', keep_vars=keep_vars)
-    for hook in module._state_dict_hooks.values():
-        hook_result = hook(module, destination, prefix, local_metadata)
-        if hook_result is not None:
-            destination = hook_result
-    return destination
-def save_checkpoint(model, filename, optimizer=None, meta=None):
-    """Save checkpoint to file.
-    The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
-    ``optimizer``. By default ``meta`` will contain version and time info.
-    Args:
-        model (Module): Module whose params are to be saved.
-        filename (str): Checkpoint filename.
-        optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.
-        meta (dict, optional): Metadata to be saved in checkpoint.
-    """
-    if meta is None:
-        meta = {}
-    elif not isinstance(meta, dict):
-        raise TypeError(f'meta must be a dict or None, but got {type(meta)}')
-    meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
-    if is_module_wrapper(model):
-        model = model.module
-    if hasattr(model, 'CLASSES') and model.CLASSES is not None:
-        # save class name to the meta
-        meta.update(CLASSES=model.CLASSES)
-    checkpoint = {
-        'meta': meta,
-        'state_dict': weights_to_cpu(get_state_dict(model))
-    }
-    # save optimizer state dict in the checkpoint
-    if isinstance(optimizer, Optimizer):
-        checkpoint['optimizer'] = optimizer.state_dict()
-    elif isinstance(optimizer, dict):
-        checkpoint['optimizer'] = {}
-        for name, optim in optimizer.items():
-            checkpoint['optimizer'][name] = optim.state_dict()
-    if filename.startswith('pavi://'):
-        try:
-            from pavi import modelcloud
-            from pavi.exception import NodeNotFoundError
-        except ImportError:
-            raise ImportError(
-                'Please install pavi to load checkpoint from modelcloud.')
-        model_path = filename[7:]
-        root = modelcloud.Folder()
-        model_dir, model_name = osp.split(model_path)
-        try:
-            model = modelcloud.get(model_dir)
-        except NodeNotFoundError:
-            model = root.create_training_model(model_dir)
-        with TemporaryDirectory() as tmp_dir:
-            checkpoint_file = osp.join(tmp_dir, model_name)
-            with open(checkpoint_file, 'wb') as f:
-                torch.save(checkpoint, f)
-                f.flush()
-            model.create_file(checkpoint_file, name=model_name)
-    else:
-        mmcv.mkdir_or_exist(osp.dirname(filename))
-        # immediately flush buffer
-        with open(filename, 'wb') as f:
-            torch.save(checkpoint, f)
-            f.flush()

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmseg/core/seg/__init__.py DELETED Viewed

@@ -1,4 +0,0 @@
-from .builder import build_pixel_sampler
-from .sampler import BasePixelSampler, OHEMPixelSampler
-__all__ = ['build_pixel_sampler', 'BasePixelSampler', 'OHEMPixelSampler']

spaces/Anonymous-sub/Rerender/gmflow_module/utils/utils.py DELETED Viewed

@@ -1,58 +0,0 @@
-import torch
-import torch.nn.functional as F
-class InputPadder:
-    """ Pads images such that dimensions are divisible by 8 """
-    def __init__(self, dims, mode='sintel', padding_factor=8):
-        self.ht, self.wd = dims[-2:]
-        pad_ht = (((self.ht // padding_factor) + 1) * padding_factor - self.ht) % padding_factor
-        pad_wd = (((self.wd // padding_factor) + 1) * padding_factor - self.wd) % padding_factor
-        if mode == 'sintel':
-            self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, pad_ht // 2, pad_ht - pad_ht // 2]
-        else:
-            self._pad = [pad_wd // 2, pad_wd - pad_wd // 2, 0, pad_ht]
-    def pad(self, *inputs):
-        return [F.pad(x, self._pad, mode='replicate') for x in inputs]
-    def unpad(self, x):
-        ht, wd = x.shape[-2:]
-        c = [self._pad[2], ht - self._pad[3], self._pad[0], wd - self._pad[1]]
-        return x[..., c[0]:c[1], c[2]:c[3]]
-def coords_grid(batch, ht, wd, normalize=False):
-    if normalize:  # [-1, 1]
-        coords = torch.meshgrid(2 * torch.arange(ht) / (ht - 1) - 1,
-                                2 * torch.arange(wd) / (wd - 1) - 1)
-    else:
-        coords = torch.meshgrid(torch.arange(ht), torch.arange(wd))
-    coords = torch.stack(coords[::-1], dim=0).float()
-    return coords[None].repeat(batch, 1, 1, 1)  # [B, 2, H, W]
-def compute_out_of_boundary_mask(flow):
-    # flow: [B, 2, H, W]
-    assert flow.dim() == 4 and flow.size(1) == 2
-    b, _, h, w = flow.shape
-    init_coords = coords_grid(b, h, w).to(flow.device)
-    corres = init_coords + flow  # [B, 2, H, W]
-    max_w = w - 1
-    max_h = h - 1
-    valid_mask = (corres[:, 0] >= 0) & (corres[:, 0] <= max_w) & (corres[:, 1] >= 0) & (corres[:, 1] <= max_h)
-    # in case very large flow
-    flow_mask = (flow[:, 0].abs() <= max_w) & (flow[:, 1].abs() <= max_h)
-    valid_mask = valid_mask & flow_mask
-    return valid_mask  # [B, H, W]
-def count_parameters(model):
-    num = sum(p.numel() for p in model.parameters() if p.requires_grad)
-    return num

spaces/Anuj-Panthri/imdb_review_sentiment/app.py DELETED Viewed

@@ -1,20 +0,0 @@
-import gradio as gr
-from fastai.text.all import *
-# to fix : NotImplementedError: cannot instantiate 'PosixPath' on your system
-# import pathlib
-# temp = pathlib.PosixPath
-# pathlib.PosixPath = pathlib.WindowsPath
-examples=['This was a fantastic end to the trilogy.','I\'ve never seen a bigger waste of my time.','Just when we thought they couldn\'t possibly make a worse TV movie than Sharknado? Syfy says, "Hold my beer!"']
-learn=load_learner('imdb_review_sentiment_model.pkl')
-class_names=['neg','pos']
-def classify(review):
-    _,_,pob=learn.predict(review)
-    return dict(zip(class_names,map(float,pob)))
-iface = gr.Interface(fn=classify, inputs=gr.inputs.Textbox(), outputs=gr.outputs.Label(),examples=examples)
-iface.launch()

spaces/Arsenii2023/Demo1/README.md DELETED Viewed

@@ -1,12 +0,0 @@
----
-title: Demo1
-emoji: 🏆
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.47.1
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Artples/LLaMA-2-CHAT/README.md DELETED Viewed

@@ -1,13 +0,0 @@
----
-title: LLaMA-2-CHAT
-emoji: ⚡
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: true
-license: apache-2.0
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/__main__.py DELETED Viewed

@@ -1,31 +0,0 @@
-import os
-import sys
-import warnings
-# Remove '' and current working directory from the first entry
-# of sys.path, if present to avoid using current directory
-# in pip commands check, freeze, install, list and show,
-# when invoked as python -m pip <command>
-if sys.path[0] in ("", os.getcwd()):
-    sys.path.pop(0)
-# If we are running from a wheel, add the wheel to sys.path
-# This allows the usage python pip-*.whl/pip install pip-*.whl
-if __package__ == "":
-    # __file__ is pip-*.whl/pip/__main__.py
-    # first dirname call strips of '/__main__.py', second strips off '/pip'
-    # Resulting path is the name of the wheel itself
-    # Add that to sys.path so we can import pip
-    path = os.path.dirname(os.path.dirname(__file__))
-    sys.path.insert(0, path)
-if __name__ == "__main__":
-    # Work around the error reported in #9540, pending a proper fix.
-    # Note: It is essential the warning filter is set *before* importing
-    #       pip, as the deprecation happens at import time, not runtime.
-    warnings.filterwarnings(
-        "ignore", category=DeprecationWarning, module=".*packaging\\.version"
-    )
-    from pip._internal.cli.main import main as _main
-    sys.exit(_main())

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_modeling.py DELETED Viewed

@@ -1,419 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import functools
-import io
-import struct
-import types
-import torch
-from detectron2.modeling import meta_arch
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.roi_heads import keypoint_head
-from detectron2.structures import Boxes, ImageList, Instances, RotatedBoxes
-from .c10 import Caffe2Compatible
-from .caffe2_patch import ROIHeadsPatcher, patch_generalized_rcnn
-from .shared import (
-    alias,
-    check_set_pb_arg,
-    get_pb_arg_floats,
-    get_pb_arg_valf,
-    get_pb_arg_vali,
-    get_pb_arg_vals,
-    mock_torch_nn_functional_interpolate,
-)
-def assemble_rcnn_outputs_by_name(image_sizes, tensor_outputs, force_mask_on=False):
-    """
-    A function to assemble caffe2 model's outputs (i.e. Dict[str, Tensor])
-    to detectron2's format (i.e. list of Instances instance).
-    This only works when the model follows the Caffe2 detectron's naming convention.
-    Args:
-        image_sizes (List[List[int, int]]): [H, W] of every image.
-        tensor_outputs (Dict[str, Tensor]): external_output to its tensor.
-        force_mask_on (Bool): if true, the it make sure there'll be pred_masks even
-            if the mask is not found from tensor_outputs (usually due to model crash)
-    """
-    results = [Instances(image_size) for image_size in image_sizes]
-    batch_splits = tensor_outputs.get("batch_splits", None)
-    if batch_splits:
-        raise NotImplementedError()
-    assert len(image_sizes) == 1
-    result = results[0]
-    bbox_nms = tensor_outputs["bbox_nms"]
-    score_nms = tensor_outputs["score_nms"]
-    class_nms = tensor_outputs["class_nms"]
-    # Detection will always success because Conv support 0-batch
-    assert bbox_nms is not None
-    assert score_nms is not None
-    assert class_nms is not None
-    if bbox_nms.shape[1] == 5:
-        result.pred_boxes = RotatedBoxes(bbox_nms)
-    else:
-        result.pred_boxes = Boxes(bbox_nms)
-    result.scores = score_nms
-    result.pred_classes = class_nms.to(torch.int64)
-    mask_fcn_probs = tensor_outputs.get("mask_fcn_probs", None)
-    if mask_fcn_probs is not None:
-        # finish the mask pred
-        mask_probs_pred = mask_fcn_probs
-        num_masks = mask_probs_pred.shape[0]
-        class_pred = result.pred_classes
-        indices = torch.arange(num_masks, device=class_pred.device)
-        mask_probs_pred = mask_probs_pred[indices, class_pred][:, None]
-        result.pred_masks = mask_probs_pred
-    elif force_mask_on:
-        # NOTE: there's no way to know the height/width of mask here, it won't be
-        # used anyway when batch size is 0, so just set them to 0.
-        result.pred_masks = torch.zeros([0, 1, 0, 0], dtype=torch.uint8)
-    keypoints_out = tensor_outputs.get("keypoints_out", None)
-    kps_score = tensor_outputs.get("kps_score", None)
-    if keypoints_out is not None:
-        # keypoints_out: [N, 4, #kypoints], where 4 is in order of (x, y, score, prob)
-        keypoints_tensor = keypoints_out
-        # NOTE: it's possible that prob is not calculated if "should_output_softmax"
-        # is set to False in HeatmapMaxKeypoint, so just using raw score, seems
-        # it doesn't affect mAP. TODO: check more carefully.
-        keypoint_xyp = keypoints_tensor.transpose(1, 2)[:, :, [0, 1, 2]]
-        result.pred_keypoints = keypoint_xyp
-    elif kps_score is not None:
-        # keypoint heatmap to sparse data structure
-        pred_keypoint_logits = kps_score
-        keypoint_head.keypoint_rcnn_inference(pred_keypoint_logits, [result])
-    return results
-def _cast_to_f32(f64):
-    return struct.unpack("f", struct.pack("f", f64))[0]
-def set_caffe2_compatible_tensor_mode(model, enable=True):
-    def _fn(m):
-        if isinstance(m, Caffe2Compatible):
-            m.tensor_mode = enable
-    model.apply(_fn)
-def convert_batched_inputs_to_c2_format(batched_inputs, size_divisibility, device):
-    """
-    See get_caffe2_inputs() below.
-    """
-    assert all(isinstance(x, dict) for x in batched_inputs)
-    assert all(x["image"].dim() == 3 for x in batched_inputs)
-    images = [x["image"] for x in batched_inputs]
-    images = ImageList.from_tensors(images, size_divisibility)
-    im_info = []
-    for input_per_image, image_size in zip(batched_inputs, images.image_sizes):
-        target_height = input_per_image.get("height", image_size[0])
-        target_width = input_per_image.get("width", image_size[1])  # noqa
-        # NOTE: The scale inside im_info is kept as convention and for providing
-        # post-processing information if further processing is needed. For
-        # current Caffe2 model definitions that don't include post-processing inside
-        # the model, this number is not used.
-        # NOTE: There can be a slight difference between width and height
-        # scales, using a single number can results in numerical difference
-        # compared with D2's post-processing.
-        scale = target_height / image_size[0]
-        im_info.append([image_size[0], image_size[1], scale])
-    im_info = torch.Tensor(im_info)
-    return images.tensor.to(device), im_info.to(device)
-class Caffe2MetaArch(Caffe2Compatible, torch.nn.Module):
-    """
-    Base class for caffe2-compatible implementation of a meta architecture.
-    The forward is traceable and its traced graph can be converted to caffe2
-    graph through ONNX.
-    """
-    def __init__(self, cfg, torch_model):
-        """
-        Args:
-            cfg (CfgNode):
-            torch_model (nn.Module): the detectron2 model (meta_arch) to be
-                converted.
-        """
-        super().__init__()
-        self._wrapped_model = torch_model
-        self.eval()
-        set_caffe2_compatible_tensor_mode(self, True)
-    def get_caffe2_inputs(self, batched_inputs):
-        """
-        Convert pytorch-style structured inputs to caffe2-style inputs that
-        are tuples of tensors.
-        Args:
-            batched_inputs (list[dict]): inputs to a detectron2 model
-                in its standard format. Each dict has "image" (CHW tensor), and optionally
-                "height" and "width".
-        Returns:
-            tuple[Tensor]:
-                tuple of tensors that will be the inputs to the
-                :meth:`forward` method. For existing models, the first
-                is an NCHW tensor (padded and batched); the second is
-                a im_info Nx3 tensor, where the rows are
-                (height, width, unused legacy parameter)
-        """
-        return convert_batched_inputs_to_c2_format(
-            batched_inputs,
-            self._wrapped_model.backbone.size_divisibility,
-            self._wrapped_model.device,
-        )
-    def encode_additional_info(self, predict_net, init_net):
-        """
-        Save extra metadata that will be used by inference in the output protobuf.
-        """
-        pass
-    def forward(self, inputs):
-        """
-        Run the forward in caffe2-style. It has to use caffe2-compatible ops
-        and the method will be used for tracing.
-        Args:
-            inputs (tuple[Tensor]): inputs defined by :meth:`get_caffe2_input`.
-                They will be the inputs of the converted caffe2 graph.
-        Returns:
-            tuple[Tensor]: output tensors. They will be the outputs of the
-                converted caffe2 graph.
-        """
-        raise NotImplementedError
-    def _caffe2_preprocess_image(self, inputs):
-        """
-        Caffe2 implementation of preprocess_image, which is called inside each MetaArch's forward.
-        It normalizes the input images, and the final caffe2 graph assumes the
-        inputs have been batched already.
-        """
-        data, im_info = inputs
-        data = alias(data, "data")
-        im_info = alias(im_info, "im_info")
-        mean, std = self._wrapped_model.pixel_mean, self._wrapped_model.pixel_std
-        normalized_data = (data - mean) / std
-        normalized_data = alias(normalized_data, "normalized_data")
-        # Pack (data, im_info) into ImageList which is recognized by self.inference.
-        images = ImageList(tensor=normalized_data, image_sizes=im_info)
-        return images
-    @staticmethod
-    def get_outputs_converter(predict_net, init_net):
-        """
-        Creates a function that converts outputs of the caffe2 model to
-        detectron2's standard format.
-        The function uses information in `predict_net` and `init_net` that are
-        available at inferene time. Therefore the function logic can be used in inference.
-        The returned function has the following signature:
-            def convert(batched_inputs, c2_inputs, c2_results) -> detectron2_outputs
-        Where
-            * batched_inputs (list[dict]): the original input format of the meta arch
-            * c2_inputs (tuple[Tensor]): the caffe2 inputs.
-            * c2_results (dict[str, Tensor]): the caffe2 output format,
-                corresponding to the outputs of the :meth:`forward` function.
-            * detectron2_outputs: the original output format of the meta arch.
-        This function can be used to compare the outputs of the original meta arch and
-        the converted caffe2 graph.
-        Returns:
-            callable: a callable of the above signature.
-        """
-        raise NotImplementedError
-class Caffe2GeneralizedRCNN(Caffe2MetaArch):
-    def __init__(self, cfg, torch_model):
-        assert isinstance(torch_model, meta_arch.GeneralizedRCNN)
-        torch_model = patch_generalized_rcnn(torch_model)
-        super().__init__(cfg, torch_model)
-        try:
-            use_heatmap_max_keypoint = cfg.EXPORT_CAFFE2.USE_HEATMAP_MAX_KEYPOINT
-        except AttributeError:
-            use_heatmap_max_keypoint = False
-        self.roi_heads_patcher = ROIHeadsPatcher(
-            self._wrapped_model.roi_heads, use_heatmap_max_keypoint
-        )
-    def encode_additional_info(self, predict_net, init_net):
-        size_divisibility = self._wrapped_model.backbone.size_divisibility
-        check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility)
-        check_set_pb_arg(
-            predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii")
-        )
-        check_set_pb_arg(predict_net, "meta_architecture", "s", b"GeneralizedRCNN")
-    @mock_torch_nn_functional_interpolate()
-    def forward(self, inputs):
-        if not self.tensor_mode:
-            return self._wrapped_model.inference(inputs)
-        images = self._caffe2_preprocess_image(inputs)
-        features = self._wrapped_model.backbone(images.tensor)
-        proposals, _ = self._wrapped_model.proposal_generator(images, features)
-        with self.roi_heads_patcher.mock_roi_heads():
-            detector_results, _ = self._wrapped_model.roi_heads(images, features, proposals)
-        return tuple(detector_results[0].flatten())
-    @staticmethod
-    def get_outputs_converter(predict_net, init_net):
-        def f(batched_inputs, c2_inputs, c2_results):
-            _, im_info = c2_inputs
-            image_sizes = [[int(im[0]), int(im[1])] for im in im_info]
-            results = assemble_rcnn_outputs_by_name(image_sizes, c2_results)
-            return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes)
-        return f
-class Caffe2RetinaNet(Caffe2MetaArch):
-    def __init__(self, cfg, torch_model):
-        assert isinstance(torch_model, meta_arch.RetinaNet)
-        super().__init__(cfg, torch_model)
-    @mock_torch_nn_functional_interpolate()
-    def forward(self, inputs):
-        assert self.tensor_mode
-        images = self._caffe2_preprocess_image(inputs)
-        # explicitly return the images sizes to avoid removing "im_info" by ONNX
-        # since it's not used in the forward path
-        return_tensors = [images.image_sizes]
-        features = self._wrapped_model.backbone(images.tensor)
-        features = [features[f] for f in self._wrapped_model.head_in_features]
-        for i, feature_i in enumerate(features):
-            features[i] = alias(feature_i, "feature_{}".format(i), is_backward=True)
-            return_tensors.append(features[i])
-        pred_logits, pred_anchor_deltas = self._wrapped_model.head(features)
-        for i, (box_cls_i, box_delta_i) in enumerate(zip(pred_logits, pred_anchor_deltas)):
-            return_tensors.append(alias(box_cls_i, "box_cls_{}".format(i)))
-            return_tensors.append(alias(box_delta_i, "box_delta_{}".format(i)))
-        return tuple(return_tensors)
-    def encode_additional_info(self, predict_net, init_net):
-        size_divisibility = self._wrapped_model.backbone.size_divisibility
-        check_set_pb_arg(predict_net, "size_divisibility", "i", size_divisibility)
-        check_set_pb_arg(
-            predict_net, "device", "s", str.encode(str(self._wrapped_model.device), "ascii")
-        )
-        check_set_pb_arg(predict_net, "meta_architecture", "s", b"RetinaNet")
-        # Inference parameters:
-        check_set_pb_arg(
-            predict_net, "score_threshold", "f", _cast_to_f32(self._wrapped_model.test_score_thresh)
-        )
-        check_set_pb_arg(
-            predict_net, "topk_candidates", "i", self._wrapped_model.test_topk_candidates
-        )
-        check_set_pb_arg(
-            predict_net, "nms_threshold", "f", _cast_to_f32(self._wrapped_model.test_nms_thresh)
-        )
-        check_set_pb_arg(
-            predict_net,
-            "max_detections_per_image",
-            "i",
-            self._wrapped_model.max_detections_per_image,
-        )
-        check_set_pb_arg(
-            predict_net,
-            "bbox_reg_weights",
-            "floats",
-            [_cast_to_f32(w) for w in self._wrapped_model.box2box_transform.weights],
-        )
-        self._encode_anchor_generator_cfg(predict_net)
-    def _encode_anchor_generator_cfg(self, predict_net):
-        # serialize anchor_generator for future use
-        serialized_anchor_generator = io.BytesIO()
-        torch.save(self._wrapped_model.anchor_generator, serialized_anchor_generator)
-        # Ideally we can put anchor generating inside the model, then we don't
-        # need to store this information.
-        bytes = serialized_anchor_generator.getvalue()
-        check_set_pb_arg(predict_net, "serialized_anchor_generator", "s", bytes)
-    @staticmethod
-    def get_outputs_converter(predict_net, init_net):
-        self = types.SimpleNamespace()
-        serialized_anchor_generator = io.BytesIO(
-            get_pb_arg_vals(predict_net, "serialized_anchor_generator", None)
-        )
-        self.anchor_generator = torch.load(serialized_anchor_generator)
-        bbox_reg_weights = get_pb_arg_floats(predict_net, "bbox_reg_weights", None)
-        self.box2box_transform = Box2BoxTransform(weights=tuple(bbox_reg_weights))
-        self.test_score_thresh = get_pb_arg_valf(predict_net, "score_threshold", None)
-        self.test_topk_candidates = get_pb_arg_vali(predict_net, "topk_candidates", None)
-        self.test_nms_thresh = get_pb_arg_valf(predict_net, "nms_threshold", None)
-        self.max_detections_per_image = get_pb_arg_vali(
-            predict_net, "max_detections_per_image", None
-        )
-        # hack to reuse inference code from RetinaNet
-        for meth in [
-            "forward_inference",
-            "inference_single_image",
-            "_transpose_dense_predictions",
-            "_decode_multi_level_predictions",
-            "_decode_per_level_predictions",
-        ]:
-            setattr(self, meth, functools.partial(getattr(meta_arch.RetinaNet, meth), self))
-        def f(batched_inputs, c2_inputs, c2_results):
-            _, im_info = c2_inputs
-            image_sizes = [[int(im[0]), int(im[1])] for im in im_info]
-            dummy_images = ImageList(
-                torch.randn(
-                    (
-                        len(im_info),
-                        3,
-                    )
-                    + tuple(image_sizes[0])
-                ),
-                image_sizes,
-            )
-            num_features = len([x for x in c2_results.keys() if x.startswith("box_cls_")])
-            pred_logits = [c2_results["box_cls_{}".format(i)] for i in range(num_features)]
-            pred_anchor_deltas = [c2_results["box_delta_{}".format(i)] for i in range(num_features)]
-            # For each feature level, feature should have the same batch size and
-            # spatial dimension as the box_cls and box_delta.
-            dummy_features = [x.clone()[:, 0:0, :, :] for x in pred_logits]
-            # self.num_classess can be inferred
-            self.num_classes = pred_logits[0].shape[1] // (pred_anchor_deltas[0].shape[1] // 4)
-            results = self.forward_inference(
-                dummy_images, dummy_features, [pred_logits, pred_anchor_deltas]
-            )
-            return meta_arch.GeneralizedRCNN._postprocess(results, batched_inputs, image_sizes)
-        return f
-META_ARCH_CAFFE2_EXPORT_TYPE_MAP = {
-    "GeneralizedRCNN": Caffe2GeneralizedRCNN,
-    "RetinaNet": Caffe2RetinaNet,
-}

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/utils/__init__.py DELETED Viewed

	@@ -1 +0,0 @@
1	- # Copyright (c) Facebook, Inc. and its affiliates.

spaces/Bart92/RVC_HF/infer/modules/train/extract/extract_f0_rmvpe.py DELETED Viewed

@@ -1,141 +0,0 @@
-import os
-import sys
-import traceback
-import parselmouth
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-import logging
-import numpy as np
-import pyworld
-from infer.lib.audio import load_audio
-logging.getLogger("numba").setLevel(logging.WARNING)
-n_part = int(sys.argv[1])
-i_part = int(sys.argv[2])
-i_gpu = sys.argv[3]
-os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
-exp_dir = sys.argv[4]
-is_half = sys.argv[5]
-f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
-def printt(strr):
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-class FeatureInput(object):
-    def __init__(self, samplerate=16000, hop_size=160):
-        self.fs = samplerate
-        self.hop = hop_size
-        self.f0_bin = 256
-        self.f0_max = 1100.0
-        self.f0_min = 50.0
-        self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
-        self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
-    def compute_f0(self, path, f0_method):
-        x = load_audio(path, self.fs)
-        # p_len = x.shape[0] // self.hop
-        if f0_method == "rmvpe":
-            if hasattr(self, "model_rmvpe") == False:
-                from infer.lib.rmvpe import RMVPE
-                print("Loading rmvpe model")
-                self.model_rmvpe = RMVPE(
-                    "assets/rmvpe/rmvpe.pt", is_half=is_half, device="cuda"
-                )
-            f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-        return f0
-    def coarse_f0(self, f0):
-        f0_mel = 1127 * np.log(1 + f0 / 700)
-        f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * (
-            self.f0_bin - 2
-        ) / (self.f0_mel_max - self.f0_mel_min) + 1
-        # use 0 or 1
-        f0_mel[f0_mel <= 1] = 1
-        f0_mel[f0_mel > self.f0_bin - 1] = self.f0_bin - 1
-        f0_coarse = np.rint(f0_mel).astype(int)
-        assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (
-            f0_coarse.max(),
-            f0_coarse.min(),
-        )
-        return f0_coarse
-    def go(self, paths, f0_method):
-        if len(paths) == 0:
-            printt("no-f0-todo")
-        else:
-            printt("todo-f0-%s" % len(paths))
-            n = max(len(paths) // 5, 1)  # 每个进程最多打印5条
-            for idx, (inp_path, opt_path1, opt_path2) in enumerate(paths):
-                try:
-                    if idx % n == 0:
-                        printt("f0ing,now-%s,all-%s,-%s" % (idx, len(paths), inp_path))
-                    if (
-                        os.path.exists(opt_path1 + ".npy") == True
-                        and os.path.exists(opt_path2 + ".npy") == True
-                    ):
-                        continue
-                    featur_pit = self.compute_f0(inp_path, f0_method)
-                    np.save(
-                        opt_path2,
-                        featur_pit,
-                        allow_pickle=False,
-                    )  # nsf
-                    coarse_pit = self.coarse_f0(featur_pit)
-                    np.save(
-                        opt_path1,
-                        coarse_pit,
-                        allow_pickle=False,
-                    )  # ori
-                except:
-                    printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc()))
-if __name__ == "__main__":
-    # exp_dir=r"E:\codes\py39\dataset\mi-test"
-    # n_p=16
-    # f = open("%s/log_extract_f0.log"%exp_dir, "w")
-    printt(sys.argv)
-    featureInput = FeatureInput()
-    paths = []
-    inp_root = "%s/1_16k_wavs" % (exp_dir)
-    opt_root1 = "%s/2a_f0" % (exp_dir)
-    opt_root2 = "%s/2b-f0nsf" % (exp_dir)
-    os.makedirs(opt_root1, exist_ok=True)
-    os.makedirs(opt_root2, exist_ok=True)
-    for name in sorted(list(os.listdir(inp_root))):
-        inp_path = "%s/%s" % (inp_root, name)
-        if "spec" in inp_path:
-            continue
-        opt_path1 = "%s/%s" % (opt_root1, name)
-        opt_path2 = "%s/%s" % (opt_root2, name)
-        paths.append([inp_path, opt_path1, opt_path2])
-    try:
-        featureInput.go(paths[i_part::n_part], "rmvpe")
-    except:
-        printt("f0_all_fail-%s" % (traceback.format_exc()))
-    # ps = []
-    # for i in range(n_p):
-    #     p = Process(
-    #         target=featureInput.go,
-    #         args=(
-    #             paths[i::n_p],
-    #             f0method,
-    #         ),
-    #     )
-    #     ps.append(p)
-    #     p.start()
-    # for i in range(n_p):
-    #     ps[i].join()

spaces/Benson/text-generation/Examples/Descarga De La Red M.hollywoodbets.net.md DELETED Viewed

@@ -1,104 +0,0 @@
-<br />
-<h1>Cómo descargar y usar m.hollywoodbets.net</h1>
-<p>Si usted está buscando una manera conveniente y rápida de apostar en deportes, carreras de caballos, juegos de casino, y más, es posible que desee descargar y utilizar m.hollywoodbets.net. Esta es la versión móvil de Hollywoodbets, una de las plataformas de apuestas en línea más populares en Sudáfrica. En este artículo, le mostraremos lo que es m.hollywoodbets.net, por qué debe descargarlo, cómo descargarlo, cómo usarlo y cómo resolver algunos problemas comunes con él. </p>
-<h2>descarga de la red m.hollywoodbets.net</h2><br /><p><b><b>Download</b> &#9658; <a href="https://bltlly.com/2v6JvX">https://bltlly.com/2v6JvX</a></b></p><br /><br />
- <h2>¿Qué es m.hollywoodbets.net? </h2>
-<p>m.hollywoodbets.net es el sitio móvil de Hollywoodbets, un operador de apuestas con licencia que ofrece una amplia gama de opciones de apuestas en varios deportes y eventos. Puedes apostar en fútbol, rugby, cricket, tenis, golf, baloncesto, etc. También puedes apostar en carreras de caballos desde Sudáfrica y otros países. Además, puedes jugar juegos de casino, tragamonedas, números de la suerte, betgames, juegos en vivo y más. Puede acceder a todas estas funciones desde su dispositivo móvil usando m.hollywoodbets.net. </p>
- <h2>¿Por qué descargar m.hollywoodbets.net? </h2>
-<p>Hay muchos beneficios de descargar y usar m.hollywoodbets.net. Estos son algunos de ellos:</p>
-<ul>
-<li><strong>Conveniencia:</strong> Puedes apostar en cualquier momento y en cualquier lugar usando tu dispositivo móvil. No necesita un ordenador o un navegador para acceder al sitio. Simplemente puede tocar el icono de la aplicación y comenzar a apostar. </li>
-<li><strong>Velocidad:</strong> El sitio móvil está optimizado para una carga rápida y un rendimiento suave. Usted puede hacer sus apuestas rápida y fácilmente sin ningún tipo de retrasos o fallos. </li>
-<li><strong>Acceso sin datos:</strong> Puedes acceder al sitio móvil sin usar ningún dato. Hollywoodbets se ha asociado con varios proveedores de red para ofrecer acceso sin datos a sus clientes. Puede comprobar si su proveedor de red es compatible visitando [1](https://sport.hollywoodbets.net/). </li>
-</ul>
- <h2>¿Cómo descargar m.hollywoodbets.net? </h2>
-<p>Si tienes un dispositivo Android, puedes descargar e instalar la aplicación para m.hollywoodbets.net siguiendo estos pasos:</p>
-<ol>
-<li>Visite [1](https://sport.hollywoodbets.net/) desde su navegador móvil e inicie sesión en su cuenta. Si aún no tiene una cuenta, puede registrarla haciendo clic en "Unirse ahora". </li>
-<li>Desplácese hacia abajo a la parte inferior de la página y haga clic en "Aplicación para teléfono de características básicas". Esto lo redirigirá a un sitio donde puede descargar la aplicación. </li>
-<li>Haga clic en "Descargar aplicación Android" y esperar a que la descarga se complete. </li>
-<li>Vaya a la configuración de seguridad y permita la instalación desde fuentes desconocidas. </li>
-<li>Abra el archivo descargado e instale la aplicación en su dispositivo. </li>
-</ol>
-<p>Tenga en cuenta que no hay una aplicación oficial para dispositivos iOS, por lo que tendrá que utilizar la versión del navegador móvil si tiene un iPhone o iPad. </p>
- <h2>¿Cómo usar m.hollywoodbets.net? </h2>
-<p>Usar m.hollywoodbets.net es fácil y simple. Estos son algunos pasos básicos para empezar:</p>
-<p></p>
-<ol>
-<li>Inicie sesión en su cuenta usando su nombre de usuario y contraseña. Si olvidó su contraseña, puede restablecerla haciendo clic en "Olvidé la contraseña". </li>
-<li>Elija la categoría de apuestas que desea hacer, como deportes, carreras de caballos, casino, etc. Puede utilizar el icono del menú en la esquina superior izquierda para navegar entre diferentes categorías. </li>
-<li>Selecciona el evento o juego en el que quieres apostar. Puedes usar la barra de búsqueda o los filtros para encontrar lo que buscas. </li>
-<li>Elija el mercado y las probabilidades en las que desea apostar. Puede pulsar en las probabilidades para agregarlas a su boleto de apuesta. </li>
-<li>Introduzca la cantidad que desea apostar y confirme su apuesta. También puede usar la función "Quick Bet" para realizar su apuesta más rápido. </li>
-<li>Revise su historial de apuestas y balance haciendo clic en "Mi cuenta". También puede ver sus apuestas pendientes, apuestas liquidadas y apuestas abiertas. </li>
-</ol>
-<p>Para depositar y retirar dinero usando m.hollywoodbets.net, necesita tener una cuenta verificada y una cuenta bancaria o tarjeta válida. Estos son algunos métodos que puedes usar:</p>
-<tabla>
-<tr><th>Método</th><th>Depósito</th><th>Retiro</th></tr>
-<tr><td>Transferencia bancaria</td><td>Sí</td><td>Sí</td></tr>
-<tr><td>Tarjeta de crédito/débito</td><td>Sí</td><td>No</td></tr>
-<tr><td>EFT</td><td>Sí</td><td>No</td></tr>
-<tr><td>Ozow</td><td>Sí</td><td>No</td></tr>
-<tr><td>Peach Payments</td><td>Sí</td><td>No</td></tr>
-<tr><td>Zapper</td><td>Sí</td><td>No</td></tr>
-<tr><td>Voucher</td><td>Sí</td><td>No</td></tr>
-<tr><td>Ramas de Hollywoodbets</td><td>Sí</td><td>Sí</td></tr>
-<tr><td>Tarjeta de cajero automático Hollywoodbets</td><td>No</td><td>Sí</td></tr>
-<tr><td>Hollywoodbets eWallet (FNB)</td><td>No</td><td>Sí</td></tr>
-<tr><td>Hollywoodbets Instant Money (Standard Bank)</td><td>No</td><td>Sí</td></tr>
-<tr><td>Envío de efectivo de Hollywoodbets (Absa)</td><td>No</td><td>Sí</td></tr>
-<tr><td>Hollywoodbets Cash Send Plus (Nedbank)</t d><td>No</td><td>Sí</td></tr>
-</tabla>
-<p>Para hacer un depósito, puedes seguir estos pasos:</p>
-<ol>
-<li>Inicie sesión en su cuenta y haga clic en "Depositar". </li>
-<li> Seleccione el método que desea utilizar e introduzca la cantidad que desea depositar. </li>
-<li>Siga las instrucciones en la pantalla para completar la transacción. </li>
-<li>Espera el mensaje de confirmación y comprueba tu saldo. </li>
-</ol>
-<p>Para realizar un retiro, puedes seguir estos pasos:</p>
-<ol>
-<li>Inicie sesión en su cuenta y haga clic en "Retirar". </li>
-<li> Seleccione el método que desea utilizar e introduzca la cantidad que desea retirar. </li>
-<li>Introduzca los datos de su cuenta bancaria o tarjeta si es necesario. </li>
-<li>Confirme su solicitud y espere la aprobación. </li>
-<li>Revise su cuenta bancaria o extracto de la tarjeta para los fondos. </li>
-</ol>
- <h2>¿Cómo contactar a atención al cliente usando m.hollywoodbets.net? </h2>
-<ul>
-<li><strong>Live Chat:</strong> Puede utilizar la función de chat en vivo en el sitio móvil para chatear con un agente amigable y útil. Puede acceder al chat en vivo haciendo clic en el icono "Ayuda" en la esquina inferior derecha de la pantalla. </li>
-<li><strong>Correo electrónico:</strong> Puede enviar un correo electrónico a [email protected] o [email protected] y esperar una respuesta en 24 horas. </li>
-<li><strong>Teléfono:</strong> Puede llamar al número gratuito 08600 42387 o al número alternativo 087 353 7634 y hablar con un representante. </li>
-<li><strong>Redes sociales:</strong> Puedes seguir a Hollywoodbets en Facebook, Twitter, Instagram, YouTube y Telegram y enviarles un mensaje o comentario en sus publicaciones. </li>
-</ul>
- <h2>Problemas comunes con m.hollywoodbets.net y cómo resolverlos</h2>
-<p>Mientras m.hollywoodbets.net está diseñado para proporcionar una experiencia de apuestas sin problemas y sin problemas, es posible que encuentre algunos problemas con él de vez en cuando. Estos son algunos de los problemas comunes y cómo resolverlos:</p>
-<ul>
-<li><strong>Errores de inicio de sesión:</strong> Si no puede iniciar sesión en su cuenta, es posible que haya introducido el nombre de usuario o contraseña incorrectos, o que su cuenta esté bloqueada debido a inactividad o razones de seguridad. Para resolver este problema, puede intentar restablecer su contraseña haciendo clic en "Olvidé la contraseña", o ponerse en contacto con el servicio de atención al cliente para desbloquear su cuenta. </li>
-<li><strong>Inactividad de la cuenta:</strong> Si no ha utilizado su cuenta durante más de 90 días, puede desactivarse debido a la inactividad. Para reactivar su cuenta, debe ponerse en contacto con el servicio de atención al cliente y proporcionarle sus documentos FICA (prueba de identidad y dirección). </li>
-<li><strong>FICA verification:</strong> FICA significa Financial Intelligence Centre Act, que es una ley que requiere que todos los operadores de apuestas verifiquen la identidad y la dirección de sus clientes. Para cumplir con esta ley, debe presentar sus documentos FICA (prueba de identidad y dirección) cuando registre una cuenta o realice un retiro. Puede subir sus documentos en línea haciendo clic en "Mi cuenta" y "FICA", o enviarlos por correo electrónico a [email protected]</li>
-</ul>
- <h2>Conclusión</h2>
-<p>m.hollywoodbets.net es una gran manera de disfrutar de las apuestas en línea en su dispositivo móvil. Puede descargarlo y usarlo fácilmente y acceder a una variedad de opciones de apuestas, promociones y características. También puede depositar y retirar dinero de forma segura y ponerse en contacto con el servicio de atención al cliente convenientemente. Si encuentra algún problema con el sitio móvil, puede seguir los consejos anteriores o comunicarse con el servicio de atención al cliente para obtener ayuda. Entonces, ¿qué estás esperando? Descargar m.hollywoodbets.net hoy y empezar a apostar! </p>
- <h2>Preguntas frecuentes</h2>
-<h3>¿Es m.hollywoodbets.net seguro y legal? </h3>
-<p>Sí, m.hollywoodbets.net es seguro y legal. Hollywoodbets está autorizado por el Western Cape Gambling and Racing Board y se adhiere a estrictas normas de seguridad. Todas las transacciones están encriptadas y protegidas por la tecnología SSL. Toda la información personal se mantiene confidencial y no se comparte con terceros. </p>
- <h3> ¿Cuáles son las apuestas mínimas y máximas en m.hollywoodbets.net? </h p>La apuesta mínima en m.hollywoodbets.net es R1, mientras que la apuesta máxima depende del evento y el mercado en el que esté apostando. Puede comprobar la apuesta máxima haciendo clic en "Max Bet" en su boleto de apuesta. </p>
- <h3>¿Cómo puedo obtener apuestas gratis en m.hollywoodbets.net? </h3>
-<p>Hay varias formas de obtener apuestas gratis en m.hollywoodbets.net. Algunas de ellas son:</p>
-<ul>
-<li>Registrar una nueva cuenta y obtener un bono de registro R25. </li>
-<li>Referir a un amigo y obtener un bono R50 por cada referencia exitosa. </li>
-<li>Uso de vales que puede comprar en los minoristas seleccionados o recibir de atención al cliente. </li>
-</ul>
- <h3>¿Cómo puedo comprobar los resultados de mis apuestas en m.hollywoodbets.net? </h3>
-<p>Puede comprobar los resultados de sus apuestas en m.hollywoodbets.net haciendo clic en "Mi cuenta" y "Historial de apuestas". También puede utilizar la función "Resultados" en el sitio móvil para comprobar los resultados de varios eventos y juegos. </p>
- <h3>¿Cómo puedo actualizar mis datos personales en m.hollywoodbets.net? </h3>
-<p>Puede actualizar sus datos personales en m.hollywoodbets.net haciendo clic en "Mi cuenta" y "Datos personales". Puede cambiar su contraseña, dirección de correo electrónico, número de teléfono y pregunta de seguridad. Sin embargo, no puede cambiar su nombre, apellido, fecha de nacimiento o número de identificación. Si necesita cambiar estos datos, debe ponerse en contacto con el servicio de atención al cliente y proporcionarle una prueba de identidad. </p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/zoneinfo/rebuild.py DELETED Viewed

@@ -1,75 +0,0 @@
-import logging
-import os
-import tempfile
-import shutil
-import json
-from subprocess import check_call, check_output
-from tarfile import TarFile
-from dateutil.zoneinfo import METADATA_FN, ZONEFILENAME
-def rebuild(filename, tag=None, format="gz", zonegroups=[], metadata=None):
-    """Rebuild the internal timezone info in dateutil/zoneinfo/zoneinfo*tar*
-    filename is the timezone tarball from ``ftp.iana.org/tz``.
-    """
-    tmpdir = tempfile.mkdtemp()
-    zonedir = os.path.join(tmpdir, "zoneinfo")
-    moduledir = os.path.dirname(__file__)
-    try:
-        with TarFile.open(filename) as tf:
-            for name in zonegroups:
-                tf.extract(name, tmpdir)
-            filepaths = [os.path.join(tmpdir, n) for n in zonegroups]
-            _run_zic(zonedir, filepaths)
-        # write metadata file
-        with open(os.path.join(zonedir, METADATA_FN), 'w') as f:
-            json.dump(metadata, f, indent=4, sort_keys=True)
-        target = os.path.join(moduledir, ZONEFILENAME)
-        with TarFile.open(target, "w:%s" % format) as tf:
-            for entry in os.listdir(zonedir):
-                entrypath = os.path.join(zonedir, entry)
-                tf.add(entrypath, entry)
-    finally:
-        shutil.rmtree(tmpdir)
-def _run_zic(zonedir, filepaths):
-    """Calls the ``zic`` compiler in a compatible way to get a "fat" binary.
-    Recent versions of ``zic`` default to ``-b slim``, while older versions
-    don't even have the ``-b`` option (but default to "fat" binaries). The
-    current version of dateutil does not support Version 2+ TZif files, which
-    causes problems when used in conjunction with "slim" binaries, so this
-    function is used to ensure that we always get a "fat" binary.
-    """
-    try:
-        help_text = check_output(["zic", "--help"])
-    except OSError as e:
-        _print_on_nosuchfile(e)
-        raise
-    if b"-b " in help_text:
-        bloat_args = ["-b", "fat"]
-    else:
-        bloat_args = []
-    check_call(["zic"] + bloat_args + ["-d", zonedir] + filepaths)
-def _print_on_nosuchfile(e):
-    """Print helpful troubleshooting message
-    e is an exception raised by subprocess.check_call()
-    """
-    if e.errno == 2:
-        logging.error(
-            "Could not find zic. Perhaps you need to install "
-            "libc-bin or some other package that provides it, "
-            "or it's not in your PATH?")

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/metadata/languages.py DELETED Viewed

@@ -1,352 +0,0 @@
-"""
-Metadata about languages used by our model training code for our
-SingleByteCharSetProbers.  Could be used for other things in the future.
-This code is based on the language metadata from the uchardet project.
-"""
-from string import ascii_letters
-from typing import List, Optional
-# TODO: Add Ukrainian (KOI8-U)
-class Language:
-    """Metadata about a language useful for training models
-    :ivar name: The human name for the language, in English.
-    :type name: str
-    :ivar iso_code: 2-letter ISO 639-1 if possible, 3-letter ISO code otherwise,
-                    or use another catalog as a last resort.
-    :type iso_code: str
-    :ivar use_ascii: Whether or not ASCII letters should be included in trained
-                     models.
-    :type use_ascii: bool
-    :ivar charsets: The charsets we want to support and create data for.
-    :type charsets: list of str
-    :ivar alphabet: The characters in the language's alphabet. If `use_ascii` is
-                    `True`, you only need to add those not in the ASCII set.
-    :type alphabet: str
-    :ivar wiki_start_pages: The Wikipedia pages to start from if we're crawling
-                            Wikipedia for training data.
-    :type wiki_start_pages: list of str
-    """
-    def __init__(
-        self,
-        name: Optional[str] = None,
-        iso_code: Optional[str] = None,
-        use_ascii: bool = True,
-        charsets: Optional[List[str]] = None,
-        alphabet: Optional[str] = None,
-        wiki_start_pages: Optional[List[str]] = None,
-    ) -> None:
-        super().__init__()
-        self.name = name
-        self.iso_code = iso_code
-        self.use_ascii = use_ascii
-        self.charsets = charsets
-        if self.use_ascii:
-            if alphabet:
-                alphabet += ascii_letters
-            else:
-                alphabet = ascii_letters
-        elif not alphabet:
-            raise ValueError("Must supply alphabet if use_ascii is False")
-        self.alphabet = "".join(sorted(set(alphabet))) if alphabet else None
-        self.wiki_start_pages = wiki_start_pages
-    def __repr__(self) -> str:
-        param_str = ", ".join(
-            f"{k}={v!r}" for k, v in self.__dict__.items() if not k.startswith("_")
-        )
-        return f"{self.__class__.__name__}({param_str})"
-LANGUAGES = {
-    "Arabic": Language(
-        name="Arabic",
-        iso_code="ar",
-        use_ascii=False,
-        # We only support encodings that use isolated
-        # forms, because the current recommendation is
-        # that the rendering system handles presentation
-        # forms. This means we purposefully skip IBM864.
-        charsets=["ISO-8859-6", "WINDOWS-1256", "CP720", "CP864"],
-        alphabet="ءآأؤإئابةتثجحخدذرزسشصضطظعغػؼؽؾؿـفقكلمنهوىيًٌٍَُِّ",
-        wiki_start_pages=["الصفحة_الرئيسية"],
-    ),
-    "Belarusian": Language(
-        name="Belarusian",
-        iso_code="be",
-        use_ascii=False,
-        charsets=["ISO-8859-5", "WINDOWS-1251", "IBM866", "MacCyrillic"],
-        alphabet="АБВГДЕЁЖЗІЙКЛМНОПРСТУЎФХЦЧШЫЬЭЮЯабвгдеёжзійклмнопрстуўфхцчшыьэюяʼ",
-        wiki_start_pages=["Галоўная_старонка"],
-    ),
-    "Bulgarian": Language(
-        name="Bulgarian",
-        iso_code="bg",
-        use_ascii=False,
-        charsets=["ISO-8859-5", "WINDOWS-1251", "IBM855"],
-        alphabet="АБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЬЮЯабвгдежзийклмнопрстуфхцчшщъьюя",
-        wiki_start_pages=["Начална_страница"],
-    ),
-    "Czech": Language(
-        name="Czech",
-        iso_code="cz",
-        use_ascii=True,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="áčďéěíňóřšťúůýžÁČĎÉĚÍŇÓŘŠŤÚŮÝŽ",
-        wiki_start_pages=["Hlavní_strana"],
-    ),
-    "Danish": Language(
-        name="Danish",
-        iso_code="da",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="æøåÆØÅ",
-        wiki_start_pages=["Forside"],
-    ),
-    "German": Language(
-        name="German",
-        iso_code="de",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="äöüßẞÄÖÜ",
-        wiki_start_pages=["Wikipedia:Hauptseite"],
-    ),
-    "Greek": Language(
-        name="Greek",
-        iso_code="el",
-        use_ascii=False,
-        charsets=["ISO-8859-7", "WINDOWS-1253"],
-        alphabet="αβγδεζηθικλμνξοπρσςτυφχψωάέήίόύώΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΣΤΥΦΧΨΩΆΈΉΊΌΎΏ",
-        wiki_start_pages=["Πύλη:Κύρια"],
-    ),
-    "English": Language(
-        name="English",
-        iso_code="en",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "WINDOWS-1252", "MacRoman"],
-        wiki_start_pages=["Main_Page"],
-    ),
-    "Esperanto": Language(
-        name="Esperanto",
-        iso_code="eo",
-        # Q, W, X, and Y not used at all
-        use_ascii=False,
-        charsets=["ISO-8859-3"],
-        alphabet="abcĉdefgĝhĥijĵklmnoprsŝtuŭvzABCĈDEFGĜHĤIJĴKLMNOPRSŜTUŬVZ",
-        wiki_start_pages=["Vikipedio:Ĉefpaĝo"],
-    ),
-    "Spanish": Language(
-        name="Spanish",
-        iso_code="es",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ñáéíóúüÑÁÉÍÓÚÜ",
-        wiki_start_pages=["Wikipedia:Portada"],
-    ),
-    "Estonian": Language(
-        name="Estonian",
-        iso_code="et",
-        use_ascii=False,
-        charsets=["ISO-8859-4", "ISO-8859-13", "WINDOWS-1257"],
-        # C, F, Š, Q, W, X, Y, Z, Ž are only for
-        # loanwords
-        alphabet="ABDEGHIJKLMNOPRSTUVÕÄÖÜabdeghijklmnoprstuvõäöü",
-        wiki_start_pages=["Esileht"],
-    ),
-    "Finnish": Language(
-        name="Finnish",
-        iso_code="fi",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ÅÄÖŠŽåäöšž",
-        wiki_start_pages=["Wikipedia:Etusivu"],
-    ),
-    "French": Language(
-        name="French",
-        iso_code="fr",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="œàâçèéîïùûêŒÀÂÇÈÉÎÏÙÛÊ",
-        wiki_start_pages=["Wikipédia:Accueil_principal", "Bœuf (animal)"],
-    ),
-    "Hebrew": Language(
-        name="Hebrew",
-        iso_code="he",
-        use_ascii=False,
-        charsets=["ISO-8859-8", "WINDOWS-1255"],
-        alphabet="אבגדהוזחטיךכלםמןנסעףפץצקרשתװױײ",
-        wiki_start_pages=["עמוד_ראשי"],
-    ),
-    "Croatian": Language(
-        name="Croatian",
-        iso_code="hr",
-        # Q, W, X, Y are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="abcčćdđefghijklmnoprsštuvzžABCČĆDĐEFGHIJKLMNOPRSŠTUVZŽ",
-        wiki_start_pages=["Glavna_stranica"],
-    ),
-    "Hungarian": Language(
-        name="Hungarian",
-        iso_code="hu",
-        # Q, W, X, Y are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="abcdefghijklmnoprstuvzáéíóöőúüűABCDEFGHIJKLMNOPRSTUVZÁÉÍÓÖŐÚÜŰ",
-        wiki_start_pages=["Kezdőlap"],
-    ),
-    "Italian": Language(
-        name="Italian",
-        iso_code="it",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ÀÈÉÌÒÓÙàèéìòóù",
-        wiki_start_pages=["Pagina_principale"],
-    ),
-    "Lithuanian": Language(
-        name="Lithuanian",
-        iso_code="lt",
-        use_ascii=False,
-        charsets=["ISO-8859-13", "WINDOWS-1257", "ISO-8859-4"],
-        # Q, W, and X not used at all
-        alphabet="AĄBCČDEĘĖFGHIĮYJKLMNOPRSŠTUŲŪVZŽaąbcčdeęėfghiįyjklmnoprsštuųūvzž",
-        wiki_start_pages=["Pagrindinis_puslapis"],
-    ),
-    "Latvian": Language(
-        name="Latvian",
-        iso_code="lv",
-        use_ascii=False,
-        charsets=["ISO-8859-13", "WINDOWS-1257", "ISO-8859-4"],
-        # Q, W, X, Y are only for loanwords
-        alphabet="AĀBCČDEĒFGĢHIĪJKĶLĻMNŅOPRSŠTUŪVZŽaābcčdeēfgģhiījkķlļmnņoprsštuūvzž",
-        wiki_start_pages=["Sākumlapa"],
-    ),
-    "Macedonian": Language(
-        name="Macedonian",
-        iso_code="mk",
-        use_ascii=False,
-        charsets=["ISO-8859-5", "WINDOWS-1251", "MacCyrillic", "IBM855"],
-        alphabet="АБВГДЃЕЖЗЅИЈКЛЉМНЊОПРСТЌУФХЦЧЏШабвгдѓежзѕијклљмнњопрстќуфхцчџш",
-        wiki_start_pages=["Главна_страница"],
-    ),
-    "Dutch": Language(
-        name="Dutch",
-        iso_code="nl",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "WINDOWS-1252", "MacRoman"],
-        wiki_start_pages=["Hoofdpagina"],
-    ),
-    "Polish": Language(
-        name="Polish",
-        iso_code="pl",
-        # Q and X are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="AĄBCĆDEĘFGHIJKLŁMNŃOÓPRSŚTUWYZŹŻaąbcćdeęfghijklłmnńoóprsśtuwyzźż",
-        wiki_start_pages=["Wikipedia:Strona_główna"],
-    ),
-    "Portuguese": Language(
-        name="Portuguese",
-        iso_code="pt",
-        use_ascii=True,
-        charsets=["ISO-8859-1", "ISO-8859-15", "WINDOWS-1252", "MacRoman"],
-        alphabet="ÁÂÃÀÇÉÊÍÓÔÕÚáâãàçéêíóôõú",
-        wiki_start_pages=["Wikipédia:Página_principal"],
-    ),
-    "Romanian": Language(
-        name="Romanian",
-        iso_code="ro",
-        use_ascii=True,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="ăâîșțĂÂÎȘȚ",
-        wiki_start_pages=["Pagina_principală"],
-    ),
-    "Russian": Language(
-        name="Russian",
-        iso_code="ru",
-        use_ascii=False,
-        charsets=[
-            "ISO-8859-5",
-            "WINDOWS-1251",
-            "KOI8-R",
-            "MacCyrillic",
-            "IBM866",
-            "IBM855",
-        ],
-        alphabet="абвгдеёжзийклмнопрстуфхцчшщъыьэюяАБВГДЕЁЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯ",
-        wiki_start_pages=["Заглавная_страница"],
-    ),
-    "Slovak": Language(
-        name="Slovak",
-        iso_code="sk",
-        use_ascii=True,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="áäčďéíĺľňóôŕšťúýžÁÄČĎÉÍĹĽŇÓÔŔŠŤÚÝŽ",
-        wiki_start_pages=["Hlavná_stránka"],
-    ),
-    "Slovene": Language(
-        name="Slovene",
-        iso_code="sl",
-        # Q, W, X, Y are only used for foreign words.
-        use_ascii=False,
-        charsets=["ISO-8859-2", "WINDOWS-1250"],
-        alphabet="abcčdefghijklmnoprsštuvzžABCČDEFGHIJKLMNOPRSŠTUVZŽ",
-        wiki_start_pages=["Glavna_stran"],
-    ),
-    # Serbian can be written in both Latin and Cyrillic, but there's no
-    # simple way to get the Latin alphabet pages from Wikipedia through
-    # the API, so for now we just support Cyrillic.
-    "Serbian": Language(
-        name="Serbian",
-        iso_code="sr",
-        alphabet="АБВГДЂЕЖЗИЈКЛЉМНЊОПРСТЋУФХЦЧЏШабвгдђежзијклљмнњопрстћуфхцчџш",
-        charsets=["ISO-8859-5", "WINDOWS-1251", "MacCyrillic", "IBM855"],
-        wiki_start_pages=["Главна_страна"],
-    ),
-    "Thai": Language(
-        name="Thai",
-        iso_code="th",
-        use_ascii=False,
-        charsets=["ISO-8859-11", "TIS-620", "CP874"],
-        alphabet="กขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลฦวศษสหฬอฮฯะัาำิีึืฺุู฿เแโใไๅๆ็่้๊๋์ํ๎๏๐๑๒๓๔๕๖๗๘๙๚๛",
-        wiki_start_pages=["หน้าหลัก"],
-    ),
-    "Turkish": Language(
-        name="Turkish",
-        iso_code="tr",
-        # Q, W, and X are not used by Turkish
-        use_ascii=False,
-        charsets=["ISO-8859-3", "ISO-8859-9", "WINDOWS-1254"],
-        alphabet="abcçdefgğhıijklmnoöprsştuüvyzâîûABCÇDEFGĞHIİJKLMNOÖPRSŞTUÜVYZÂÎÛ",
-        wiki_start_pages=["Ana_Sayfa"],
-    ),
-    "Vietnamese": Language(
-        name="Vietnamese",
-        iso_code="vi",
-        use_ascii=False,
-        # Windows-1258 is the only common 8-bit
-        # Vietnamese encoding supported by Python.
-        # From Wikipedia:
-        # For systems that lack support for Unicode,
-        # dozens of 8-bit Vietnamese code pages are
-        # available.[1] The most common are VISCII
-        # (TCVN 5712:1993), VPS, and Windows-1258.[3]
-        # Where ASCII is required, such as when
-        # ensuring readability in plain text e-mail,
-        # Vietnamese letters are often encoded
-        # according to Vietnamese Quoted-Readable
-        # (VIQR) or VSCII Mnemonic (VSCII-MNEM),[4]
-        # though usage of either variable-width
-        # scheme has declined dramatically following
-        # the adoption of Unicode on the World Wide
-        # Web.
-        charsets=["WINDOWS-1258"],
-        alphabet="aăâbcdđeêghiklmnoôơpqrstuưvxyAĂÂBCDĐEÊGHIKLMNOÔƠPQRSTUƯVXY",
-        wiki_start_pages=["Chữ_Quốc_ngữ"],
-    ),
-}

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/requests/cookies.py DELETED Viewed

@@ -1,561 +0,0 @@
-"""
-requests.cookies
-~~~~~~~~~~~~~~~~
-Compatibility code to be able to use `cookielib.CookieJar` with requests.
-requests.utils imports from here, so be careful with imports.
-"""
-import calendar
-import copy
-import time
-from ._internal_utils import to_native_string
-from .compat import Morsel, MutableMapping, cookielib, urlparse, urlunparse
-try:
-    import threading
-except ImportError:
-    import dummy_threading as threading
-class MockRequest:
-    """Wraps a `requests.Request` to mimic a `urllib2.Request`.
-    The code in `cookielib.CookieJar` expects this interface in order to correctly
-    manage cookie policies, i.e., determine whether a cookie can be set, given the
-    domains of the request and the cookie.
-    The original request object is read-only. The client is responsible for collecting
-    the new headers via `get_new_headers()` and interpreting them appropriately. You
-    probably want `get_cookie_header`, defined below.
-    """
-    def __init__(self, request):
-        self._r = request
-        self._new_headers = {}
-        self.type = urlparse(self._r.url).scheme
-    def get_type(self):
-        return self.type
-    def get_host(self):
-        return urlparse(self._r.url).netloc
-    def get_origin_req_host(self):
-        return self.get_host()
-    def get_full_url(self):
-        # Only return the response's URL if the user hadn't set the Host
-        # header
-        if not self._r.headers.get("Host"):
-            return self._r.url
-        # If they did set it, retrieve it and reconstruct the expected domain
-        host = to_native_string(self._r.headers["Host"], encoding="utf-8")
-        parsed = urlparse(self._r.url)
-        # Reconstruct the URL as we expect it
-        return urlunparse(
-            [
-                parsed.scheme,
-                host,
-                parsed.path,
-                parsed.params,
-                parsed.query,
-                parsed.fragment,
-            ]
-        )
-    def is_unverifiable(self):
-        return True
-    def has_header(self, name):
-        return name in self._r.headers or name in self._new_headers
-    def get_header(self, name, default=None):
-        return self._r.headers.get(name, self._new_headers.get(name, default))
-    def add_header(self, key, val):
-        """cookielib has no legitimate use for this method; add it back if you find one."""
-        raise NotImplementedError(
-            "Cookie headers should be added with add_unredirected_header()"
-        )
-    def add_unredirected_header(self, name, value):
-        self._new_headers[name] = value
-    def get_new_headers(self):
-        return self._new_headers
-    @property
-    def unverifiable(self):
-        return self.is_unverifiable()
-    @property
-    def origin_req_host(self):
-        return self.get_origin_req_host()
-    @property
-    def host(self):
-        return self.get_host()
-class MockResponse:
-    """Wraps a `httplib.HTTPMessage` to mimic a `urllib.addinfourl`.
-    ...what? Basically, expose the parsed HTTP headers from the server response
-    the way `cookielib` expects to see them.
-    """
-    def __init__(self, headers):
-        """Make a MockResponse for `cookielib` to read.
-        :param headers: a httplib.HTTPMessage or analogous carrying the headers
-        """
-        self._headers = headers
-    def info(self):
-        return self._headers
-    def getheaders(self, name):
-        self._headers.getheaders(name)
-def extract_cookies_to_jar(jar, request, response):
-    """Extract the cookies from the response into a CookieJar.
-    :param jar: cookielib.CookieJar (not necessarily a RequestsCookieJar)
-    :param request: our own requests.Request object
-    :param response: urllib3.HTTPResponse object
-    """
-    if not (hasattr(response, "_original_response") and response._original_response):
-        return
-    # the _original_response field is the wrapped httplib.HTTPResponse object,
-    req = MockRequest(request)
-    # pull out the HTTPMessage with the headers and put it in the mock:
-    res = MockResponse(response._original_response.msg)
-    jar.extract_cookies(res, req)
-def get_cookie_header(jar, request):
-    """
-    Produce an appropriate Cookie header string to be sent with `request`, or None.
-    :rtype: str
-    """
-    r = MockRequest(request)
-    jar.add_cookie_header(r)
-    return r.get_new_headers().get("Cookie")
-def remove_cookie_by_name(cookiejar, name, domain=None, path=None):
-    """Unsets a cookie by name, by default over all domains and paths.
-    Wraps CookieJar.clear(), is O(n).
-    """
-    clearables = []
-    for cookie in cookiejar:
-        if cookie.name != name:
-            continue
-        if domain is not None and domain != cookie.domain:
-            continue
-        if path is not None and path != cookie.path:
-            continue
-        clearables.append((cookie.domain, cookie.path, cookie.name))
-    for domain, path, name in clearables:
-        cookiejar.clear(domain, path, name)
-class CookieConflictError(RuntimeError):
-    """There are two cookies that meet the criteria specified in the cookie jar.
-    Use .get and .set and include domain and path args in order to be more specific.
-    """
-class RequestsCookieJar(cookielib.CookieJar, MutableMapping):
-    """Compatibility class; is a cookielib.CookieJar, but exposes a dict
-    interface.
-    This is the CookieJar we create by default for requests and sessions that
-    don't specify one, since some clients may expect response.cookies and
-    session.cookies to support dict operations.
-    Requests does not use the dict interface internally; it's just for
-    compatibility with external client code. All requests code should work
-    out of the box with externally provided instances of ``CookieJar``, e.g.
-    ``LWPCookieJar`` and ``FileCookieJar``.
-    Unlike a regular CookieJar, this class is pickleable.
-    .. warning:: dictionary operations that are normally O(1) may be O(n).
-    """
-    def get(self, name, default=None, domain=None, path=None):
-        """Dict-like get() that also supports optional domain and path args in
-        order to resolve naming collisions from using one cookie jar over
-        multiple domains.
-        .. warning:: operation is O(n), not O(1).
-        """
-        try:
-            return self._find_no_duplicates(name, domain, path)
-        except KeyError:
-            return default
-    def set(self, name, value, **kwargs):
-        """Dict-like set() that also supports optional domain and path args in
-        order to resolve naming collisions from using one cookie jar over
-        multiple domains.
-        """
-        # support client code that unsets cookies by assignment of a None value:
-        if value is None:
-            remove_cookie_by_name(
-                self, name, domain=kwargs.get("domain"), path=kwargs.get("path")
-            )
-            return
-        if isinstance(value, Morsel):
-            c = morsel_to_cookie(value)
-        else:
-            c = create_cookie(name, value, **kwargs)
-        self.set_cookie(c)
-        return c
-    def iterkeys(self):
-        """Dict-like iterkeys() that returns an iterator of names of cookies
-        from the jar.
-        .. seealso:: itervalues() and iteritems().
-        """
-        for cookie in iter(self):
-            yield cookie.name
-    def keys(self):
-        """Dict-like keys() that returns a list of names of cookies from the
-        jar.
-        .. seealso:: values() and items().
-        """
-        return list(self.iterkeys())
-    def itervalues(self):
-        """Dict-like itervalues() that returns an iterator of values of cookies
-        from the jar.
-        .. seealso:: iterkeys() and iteritems().
-        """
-        for cookie in iter(self):
-            yield cookie.value
-    def values(self):
-        """Dict-like values() that returns a list of values of cookies from the
-        jar.
-        .. seealso:: keys() and items().
-        """
-        return list(self.itervalues())
-    def iteritems(self):
-        """Dict-like iteritems() that returns an iterator of name-value tuples
-        from the jar.
-        .. seealso:: iterkeys() and itervalues().
-        """
-        for cookie in iter(self):
-            yield cookie.name, cookie.value
-    def items(self):
-        """Dict-like items() that returns a list of name-value tuples from the
-        jar. Allows client-code to call ``dict(RequestsCookieJar)`` and get a
-        vanilla python dict of key value pairs.
-        .. seealso:: keys() and values().
-        """
-        return list(self.iteritems())
-    def list_domains(self):
-        """Utility method to list all the domains in the jar."""
-        domains = []
-        for cookie in iter(self):
-            if cookie.domain not in domains:
-                domains.append(cookie.domain)
-        return domains
-    def list_paths(self):
-        """Utility method to list all the paths in the jar."""
-        paths = []
-        for cookie in iter(self):
-            if cookie.path not in paths:
-                paths.append(cookie.path)
-        return paths
-    def multiple_domains(self):
-        """Returns True if there are multiple domains in the jar.
-        Returns False otherwise.
-        :rtype: bool
-        """
-        domains = []
-        for cookie in iter(self):
-            if cookie.domain is not None and cookie.domain in domains:
-                return True
-            domains.append(cookie.domain)
-        return False  # there is only one domain in jar
-    def get_dict(self, domain=None, path=None):
-        """Takes as an argument an optional domain and path and returns a plain
-        old Python dict of name-value pairs of cookies that meet the
-        requirements.
-        :rtype: dict
-        """
-        dictionary = {}
-        for cookie in iter(self):
-            if (domain is None or cookie.domain == domain) and (
-                path is None or cookie.path == path
-            ):
-                dictionary[cookie.name] = cookie.value
-        return dictionary
-    def __contains__(self, name):
-        try:
-            return super().__contains__(name)
-        except CookieConflictError:
-            return True
-    def __getitem__(self, name):
-        """Dict-like __getitem__() for compatibility with client code. Throws
-        exception if there are more than one cookie with name. In that case,
-        use the more explicit get() method instead.
-        .. warning:: operation is O(n), not O(1).
-        """
-        return self._find_no_duplicates(name)
-    def __setitem__(self, name, value):
-        """Dict-like __setitem__ for compatibility with client code. Throws
-        exception if there is already a cookie of that name in the jar. In that
-        case, use the more explicit set() method instead.
-        """
-        self.set(name, value)
-    def __delitem__(self, name):
-        """Deletes a cookie given a name. Wraps ``cookielib.CookieJar``'s
-        ``remove_cookie_by_name()``.
-        """
-        remove_cookie_by_name(self, name)
-    def set_cookie(self, cookie, *args, **kwargs):
-        if (
-            hasattr(cookie.value, "startswith")
-            and cookie.value.startswith('"')
-            and cookie.value.endswith('"')
-        ):
-            cookie.value = cookie.value.replace('\\"', "")
-        return super().set_cookie(cookie, *args, **kwargs)
-    def update(self, other):
-        """Updates this jar with cookies from another CookieJar or dict-like"""
-        if isinstance(other, cookielib.CookieJar):
-            for cookie in other:
-                self.set_cookie(copy.copy(cookie))
-        else:
-            super().update(other)
-    def _find(self, name, domain=None, path=None):
-        """Requests uses this method internally to get cookie values.
-        If there are conflicting cookies, _find arbitrarily chooses one.
-        See _find_no_duplicates if you want an exception thrown if there are
-        conflicting cookies.
-        :param name: a string containing name of cookie
-        :param domain: (optional) string containing domain of cookie
-        :param path: (optional) string containing path of cookie
-        :return: cookie.value
-        """
-        for cookie in iter(self):
-            if cookie.name == name:
-                if domain is None or cookie.domain == domain:
-                    if path is None or cookie.path == path:
-                        return cookie.value
-        raise KeyError(f"name={name!r}, domain={domain!r}, path={path!r}")
-    def _find_no_duplicates(self, name, domain=None, path=None):
-        """Both ``__get_item__`` and ``get`` call this function: it's never
-        used elsewhere in Requests.
-        :param name: a string containing name of cookie
-        :param domain: (optional) string containing domain of cookie
-        :param path: (optional) string containing path of cookie
-        :raises KeyError: if cookie is not found
-        :raises CookieConflictError: if there are multiple cookies
-            that match name and optionally domain and path
-        :return: cookie.value
-        """
-        toReturn = None
-        for cookie in iter(self):
-            if cookie.name == name:
-                if domain is None or cookie.domain == domain:
-                    if path is None or cookie.path == path:
-                        if toReturn is not None:
-                            # if there are multiple cookies that meet passed in criteria
-                            raise CookieConflictError(
-                                f"There are multiple cookies with name, {name!r}"
-                            )
-                        # we will eventually return this as long as no cookie conflict
-                        toReturn = cookie.value
-        if toReturn:
-            return toReturn
-        raise KeyError(f"name={name!r}, domain={domain!r}, path={path!r}")
-    def __getstate__(self):
-        """Unlike a normal CookieJar, this class is pickleable."""
-        state = self.__dict__.copy()
-        # remove the unpickleable RLock object
-        state.pop("_cookies_lock")
-        return state
-    def __setstate__(self, state):
-        """Unlike a normal CookieJar, this class is pickleable."""
-        self.__dict__.update(state)
-        if "_cookies_lock" not in self.__dict__:
-            self._cookies_lock = threading.RLock()
-    def copy(self):
-        """Return a copy of this RequestsCookieJar."""
-        new_cj = RequestsCookieJar()
-        new_cj.set_policy(self.get_policy())
-        new_cj.update(self)
-        return new_cj
-    def get_policy(self):
-        """Return the CookiePolicy instance used."""
-        return self._policy
-def _copy_cookie_jar(jar):
-    if jar is None:
-        return None
-    if hasattr(jar, "copy"):
-        # We're dealing with an instance of RequestsCookieJar
-        return jar.copy()
-    # We're dealing with a generic CookieJar instance
-    new_jar = copy.copy(jar)
-    new_jar.clear()
-    for cookie in jar:
-        new_jar.set_cookie(copy.copy(cookie))
-    return new_jar
-def create_cookie(name, value, **kwargs):
-    """Make a cookie from underspecified parameters.
-    By default, the pair of `name` and `value` will be set for the domain ''
-    and sent on every request (this is sometimes called a "supercookie").
-    """
-    result = {
-        "version": 0,
-        "name": name,
-        "value": value,
-        "port": None,
-        "domain": "",
-        "path": "/",
-        "secure": False,
-        "expires": None,
-        "discard": True,
-        "comment": None,
-        "comment_url": None,
-        "rest": {"HttpOnly": None},
-        "rfc2109": False,
-    }
-    badargs = set(kwargs) - set(result)
-    if badargs:
-        raise TypeError(
-            f"create_cookie() got unexpected keyword arguments: {list(badargs)}"
-        )
-    result.update(kwargs)
-    result["port_specified"] = bool(result["port"])
-    result["domain_specified"] = bool(result["domain"])
-    result["domain_initial_dot"] = result["domain"].startswith(".")
-    result["path_specified"] = bool(result["path"])
-    return cookielib.Cookie(**result)
-def morsel_to_cookie(morsel):
-    """Convert a Morsel object into a Cookie containing the one k/v pair."""
-    expires = None
-    if morsel["max-age"]:
-        try:
-            expires = int(time.time() + int(morsel["max-age"]))
-        except ValueError:
-            raise TypeError(f"max-age: {morsel['max-age']} must be integer")
-    elif morsel["expires"]:
-        time_template = "%a, %d-%b-%Y %H:%M:%S GMT"
-        expires = calendar.timegm(time.strptime(morsel["expires"], time_template))
-    return create_cookie(
-        comment=morsel["comment"],
-        comment_url=bool(morsel["comment"]),
-        discard=False,
-        domain=morsel["domain"],
-        expires=expires,
-        name=morsel.key,
-        path=morsel["path"],
-        port=None,
-        rest={"HttpOnly": morsel["httponly"]},
-        rfc2109=False,
-        secure=bool(morsel["secure"]),
-        value=morsel.value,
-        version=morsel["version"] or 0,
-    )
-def cookiejar_from_dict(cookie_dict, cookiejar=None, overwrite=True):
-    """Returns a CookieJar from a key/value dictionary.
-    :param cookie_dict: Dict of key/values to insert into CookieJar.
-    :param cookiejar: (optional) A cookiejar to add the cookies to.
-    :param overwrite: (optional) If False, will not replace cookies
-        already in the jar with new ones.
-    :rtype: CookieJar
-    """
-    if cookiejar is None:
-        cookiejar = RequestsCookieJar()
-    if cookie_dict is not None:
-        names_from_jar = [cookie.name for cookie in cookiejar]
-        for name in cookie_dict:
-            if overwrite or (name not in names_from_jar):
-                cookiejar.set_cookie(create_cookie(name, cookie_dict[name]))
-    return cookiejar
-def merge_cookies(cookiejar, cookies):
-    """Add cookies to cookiejar and returns a merged CookieJar.
-    :param cookiejar: CookieJar object to add the cookies to.
-    :param cookies: Dictionary or CookieJar object to be added.
-    :rtype: CookieJar
-    """
-    if not isinstance(cookiejar, cookielib.CookieJar):
-        raise ValueError("You can only merge into CookieJar")
-    if isinstance(cookies, dict):
-        cookiejar = cookiejar_from_dict(cookies, cookiejar=cookiejar, overwrite=False)
-    elif isinstance(cookies, cookielib.CookieJar):
-        try:
-            cookiejar.update(cookies)
-        except AttributeError:
-            for cookie_in_jar in cookies:
-                cookiejar.set_cookie(cookie_in_jar)
-    return cookiejar

spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/utils/ans_punct.py DELETED Viewed

@@ -1,105 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Yuhao Cui https://github.com/cuiyuhao1996
-# based on VQA Evaluation Code
-# --------------------------------------------------------
-import re
-contractions = {
-    "aint": "ain't", "arent": "aren't", "cant": "can't", "couldve":
-    "could've", "couldnt": "couldn't", "couldn'tve": "couldn't've",
-    "couldnt've": "couldn't've", "didnt": "didn't", "doesnt":
-    "doesn't", "dont": "don't", "hadnt": "hadn't", "hadnt've":
-    "hadn't've", "hadn'tve": "hadn't've", "hasnt": "hasn't", "havent":
-    "haven't", "hed": "he'd", "hed've": "he'd've", "he'dve":
-    "he'd've", "hes": "he's", "howd": "how'd", "howll": "how'll",
-    "hows": "how's", "Id've": "I'd've", "I'dve": "I'd've", "Im":
-    "I'm", "Ive": "I've", "isnt": "isn't", "itd": "it'd", "itd've":
-    "it'd've", "it'dve": "it'd've", "itll": "it'll", "let's": "let's",
-    "maam": "ma'am", "mightnt": "mightn't", "mightnt've":
-    "mightn't've", "mightn'tve": "mightn't've", "mightve": "might've",
-    "mustnt": "mustn't", "mustve": "must've", "neednt": "needn't",
-    "notve": "not've", "oclock": "o'clock", "oughtnt": "oughtn't",
-    "ow's'at": "'ow's'at", "'ows'at": "'ow's'at", "'ow'sat":
-    "'ow's'at", "shant": "shan't", "shed've": "she'd've", "she'dve":
-    "she'd've", "she's": "she's", "shouldve": "should've", "shouldnt":
-    "shouldn't", "shouldnt've": "shouldn't've", "shouldn'tve":
-    "shouldn't've", "somebody'd": "somebodyd", "somebodyd've":
-    "somebody'd've", "somebody'dve": "somebody'd've", "somebodyll":
-    "somebody'll", "somebodys": "somebody's", "someoned": "someone'd",
-    "someoned've": "someone'd've", "someone'dve": "someone'd've",
-    "someonell": "someone'll", "someones": "someone's", "somethingd":
-    "something'd", "somethingd've": "something'd've", "something'dve":
-    "something'd've", "somethingll": "something'll", "thats":
-    "that's", "thered": "there'd", "thered've": "there'd've",
-    "there'dve": "there'd've", "therere": "there're", "theres":
-    "there's", "theyd": "they'd", "theyd've": "they'd've", "they'dve":
-    "they'd've", "theyll": "they'll", "theyre": "they're", "theyve":
-    "they've", "twas": "'twas", "wasnt": "wasn't", "wed've":
-    "we'd've", "we'dve": "we'd've", "weve": "we've", "werent":
-    "weren't", "whatll": "what'll", "whatre": "what're", "whats":
-    "what's", "whatve": "what've", "whens": "when's", "whered":
-    "where'd", "wheres": "where's", "whereve": "where've", "whod":
-    "who'd", "whod've": "who'd've", "who'dve": "who'd've", "wholl":
-    "who'll", "whos": "who's", "whove": "who've", "whyll": "why'll",
-    "whyre": "why're", "whys": "why's", "wont": "won't", "wouldve":
-    "would've", "wouldnt": "wouldn't", "wouldnt've": "wouldn't've",
-    "wouldn'tve": "wouldn't've", "yall": "y'all", "yall'll":
-    "y'all'll", "y'allll": "y'all'll", "yall'd've": "y'all'd've",
-    "y'alld've": "y'all'd've", "y'all'dve": "y'all'd've", "youd":
-    "you'd", "youd've": "you'd've", "you'dve": "you'd've", "youll":
-    "you'll", "youre": "you're", "youve": "you've"
-}
-manual_map = { 'none': '0',
-              'zero': '0',
-              'one': '1',
-              'two': '2',
-              'three': '3',
-              'four': '4',
-              'five': '5',
-              'six': '6',
-              'seven': '7',
-              'eight': '8',
-               'nine': '9',
-              'ten': '10'}
-articles = ['a', 'an', 'the']
-period_strip = re.compile("(?!<=\d)(\.)(?!\d)")
-comma_strip = re.compile("(\d)(\,)(\d)")
-punct = [';', r"/", '[', ']', '"', '{', '}',
-                '(', ')', '=', '+', '\\', '_', '-',
-                '>', '<', '@', '`', ',', '?', '!']
-def process_punctuation(inText):
-    outText = inText
-    for p in punct:
-        if (p + ' ' in inText or ' ' + p in inText) \
-           or (re.search(comma_strip, inText) != None):
-            outText = outText.replace(p, '')
-        else:
-            outText = outText.replace(p, ' ')
-    outText = period_strip.sub("", outText, re.UNICODE)
-    return outText
-def process_digit_article(inText):
-    outText = []
-    tempText = inText.lower().split()
-    for word in tempText:
-        word = manual_map.setdefault(word, word)
-        if word not in articles:
-            outText.append(word)
-        else:
-            pass
-    for wordId, word in enumerate(outText):
-        if word in contractions:
-            outText[wordId] = contractions[word]
-    outText = ' '.join(outText)
-    return outText
-def prep_ans(answer):
-    answer = process_digit_article(process_punctuation(answer))
-    answer = answer.replace(',', '')
-    return answer

spaces/CVPR/LIVE/thrust/thrust/detail/config/config.h DELETED Viewed

@@ -1,39 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-/*! \file config.h
- *  \brief Defines platform configuration.
- */
-#pragma once
-// NOTE: The order of these #includes matters.
-#include <thrust/detail/config/simple_defines.h>
-#include <thrust/detail/config/compiler.h>
-#include <thrust/detail/config/cpp_dialect.h>
-#include <thrust/detail/config/cpp_compatibility.h>
-#include <thrust/detail/config/deprecated.h>
-// host_system.h & device_system.h must be #included as early as possible
-// because other config headers depend on it
-#include <thrust/detail/config/host_system.h>
-#include <thrust/detail/config/device_system.h>
-#include <thrust/detail/config/host_device.h>
-#include <thrust/detail/config/debug.h>
-#include <thrust/detail/config/forceinline.h>
-#include <thrust/detail/config/exec_check_disable.h>
-#include <thrust/detail/config/global_workarounds.h>

spaces/CVPR/LIVE/thrust/thrust/system/tbb/detail/binary_search.h DELETED Viewed

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

spaces/CVPR/WALT/mmdet/core/evaluation/bbox_overlaps.py DELETED Viewed

@@ -1,48 +0,0 @@
-import numpy as np
-def bbox_overlaps(bboxes1, bboxes2, mode='iou', eps=1e-6):
-    """Calculate the ious between each bbox of bboxes1 and bboxes2.
-    Args:
-        bboxes1(ndarray): shape (n, 4)
-        bboxes2(ndarray): shape (k, 4)
-        mode(str): iou (intersection over union) or iof (intersection
-            over foreground)
-    Returns:
-        ious(ndarray): shape (n, k)
-    """
-    assert mode in ['iou', 'iof']
-    bboxes1 = bboxes1.astype(np.float32)
-    bboxes2 = bboxes2.astype(np.float32)
-    rows = bboxes1.shape[0]
-    cols = bboxes2.shape[0]
-    ious = np.zeros((rows, cols), dtype=np.float32)
-    if rows * cols == 0:
-        return ious
-    exchange = False
-    if bboxes1.shape[0] > bboxes2.shape[0]:
-        bboxes1, bboxes2 = bboxes2, bboxes1
-        ious = np.zeros((cols, rows), dtype=np.float32)
-        exchange = True
-    area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] - bboxes1[:, 1])
-    area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] - bboxes2[:, 1])
-    for i in range(bboxes1.shape[0]):
-        x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])
-        y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])
-        x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])
-        y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])
-        overlap = np.maximum(x_end - x_start, 0) * np.maximum(
-            y_end - y_start, 0)
-        if mode == 'iou':
-            union = area1[i] + area2 - overlap
-        else:
-            union = area1[i] if not exchange else area2
-        union = np.maximum(union, eps)
-        ious[i, :] = overlap / union
-    if exchange:
-        ious = ious.T
-    return ious

spaces/CVPR/WALT/mmdet/models/losses/smooth_l1_loss.py DELETED Viewed

@@ -1,139 +0,0 @@
-import mmcv
-import torch
-import torch.nn as nn
-from ..builder import LOSSES
-from .utils import weighted_loss
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def smooth_l1_loss(pred, target, beta=1.0):
-    """Smooth L1 loss.
-    Args:
-        pred (torch.Tensor): The prediction.
-        target (torch.Tensor): The learning target of the prediction.
-        beta (float, optional): The threshold in the piecewise function.
-            Defaults to 1.0.
-    Returns:
-        torch.Tensor: Calculated loss
-    """
-    assert beta > 0
-    assert pred.size() == target.size() and target.numel() > 0
-    diff = torch.abs(pred - target)
-    loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
-                       diff - 0.5 * beta)
-    return loss
-@mmcv.jit(derivate=True, coderize=True)
-@weighted_loss
-def l1_loss(pred, target):
-    """L1 loss.
-    Args:
-        pred (torch.Tensor): The prediction.
-        target (torch.Tensor): The learning target of the prediction.
-    Returns:
-        torch.Tensor: Calculated loss
-    """
-    assert pred.size() == target.size() and target.numel() > 0
-    loss = torch.abs(pred - target)
-    return loss
-@LOSSES.register_module()
-class SmoothL1Loss(nn.Module):
-    """Smooth L1 loss.
-    Args:
-        beta (float, optional): The threshold in the piecewise function.
-            Defaults to 1.0.
-        reduction (str, optional): The method to reduce the loss.
-            Options are "none", "mean" and "sum". Defaults to "mean".
-        loss_weight (float, optional): The weight of loss.
-    """
-    def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0):
-        super(SmoothL1Loss, self).__init__()
-        self.beta = beta
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None,
-                **kwargs):
-        """Forward function.
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction.
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_bbox = self.loss_weight * smooth_l1_loss(
-            pred,
-            target,
-            weight,
-            beta=self.beta,
-            reduction=reduction,
-            avg_factor=avg_factor,
-            **kwargs)
-        return loss_bbox
-@LOSSES.register_module()
-class L1Loss(nn.Module):
-    """L1 loss.
-    Args:
-        reduction (str, optional): The method to reduce the loss.
-            Options are "none", "mean" and "sum".
-        loss_weight (float, optional): The weight of loss.
-    """
-    def __init__(self, reduction='mean', loss_weight=1.0):
-        super(L1Loss, self).__init__()
-        self.reduction = reduction
-        self.loss_weight = loss_weight
-    def forward(self,
-                pred,
-                target,
-                weight=None,
-                avg_factor=None,
-                reduction_override=None):
-        """Forward function.
-        Args:
-            pred (torch.Tensor): The prediction.
-            target (torch.Tensor): The learning target of the prediction.
-            weight (torch.Tensor, optional): The weight of loss for each
-                prediction. Defaults to None.
-            avg_factor (int, optional): Average factor that is used to average
-                the loss. Defaults to None.
-            reduction_override (str, optional): The reduction method used to
-                override the original reduction method of the loss.
-                Defaults to None.
-        """
-        assert reduction_override in (None, 'none', 'mean', 'sum')
-        reduction = (
-            reduction_override if reduction_override else self.reduction)
-        loss_bbox = self.loss_weight * l1_loss(
-            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
-        return loss_bbox

spaces/CVPR/lama-example/saicinpainting/training/modules/__init__.py DELETED Viewed

@@ -1,31 +0,0 @@
-import logging
-from saicinpainting.training.modules.ffc import FFCResNetGenerator
-from saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \
-    NLayerDiscriminator, MultidilatedNLayerDiscriminator
-def make_generator(config, kind, **kwargs):
-    logging.info(f'Make generator {kind}')
-    if kind == 'pix2pixhd_multidilated':
-        return MultiDilatedGlobalGenerator(**kwargs)
-    if kind == 'pix2pixhd_global':
-        return GlobalGenerator(**kwargs)
-    if kind == 'ffc_resnet':
-        return FFCResNetGenerator(**kwargs)
-    raise ValueError(f'Unknown generator kind {kind}')
-def make_discriminator(kind, **kwargs):
-    logging.info(f'Make discriminator {kind}')
-    if kind == 'pix2pixhd_nlayer_multidilated':
-        return MultidilatedNLayerDiscriminator(**kwargs)
-    if kind == 'pix2pixhd_nlayer':
-        return NLayerDiscriminator(**kwargs)
-    raise ValueError(f'Unknown discriminator kind {kind}')

spaces/CVPR/regionclip-demo/detectron2/evaluation/cityscapes_evaluation.py DELETED Viewed

@@ -1,194 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import glob
-import logging
-import numpy as np
-import os
-import tempfile
-from collections import OrderedDict
-import torch
-from PIL import Image
-from detectron2.data import MetadataCatalog
-from detectron2.utils import comm
-from detectron2.utils.file_io import PathManager
-from .evaluator import DatasetEvaluator
-class CityscapesEvaluator(DatasetEvaluator):
-    """
-    Base class for evaluation using cityscapes API.
-    """
-    def __init__(self, dataset_name):
-        """
-        Args:
-            dataset_name (str): the name of the dataset.
-                It must have the following metadata associated with it:
-                "thing_classes", "gt_dir".
-        """
-        self._metadata = MetadataCatalog.get(dataset_name)
-        self._cpu_device = torch.device("cpu")
-        self._logger = logging.getLogger(__name__)
-    def reset(self):
-        self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
-        self._temp_dir = self._working_dir.name
-        # All workers will write to the same results directory
-        # TODO this does not work in distributed training
-        self._temp_dir = comm.all_gather(self._temp_dir)[0]
-        if self._temp_dir != self._working_dir.name:
-            self._working_dir.cleanup()
-        self._logger.info(
-            "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
-        )
-class CityscapesInstanceEvaluator(CityscapesEvaluator):
-    """
-    Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
-    Note:
-        * It does not work in multi-machine distributed training.
-        * It contains a synchronization, therefore has to be used on all ranks.
-        * Only the main process runs evaluation.
-    """
-    def process(self, inputs, outputs):
-        from cityscapesscripts.helpers.labels import name2label
-        for input, output in zip(inputs, outputs):
-            file_name = input["file_name"]
-            basename = os.path.splitext(os.path.basename(file_name))[0]
-            pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
-            if "instances" in output:
-                output = output["instances"].to(self._cpu_device)
-                num_instances = len(output)
-                with open(pred_txt, "w") as fout:
-                    for i in range(num_instances):
-                        pred_class = output.pred_classes[i]
-                        classes = self._metadata.thing_classes[pred_class]
-                        class_id = name2label[classes].id
-                        score = output.scores[i]
-                        mask = output.pred_masks[i].numpy().astype("uint8")
-                        png_filename = os.path.join(
-                            self._temp_dir, basename + "_{}_{}.png".format(i, classes)
-                        )
-                        Image.fromarray(mask * 255).save(png_filename)
-                        fout.write(
-                            "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
-                        )
-            else:
-                # Cityscapes requires a prediction file for every ground truth image.
-                with open(pred_txt, "w") as fout:
-                    pass
-    def evaluate(self):
-        """
-        Returns:
-            dict: has a key "segm", whose value is a dict of "AP" and "AP50".
-        """
-        comm.synchronize()
-        if comm.get_rank() > 0:
-            return
-        import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
-        self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-        # set some global states in cityscapes evaluation API, before evaluating
-        cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
-        cityscapes_eval.args.predictionWalk = None
-        cityscapes_eval.args.JSONOutput = False
-        cityscapes_eval.args.colorized = False
-        cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
-        # These lines are adopted from
-        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
-        gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
-        groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
-        assert len(
-            groundTruthImgList
-        ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
-            cityscapes_eval.args.groundTruthSearch
-        )
-        predictionImgList = []
-        for gt in groundTruthImgList:
-            predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
-        results = cityscapes_eval.evaluateImgLists(
-            predictionImgList, groundTruthImgList, cityscapes_eval.args
-        )["averages"]
-        ret = OrderedDict()
-        ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
-        self._working_dir.cleanup()
-        return ret
-class CityscapesSemSegEvaluator(CityscapesEvaluator):
-    """
-    Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
-    Note:
-        * It does not work in multi-machine distributed training.
-        * It contains a synchronization, therefore has to be used on all ranks.
-        * Only the main process runs evaluation.
-    """
-    def process(self, inputs, outputs):
-        from cityscapesscripts.helpers.labels import trainId2label
-        for input, output in zip(inputs, outputs):
-            file_name = input["file_name"]
-            basename = os.path.splitext(os.path.basename(file_name))[0]
-            pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
-            output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
-            pred = 255 * np.ones(output.shape, dtype=np.uint8)
-            for train_id, label in trainId2label.items():
-                if label.ignoreInEval:
-                    continue
-                pred[output == train_id] = label.id
-            Image.fromarray(pred).save(pred_filename)
-    def evaluate(self):
-        comm.synchronize()
-        if comm.get_rank() > 0:
-            return
-        # Load the Cityscapes eval script *after* setting the required env var,
-        # since the script reads CITYSCAPES_DATASET into global variables at load time.
-        import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
-        self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
-        # set some global states in cityscapes evaluation API, before evaluating
-        cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
-        cityscapes_eval.args.predictionWalk = None
-        cityscapes_eval.args.JSONOutput = False
-        cityscapes_eval.args.colorized = False
-        # These lines are adopted from
-        # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
-        gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
-        groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
-        assert len(
-            groundTruthImgList
-        ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
-            cityscapes_eval.args.groundTruthSearch
-        )
-        predictionImgList = []
-        for gt in groundTruthImgList:
-            predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
-        results = cityscapes_eval.evaluateImgLists(
-            predictionImgList, groundTruthImgList, cityscapes_eval.args
-        )
-        ret = OrderedDict()
-        ret["sem_seg"] = {
-            "IoU": 100.0 * results["averageScoreClasses"],
-            "iIoU": 100.0 * results["averageScoreInstClasses"],
-            "IoU_sup": 100.0 * results["averageScoreCategories"],
-            "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
-        }
-        self._working_dir.cleanup()
-        return ret

spaces/CognitiveLabs/Research-Assistant/README.md DELETED Viewed

@@ -1,78 +0,0 @@
----
-title: AI-Research-Assistant
-app_file: app.py
-sdk: gradio
-sdk_version: 3.38.0
-duplicated_from: zej97/AI-Research-Assistant
----
-<div style="width: 100%;">
-    <img src="./statics/title.svg" style="width: 100%;">
-    <div align="right">
-        <a href="./README.md">English</a> |
-        <a href="./statics/README_zh.md">中文</a>
-    </div>
-</div>
-Inspired by [gpt-researcher](https://github.com/assafelovic/gpt-researcher). This project endeavors to develop an AI research assistant capable of **generating research reports** effortlessly for researchers. For instance, researchers can request the AI research assistant to compose a report on *the latest advancements in the field of superconductors as of 2023*, which is currently a trending topic. The AI research assistant will subsequently compile a report based on the relevant information obtained from the internet. Now, AIRA also offers support for **academic English polishing**.
-<!-- make a table -->
-|              Example1-1              |              Example1-2              |              Example1-3              |
-| :----------------------------------: | :----------------------------------: | :----------------------------------: |
-| <img src="./statics/example1-1.png"> | <img src="./statics/example1-2.png"> | <img src="./statics/example1-3.png"> |
-The currently supported agents encompass a wide range of fields, including *finance, business analysis, clinical medicine, basic medicine, travel, academic research and sociology*.
-In addition to official api, this project offers an alternative approach to generating research reports by utilizing a third-party API. For access to this third-party API, please refer to [chimeragpt](https://chimeragpt.adventblocks.cc/) or [GPT-API-free](https://github.com/chatanywhere/GPT_API_free). Before running the project, kindly ensure that you set the environment variables `OPENAI_API_KEY` and `OPENAI_API_BASE`.
-```shell
-$ export OPENAI_API_KEY = your_api_key
-$ export OPENAI_API_BASE = your_api_base
-```
-or you can set the api key and base in `.env` file.
-## Installation
-1. Clone the repository
-    ```shell
-    $ git clone [email protected]:paradoxtown/ai_research_assistant.git
-    $ cd ai_research_assistant
-    ```
-2. Install the dependencies
-    ```shell
-    $ pip install -r requirements.txt
-    ```
-3. Export evnironment variables
-    ```shell
-    $ export OPENAI_API_KEY = your_api_key
-    $ export OPENAI_API_BASE = your_api_base
-    ```
-    or modify the `.env` file.
-4. Run the project
-    ```shell
-    $ python app.py
-    ```
-## TODO
-- [x] Switch Google Search to DuckDuckGo
-- [ ] Literature review
-- [x] Third-party API
-- [ ] Prettify report
-- [x] Add medical agent and social agent
-- [ ] Add option for users to customize the number of words and temperature
-- [ ] Copy and download buttons
-- [ ] Allows the user to choose the degree of research.
-- [ ] Wikipedia Understanding
----
-<div align="center">Happy researching! 🚀</div>

spaces/Cropinky/hana_hanak_houses/networks_fastgan.py DELETED Viewed

@@ -1,179 +0,0 @@
-# original implementation: https://github.com/odegeasslbc/FastGAN-pytorch/blob/main/models.py
-#
-# modified by Axel Sauer for "Projected GANs Converge Faster"
-#
-import torch.nn as nn
-from blocks import (InitLayer, UpBlockBig, UpBlockBigCond, UpBlockSmall, UpBlockSmallCond, SEBlock, conv2d)
-from huggingface_hub import PyTorchModelHubMixin
-def normalize_second_moment(x, dim=1, eps=1e-8):
-    return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
-class DummyMapping(nn.Module):
-    def __init__(self):
-        super().__init__()
-    def forward(self, z, c, **kwargs):
-        return z.unsqueeze(1)  # to fit the StyleGAN API
-class FastganSynthesis(nn.Module):
-    def __init__(self, ngf=128, z_dim=256, nc=3, img_resolution=256, lite=False):
-        super().__init__()
-        self.img_resolution = img_resolution
-        self.z_dim = z_dim
-        # channel multiplier
-        nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5,
-                     512:0.25, 1024:0.125}
-        nfc = {}
-        for k, v in nfc_multi.items():
-            nfc[k] = int(v*ngf)
-        # layers
-        self.init = InitLayer(z_dim, channel=nfc[2], sz=4)
-        UpBlock = UpBlockSmall if lite else UpBlockBig
-        self.feat_8   = UpBlock(nfc[4], nfc[8])
-        self.feat_16  = UpBlock(nfc[8], nfc[16])
-        self.feat_32  = UpBlock(nfc[16], nfc[32])
-        self.feat_64  = UpBlock(nfc[32], nfc[64])
-        self.feat_128 = UpBlock(nfc[64], nfc[128])
-        self.feat_256 = UpBlock(nfc[128], nfc[256])
-        self.se_64  = SEBlock(nfc[4], nfc[64])
-        self.se_128 = SEBlock(nfc[8], nfc[128])
-        self.se_256 = SEBlock(nfc[16], nfc[256])
-        self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True)
-        if img_resolution > 256:
-            self.feat_512 = UpBlock(nfc[256], nfc[512])
-            self.se_512 = SEBlock(nfc[32], nfc[512])
-        if img_resolution > 512:
-            self.feat_1024 = UpBlock(nfc[512], nfc[1024])
-    def forward(self, input, c, **kwargs):
-        # map noise to hypersphere as in "Progressive Growing of GANS"
-        input = normalize_second_moment(input[:, 0])
-        feat_4 = self.init(input)
-        feat_8 = self.feat_8(feat_4)
-        feat_16 = self.feat_16(feat_8)
-        feat_32 = self.feat_32(feat_16)
-        feat_64 = self.se_64(feat_4, self.feat_64(feat_32))
-        feat_128 = self.se_128(feat_8,  self.feat_128(feat_64))
-        if self.img_resolution >= 128:
-            feat_last = feat_128
-        if self.img_resolution >= 256:
-            feat_last = self.se_256(feat_16, self.feat_256(feat_last))
-        if self.img_resolution >= 512:
-            feat_last = self.se_512(feat_32, self.feat_512(feat_last))
-        if self.img_resolution >= 1024:
-            feat_last = self.feat_1024(feat_last)
-        return self.to_big(feat_last)
-class FastganSynthesisCond(nn.Module):
-    def __init__(self, ngf=64, z_dim=256, nc=3, img_resolution=256, num_classes=1000, lite=False):
-        super().__init__()
-        self.z_dim = z_dim
-        nfc_multi = {2: 16, 4:16, 8:8, 16:4, 32:2, 64:2, 128:1, 256:0.5,
-                     512:0.25, 1024:0.125, 2048:0.125}
-        nfc = {}
-        for k, v in nfc_multi.items():
-            nfc[k] = int(v*ngf)
-        self.img_resolution = img_resolution
-        self.init = InitLayer(z_dim, channel=nfc[2], sz=4)
-        UpBlock = UpBlockSmallCond if lite else UpBlockBigCond
-        self.feat_8   = UpBlock(nfc[4], nfc[8], z_dim)
-        self.feat_16  = UpBlock(nfc[8], nfc[16], z_dim)
-        self.feat_32  = UpBlock(nfc[16], nfc[32], z_dim)
-        self.feat_64  = UpBlock(nfc[32], nfc[64], z_dim)
-        self.feat_128 = UpBlock(nfc[64], nfc[128], z_dim)
-        self.feat_256 = UpBlock(nfc[128], nfc[256], z_dim)
-        self.se_64 = SEBlock(nfc[4], nfc[64])
-        self.se_128 = SEBlock(nfc[8], nfc[128])
-        self.se_256 = SEBlock(nfc[16], nfc[256])
-        self.to_big = conv2d(nfc[img_resolution], nc, 3, 1, 1, bias=True)
-        if img_resolution > 256:
-            self.feat_512 = UpBlock(nfc[256], nfc[512])
-            self.se_512 = SEBlock(nfc[32], nfc[512])
-        if img_resolution > 512:
-            self.feat_1024 = UpBlock(nfc[512], nfc[1024])
-        self.embed = nn.Embedding(num_classes, z_dim)
-    def forward(self, input, c, update_emas=False):
-        c = self.embed(c.argmax(1))
-        # map noise to hypersphere as in "Progressive Growing of GANS"
-        input = normalize_second_moment(input[:, 0])
-        feat_4 = self.init(input)
-        feat_8 = self.feat_8(feat_4, c)
-        feat_16 = self.feat_16(feat_8, c)
-        feat_32 = self.feat_32(feat_16, c)
-        feat_64 = self.se_64(feat_4, self.feat_64(feat_32, c))
-        feat_128 = self.se_128(feat_8,  self.feat_128(feat_64, c))
-        if self.img_resolution >= 128:
-            feat_last = feat_128
-        if self.img_resolution >= 256:
-            feat_last = self.se_256(feat_16, self.feat_256(feat_last, c))
-        if self.img_resolution >= 512:
-            feat_last = self.se_512(feat_32, self.feat_512(feat_last, c))
-        if self.img_resolution >= 1024:
-            feat_last = self.feat_1024(feat_last, c)
-        return self.to_big(feat_last)
-class MyGenerator(nn.Module, PyTorchModelHubMixin):
-    def __init__(
-        self,
-        z_dim=256,
-        c_dim=0,
-        w_dim=0,
-        img_resolution=256,
-        img_channels=3,
-        ngf=128,
-        cond=0,
-        mapping_kwargs={},
-        synthesis_kwargs={}
-    ):
-        super().__init__()
-        #self.config = kwargs.pop("config", None)
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        # Mapping and Synthesis Networks
-        self.mapping = DummyMapping()  # to fit the StyleGAN API
-        Synthesis = FastganSynthesisCond if cond else FastganSynthesis
-        self.synthesis = Synthesis(ngf=ngf, z_dim=z_dim, nc=img_channels, img_resolution=img_resolution, **synthesis_kwargs)
-    def forward(self, z, c, **kwargs):
-        w = self.mapping(z, c)
-        img = self.synthesis(w, c)
-        return img

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/McIdasImagePlugin.py DELETED Viewed

@@ -1,75 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# Basic McIdas support for PIL
-#
-# History:
-# 1997-05-05 fl  Created (8-bit images only)
-# 2009-03-08 fl  Added 16/32-bit support.
-#
-# Thanks to Richard Jones and Craig Swank for specs and samples.
-#
-# Copyright (c) Secret Labs AB 1997.
-# Copyright (c) Fredrik Lundh 1997.
-#
-# See the README file for information on usage and redistribution.
-#
-import struct
-from . import Image, ImageFile
-def _accept(s):
-    return s[:8] == b"\x00\x00\x00\x00\x00\x00\x00\x04"
-##
-# Image plugin for McIdas area images.
-class McIdasImageFile(ImageFile.ImageFile):
-    format = "MCIDAS"
-    format_description = "McIdas area file"
-    def _open(self):
-        # parse area file directory
-        s = self.fp.read(256)
-        if not _accept(s) or len(s) != 256:
-            msg = "not an McIdas area file"
-            raise SyntaxError(msg)
-        self.area_descriptor_raw = s
-        self.area_descriptor = w = [0] + list(struct.unpack("!64i", s))
-        # get mode
-        if w[11] == 1:
-            mode = rawmode = "L"
-        elif w[11] == 2:
-            # FIXME: add memory map support
-            mode = "I"
-            rawmode = "I;16B"
-        elif w[11] == 4:
-            # FIXME: add memory map support
-            mode = "I"
-            rawmode = "I;32B"
-        else:
-            msg = "unsupported McIdas format"
-            raise SyntaxError(msg)
-        self.mode = mode
-        self._size = w[10], w[9]
-        offset = w[34] + w[15]
-        stride = w[15] + w[10] * w[11] * w[14]
-        self.tile = [("raw", (0, 0) + self.size, offset, (rawmode, stride, 1))]
-# --------------------------------------------------------------------
-# registry
-Image.register_open(McIdasImageFile.format, McIdasImageFile, _accept)
-# no default extension

spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/click/_termui_impl.py DELETED Viewed

@@ -1,739 +0,0 @@
-"""
-This module contains implementations for the termui module. To keep the
-import time of Click down, some infrequently used functionality is
-placed in this module and only imported as needed.
-"""
-import contextlib
-import math
-import os
-import sys
-import time
-import typing as t
-from gettext import gettext as _
-from io import StringIO
-from types import TracebackType
-from ._compat import _default_text_stdout
-from ._compat import CYGWIN
-from ._compat import get_best_encoding
-from ._compat import isatty
-from ._compat import open_stream
-from ._compat import strip_ansi
-from ._compat import term_len
-from ._compat import WIN
-from .exceptions import ClickException
-from .utils import echo
-V = t.TypeVar("V")
-if os.name == "nt":
-    BEFORE_BAR = "\r"
-    AFTER_BAR = "\n"
-else:
-    BEFORE_BAR = "\r\033[?25l"
-    AFTER_BAR = "\033[?25h\n"
-class ProgressBar(t.Generic[V]):
-    def __init__(
-        self,
-        iterable: t.Optional[t.Iterable[V]],
-        length: t.Optional[int] = None,
-        fill_char: str = "#",
-        empty_char: str = " ",
-        bar_template: str = "%(bar)s",
-        info_sep: str = "  ",
-        show_eta: bool = True,
-        show_percent: t.Optional[bool] = None,
-        show_pos: bool = False,
-        item_show_func: t.Optional[t.Callable[[t.Optional[V]], t.Optional[str]]] = None,
-        label: t.Optional[str] = None,
-        file: t.Optional[t.TextIO] = None,
-        color: t.Optional[bool] = None,
-        update_min_steps: int = 1,
-        width: int = 30,
-    ) -> None:
-        self.fill_char = fill_char
-        self.empty_char = empty_char
-        self.bar_template = bar_template
-        self.info_sep = info_sep
-        self.show_eta = show_eta
-        self.show_percent = show_percent
-        self.show_pos = show_pos
-        self.item_show_func = item_show_func
-        self.label: str = label or ""
-        if file is None:
-            file = _default_text_stdout()
-            # There are no standard streams attached to write to. For example,
-            # pythonw on Windows.
-            if file is None:
-                file = StringIO()
-        self.file = file
-        self.color = color
-        self.update_min_steps = update_min_steps
-        self._completed_intervals = 0
-        self.width: int = width
-        self.autowidth: bool = width == 0
-        if length is None:
-            from operator import length_hint
-            length = length_hint(iterable, -1)
-            if length == -1:
-                length = None
-        if iterable is None:
-            if length is None:
-                raise TypeError("iterable or length is required")
-            iterable = t.cast(t.Iterable[V], range(length))
-        self.iter: t.Iterable[V] = iter(iterable)
-        self.length = length
-        self.pos = 0
-        self.avg: t.List[float] = []
-        self.last_eta: float
-        self.start: float
-        self.start = self.last_eta = time.time()
-        self.eta_known: bool = False
-        self.finished: bool = False
-        self.max_width: t.Optional[int] = None
-        self.entered: bool = False
-        self.current_item: t.Optional[V] = None
-        self.is_hidden: bool = not isatty(self.file)
-        self._last_line: t.Optional[str] = None
-    def __enter__(self) -> "ProgressBar[V]":
-        self.entered = True
-        self.render_progress()
-        return self
-    def __exit__(
-        self,
-        exc_type: t.Optional[t.Type[BaseException]],
-        exc_value: t.Optional[BaseException],
-        tb: t.Optional[TracebackType],
-    ) -> None:
-        self.render_finish()
-    def __iter__(self) -> t.Iterator[V]:
-        if not self.entered:
-            raise RuntimeError("You need to use progress bars in a with block.")
-        self.render_progress()
-        return self.generator()
-    def __next__(self) -> V:
-        # Iteration is defined in terms of a generator function,
-        # returned by iter(self); use that to define next(). This works
-        # because `self.iter` is an iterable consumed by that generator,
-        # so it is re-entry safe. Calling `next(self.generator())`
-        # twice works and does "what you want".
-        return next(iter(self))
-    def render_finish(self) -> None:
-        if self.is_hidden:
-            return
-        self.file.write(AFTER_BAR)
-        self.file.flush()
-    @property
-    def pct(self) -> float:
-        if self.finished:
-            return 1.0
-        return min(self.pos / (float(self.length or 1) or 1), 1.0)
-    @property
-    def time_per_iteration(self) -> float:
-        if not self.avg:
-            return 0.0
-        return sum(self.avg) / float(len(self.avg))
-    @property
-    def eta(self) -> float:
-        if self.length is not None and not self.finished:
-            return self.time_per_iteration * (self.length - self.pos)
-        return 0.0
-    def format_eta(self) -> str:
-        if self.eta_known:
-            t = int(self.eta)
-            seconds = t % 60
-            t //= 60
-            minutes = t % 60
-            t //= 60
-            hours = t % 24
-            t //= 24
-            if t > 0:
-                return f"{t}d {hours:02}:{minutes:02}:{seconds:02}"
-            else:
-                return f"{hours:02}:{minutes:02}:{seconds:02}"
-        return ""
-    def format_pos(self) -> str:
-        pos = str(self.pos)
-        if self.length is not None:
-            pos += f"/{self.length}"
-        return pos
-    def format_pct(self) -> str:
-        return f"{int(self.pct * 100): 4}%"[1:]
-    def format_bar(self) -> str:
-        if self.length is not None:
-            bar_length = int(self.pct * self.width)
-            bar = self.fill_char * bar_length
-            bar += self.empty_char * (self.width - bar_length)
-        elif self.finished:
-            bar = self.fill_char * self.width
-        else:
-            chars = list(self.empty_char * (self.width or 1))
-            if self.time_per_iteration != 0:
-                chars[
-                    int(
-                        (math.cos(self.pos * self.time_per_iteration) / 2.0 + 0.5)
-                        * self.width
-                    )
-                ] = self.fill_char
-            bar = "".join(chars)
-        return bar
-    def format_progress_line(self) -> str:
-        show_percent = self.show_percent
-        info_bits = []
-        if self.length is not None and show_percent is None:
-            show_percent = not self.show_pos
-        if self.show_pos:
-            info_bits.append(self.format_pos())
-        if show_percent:
-            info_bits.append(self.format_pct())
-        if self.show_eta and self.eta_known and not self.finished:
-            info_bits.append(self.format_eta())
-        if self.item_show_func is not None:
-            item_info = self.item_show_func(self.current_item)
-            if item_info is not None:
-                info_bits.append(item_info)
-        return (
-            self.bar_template
-            % {
-                "label": self.label,
-                "bar": self.format_bar(),
-                "info": self.info_sep.join(info_bits),
-            }
-        ).rstrip()
-    def render_progress(self) -> None:
-        import shutil
-        if self.is_hidden:
-            # Only output the label as it changes if the output is not a
-            # TTY. Use file=stderr if you expect to be piping stdout.
-            if self._last_line != self.label:
-                self._last_line = self.label
-                echo(self.label, file=self.file, color=self.color)
-            return
-        buf = []
-        # Update width in case the terminal has been resized
-        if self.autowidth:
-            old_width = self.width
-            self.width = 0
-            clutter_length = term_len(self.format_progress_line())
-            new_width = max(0, shutil.get_terminal_size().columns - clutter_length)
-            if new_width < old_width:
-                buf.append(BEFORE_BAR)
-                buf.append(" " * self.max_width)  # type: ignore
-                self.max_width = new_width
-            self.width = new_width
-        clear_width = self.width
-        if self.max_width is not None:
-            clear_width = self.max_width
-        buf.append(BEFORE_BAR)
-        line = self.format_progress_line()
-        line_len = term_len(line)
-        if self.max_width is None or self.max_width < line_len:
-            self.max_width = line_len
-        buf.append(line)
-        buf.append(" " * (clear_width - line_len))
-        line = "".join(buf)
-        # Render the line only if it changed.
-        if line != self._last_line:
-            self._last_line = line
-            echo(line, file=self.file, color=self.color, nl=False)
-            self.file.flush()
-    def make_step(self, n_steps: int) -> None:
-        self.pos += n_steps
-        if self.length is not None and self.pos >= self.length:
-            self.finished = True
-        if (time.time() - self.last_eta) < 1.0:
-            return
-        self.last_eta = time.time()
-        # self.avg is a rolling list of length <= 7 of steps where steps are
-        # defined as time elapsed divided by the total progress through
-        # self.length.
-        if self.pos:
-            step = (time.time() - self.start) / self.pos
-        else:
-            step = time.time() - self.start
-        self.avg = self.avg[-6:] + [step]
-        self.eta_known = self.length is not None
-    def update(self, n_steps: int, current_item: t.Optional[V] = None) -> None:
-        """Update the progress bar by advancing a specified number of
-        steps, and optionally set the ``current_item`` for this new
-        position.
-        :param n_steps: Number of steps to advance.
-        :param current_item: Optional item to set as ``current_item``
-            for the updated position.
-        .. versionchanged:: 8.0
-            Added the ``current_item`` optional parameter.
-        .. versionchanged:: 8.0
-            Only render when the number of steps meets the
-            ``update_min_steps`` threshold.
-        """
-        if current_item is not None:
-            self.current_item = current_item
-        self._completed_intervals += n_steps
-        if self._completed_intervals >= self.update_min_steps:
-            self.make_step(self._completed_intervals)
-            self.render_progress()
-            self._completed_intervals = 0
-    def finish(self) -> None:
-        self.eta_known = False
-        self.current_item = None
-        self.finished = True
-    def generator(self) -> t.Iterator[V]:
-        """Return a generator which yields the items added to the bar
-        during construction, and updates the progress bar *after* the
-        yielded block returns.
-        """
-        # WARNING: the iterator interface for `ProgressBar` relies on
-        # this and only works because this is a simple generator which
-        # doesn't create or manage additional state. If this function
-        # changes, the impact should be evaluated both against
-        # `iter(bar)` and `next(bar)`. `next()` in particular may call
-        # `self.generator()` repeatedly, and this must remain safe in
-        # order for that interface to work.
-        if not self.entered:
-            raise RuntimeError("You need to use progress bars in a with block.")
-        if self.is_hidden:
-            yield from self.iter
-        else:
-            for rv in self.iter:
-                self.current_item = rv
-                # This allows show_item_func to be updated before the
-                # item is processed. Only trigger at the beginning of
-                # the update interval.
-                if self._completed_intervals == 0:
-                    self.render_progress()
-                yield rv
-                self.update(1)
-            self.finish()
-            self.render_progress()
-def pager(generator: t.Iterable[str], color: t.Optional[bool] = None) -> None:
-    """Decide what method to use for paging through text."""
-    stdout = _default_text_stdout()
-    # There are no standard streams attached to write to. For example,
-    # pythonw on Windows.
-    if stdout is None:
-        stdout = StringIO()
-    if not isatty(sys.stdin) or not isatty(stdout):
-        return _nullpager(stdout, generator, color)
-    pager_cmd = (os.environ.get("PAGER", None) or "").strip()
-    if pager_cmd:
-        if WIN:
-            return _tempfilepager(generator, pager_cmd, color)
-        return _pipepager(generator, pager_cmd, color)
-    if os.environ.get("TERM") in ("dumb", "emacs"):
-        return _nullpager(stdout, generator, color)
-    if WIN or sys.platform.startswith("os2"):
-        return _tempfilepager(generator, "more <", color)
-    if hasattr(os, "system") and os.system("(less) 2>/dev/null") == 0:
-        return _pipepager(generator, "less", color)
-    import tempfile
-    fd, filename = tempfile.mkstemp()
-    os.close(fd)
-    try:
-        if hasattr(os, "system") and os.system(f'more "{filename}"') == 0:
-            return _pipepager(generator, "more", color)
-        return _nullpager(stdout, generator, color)
-    finally:
-        os.unlink(filename)
-def _pipepager(generator: t.Iterable[str], cmd: str, color: t.Optional[bool]) -> None:
-    """Page through text by feeding it to another program.  Invoking a
-    pager through this might support colors.
-    """
-    import subprocess
-    env = dict(os.environ)
-    # If we're piping to less we might support colors under the
-    # condition that
-    cmd_detail = cmd.rsplit("/", 1)[-1].split()
-    if color is None and cmd_detail[0] == "less":
-        less_flags = f"{os.environ.get('LESS', '')}{' '.join(cmd_detail[1:])}"
-        if not less_flags:
-            env["LESS"] = "-R"
-            color = True
-        elif "r" in less_flags or "R" in less_flags:
-            color = True
-    c = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, env=env)
-    stdin = t.cast(t.BinaryIO, c.stdin)
-    encoding = get_best_encoding(stdin)
-    try:
-        for text in generator:
-            if not color:
-                text = strip_ansi(text)
-            stdin.write(text.encode(encoding, "replace"))
-    except (OSError, KeyboardInterrupt):
-        pass
-    else:
-        stdin.close()
-    # Less doesn't respect ^C, but catches it for its own UI purposes (aborting
-    # search or other commands inside less).
-    #
-    # That means when the user hits ^C, the parent process (click) terminates,
-    # but less is still alive, paging the output and messing up the terminal.
-    #
-    # If the user wants to make the pager exit on ^C, they should set
-    # `LESS='-K'`. It's not our decision to make.
-    while True:
-        try:
-            c.wait()
-        except KeyboardInterrupt:
-            pass
-        else:
-            break
-def _tempfilepager(
-    generator: t.Iterable[str], cmd: str, color: t.Optional[bool]
-) -> None:
-    """Page through text by invoking a program on a temporary file."""
-    import tempfile
-    fd, filename = tempfile.mkstemp()
-    # TODO: This never terminates if the passed generator never terminates.
-    text = "".join(generator)
-    if not color:
-        text = strip_ansi(text)
-    encoding = get_best_encoding(sys.stdout)
-    with open_stream(filename, "wb")[0] as f:
-        f.write(text.encode(encoding))
-    try:
-        os.system(f'{cmd} "{filename}"')
-    finally:
-        os.close(fd)
-        os.unlink(filename)
-def _nullpager(
-    stream: t.TextIO, generator: t.Iterable[str], color: t.Optional[bool]
-) -> None:
-    """Simply print unformatted text.  This is the ultimate fallback."""
-    for text in generator:
-        if not color:
-            text = strip_ansi(text)
-        stream.write(text)
-class Editor:
-    def __init__(
-        self,
-        editor: t.Optional[str] = None,
-        env: t.Optional[t.Mapping[str, str]] = None,
-        require_save: bool = True,
-        extension: str = ".txt",
-    ) -> None:
-        self.editor = editor
-        self.env = env
-        self.require_save = require_save
-        self.extension = extension
-    def get_editor(self) -> str:
-        if self.editor is not None:
-            return self.editor
-        for key in "VISUAL", "EDITOR":
-            rv = os.environ.get(key)
-            if rv:
-                return rv
-        if WIN:
-            return "notepad"
-        for editor in "sensible-editor", "vim", "nano":
-            if os.system(f"which {editor} >/dev/null 2>&1") == 0:
-                return editor
-        return "vi"
-    def edit_file(self, filename: str) -> None:
-        import subprocess
-        editor = self.get_editor()
-        environ: t.Optional[t.Dict[str, str]] = None
-        if self.env:
-            environ = os.environ.copy()
-            environ.update(self.env)
-        try:
-            c = subprocess.Popen(f'{editor} "{filename}"', env=environ, shell=True)
-            exit_code = c.wait()
-            if exit_code != 0:
-                raise ClickException(
-                    _("{editor}: Editing failed").format(editor=editor)
-                )
-        except OSError as e:
-            raise ClickException(
-                _("{editor}: Editing failed: {e}").format(editor=editor, e=e)
-            ) from e
-    def edit(self, text: t.Optional[t.AnyStr]) -> t.Optional[t.AnyStr]:
-        import tempfile
-        if not text:
-            data = b""
-        elif isinstance(text, (bytes, bytearray)):
-            data = text
-        else:
-            if text and not text.endswith("\n"):
-                text += "\n"
-            if WIN:
-                data = text.replace("\n", "\r\n").encode("utf-8-sig")
-            else:
-                data = text.encode("utf-8")
-        fd, name = tempfile.mkstemp(prefix="editor-", suffix=self.extension)
-        f: t.BinaryIO
-        try:
-            with os.fdopen(fd, "wb") as f:
-                f.write(data)
-            # If the filesystem resolution is 1 second, like Mac OS
-            # 10.12 Extended, or 2 seconds, like FAT32, and the editor
-            # closes very fast, require_save can fail. Set the modified
-            # time to be 2 seconds in the past to work around this.
-            os.utime(name, (os.path.getatime(name), os.path.getmtime(name) - 2))
-            # Depending on the resolution, the exact value might not be
-            # recorded, so get the new recorded value.
-            timestamp = os.path.getmtime(name)
-            self.edit_file(name)
-            if self.require_save and os.path.getmtime(name) == timestamp:
-                return None
-            with open(name, "rb") as f:
-                rv = f.read()
-            if isinstance(text, (bytes, bytearray)):
-                return rv
-            return rv.decode("utf-8-sig").replace("\r\n", "\n")  # type: ignore
-        finally:
-            os.unlink(name)
-def open_url(url: str, wait: bool = False, locate: bool = False) -> int:
-    import subprocess
-    def _unquote_file(url: str) -> str:
-        from urllib.parse import unquote
-        if url.startswith("file://"):
-            url = unquote(url[7:])
-        return url
-    if sys.platform == "darwin":
-        args = ["open"]
-        if wait:
-            args.append("-W")
-        if locate:
-            args.append("-R")
-        args.append(_unquote_file(url))
-        null = open("/dev/null", "w")
-        try:
-            return subprocess.Popen(args, stderr=null).wait()
-        finally:
-            null.close()
-    elif WIN:
-        if locate:
-            url = _unquote_file(url.replace('"', ""))
-            args = f'explorer /select,"{url}"'
-        else:
-            url = url.replace('"', "")
-            wait_str = "/WAIT" if wait else ""
-            args = f'start {wait_str} "" "{url}"'
-        return os.system(args)
-    elif CYGWIN:
-        if locate:
-            url = os.path.dirname(_unquote_file(url).replace('"', ""))
-            args = f'cygstart "{url}"'
-        else:
-            url = url.replace('"', "")
-            wait_str = "-w" if wait else ""
-            args = f'cygstart {wait_str} "{url}"'
-        return os.system(args)
-    try:
-        if locate:
-            url = os.path.dirname(_unquote_file(url)) or "."
-        else:
-            url = _unquote_file(url)
-        c = subprocess.Popen(["xdg-open", url])
-        if wait:
-            return c.wait()
-        return 0
-    except OSError:
-        if url.startswith(("http://", "https://")) and not locate and not wait:
-            import webbrowser
-            webbrowser.open(url)
-            return 0
-        return 1
-def _translate_ch_to_exc(ch: str) -> t.Optional[BaseException]:
-    if ch == "\x03":
-        raise KeyboardInterrupt()
-    if ch == "\x04" and not WIN:  # Unix-like, Ctrl+D
-        raise EOFError()
-    if ch == "\x1a" and WIN:  # Windows, Ctrl+Z
-        raise EOFError()
-    return None
-if WIN:
-    import msvcrt
-    @contextlib.contextmanager
-    def raw_terminal() -> t.Iterator[int]:
-        yield -1
-    def getchar(echo: bool) -> str:
-        # The function `getch` will return a bytes object corresponding to
-        # the pressed character. Since Windows 10 build 1803, it will also
-        # return \x00 when called a second time after pressing a regular key.
-        #
-        # `getwch` does not share this probably-bugged behavior. Moreover, it
-        # returns a Unicode object by default, which is what we want.
-        #
-        # Either of these functions will return \x00 or \xe0 to indicate
-        # a special key, and you need to call the same function again to get
-        # the "rest" of the code. The fun part is that \u00e0 is
-        # "latin small letter a with grave", so if you type that on a French
-        # keyboard, you _also_ get a \xe0.
-        # E.g., consider the Up arrow. This returns \xe0 and then \x48. The
-        # resulting Unicode string reads as "a with grave" + "capital H".
-        # This is indistinguishable from when the user actually types
-        # "a with grave" and then "capital H".
-        #
-        # When \xe0 is returned, we assume it's part of a special-key sequence
-        # and call `getwch` again, but that means that when the user types
-        # the \u00e0 character, `getchar` doesn't return until a second
-        # character is typed.
-        # The alternative is returning immediately, but that would mess up
-        # cross-platform handling of arrow keys and others that start with
-        # \xe0. Another option is using `getch`, but then we can't reliably
-        # read non-ASCII characters, because return values of `getch` are
-        # limited to the current 8-bit codepage.
-        #
-        # Anyway, Click doesn't claim to do this Right(tm), and using `getwch`
-        # is doing the right thing in more situations than with `getch`.
-        func: t.Callable[[], str]
-        if echo:
-            func = msvcrt.getwche  # type: ignore
-        else:
-            func = msvcrt.getwch  # type: ignore
-        rv = func()
-        if rv in ("\x00", "\xe0"):
-            # \x00 and \xe0 are control characters that indicate special key,
-            # see above.
-            rv += func()
-        _translate_ch_to_exc(rv)
-        return rv
-else:
-    import tty
-    import termios
-    @contextlib.contextmanager
-    def raw_terminal() -> t.Iterator[int]:
-        f: t.Optional[t.TextIO]
-        fd: int
-        if not isatty(sys.stdin):
-            f = open("/dev/tty")
-            fd = f.fileno()
-        else:
-            fd = sys.stdin.fileno()
-            f = None
-        try:
-            old_settings = termios.tcgetattr(fd)
-            try:
-                tty.setraw(fd)
-                yield fd
-            finally:
-                termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
-                sys.stdout.flush()
-                if f is not None:
-                    f.close()
-        except termios.error:
-            pass
-    def getchar(echo: bool) -> str:
-        with raw_terminal() as fd:
-            ch = os.read(fd, 32).decode(get_best_encoding(sys.stdin), "replace")
-            if echo and isatty(sys.stdout):
-                sys.stdout.write(ch)
-            _translate_ch_to_exc(ch)
-            return ch